ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
THE INTERNET OF METAMATERIAL THINGS AND THEIR SOFTWARE ENABLERS
Christos Liaskos1,2, Georgios G. Pyrialakos2,3, Alexandros Pitilakis2,3, Ageliki Tsioliaridou2, Michail Christodoulou2,3, Nikolaos Kantartzis2,3, Sotiris Ioannidis4,2, Andreas Pitsillides5, Ian F. Akyildiz5 1Computer Science Engineering Dept., University of Ioannina, Ioannina, Greece, 2Foundation for Research and Technology - Hellas, Heraklion, Greece, 3Electrical and Computer Engineering Dept., Aristotle University, Greece, 4Technical University of Chania, Crete, Greece, 5Computer Science Dept., University of Cyprus, Nicosia, Cyprus,
NOTE: Corresponding author: Christos Liaskos, [email protected]
Abstract A new paradigm called the Internet of MetaMaterial Things (IoMMT) is introduced in this paper where arti cial materials with real-time tunable physical properties can be interconnected to form a network to realize com- munication through software-controlled electromagnetic, acoustic, and mechanical energy waves. The IoMMT will signi cantly enrich the Internet of Things ecosystem by connecting anything at any place by optimizing the physical energy propagation between the metamaterial devices during their lifetime, via eco- rmware updates. First, the means for abstracting the complex physics behind these materials are explored, showing their integration into the IoT world. Subsequently, two novel software categories for the material things are proposed, namely the metamaterial Application Programming Interface and Metamaterial Middleware, which will be in charge of the application and physical domains, respectively. Regarding the API, the paper provides the data model and work ows for obtaining and setting the physical properties of a material via callbacks. The Metamaterial Middleware is tasked with matching these callbacks to the corresponding material-altering actuations through embedded elements. Furthermore, a full stack implementation of the software for the electromagnetic metamaterial case is presented and evaluated, incorporating all the aforementioned aspects. Finally, interesting extensions and envisioned use cases of the IoMMT concept are discussed. Keywords Internet of Things, metamaterials, programming interface, software-de ned networking.
1. INTRODUCTION that has enabled the creation of arti cial materials with real-time tunable physical properties [2, 3]. Metama- Recent years have witnessed the advent of the Internet- terials are based on the fundamental idea stating that of-Things (IoT), denoting the interconnection of every the physical properties of matter stem from its atomic electronic device and the smart, orchestrated automa- structure. Therefore, one can create arti cially struc- tion it entails [1]. Vehicles, smart phones, sensors, home tured materials (comprising su ciently small elemen- and industrial appliances of any kind expose a function- tary units of composition and geometry) to yield any ality interface expressed in software, allowing for devel- required energy manipulating behavior, including types opers to create end-to-end work ows. As an upshot, not found in natural materials. Metamaterials manipu- smart buildings and even smart cities that automatically lating electromagnetic (EM) energy were the rst kind adapt, e.g., power generation, tra c and heat manage- of metamaterials to be studied in depth, mainly due ment to the needs of residents, have been devised in to the relative ease of manufacturing as low-complexity recent years. This current IoT potential stems from ex- electronic boards [3]. posing and controlling a high-level functionality of an Going beyond EM waves, the collectively termed elas- electronic device, such as turning on/o lights and air- todynamic metamaterials can manipulate acoustic, me- conditioning units based on the time of day and temper- chanical and structural waves, whereas thermodynamic ature. This paper proposes the expansion of the IoT to and quantum-mechanic metamaterials have also been the level of physical material properties, such as electri- postulated [4]. Elastodynamic metamaterials, empow- cal and thermal conductivity, mechanical elasticity, and ered by recent advances in nano- and micro-fabrication acoustic absorption. This novel direction is denoted as (e.g. additive manufacturing/3D printing), can ex- the Internet of MetaMaterial Things (IoMMT), and can hibit eective/macroscopic nonphysical properties such have groundbreaking potential across many industrial as tunable stiness and absorption/re ection, extreme sectors, as outlined in this paper. There are two key mass-volume ratios, negative sonic refraction, etc [5]. enablers for the proposed IoMMT: Their cell-size spans several length scales, depending on Key Enabler 1: the application: acoustic cloaking/anisotropy/isolation, The rst key enabler of the proposed IoMMT are the ultra-lightweight and resilient materials, devices for metamaterials, the outcome of recent research in physics medical/surgical applications and food/drug adminis-
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tration, MEMS, anti-seismic structures, etc. Tunability Sensor Networked IoT MetaMaterials of elastodynamic metamaterials can be achieved with Passive part Gateway Actuate Communicate electric, magnetic, optical, thermal or chemical stimuli. Actuator & Sense In a nutshell, their operation is as follows: Impinging EM Compute Behavior EM waves create inductive currents over the material, Source LabelSPLIT NETWORK which can be modi ed by tuning the actuator elements Source LabelFOCUS API within it (e.g., simple switches) accordingly. The Huy- Source gens principle states that any EM wavefront departing ABSORBLabel MIDDLEWARE Source from a surface can be traced back to an equivalent cur- POLARIZATIONLabel Alter Source LabelSTEER rent distribution over a surface [3]. Thus, in principle, Source PHASELabel Alter metamaterials can produce any custom departing EM wave as a response to any impinging wave, just by tun- ing the state of embedded switches/actuators. Such EM interactions are shown in Fig. 1 (on the right side). The Fig. 1 Networked metamaterial structure and possible energy same principle of operation applies to mechanical, acous- wave interactions [8]. tic and thermal metamaterials [6]. Key Enabler 2: collimate() The second key enabler of the IoMMT is the concept absorb() of networked metamaterials. These will come with an application programming interface (API), an accompa- nying software middleware and a network integration steer() architecture that enable the hosting of any kind of en- refract() ergy manipulation over a metamaterial in real time (e.g., steering, absorbing, splitting of EM, mechanical, avoid() thermal or acoustic waves), via simple software call- follow() backs executed from a standard PC (desktop or lap- steer() focus() top), while abstracting the underlying physics. The goal steer() focus() is to constitute the IoMMT directly accessible to the steer() IoT and software development industries, without car- focus() ing for the intrinsic and potentially complicated phys- ical principles. Regarding the IoMMT potential, large scale deployments of EM metamaterials in indoor setups have introduced the groundbreaking concept of pro- grammable/intelligent wireless environment (Fig. 2) [7]. Fig. 2 The programmable wireless environment introduced in [7], is created by coating walls with networked metamateri- By coating all major surfaces in a space (e.g., indoors) als. This allows for customized wireless propagation-as-an-app per with EM metamaterials, the wireless propagation can communicating device pair, introducing novel potential in data be controlled and customized via software. As detailed rates, communication quality, security and wireless power trans- in [7] this can enable the mitigation of path loss, fad- fer. ing and Doppler phenomena, while also allowing waves The acoustic metamaterials can surround noisy to follow improbable air-routes to avoid eavesdroppers devices or be applied on windows to provide a (a type of physical-layer security). In cases where the more silent environment, but to also harvest energy device beamforming and the EM metamaterials in the which can be added to a system such as a smart- space are orchestrated together, intelligent wireless envi- household. ronments can attain previously unattainable communi- cation quality and wireless power transfer [7]. Extend- Assuming a central controller to optimize a given ing the EM case, we envision the generalized IoMMT IoMMT deployment allows for further potential. For deployed as structural parts of products, as shown in instance, one can allow for quickly patching of over- Fig. 3: looked physical aspects (e.g., poor ecological perfor- EM interference and unwanted emissions can be mance) of IoMM-enabled products during operation, harvested by IoMM-coated walls and be trans- without overburdening the product design phase with formed back to usable EM or mechanical energy. such concerns. The patching may also be deferred in the form of eco- rmware, distributed via the Internet Thermoelectric and mechanical metamaterials can to ecologically tune a single product or horizontal sets micro-manage emanated heat and vibrations from of products. devices, such as any kind of motor, to recycle it In this context, the principal contributions of the paper as energy while eectively cooling it. The same are as follows: principle can be applied to a smart household or a noisy factory. We propose the concept of the IoMMT and discuss ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
agation within a space, in every physical domain, i.e., for any physical material property and corresponding information-carrying wave. For instance, control over the equivalent RLC parameters of an electric load con- trols the power that can be delivered to it by an EM wave. Moreover, the presented software is a mature pro- totype platform for the development of IoMMT appli- cations. This constitutes a major leap towards a new research direction. On the other hand, other research directions have proposed and explored the Internet of NanoThings [9]. Although similarly named, these direc- tions are not related to the IoMMT, as they are about embedding nano-sized computers into materials in order to augment the penetration level of applications (e.g., sense structural, temperature, humidity changes within a material, rather than just over it, etc.), and not to control the energy propagation within them. The remainder of this paper is organized as follows, devoting a section to each of the principal contribu- tions of our work. In Section 2 we provide the related work overview and the necessary prerequisite knowledge (a) Conceptual IoMMT deployment within a smart home. for networked metamaterial. In Section 3 we present the architecture for integrating the IoMMT in existing Software-De ned Networks (SDNs) and systems. In Sec- tion 4 we present the novel metamaterial API, and Sec- tion 5 follows with the description of the Metamaterial Middleware and its assorted work ows. In Section 6 we present the implemented version of the software for the EM metamaterial case, along with a description of the employed evaluation test bed. Finally, novel realistic ap- plications enabled with our new paradigm are discussed in Section 7, and we conclude the paper in Section 8.
