SWARM CENTRIC SOLUTIONS FOR INTELLIGENT SENSOR NETWORKS A. Farina(1), L. Chisci(2), F. Fedi (3) (1) FIEEE, FIET, FREng, Fellow of EURASIP IEEE AESS Board of Governors CTIF Industry Advisory Chair Visiting Professor at UCL Dept Electronics Consultant, Land & Naval Defence Electronics Division, Leonardo Company, Roma, Italy (2) Dpt. of Information Engineering (DINFO) University of Florence (3) Land and Naval Defence Electronics Division, Leonardo Company, Roma, Italy Key Note Speech ”Swarm Theoretical Concepts & Applications. An introduction” SPECIALISTS’ MEETING - SET- 222 Venue: Centro Alti Studi Della Difesa, P.za Della Rovere, Roma Summary Specialists’ Meeting Topics Definition of SWARM Complexity of a swarm Measuring complexity Intelligence of a swarm Books and papers defining the state of art Enabling algorithms Operational needs Potential systems and applications Way ahead 2 Specialists’ Meeting Topics: Swarm Operations: Does the swarm-centric sensor network and architecture extend the operational capabilities and range? For which use cases the swarm is more relevant than hierarchical structures? Which issues in the adoption of swarm of sensor in operation have been identified? Swarm Applications: Which families of applications will benefit from the adoption of swarm concepts? Which innovative kind of applications will be enabled? Swarm Systems Engineering: How to design, develop and validate a swarm- centric system? How to find the key performance indicators? How to handle dependability, security, interoperability, adaptivity, (self-) organization of the swarm of sensors? What kind of enhancements or of new protocols is needed? Symbiotic Human-Swarm Interaction: How could a swarm of sensors improves situational awareness? How would the key design features of HCIs for swarms look like? Which degree of autonomy is desirable? Power management and energy harvesting: Which methods/technologies/techniques are available to extend the operational lifetime of the swarm? Modeling and Simulation (M&S): How does M&S improve the understanding of swarm behaviors? Applied mathematics: What kind of maths is available to analyze swarms? How to estimate the computational load associated with swarm-centric algorithms? Signal processing: What kind of algorithms are suited best to be implemented on 3 (massive) sensor networks whose elements may have processing constrains? Summary Specialists’ Meeting Topics Definition of SWARM Complexity of a swarm Measuring complexity Intelligence of a swarm Books and papers defining the state of art Enabling algorithms Operational needs Potential systems and applications Way ahead 4 Definition of Swarm (https://en.wikipedia.org/wiki/Swarm_behaviour) Swarm behaviour, or swarming, is a collective behaviour exhibited by entities, particularly animals, of similar size which aggregate together, perhaps milling about the same spot or perhaps moving en masse or migrating in some direction. It is highly interdisciplinary topic. Swarming is applied to insects, any other entity or animal that exhibits collective behaviour. The term flocking is used to refer to swarm behaviour in birds, herding to refer to swarm behaviour in quadrupeds, shoaling or schooling to refer to swarm behaviour in fish. …. By extension, the term swarm is applied also to inanimate entities which exhibit parallel behaviours, as in a robot swarm, an earthquake swarm, or a swarm of stars. Swarm behaviour is the collective motion of a large number of self-propelled entities. It is an emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination. 5 METRONOMES Summary Specialists’ Meeting Topics Definition of SWARM Complexity of a swarm Measuring complexity Intelligence of a swarm Books and papers defining the state of art Enabling algorithms Operational needs Potential systems and applications Way ahead 6 Taxonomy of systems • Simple – They have a small number of components which have well-defined roles and are governed by well understood rules. • Complicated – They have a large number of components which have well- defined roles and are governed by well understood rules; 346 AM – Robustness is achieved through redundancy. • Complex – They have a large number of similar components which may act according to rules that may change over time and that may not be well understood; – The connectivity of the components may be quite plastic and roles may be fluid; – Robustness is achieved by enabling the parts to adapt to the changing environment and adopt different roles; – Need to distinguish between complex system and complex dynamics (complex time behaviour may arise from simple systems). 7 Definition of “Complex System” • Complex systems contain many constituents; • constituents of a complex system are interdependent and interact non-linearly; • complex system possesses a structure spanning several spatial scales; • complex system is capable of emerging behavior (i.e. a self-organizing collective behaviour difficult to anticipate from a