2. PREREQUISITES AND RELATED (b) Conceptual IoMMT deployment within single products. WORK
Fig. 3 Envisioned applications of the IoMMT in smart houses Metamaterials are simple structures that are created by and products. periodically repeating a basic structure, called a cell or a its architecture and interoperability with existing meta-atom [3]. Some examples across physical domains network infrastructures. are shown in Fig. 4. The planar (2D) assemblies of meta- atoms, known as metasurfaces, are of particular interest We de ne two novel categories of software: the currently [10, 11]. For instance, EM are currently heav- Metamaterial API and the Metamaterial Middle- ily investigated by the electromagnetic/high-frequency ware, which enable any software developer to in- community, for novel communications, sensing and en- teract with a set of networked metamaterials, in ergy applications. [1214]. a physics-agnostic manner. We establish the data A notable trait of metamaterials is that they are sim- models, work ows, and test bed processes re- ple structures and, therefore, there exists a variety of quired for pro ling and, subsequently, componetiz- techniques for generally low-cost and scalable produc- ing metamaterials. tion [3]. The techniques such as printed circuit broads, exible materials such as Kapton, 3D printing, Large We present an implemented and experimentally Area Electronics, bio-skins and micro uidics have been veri ed version of the metamaterial API and the successfully employed for manufacturing [3]. Metamaterial Middleware for the EM case. In each physical domain, a properly con gured metama- We highlight promising, new applications empow- terial has the capacity to steer and focus an incoming ered by the featured IoMMT concept. energy wave towards an arbitrary direction or even com- pletely absorb the impinging power. In the EM case this In this aspect, the potential of our IoMMT paradigm capability can be exploited for advanced wireless com- is the rst to oer true control over the energy prop- munications [7, 1519], oering substantially increased ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
our paper focuses more on the networking approaches for metamaterials, which has only been treated in our previous work [8], and only for the EM case. Finally, the work of Chen et al. [27] also advocates for the use of metamaterial in any physical domain for distributed energy harvesting, e.g., in a smart house or a city. How- ever, software enablers and networking considerations are not discussed or solved in [27]. Moreover, the energy manipulation type is restricted to harvesting which can be viewed as a subset of our proposed IoMMT potential.
Metamaterials: Principles of Operation, Classi- cation and Supported Functionalities A conceptual metamaterial is illustrated in Fig. 5 [3]. Basically, a metamaterial consists of periodically re- peated meta-atoms arranged in a 3D grid layout, with the metasurfaces being a sub-case. In particular, unit Fig. 4 Energy manipulation domains of arti cial materials: cells comprise passive and tunable parts, required in (a) Electromagnetic [20] (b) Mechanical [21] (c) Acoustic [22] (d) recon gurable metamaterials as well as optional inte- Thermoelectric [23]. grated sensory circuits, which can extract information bandwidth and security between two communicating of the incident energy wave. Furthermore, tunable parts parties. are crucial for metamaterials, as they enable recon g- The potential stemming from interconnected metama- urability and switching between dierent functions. For terials has begun to be studied only recently [8]. The illustration, in EM metamaterials at microwave frequen- perspective networking architecture and protocols [7,8], cies, the tunable parts embedded inside the unit cells metamaterial control latency models [24], and smart en- can be voltage-controlled resistors (varistors) and/or ca- vironment orchestration issues have been recently stud- pacitors (varactors), micro-electromechanical switches ied for the EM case [25,26]. (MEMS), to name a few [3,19]. Notably, a similarly named concept, i.e., the Internet On the other hand, in mechanical and acoustic meta- of NanoThings [9], was recently proposed to refer to materials, the tunable parts can be micro-springs with materials with embedded, nano-sized computing and a tunable elasticity rate [28, 29]. The meta-atoms may communicating elements. In general, these materials also form larger groups, called super-atoms or super- are derived from miniaturizing electronic elements and cells, repeated in speci c patterns that can serve more placing them over or embedding them into fabrics and complex functionalities, as discussed later in this paper. gadgets, to increase their application-layer capabilities. Lastly, the software-de ned metamaterials include a For instance, this could make a glass window become gateway [7], i.e., an on-board computer, whose main a giant, self-powered touchpad for another IoT device. tasks are to: i) power the whole device and ii) control Originally. the concept of software-de ned metasurfaces (get/set) the state of the embedded tunable elements, wa based on the nano-IoT as the actuation/control en- iii) interoperate with the embedded sensors, and iv) abler [17]. Nano-devices can indeed act as the con- interconnect with the outside world, using well-known trollers governing the state of the active cells, oer- legacy networks and protocol stacks (e.g. Ethernet). ing manufacturing versatility and extreme energy e - The relative size of a meta-atom compared to the wave- ciency. Nonetheless, until nano-IoT becomes a main- length of the excitation (impinging wave) de nes the en- stream technology, other approaches can be adopted ergy manipulation precision and e ciency of a metama- for manufacturing software-de ned metasurfaces, as re- terial. For example, EM metasurfaces share many com- ported in the related physics-oriented literature [6]. It is mon attributes with classic antenna-arrays and re ect- also noted that nano-IoT as a general concept is about arrays. Antenna arrays can be viewed as independently embedding nano-sized computers into materials in order operating antennas, being very eective for coarse beam to augment the penetration level of applications (e.g., steering as a whole. Re ect-arrays typically consist of sense structural, temperature, humidity changes within smaller elements (still subwavelength), permitting more a material, rather than just over it, etc.), and not specif- ne-grained beam steering and a very coarse polariza- ically to control the energy propagation within them. tion control. Metamaterials comprise orders of magni- In contrast, our work refers speci cally to the case of tude smaller meta-atoms, and may also include tunable metamaterials and the capabilities they oer for the ma- elements and sensors. Their meta-atoms are generally nipulation of energy across physical domains. Moreover, considered tiny with regard to the exciting wavelength, our paper introduces the software enablers for this direc- hence allowing full control over the form of the departing tion, which has not been proposed before. Additionally, energy wave. ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
Departing waves: Directions, {D'(i)}. Frequency, {f'(i)}. Global switching frequency f Impinging wave: Bandwidth, {B'(i)}. sw Direction, D. Block size << 1/fmax Amplitudes, {A'(i)}. Frequency, fmax. Phases,{φ'(i)}. Bandwidth, B. Polarizations, {J'(i)}. Amplitude, A. if (fsw>fmax) Phase, φ. Modulations, {M(i)'}. Polarization, J. Modulation, M.
IoT Chipset-based gateway, NICs, and power supply connection
Passive Commands block Tunable >STEER(); and/or >FOCUS(); sensory >FILTER(); IC >ABSORB(); Tunable block and Sensory >PHASE_ALTER(); IC-to-gateway >POLAR_ALTER(); interconnection fabric offering Middleware Switch or Bus-type control >MODULATE(); >SENSE();
META-API
Fig. 5 Overview of the metasurface/metamaterial structure and operating principles.
Regardless of their geometry and composition, the oper- ting, focusing, collimating, beamforming, scatter- ating principle of metamaterials remains the same. As ing. depicted in Fig. 5, an impinging wave of any physical nature (e.g., EM, mechanical, acoustic, thermal) excites Bandwidth: Filtering. the surface elements of a metamaterial, initiating a spa- tial distribution of energy over and within it. We will call this distribution exciting-source. On the other Modulation: Requires embedded actuators that can hand, well-known and cross-domain principles state that switch states fast enough to yield the targeted mod- any energy wavefront, which we demand to be emitted ulation type [30]. by the metamaterial as a response to the excitation, can be traced back to a corresponding surface energy distri- Frequency: Filtering, channel conversion. bution denoted as producing-source [3, 6]. Therefore, a metamaterial con gures its tunable elements to cre- Doppler eect mitigation and non-linear eects [8]. ate a circuit that morphs the exciting-source into the producing-source. In this way, a metamaterial with high Additionally, sensing impinging waves may be consid- meta-atom density can perform any kind of energy wave ered one of the above functionalities and, as an outcome, manipulation that respects the energy preservation prin- the embedded sensors can extract information of any of ciple. Arguably, the electromagnetism constitutes a very the above parameters related to the incident wave. complex energy type to describe and, as a consequence, In this aspect, the role of the contributed metamate- manipulate in this manner, as it is described by two de- rial API is to model these manipulation types into a pendent vectors (electric and magnetic eld) as well as library of software callbacks with appropriate parame- their relative orientation in space, i.e. polarization (me- ters. Then, for each callback and assorted parameters, chanical, acoustic and thermal waves can be described the Metamaterial Middleware produces the correspond- by a single scalar eld in space). As such, incoming EM ing states of the embedded tunable elements that in- waves can be treated in more ways than other energy deed yield the required energy manipulation type. In types. The common types of EM wave manipulation via other words, a metamaterial coupled with an API and metamaterials, reported in the literature [3], can desig- a Metamaterial Middleware can be viewed as a hypervi- nate a set of high-level functionality types as follows: sor that can host metamaterial functionalities upon user Amplitude: Filtering (band-stop, -pass), absorp- request [8]. tion. In the following, we focus on EM metamaterials which, Polarization: Waveplates (polarization conversion, as described, yield the richest API and most complex modulation). Metamaterial Middleware. The expansion to other en- ergy domains is discussed via derivation in Section 7. Wavefront: Steering (re ecting or refracting), split- ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
API callbacks into metamaterial hardware directives. SDN app ecosystem Envisioned IoMMT apps A notable trait of the Metamaterial Middleware is that Device discovery Device access control Wireless Power Energy isolation and and registration it is divided into two parts, in terms of system deploy- & permissions app Transfer security app
Device location discovery app Propagation optimization ment [32]: 1. The metamaterial manufacturing stage component, Northbound Inteface IoMMT API a complex, o ine process requiring special meta- SDN Controller IoMMT Middleware DB material measurement and evaluation setups (dis- Southbound Inteface - Unified IoT and IoMMT interface cussed in Section 5), and 2. The metamaterial operation stage component, which operates in real time based on a codebook.
SDN This codebook is a database populated once by Existing IoT, Networking and Controller Sensor infrastructure the manufacturing stage component and contains
IoMMT API callback a comprehensive set of con gurations for all meta- Metamaterial unit connected to the material API callbacks, supported by a given meta- SDN Controller material. Contained and optimized propagation
IoMMT API IoMMT API The operation stage component simply retrieves con- callback callback Interference-protected area Environment gurations from the codebook and optionally combines OR enabling Avoided obstacle them as needed, using an interleaving process described OR software- Avoided untrusted device defined in Section 5. propagation Transmitter Notably, other studies propose the use of online machine Receiver learning as a one-shot process, which can be more prac- tical when response time is not a major concern [33]. However, in this work we propose the aforementioned Fig. 6 SDN schematic display of the system model and the separation in deployment, to ensure the fastest opera- entire work ow abstraction. tion possible overall, thus covering even the most de- 3. NETWORKED METAMATERIALS manding cases. AND SDN WORKFLOWS It is noted that SDN is not a choice due to restric- tions, but rather a choice due to compatibility. In the Many metamaterials deployed within an environment software-de ned metasurfaces presented in this paper, a can be networked through their gateways. This means key point is the abstraction of physics via an API that that they may become centrally monitored and con g- allows networking logic to be reused in PWEs, without ured via a server/access point in order to serve a partic- requiring a deep understanding of physics. SDN has ular end objective. (among other things) already introduced this separa- An example is given in Fig. 6, where a set of meta- tion of control logic from the underlying hardware and materials is designed with the proper commands for its administrative peculiarities. Therefore, we propose energy wave steering and focusing, in order to route an integration of PWE within SDN to better convey the the energy waves exchanged between two wireless users, logical alignment of the two concepts. thus avoiding obstacles or eavesdroppers. Other ap- plications include wireless power transfer and wireless 4. APPLICATION PROGRAMMING channel customization for an advanced quality of service INTERFACE FOR METAMATERI- (QoS) [7, 13]. Such a space, where energy propagation ALS becomes software de ned via metamaterials is called a programmable wireless environment (PWE) [8]. In the following, we consider a metamaterial in the form As shown in [7], the PWE architecture is based of a rectangular tile. The term tile is used to refer to a on the software-de ned networking (SDN) principles. practical metamaterial product unit, which can be used The PWE server is implemented within an SDN con- to cover large objects such as walls and ceilings in a troller [31]; the southbound interface abstracts the meta- oorplan. material hardware, treating metamaterial devices as A software process can be initiated for any metamaterial networking equipment that can route energy waves (e.g. tile supporting a unique, one-to-one correspondence be- similar to a router, albeit with a more extended and tween its available switch element con gurations and a unique parameterization). Thus, the metamaterial API large number of metamaterial functionalities. The meta- constitutes a part of the northbound SDN interface, material tiles in this work incorporate tunable switch atop of which the security, QoS and power transfer con- elements, which dictate the response of each individual cepts can be implemented as SDN controller applica- cell, locally. In this way, providing an arrangement of all tions. On the other hand, the Metamaterial Middleware the tile cells allows the tuning of the concerted meta- is part of the SDN middleware, translating metamaterial material response of the entire tile. ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
Associate Tile Enumerate Database Identifier Supported Tiles Interrupt user Initialize Configuration Environment Set Tile Handle State Database Interrupt Enumerate Sup- ported EM fucntions Get Tile Gateway State Interrupt (a) Tile Health Hypersurface Hardware error Check Gateway Communication error
HyperSurface Functionality
ID ID ID TID TypeID Type “Steering” Type “R,X,D” FreqID Ni DoAinID
DIRECTION_RANGE_1
Fig. 7 (a) Case diagram of the main functions supported by the three basic entities. Tasks highlighted with the database icon indicate that a set of data is to be retrieved from the Con guration Database. (b) Wavefront description in data object format. (c) A simpli ed overview of the structure of the Con guration Database. The
In this section, we present the API that grants access to is parameterized by a discrete variable corresponding to the tile's metamaterial applications by de ning an ab- the number of outgoing beams, their directivity ampli- stract representation of the metamaterial, its switch ele- tudes, and an appropriate number of (θ, ϕ) pairs, in- ment con gurations and their respective functionalities. dicating the steering angles. The most complex func- Speci cally, the API resides between a user, operating a tionality can be generally described by a custom scat- common PC (desktop, smartphone, etc.) and a tile gate- tering pattern and represented herein by a collection of way, linked to the network of switch element controllers. variables that indicate the re ected power towards all The case diagram of the proposed concept, presented in directions within the tile's viewing area. Fig. 7(a), involves the following main entities: It is, also, worth noting that the data objects being A Con guration Database which stores all informa- passed as arguments in the callbacks are primarily de- tion regarding the tiles, the switch element con g- scriptions of wavefronts. A simpli ed data structure is urations, and their corresponding functionalities. illustrated in Fig. 7(b). Hence, a wavefront is described by a type (string identi er), such as Planar, Elliptic, A User which initiates all API callbacks though ei- Gaussian, Custom, etc. For each type, a series of ther the source code or button click events in the headers de nes the location and attributes of the cre- Graphical User Interface (GUI). ating source (for impinging wavefronts only) as well as the coordinate system origin with respect to which all The HyperSurface Gateway which represents the distances are measured. Moreover, the direction of ar- electronic controller of the hardware. rival and departure are arrays that can be used to de ne An Interrupt handling service which acts as a per- multiple impinging or departing wavefronts at the same sistent daemon, receiving and dispatching com- time. Notably, the information within the headers may mands to the Hypersurface Gateway. be su cient to produce any value of the wavefront via simply analytical means. In such cases, the data part 4.1 Data Structures of the Metamaterial API can be left empty. In custom wavefronts, the data is populated accordingly. A mechanism for de ning peri- In the con guration Database (DB), each tile is associ- odicity is supplied via the notion of ranges (i.e. coor- ated with an element array S that represents all possible dinate ranges where the energy eld is approximately arrangements of switch element states on the metama- equal), to potentially limit the size of the overall data terial under study. Each switch element is represented object. by either a discrete or a continuous variable, creating a The parameters that represent the functionalities and mathematical space of V1 × N × M + ··· + Vn × N × M con gurations of a tile constitute the set of variables dimensions, where Vi is the number of elements of the that are exposed to the programmer through the meta- same type (e.g. capacitors) and N,M the number of material API. They are organized in a uni ed manner unit cells towards the two perpendicular directions. Fur- within the Database, as shown in Fig. 7(c) which pro- thermore, every object in this space corresponds to a dif- vides an illustration of the unique association between ferent state of S and therefore a dierent con guration. all primary tables. Particularly, the
proper set of hardware commands, when a suitable call- of the element state variables, which is, then, conveyed back is executed. In general, an API callback can refer to the tile Gateway using the corresponding protocol. to a number of common requests such as: This noti es the intra-tile control network to assign the switch element states to their suitable values. Finally, Detect the number and type of accessible tiles in feedback from a successful or failed con guration setup the environment. is received from the tile, notifying the user. The state of Get the current state of all switch elements or set the newly set con guration is evaluated through either them to a speci c con guration. the identi cation of failed or unresponsive switches, or by activating the sensing app, in controlled conditions, Check the health status and handle interrupts from as a self-diagnosing tool for the tile. the tiles or the Database. A more advanced API callback may involve the assign- Prior to any other callback, the API follows an initi- ment of a secondary or supplementary functionality, on ation process, while the software detects all presently top of an already existing operation. For instance, the active and connected (discoverable) tiles by broadcast- Metamaterial Middleware may receive independent re- ing a corresponding network message. The tiles report quests from dierent users to steer the wavefront of sev- their location and a unique identi er, e.g. a xed value, eral point power sources (i.e. the users' cellphones) to- that associates all tiles with the same hardware speci- wards the direction of a single nearby network hotspot. cations. The API validates the support of the active This can be handled by the API in many ways. In the tiles by checking if the identi er exists in the tile list case where several tiles are present in the environment, present in the database. It, then, retrieves the switch each tile can be repurposed to host a separate func- element arrays that correspond to these tiles and re- tionality, distributing all users to their own active tiles. mains idle until a new get or set request is received When this is not feasible, the API can divide a single for a currently active or new con guration, respectively. tile into separate areas and associate the respective el- This means that the API is now open to receive new ement switches to dierent con gurations (the division functionality requests from a user, physically operating is usually performed in equal-sized rectangular patterns, the software, or generate its own requests by reacting but interlacing can, also, be used). Lastly, two function- to unexpected changes in the environment of devices alities can be combined into a single one, when a cor- linked to the MS network. When a new functionality responding physical interpretation exists. For example, request is received, the API retrieves one of the avail- two separate steering operations, from the same source, able con gurations from the Database and translates it may be combined into a single dual-splitting operation, to a proper set of element states on an active metama- expressed by a single uni ed pattern on the metamate- terial tile. The corresponding API callback process is rial. illustrated in Fig. 8. In particular, the Caller (user) In other cases, the con guration resolver may need to executes a metamaterial Function Deployment request, combine several functionalities to produce a special new which, in turn, invokes the Con guration Resolver, iden- application. This occurs when a functionality is pa- tifying a tile that supports the requested functionality. rameterized by a continuous variable (e.g. a steering Next, the resolver queries the Database and returns a angle), while the Metamaterial Middleware can eval- con guration that matches the intended metamaterial uate and store only a nite number of entries in the application, looking for a proper entry in the
ter a certain timeout), the API is also responsible for API Callback: informing an available user. (HSF type, Impinging wave, R equired departing wavefront )
Phased array model Neural network 5. THE METAMATERIAL MIDDLE- Workflow model equivalent rays model WARE Phased array + circuit equivalent model Raw EM field model For the metamaterial to be recon gured between dif- Analytical model- based initial ferent functionalities, a physical mechanism for locally solution Period bounds tuning each unit cell response must be infused [8]. In the detection context of the present work, we assume that the response Smart solution- Effective variable of the unit cells is controlled by variable impedance loads space reduction discretization Goodness-of-Fit connected to the front side metallization layer of the metric Physics-derived metamaterial, where structures such as the resonant Model-based variable restrictions Regression calibration patch pair resides [3]. The loads are complex valued Approach (XCSF) variables, comprising resistors and capacitors or induc- Optimization engine selection & initialization tors. The value of the i-th load, Zi = Ri + jXi, com- prises two parameters: its resistance (Ri > 0) and re- −1 Fitness Function actance (Xi = −(ωCi) or Xi = +ωLi), for capacitive Simulations-driven and inductive loads, respectively. The loads are, thus, Optimization Loop electromagnetically connected to the surface impedance Measurements-driven Machine learning- of the unloaded unit cell and by tuning their values based accelerator we can regulate the unit cell response, e.g. the ampli- Store optimal (Populate training tude and phase of its re ection coe cient. The latter is configuration to DB dataset) naturally a function of frequency and incoming ray di- Fig. 9 The Metamaterial Middleware functionality optimization rection and polarization. When the metamaterial unit work ow. The work ow seeks to match an analytical metamate- cells are properly orchestrated by means of tuning the rial model and its parameters to a speci c parameterized API callback. A selected analytical model is rst calibrated. Then, an attached (Ri,Xi) loads, the desired functionality (global response) of the metamaterial is attained. iterative process (simulation or measurement-based) optimizes the input parameters of the model that best yield the API callback. In the most rigorous approach, the metamaterial re- sponse can be computed by full-wave simulations, and/or the response. For instance, broadband simula- tion for the response of a unit cell of volume λ 3 on a which implement Maxwell's laws, given the geome- ( 5 ) try and metamaterial properties of the structure as contemporary desktop computer could take several min- well as a complex vector excitation, i.e. the imping- utes, especially if the cell includes ne subwavelength ing wave polarization and wavefront shape (phase and features. The memory and CPU resources scale-up lin- amplitude pro le). The full-wave simulation captures early for metasurfaces comprising hundreds of thousands the entire physical problem and, hence, does not re- of unit cells. Moreover, full-wave simulations do not ex- quire a metamaterial-level abstraction for the structure. plicitly unveil the underlying principles that govern the Frequency-domain solvers, which assume linear media metamaterial functionality. and harmonic excitation (i.e. the same frequency com- ponent in both the excitation and the response), are the 5.1 Functionality Optimization Work ow: prime candidates for full-wave simulation. They typ- Metamaterial Modeling and State Cali- ically discretize the structure's volumes or surfaces at bration a minimum of λ/10 resolution, formulate the problem with an appropriate method (e.g. the nite-element or In this section we establish the optimization work ow the boundary-element method) and, then, numerically of Fig. 9 that drives the calibration process of the meta- solve a large sparse- or full-array system to compute the material via an appropriate approximation model. Here response, in our case, the scattered eld. Conversely, calibration denotes the matching of actual active ele- time-domain solvers assume a pulsed excitation, cov- ment states (e.g., the states of a tunable varactor) to ering a prede ned spectral bandwidth and iteratively the corresponding model parameter values (e.g., phase propagate it across the structure, solving Maxwell's dierence per cell in the re ectarray model). In this equations to compute its response; they, typically, re- work ow, the optical scattering response is initially in- quire a dense discretization of the structure, e.g. a min- vestigated and, then, the solution is hill-climbed via an imum of λ/20 resolution. From this process, it becomes optimization loop relying on either eld measurements evident that metamaterial with a wide aperture, i.e. or full-wave simulations. The approximate models are spanning over several wavelengths along the maximum as follows. dimension, require high computational resources in the The simplest model is the phased antenna-array anal- full-wave regime, scaling linearly, when parametric sim- ysis, where each single unit cell is treated as an inde- ulations need to be performed to optimize the structure pendent antenna, excited by a single impinging ray and ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
emitting a single ray in response, with a local phase and provided in the proper format, the optimization work- amplitude alteration. Assuming a metamaterial consist- ow of Fig. 9 continues, once again, with the calibration ing of M × N unit cells, the scattered E- eld complex phase, which is identical as before. The key dierence amplitude pattern at a given frequency can be calcu- and merit is that the calibration is, now, extremely pre- lated by the envelope (coherent superposition) of all rays cise with regard to the full-wave simulations, while it scattered from the metamaterial [2] takes much less time to complete, as detailed in the cor- responding study of [2]. M N An intermediate solution, combining the precision of X X jαmn E(θ, ϕ) = Amne fmn(θmn, ϕmn) the circuit model and the automation of the antenna- m=1 n=1 array model, is an equivalent propagation model, men- jγmn jΦmn(θ,ϕ) · Γmne fmn(θ, ϕ)e . (2) tioned here for the sake of completion. The main idea is to introduce a generic mechanism to capture the In (2), ϕ and θ are the azimuth and elevation angles in cross-interactions among meta-atoms (as opposed to the the scattering direction, (θmn, ϕmn) denotes the direc- strict, physics-derived nature of the circuit model) and tion of the wavefront `ray' incident on the mn-th cell, then proceed with automatic model calibration, avoid- Amn and αmn are the amplitude and phase of the in- ing the need for expert input. The equivalent ray model cident wavefront on the mn-th cell, Γmn and γmn form uses a neural network approach as the generic cross-talk the re ection coe cient (amplitude and phase) of the descriptor [35]. A short summary is as follows. Each mn-th cell, while fmn de nes the scattering pattern of meta-atom is mapped to a neural network node, and the mn-th cell, which, according to reciprocity, is iden- the locally impinging wave amplitude and phase are its tical for the incident and scattered direction, and, in inputs. Then, we clone this layer (omitting the inputs) this work, is assumed that fmn(θ, ϕ) = cos(θ). Finally, and form a number of intermediate, fully connected lay- Φmn(θ, ϕ) is the phase shift in the mn-th cell stemming ers (usually 3-5), thereby emulating a recurrent network from its geometrical placement, as with a nite number of steps. We de ne links per node (shared among all node clones), which de ne an alter-
Φmn(θ, ϕ) = k sin θ [dxm cos ϕ + dyn sin ϕ] + φ0(θ, ϕ), ation of the local phase and amplitude, and its distribu- (3) tion to other neighboring meta-atoms/nodes. Next, we where dx,y are the rectangular unit-cell lateral dimen- proceed to calibrate the model via feed-forward/back- sions, k = 2π/λ is the wavenumber in the medium en- propagation, thereby obtaining a match between R, X, closing the metamaterial, and is the reference phase φ0 Γmn, and γmn values. Nonetheless, despite its auto- denoting the spherical coordinate system center, typi- mated nature, a major drawback of this model is the cally in the middle of the metamaterial aperture. Given need for considerable computational resources, without a uniform, single-frequency impinging wave, any depart- which the model loses its value, since it becomes re- ing wavefront is essentially a Fourier composition of the stricted only to very simple metamaterial designs. individual meta-atom responses. Thus, we can, also, cal- Since computational complexity is a concern regard- culate the meta-atom amplitudes Γmn and phases γmn less of the chosen model, the Metamaterial Middle- that yield a desired departing wavefront, by applying ware work ow allows the user to de ne solution reduc- an inverse Fourier transform, as elaborately discussed tion across three directions. First, meta-atoms may be in [2]. The calculated Γmn and γmn values must be grouped into periodically repeated super-cells. Thus, mapped to the Ri and Xi values that generate them, the optimization work ow needs only to optimize the since the latter are the actual tunable metamaterial pa- con guration parameters of a super-cell, as opposed rameters. This process requires a set of simulations yet to optimizing the complete metamaterial. Second, the it can be automated: existing model calibration tech- range of possible R and X values per meta-atom can be niques, such as the Regression and Goodness of Fit can discretized into regular or irregular steps, reducing the be employed [34]. solution space further1. Finally, some R and X values The shortcoming of the antenna-array approach is that or ranges can be discarded due to the physical nature the coupling between adjacent unit cells (e.g., compare of the optimization request. For instance, if we seek against Fig. 5) is not properly accounted for, which can to optimize a wave steering approach with an empha- result to model imprecision [2]. To this aim, the Meta- sis on minimal losses over the metamaterial (maximum material Middleware user is presented with an alterna- re ection amplitude), the Ohmic resistance R needs to tive model. It utilizes the phased array and equivalent receive its boundary value. On a related track, ma- circuit model, which assumes not only the transmitting- chine learning-based approaches can quickly estimate responding antenna per meta-atom, but, also, circuit the performance deriving from one set of R and X val- elements that interconnect them and account for the ues, thereby discarding non-promising ones and acceler- cross-meta-atom metamaterial interactions. The disad- ating convergence [36]. vantage of this approach is that an expert needs to de ne 1Notably, contemporary optimization engines already incorporate this circuit model, that is generally unique per metama- equivalents to this direction, as they are able to detect strongly terial design [3]. Once this model has been selected and and loosely connected inputs-outputs [34]). ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
Subsequently, the Metamaterial Middleware work ow API Callback: moves to the optimization stage, where it attempts to (HSF type, Impinging wave, HSF Configuration , R esulting departing wavefront) hill-climb the initial solution detected via any of the Machine-learning based described approximate models. At this point, the work- estimation ow is compatible to any modern optimization engine, which receives an input solution and outputs one or Store estimated Estimation Y departing wavefront more proposed improvements upon it at each iteration. confidence and calculated sufficient Herein, we stress the existence of engines that, also, in- metrics to DB corporate machine learning mechanisms, to accelerate foreach metric the optimization cycle [34]. The optimization can be Analysis-driven N based either on full-wave simulations or a real measure- Wavefront metric ment test bed, described in Section 6. The optimiza- Simulations-driven Evaluation type calculation (reduction) tion metric can be any reduction of the produced de- parting wavefront. Various metrics relevant to antenna Measurements-driven and propagation theory may be extracted, namely: the Store departing number of main lobes (beam directions), the directiv- wavefront and (Populate training calculated metrics data set) ity of main lobes, the side (parasitic) lobes and their to DB levels, the beam widths, etc. Such metrics can be used to quantify the metamaterial performance for the re- Fig. 10 Work ow for pro ling a metamaterial functionality. The work ow seeks to produce a data set that describes the metama- quested functionality, e.g. the main lobe directivity and terial behavior for any impinging wave type that does not match beam width measures how well a metamaterial steers the one speci ed in the current metamaterial con guration. An an incoming wavefront to a desired outgoing direction. exhaustive evaluation takes place rst for a wide set of possible im- Lastly, the hill-climbed metamaterial con guration per- pinging waves. For intermediate impinging wave cases, the work- ow can rely on estimations produced by machine learning algo- taining to the metamaterial API callback is stored into rithms or simple extrapolation means, provided that it yields an a database for any future use by metamaterial users. acceptable degree of con dence. Finally, we note that multiple simultaneous functionali- ties can be supported by interlacing dierent scattering impinging and departing wavefronts. However, in real pro les across the metamaterial. In general, this is pre- deployments, it is not certain that a metamaterial will formed by spatially mixing the pro les in phasor form always be illuminated by the intended wavefront [24]. For instance, user mobility can alter the impinging wave-
Nc front in a manner that has limited relation to the in- jαmn X jαc,mn tended one and, consequently, to the running metama- Amne = Ac,mne , (4) c=1 terial con guration. As such, there is a need for fully pro ling a metamaterial, i.e. calculate and cache its where c iterates over single, "low-level" functionalities expected response for each intended metamaterial con- and n, m are the unit cell indices. Typically, low-level guration, but, also, for each possible (matching or not) functionalities correspond to simple beam steering op- impinging wavefront of interest. This pro ling process erations, which are produced exclusively by phase vari- is outlined in Fig. 10. ations on the metamaterial ( ). In this case, Ac,mn = 1 The pro ling process begins by querying the existing a "high-level" functionality will correspond to a multi- cache (part of the DB) or trained model for the given splitting operation with variable spatial distribution of metamaterial and an estimation (or existing calculated amplitude, raising the hardware requirements for Amn outcome) of the expected metamaterial response for a the metamaterial. Therefore, a metamaterial with no given impinging wave. If it exists, this response is stored absorption capabilities (and thus no control over ) Amn into a separate pro le entry for the metamaterial in the will have limited access to high-level operations, unless Metamaterial Middleware DB2. If the response needs a mathematical approximation is to be applied, skew- to be calculated anew, the process proceeds with either ing the scattering response from its ideal state. As dis- an analysis-, simulation- or measurement-driven eval- cussed in Section 6, a method for minimizing amplitude uation. Therefore, the choice is given as a means to variations has been successfully investigated by increas- facilitate the expert into reducing the required compu- ing the number of secondary parasitic lobes. Such a tational time, as allowed per case. Then, the pro ler problem can be easily reformulated into an optimization proceeds to, also, calculate all possible reductions of task, where an optimal match to the ideal high-level op- the departing wavefront, e.g. the number of main lobes eration can be pursued under speci c constrains (e.g. (beam directions), the directivity of the main lobes, the ). Amn > const., ∀m, n side (parasitic) lobes and their levels, the beam widths,
5.2 The Metamaterial Functionality Pro ler 2In case of an estimated response, the user has control over the process to lter out estimations with low con dence. However, The optimization work ow of Fig. 9 opts for the best the selected estimation engine must be able to provide a con - metamaterial con guration for a given, speci c pair of dence degree for this automation. ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
etc. Finally, once all required impinging wavefronts have The association of the metamaterial pro le to the been successfully processed, the pro ling process is con- set of element states, through the use of numerical cluded. simulations. It is clari ed, that the middleware operations are one- time only, i.e., once the database containing the behav- The experimental evaluation of the exported con- ior pro le of a metasurface is complete, it can be used guration through physical measurement of a meta- in any application setting in the real world by any tile material prototype. Notably, the presented soft- of the same type. ware has been veri ed experimentally, and a full report can be found online [20].
6. SOFTWARE IMPLEMENTATION The nal storing of all con guration parameters AND EVALUATION into the con guration DB to complete the function optimization process. Employing the concepts of the previous sections, we developed a complete Java implementation of the de- In this context, Fig. 11(a) depicts all the individual steps scribed software. The software is subdivided into two in separate panels. If a new con guration is to be de- integral modules: i) an implementation of the metama- ned for a tile already present in the con guration DB, terial API that handles the communication and alloca- then, the rst step can be skipped. Alternatively, the tion of existing con gurations and ii) the Metamaterial user must input all essential parameters of the unit cell Middleware that populates the con guration DB with structure, i.e. the number and type of all variables that new data (new tiles, con gurations, and functionalities). correspond to the sum of recon gurable metamaterial The Metamaterial Middleware incorporates a full GUI elements. The de nition of a new con guration begins environment, guiding the user through a step-by-step with the parameterization of the desired functionality process to produce new con gurations. It utilizes all (Fig. 11(a.2)). The current implementation supports available theoretical and computational tools for the ac- plane wave or point source inputs (for far- and near- eld curate characterization of a metamaterial tile. Further- energy sources) and a set of output options correspond- more, it oers direct access to the con guration DB, ing to all basic metamaterial functionalities, discussed manually, via a custom-made Structured Query Lan- in Section 2. Here, we select a beam splitting operation guage (SQL) manager or through the automated pro- and proceed to the rst main step of the characterization cess following a successful metamaterial characteriza- process. tion. Through this process, all the necessary data re- The analytical evaluation of the energy scattering pro- lated to a newly produced con guration become explic- le is performed in the software locally and in real time. itly available to the API. During this step, the Metamaterial Middleware calcu- A microwave metasurface was selected to demonstrate lates the proper scattered eld response of the imping- the capabilities of the developed concepts and methods ing wave, for each unit cell at the N × M tile, via either for software-tunable metamaterials. We adapted the de- the analytical methods of Section 5 or through an op- sign of [37], where a set of RF diodes can be employed to timization process. The scattered elds are evaluated toggle the re ection-phase of each cell between 16 states. iφ1 for each unit cell as a double complex variable (A1e We numerically extracted the response of the metasur- iφ2 and A2e , magnitude and phase of the re ected TE face (i.e., its scattering pattern), and nally used the and TM polarizations, respectively), a process physi- developed software to demonstrate how the metasurface cally correct under the condition that the unit cell is a response can be controlled. For the practical demonstra- subwavelength entity. This simply implies that the in- tion of the developed software in the same measurement put of the physical size eld in Fig. 11(a.1) must com- environment (anechoic chamber) with a simpler, 1-bit ply with this speci cation or a warning message will ap- metasurface hardware, we redirect the reader to [20,30], pear. In our example, the selected tile consists of binary since the hardware manufacturing topic is quite exten- elements (D stands for diodes), which may only control sive and clearly beyond the software aspects that con- the phase of the co-polarized scattered eld (φ1 term). stitute the focus of this paper. By clicking the View suggested button, we analytically In the following, we list and comprehensively describe calculate and display φ0 − φ1 (φ0 is the phase of the in- the steps undertaken during a optimization process, as coming wavefront), which should give an indication of seen through the GUI environment of the Metamaterial the diode states at the metamaterial. Alternatively, a Middleware. In summary, this process involves: similar result can be extracted by launching the meta- heuristic optimizer, either via a blind optimization pro- The de nition of a new unit cell structure and tile cess (all-0 initial solution) or an assisted optimization, array (if required). using the analytically evaluated pro le as an initial so- The parameterization of a new functionality. lution. The latter practice leads to more re ned results, over an analytical evaluation, by considering the nite The analytical evaluation of the scattering pro le size of the tile and the non-in nitesimal size of the unit on the metamaterial for the selected functionality. cell. ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
3. Evaluate the scattering profile 4. Perform switch-state calibration 1. Design a virtual tile 40x40 unit cellls analytical outcome
Phase φ Amplitude A
0 A=1,φ=2π
blind optimization outcome - binary space
Phase φ Amplitude A = 1 2. Define a new functionality (a)
Save configuration to Database Perform evaluation measurement
(b) (c)
Mast Positioner Controller angle Rx Antenna Rx Antenna VNA
Tx Antenna Turn Table angle Compiler Host
(d)
Fig. 11 (a) Snapshots of the Metamaterial Middleware GUI during the metamaterial characterization process. The user may select an existing tile or create a new one by drawing its unit cell topology and variable elements within. A new functionality for this tile is assigned by selecting the desired parameters for the impinging and outgoing waves. The process begins by evaluating the scattering response pro le, calculated as a phase and amplitude array for the selected metamaterial function. During the nal step the Metamaterial Middleware seeks the set of optimal states that can realize the scattering response into an actual hardware con guration. (b) After a successful characterization process the con guration is saved to the DB. Herein, as an instance, a snapshot of the DB manager that was created during development of the software is presented. (c) The Metamaterial Middleware oers extensive experimental capabilities through a dedicated module. (d) Utilizing the hardware present in an anechoic chamber, the Metamaterial Middleware was able to acquire full scattering diagrams of a metamaterial tile, mounted on the available positioning equipment. The three-dimensional (3D) schematic, shown on the left, is updated live as the turntable or the positioner head rotates during a measurement or optimization process. On the right hand side, the Metamaterial Middleware host (white laptop) connects via Ethernet to the VNA (measuring the Rx/Tx antennas) and to the positioner controller. ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
In the nal step, we seek to match the scattering eld Algorithm 1 Physical Element Calibration. High-
iφ1 iφ2 response (i.e. the (A1e ,A2e )i,j pairs calculated lighted commands run in a separate thread. in the previous step) to the appropriate set of element Set index to i = 0, j = 0 states, such as the resistance R and reactance X (ca- while index does not exceed max do pacitance or inductance) of the loads, for all unit cells Get Sij; Get T ol indexed by i, j. The correspondence between the scat- if !(exists Sdb where |Sdb − Sij| < T ol) then tering response of a unit cell and its physical structure while searching do constitutes a highly complex and demanding propaga- Select node where state == free tion problem. Actually, the search for a proper set of Set to busy states for a fully de ned response constitutes an inverse Initiate parallel thread; problem with closed-form solutions available only for Optimizer: Fixes (R, L, C, D) variables; very simple unit cell types. As such, it demands the use Optimizer → Requests new simulation; of highly e cient optimization algorithms and strong node ← Returns Ssim computation power to perform the necessary numerical S = S ; simulations. In our implementation, we employ a cluster db sim if |Ssim − Sij| < T ol then of interconnected computer clients (that serve as simu- searching = false lation nodes) receiving instructions through a TCP/IP Set (R, L, C, D) variables as optimal network from the host Metamaterial Middleware run- end if ning on the main machine. In a lightweight scenario, Set node state to free a single-cell simulation assigned to one of the clients end while can be completed in less than a minute but this may Iterate index grow substantially when the complexity of the design is end if increased. An acceptable convergence is expected to be end while reached within a few thousands simulation trials. In per- spective, the prototype studied in this work displayed a process and a new series of simulations begins un- typical evaluation time of 8 to 12 hours for a full char- til a su ciently close convergence to the targeted iφ1 iφ2 acterization of its con guration space and all possible scattering eld response Sij=(A1e ,A2e )i,j is impinging plane waves (θ,φ sets) at the operation fre- achieved. Prior to each simulation, the optimizer quency of the hardware. The evaluation is performed looks up all entries in the associated table of the through Algorithm 1 (see next page) whose steps are con guration DB, in case a result (Sdb) is already described below. Speci cally, the user: stored. If not, the simulation will start and the result will be stored afterwards. Over time, this selects one of the three options: i) a gradient based process can populate the DB with enough results, optimizer, recommended for a small number of vari- making option (iii) of the initial step a highly e - ables (less than four), ii) a metaheuristic optimizer, cient method for evaluating new functionalities for recommended for a higher count of variables (more this particular tile. than three), and iii) a database-based optimization process that con gures the proper switch-states, By completing the previous steps, the software has suc- through simulation results already stored in the cessfully de ned a new con guration for the chosen tile, con guration DB. which, now, remains to be stored in the con guration DB. Before doing so, we can apply an additional evalu- suggests a convergence limit to the optimizer T ol ation step by conducting an experimental measurement in the Tolerance text box for all variable targets, on a physical prototype (if available). Our current im- lls the IP list with all available simulation nodes plementation is able to assess multi-splitting or absorp- and initiates a connection. All nodes will launch the tion functionalities by measuring the full scattering dia- installed simulation software, open the correspond- gram on the front metamaterial hemisphere, which can ing geometry model, and remain idle until further then be compared to the scattering pro le produced instructions are received. by the software. As presented in the corresponding technical report [20], several tests with a metamaterial modi es, if necessary, the value range, step and ini- unit have already been successfully conducted in a fully tial value for each variable in the unit cell. equipped anechoic chamber (Fig. 11). This test bed incorporates a variety of algorithms, meeting individual begins the optimization process by clicking the needs for accuracy and speed for various cases of scat- RUN button. The software will iterate over tering response measurements (e.g. a full 3D-pattern all unit cells (from top-left (0, 0) to bottom-right versus a 2D-slice might be required for arbitrary lobe (N,M)), seeking a set of optimal values for the scattering). A simple case is outlined in Algorithm 2, variables of each cell. During this sequence, each where the user has previously set the appropriate pa- unit cell (i, j) initiates an independent optimization rameters in the GUI (Fig. 11(c)). The GUI automates ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
Dual splitting 00 01 10 11 Quad splitting Incident plane wave 20x20 pattern
E2 linear normalized Circular beam Dual-circular beam pattern (40x40) pattern Dual splitting - shifted Quad splitting - shifted 1
Ε2
0
(a) E2 linear normalized
E2 logarithmic (dB) Phase φ Amplitude A (b) 0 Ε2 20 0 φ 2π 0 Α 1
(c)
Fig. 12 (a) Element states and far- eld scattering diagram of a 4-bit metamaterial array for 6 dierent cases. (b) Element states and scattering diagrams for triple- and quintuple-beam scattering, assuming continuously adjustable states with absorption capabilities. (c) Comparison between a tile with resistor elements (left) and a tile without (right) for an in-plane triple-beam splitting functionality. A non-uniform amplitude pattern (Amn) can eliminate all side-lobes and provide increased security to the signal. The incident eld in all cases is a vertically impinging plane wave, chosen for simplicity; yet any plane wave or point source input can be considered by a corresponding shift of the phase for each unit cell. ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
Algorithm 2 Evaluate Scattering Response, 3D, slow rotation case. Highlighted commands refer to HW in- structions. Set N ← number of equidistant points on the hemi- sphere Set P [N] ← Struct of (θ, φ) points for all elements i in P do Send rotation command → Positioner (Mast - P [i].theta, Head - P [i].phi, Speed) Receive ← Positioner feedback Refresh 3D Figure Send measurement command → VNA E[i] = Receive ← S21 power Fig. 13 Arbitrary functionality optimization test. A smiley face- Refresh Plot graph shaped scattering pattern is successfully produced (left). The cor- responding metamaterial element con guration (dierent meta- end for atom states expressed in colormap) is shown to the left. Save (P [i],E[i]) to DB unit cell with re ection phases (−90, 0, 90, 180) and full the process of measuring metamaterial devices in the re ection amplitude (A = 1) [14,38]. The outcome com- anechoic chamber by supporting several communication plies fully with the results provided by the corresponding interfaces for the following equipment: authors, indicating that the Metamaterial Middleware may cooperate with arbitrary hardware con gurations Vector network analyzer (VNA): produces the en- and thus be compatible with any reasonable design in ergy signal and receives the response (i.e. S21- a future diverse metamaterial market. Following these parameter) from the antenna setup. results, we, also, test four exclusive cases that highlight the additional capabilities of our software. In particu- Positioner: allows the mechanical support of the lar, Fig. 12(b) displays the results for a triple- and a metamaterial and antenna devices. Its controller quintuple-beam splitting case, while Fig. 12(c) shows can instruct the rotation of both heads (towards the optimization outcome for an in-plane triple-beam ), allowing a complete characterization of the θ, φ splitting functionality. For the latter case, the inte- scattering pro le. grated theoretical algorithms were able to eliminate all metamaterial controllers: A metamaterial hosting side lobes by suggesting a non-uniform pattern for the recon gurable elements incorporates a communica- amplitude A of the co-polarized scattered eld. This tion network for the explicit control of its element particular case demands a tile with controllable absorp- states. The Metamaterial Middleware implements tion elements (resistors). Finally, in Fig. 13 we proceed the proper interface for the evaluation prototypes to showcase the optimization of an arbitrary departing (serial port connection, WiFi, and Bluetooth have wavefront formation. A smiley-face shaped scattering been integrated). The same interfaces are, also, pattern is selected as the required energy wave response used for the metamaterial API developed in Sec- of the metamaterial to a planar impinging wave. The tion 4. optimization process successfully produces the required wavefront, and the corresponding metamaterial element A supplementary note is that the nal switch-state con- states are shown to the right of Fig. 13. guration can be re-evaluated using the same meta- heuristic optimizer utilized in step 3 of Fig. 12(a) 7. DISCUSSION: THE TRANSFOR- via actual experimental results. The optimizer starts MATIONAL POTENTIAL OF THE with the software-de ned con guration as an initial so- IOMMT AND FUTURE DIREC- lution and gradually adjusts the switch-state matrices to more optimally converge to the pursued functional- TIONS ity under true operational conditions. The implemented While the potential of the IoMMT paradigm alone may algorithm follows the template of Algorithm 2, where be worth the investigation, here we evaluate its practical N correspond to the number or optimization variables opportunities aecting the industry, the end users and (e.g. the number of scattering lobes) and a second for the environment, namely: loop nests the existing loop, seeking to maximize the
Sumi(E[i]) metric. How can the IoMMT prolong the life cycle of prod- For further evaluation purposes, we validate a number of ucts across deployment scales? indicative examples from the literature, based on previ- ously measured and simulated results. Hence, Fig. 12(a) How can the IoMMT help maintain a high-speed presents the optimization outcome for a 4-bit metamate- product development pace, without sacri cing eco- rial array, which can switch over four available states per logical concerns during the product design phase? ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
To these ends, we believe that the concept of Circular Micro-manage energy losses Energy manipula on as Economy (CE) and its associated performance indices and parasi c dissipa ons via an app framework so ware is a tting framework for the initial exploration and evaluation of the IoMMT paradigm [39, 40]. CE seeks SONIC Electromagne c, Mechanical, to make technological products reusable, repairable and Acous c and Thermal proper es API Northbound Interface recyclable across their lifetime (i.e., development, pur- EM chase, usage and disposal) by introducing cross-product Smart Environment Configura on and cross-manufacturer interactions. Instead of the tra- Consistency and Orchestra on ditional, linear order of life cycles phases, i.e., i) raw re- MECH Material Mul -physics M-ware source acquirement, ii) processing, iii) distribution, iv) Monitoring SONIC HEAT its use and v) disposal, the CE advocates to create links Modules EM MECH from disposal to all preceding phases, promoting i) re- THERMAL Southbound Interface processing or refurbishment, ii) redeployment and redis- Inteconnectivity, deployment and configuration tribution, and iii) multiple uses. IoMMT varia ons & However, according to the literature [41], the CE intro- AI-driven composi on profiling and rec- duces a paradoxical tension in the industry: While the ommenda on systems industry is pressed for faster growth and, hence, a faster Equipment-level Outdoors, (Motors, Cooling/ Indoors- vehicular, IoMMT data and energy product development rate, CE can introduce a series of Hea ng/Insula ng level smart-city models surfaces) design considerations that make for a slower product de- velopment rate. In this view, the paradigm of IoMMT Fig. 14 Envisioned future research directions for the Internet is by its nature impactful for the energy and ecologi- of MetaMaterials. cal footprint of multiple products, across disciplines and als, to match the requirements and speci cations of any scales. envisioned application. The fact that it enables the tuning and optimization of Moving to the networking layer, research can follow the the physical properties of matter allows for a tremen- proposed northbound/southbound abstraction model dous impact both in terms of quality of service per inspired from the SDN paradigm. This will provide the product and scale, but also for energy savings in a hor- necessary platform for: i) Interconnecting the IoMMT to izontal manner. Moreover, the IoMMT can contribute the vast array of existing networked devices and assorted a software-driven way for optimizing material proper- standards, models and protocols. ii) Provide the neces- ties. Using this new technology, the industrial players sary software abstractions, to open the eld of energy can maintain a fast-paced product design, where energy micromanagement to software developers (i.e., without e ciency and sustainability can be upgraded program- specialty in Physics), enabling the energy-propagation- matically via eco- rmware during its use, thereby of- as-an-app paradigm. In this aspect, we also envision the oading the product design phase of such concerns. need for algorithms optimizing IoMMT deployments for An important future research direction of the IoMMT is minimal-investment-maximal-control, and orchestrating to quantify the nancial savings stemming from adopt- IoMMT deployments for any set of generic energy micro- ing this technique. While the Circular Economy-derived management objective. paradoxical tensions have been around for long and are hard to eradicate, IoMMT can facilitate their resolu- The control time granularity depends on the application tion by quantifying them, potentially aligning fast-paced scenario and the volatility of the factors aecting the en- marketing with environmental sustainability. ergy propagation within an environment. In an indoors Apart from the CE line of work, future work will seek wireless communications setting, such as the one studied to provide the theoretical and modeling foundations of in [7], where the Intelligent Wireless Environment needs the IoMMT. Our vision, overviewed in Fig. 14, is for to continuously adapt to the position of user devices, a full-stack study of this new concept, covering: the the control granularity can be considered to be 10-25 physical layer modeling, the internetworking and com- msec (i.e., randomly walking users running a mobile ap- munications layer, and the application layer. plication). At the physical layer, future research needs to classify Finally, at the application layer research can de ne key- and model metamaterials in a functionality-centric way, applications of the IoMMT at multiple scales, start- and introduce tting Key-Performance Indicators for ing from the internals of equipment, such as motors, each oered energy manipulation type. Metasurface- heating, cooling and insulating surfaces. This will pro- internal control variations (technologies and ways of vide the basic units for IoMMT incorporation to de- monitoring embedded active elements) can be taken into vices spanning home appliances (ovens, refrigerators, account, and aspire to deduce models covering the as- washers, heating and cooling units), electronics (from pects of control data tra c, energy expenditure and interference cancellation, to smart cooling) and build- feature-based manufacturing cost estimation. This will ing materials (acoustic, thermal and mechanical insula- enable the creation of the rst, cross-physical domain tors). Various scales can be taken into consideration, pro ling and recommendation system for metamateri- from indoor (smart-house) and outdoor (city-level, ve- ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
hicular networks) IoMMT deployments. no. 6, pp. 527537, Dec. 2015. [Online]. Available: https://doi.org/10.1109/jiot.2015.2413397 8. CONCLUSION [2] F. Capolino, Theory and phenomena of metamate- In this paper, we pursue the systematic expansion of the rials. CRC press, 2017. concept of software-de ned metamaterials by establish- [3] A. Li, S. Singh, and D. Sievenpiper, Metasur- ing the key software elements of a metamaterial network. faces and their applications, Nanophotonics, vol. 7, With this goal in mind, we introduce two special cate- no. 6, pp. 9891011, 2018. gories of software, the Metamaterial Middleware, which can methodically produce novel con gurations for a sin- [4] M. Kadic, T. Bückmann, R. Schittny, and M. We- gle metamaterial tile and the metamaterial API, which gener, Metamaterials beyond electromagnetism, is in charge of supervising the energy propagation within Reports on Progress in physics, vol. 76, no. 12, p. a metamaterial-coated space. The two components were 126501, 2013. studied and developed autonomously, and then merged into a uni ed application via a common layer of abstrac- [5] J. U. Surjadi, L. Gao, H. Du, X. Li, X. Xiong, tion. N. X. Fang, and Y. Lu, Mechanical metamateri- For the API, we explored the means to interpret a meta- als and their engineering applications, Advanced material, its con guration space, and the supported en- Engineering Materials, vol. 21, no. 3, p. 1800864, ergy manipulation functionalities via a group of well- 2019. de ned software objects. The key objective was to suc- [6] M. Pishvar and R. L. Harne, Foundations for soft, cessfully conceal the physical layer of the metamaterial, smart matter by active mechanical metamaterials, and only expose the essential parameters for con guring Advanced Science, p. 2001384, 2020. an operation. In light of this de nition, the API is capa- ble of instructing its environment to steer, focus, absorb [7] C. Liaskos, A. Tsioliaridou, A. Pitsillides, S. Ioanni- or split the incoming signals through a simple interface, dis, and I. Akyildiz, Using any surface to realize a without any reference to the underlying physics. new paradigm for wireless communications, Com- The Metamaterial Middleware was developed by means mun. ACM, vol. 61, pp. 3033, 2018. of various theoretical and computational tools, includ- ing analytical algorithms that assess the scattering re- [8] C. Liaskos, A. Tsioliaridou, S. Nie, A. Pitsillides, sponse, full-wave simulations that accurately evaluate S. Ioannidis, and I. Akyildiz, On the network- a unit cell response, and experimental modules that layer modeling and con guration of programmable can perform physical measurements of metamaterial de- wireless environments, IEEE/ACM Trans. Netw., vices. Through a step-by-step process we de ned a ro- vol. 27, no. 4, pp. 16961713, 2019. bust characterization methodology, supporting a large [9] I. F. Akyildiz and J. M. Jornet, The internet set of metamaterial operations with no restriction on the of nano-things, IEEE Wireless Communications, speci cations for the metamaterial. We also discuss the vol. 17, no. 6, pp. 5863, 2010. prospect of applying dierent optimization algorithms for each stage of the metamaterial characterization pro- [10] S. B. Glybovski, S. A. Tretyakov, P. A. Belov, Y. S. cess. Finally, the evaluation of the middleware outcomes Kivshar, and C. R. Simovski, Metasurfaces: From demonstrated that the IoMMT paradigm can be em- microwaves to visible, Phys. Rep., vol. 634, pp. ployed successfully within a uni ed framework. 172, 2016. [11] H.-T. Chen, A. J. Taylor, and N. Yu, A review ACKNOWLEDGMENT of metasurfaces: Physics and applications, Rep. This work was supported by the European Union's Hori- Prog. Phys., vol. 79, no. 7, pp. 076 401(140), 2016. zon 2020 research and innovation programme-project [12] C. Huang, C. Zhang, J. Yang, B. Sun, B. Zhao, and C4IIoT, GA EU833828. The authors also acknowl- X. Luo, Recon gurable metasurface for multifunc- edge FETOPEN-RIA project VISORSURF. All pre- tional control of electromagnetic waves, Adv. Opt. sented software modules were conceived, designed and Mater., vol. 5, no. 22, pp. 1 700 485(16), 2017. implemented in full by the Foundation for Research and Technology-Hellas (FORTH) and G. Pyrialakos served [13] Ö. Özdogan, E. Björnson, and E. Larsson, Intelli- as the lead developer. gent re ecting surfaces: Physics, propagation, and pathloss modeling, IEEE Wireless Commun. Lett., REFERENCES vol. 9, no. 5, pp. 581585, 2019. [1] J. Pan, R. Jain, S. Paul, T. Vu, A. Saifullah, [14] Q. Zhang, X. Wan, S. Liu, J. Yin, L. Zhang, and M. Sha, An internet of things framework and T. Cui, Shaping electromagnetic waves us- for smart energy in buildings: Designs, prototype, ing software-automatically-designed metasurfaces, and experiments, IEEE Internet Things J., vol. 2, Sci. Rep., vol. 7, no. 1, pp. 3588(111), 2017. ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
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[24] C. Liaskos, S. Nie, A. Tsioliaridou, A. Pitsil- [34] M. Laguna and R. Marti, The OptQuest callable lides, S. Ioannidis, and I. F. Akyildiz, Mobility- library, in Optimization Software Class Libraries. aware beam steering in metasurface-based pro- Springer, 2003, pp. 193218. grammable wireless environments, in ICASSP 2020-2020 IEEE International Conference on [35] C. Liaskos, A. Tsioliaridou, S. Nie, A. Pitsillides, Acoustics, Speech and Signal Processing (ICASSP). S. Ioannidis, and I. Akyildiz, An interpretable neu- IEEE, 2020, pp. 91509154. ral network for con guring programmable wireless ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
environments, in IEEE Int. Workshop Signal Pro- AUTHORS cessing Adv. Wireless Commun. (SPAWC 2019), 2019, pp. 15. [36] H. Taghvaee, A. Jain, X. Timoneda, C. Liaskos, S. Abadal, E. Alarcón, and A. Cabellos-Aparicio, Radiation pattern prediction for metasurfaces: A neural network based approach, arXiv preprint arXiv:2007.08035, 2020. [37] Y. Saifullah, A. B. Waqas, G.-M. Yang, F. Zhang, and F. Xu, 4-bit optimized coding metasurface for Christos Liaskos received a Diploma in Electrical and wideband rcs reduction, IEEE Access, vol. 7, pp. Computer Engineering from the Aristotle University of 122 378122 386, 2019. Thessaloniki (AUTH), Greece in 2004, an MSc degree in Medical Informatics in 2008 from the Medical School, [38] S. Liu, T. J. Cui, L. Zhang, Q. Xu, W. Qiu, X. Wan, AUTH and PhD degree in Computer Networking from J. Q. Gu, W. X. Tang, M. Q. Qi, J. G. Han, W. L. the Dept. of Informatics, AUTH in 2014. He has pub- Zhang, X. Y. Zhou, and Q. Cheng, Convolution lished work in several journals and conferences, such as operations on coding metasurface to reach exible IEEE Transactions on: Networking, Computers, Vehic- and continuous controls of terahertz beams, Adv. ular Technology, Broadcasting, Systems Man and Cy- Science, vol. 3, no. 10, pp. 1 600 156(112), Jul. bernetics, Networks and Service Management, Commu- 2016. nications, INFOCOM. He is currently an assistant pro- [39] W. R. Stahel, The circular economy, Nature, vol. fessor with the University of Ioannina, Greece, and an 531, no. 7595, pp. 435438, 2016. a liated researcher at the Foundation of Research and Technology, Hellas (FORTH). His research interests in- [40] C. Liaskos, A. Tsioliaridou, and S. Ioannidis, To- clude computer networks, security and nanotechnology, wards a circular economy via intelligent metamate- with a focus on developing nanonetwork architectures rials, in 2018 IEEE 23rd International Workshop and communication protocols for future applications. on Computer Aided Modeling and Design of Com- munication Links and Networks (CAMAD). IEEE, 2018, pp. 16. [41] T. Daddi, D. Ceglia, G. Bianchi, and M. D. de Bar- cellos, Paradoxical tensions and corporate sus- tainability: A focus on circular economy business cases, Corporate Social Responsibility and Envi- ronmental Management, vol. 26, no. 4, pp. 770780, 2019. George Pyrialakos received his Diploma and Ph.D. degree from the Dept. of Electrical and Computer En- gineering, Aristotle University of Thessaloniki (AUTH), Greece, in 2013 and 2019, respectively. He is currently a post-doc researcher in AUTH, a liated with the Foun- dation for Research and Technology Hellas (FORTH). His research interests include computational electromag- netics, metamaterials, photonics, and condensed matter physics.
Alexandros Pitilakis received his Diploma and Ph.D. degree from the Dept. of Electrical and Computer En- ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
gineering, Aristotle University of Thessaloniki (AUTH), Greece, in 2005 and 2013. He, also, holds an MSc. de- gree from the ENST Paris, 2007. He is a post-doc re- searcher in AUTH, a liated with the Foundation for Research and Technology Hellas (FORTH). His research interests include computational electromagnetics, meta- materials, and nonlinear optics.
Nikolaos Kantartzis is a professor at the School of Electrical and Computer Engineering, Aristotle Univer- sity of Thessaloniki (AUTH), Greece, from where he received the Diploma and Ph.D. degrees in 1994 and 1999, respectively. His primary research interests in- clude computational electromagnetics, metamaterials, graphene, EMC/EMI problems, microwaves, and nano devices. Dr. Kantartzis is a Senior Member of IEEE, an ICS and ACES member. Ageliki Tsioliaridou received a Diploma and PhD degrees in Electrical and Computer Engineering from the Democritus University of Thrace (DUTH), Greece, in 2004 and 2010, respectively. Her research work is mainly in the eld of Quality of Service in computer networks. Additionally, her recent research interests lie in the area of nanonetworks, with a speci c focus on ar- chitecture, protocols, security and authorization issues. She has contributed to a number of EU, ESA and Na- tional research projects. She is currently a researcher at the Foundation of Research and Technology, Hellas Sotiris Ioannidis received a BSc degree in Mathemat- (FORTH). ics and an MSc degree in Computer Science from the University of Crete in 1994 and 1996 respectively. In 1998 he received an MSc degree in Computer Science from the University of Rochester and in 2005 he received his PhD from the University of Pennsylvania. Ioanni- dis held a Research Scholar position at the Stevens In- stitute of Technology until 2007, and since then he is Research Director at the Institute of Computer Science of the Foundation for Research and Technology - Hel- las. Since November 2017 he is a member of the Eu- ropean Union Agency for Network and Information Se- Michail Christodoulou received his Diploma and curity (ENISA) Permanent Stakeholders Group (PSG). Ph.D. degree in Electrical and Computer Engineering His research interests are in the area of systems, net- from the Dept. of Electrical and Computer Engineering, works, and security. Ioannidis has authored more than Aristotle University of Thessaloniki (AUTH) in 1994 100 publications in international conferences and jour- and 2006, respectively. In 1995, he received an MSc. nals, as well as book chapters, including ACM CCS, degree from the University of Bradford, UK. In 2014, ACM/IEEE ToN, USENIX ATC, NDSS, and has both he joined AUTH as a Postdoctoral Research Fellow. chaired and served in numerous program committees His research interests include RF circuits, antennas, RF in prestigious international conferences. Ioannidis is MEMS, and electromagnetic measurements. a Marie-Curie Fellow and has participated in numer- ous international and European projects. He has coor- dinated a number of European and National projects (e.g. PASS, EU-INCOOP, GANDALF, SHARCS) and is currently the project coordinator of the THREAT- ARREST, I-BiDaaS, BIO-PHOENIX, IDEAL-CITIES, CYBERSURE, and CERTCOOP European projects. ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1, 11 December 2020
Academy of Sciences, in Moscow, Russia, since May 2018. He serves on the Advisory Board for the newly es- tablished research center called Technology Innovation Institute (TII) in Abu Dhabi, United Arab Emirates since June 2020. He is the President of the Truva Inc. since March 1989 and Scienti c Advisor for the newly es- tablished company Airanaculus since April 2020. He is a Visiting Distinguished Professor with the SSN College of Andreas Pitsillides is a Professor in the Depart- Engineering in Chennai, India since October 2019. Dr. ment of Computer Science, University of Cyprus, heads Akyildiz is an Adjunct Professor with the Department NetRL, the Networks Research Laboratory he founded of Electrical and Computer Engineering at the Univer- in 2002, and is appointed Visiting Professor at the sity of Iceland since September 2020. He is the Ken University of the Witwatersrand (Wits), School of Byers Chair Professor Emeritus in Telecommunications Electrical and Information engineering, Johannesburg, at the Georgia Institute of Technology, and the Direc- South Africa. Earlier (2014-2017) Andreas was ap- tor of the Broadband Wireless Networking Laboratory pointed Visiting Professor at the University of Johan- and Former Chair of the Telecom Group from 1985- nesburg, Department of Electrical and Electronic En- 2020. He is also former Finnish Distinguished Professor gineering Science, South Africa. His broad research with the University of Tampere, Finland, supported by interests include communication networks ( xed and the Finnish Academy of Sciences from 2012-2016. He mobile/wireless), Nanonetworks and Software De ned is the founder of NanoNetworking Center and Former Metasurfaces/Metamaterials, the Internet- and Web- of Honorary Professor at the University of Politecnica de Things, Smart Spaces (Home, Grid, City), and Internet Cataluna in Barcelona from 2008-2017. Dr. Akyildiz is technologies and their application in Mobile e-Services, also former Distinguished Professor and Founder of the especially e-health, and security. He has a particular Advanced Wireless Networks Lab with the King Ab- interest in adapting tools from various elds of applied dulaziz University in Jeddah, Saudi Arabia from 2011- mathematics such as adaptive non-linear control theory, 2018. He is also the Founder of the Advanced Wireless computational intelligence, game theory, and recently Sensor Networks lab and former ExtraOrdinary Profes- complex systems and nature inspired techniques, to sor with the University of Pretoria, South Africa from solve problems in communication networks. He has pub- 2007-2012. lished over 270 referred papers in agship journals (e.g. He is the Founder and Editor in Chief of the newly es- IEEE, Elsevier, IFAC, Springer), international confer- tablished ITU J-FET (International Telecommunication ences and book chapters, 2 books (one edited), par- Union Journal for Future and Evolving Technologies ticipated in over 30 European Commission and locally since July 2020. Dr. Akyildiz is Editor-in-Chief Emer- funded research projects as principal or co-principal in- itus of Computer Networks Journal (Elsevier) (1999- vestigator, received several awards, including best pa- 2019), the founding Editor-in-Chief Emeritus of the per, presented keynotes, invited lectures at major re- Ad Hoc Networks Journal (Elsevier) (2003-2019), the search organisations, short courses at international con- founding Editor-in-Chief Emeritus of the Physical Com- ferences and short courses to industry, and serves/served munication (PHYCOM) Journal (Elsevier) (2008-2017), on several journal and conference executive committees. and the founding Editor-in-Chief Emeritus of the Nano Communication Networks (NANOCOMNET) Journal (Elsevier) (2010-2017). Dr. Akyildiz launched many IEEE and ACM conferences. He is an IEEE Fel- low (1995) and ACM Fellow (1996) and received nu- merous awards from IEEE and ACM and other pro- fessional organizations. His current research inter- ests are in 6G Wireless Systems, Recon gurable Intel- ligent Surfaces, Terahertz Communications, Nanonet- works, Internet of xThings (x=Underwater, Under- ground, Space/CubeSats, Nano and BioNano). He Ian F. Akyildiz received his BS, MS, and PhD de- graduated 45 PhD students and 30 of them are in grees in Electrical and Computer Engineering from the academia in very prestigious academic positions world- University of Erlangen-Nï¾÷rnberg, Germany, in 1978, wide. He advised 13 Postdoctoral researchers. Accord- 1981 and 1984, respectively. Currently he serves as a ing to Google Scholar as of September 2020, his H-index Consulting Chair Professor with the Computer Science is 124 and the total number of citations to his papers is Department at the University of Cyprus since January 119+K. His rank in terms of h-index in the world is 46 2017. and in the USA 32. He is also the Megagrant Leader with the Institute for Information Transmission Problems at the Russian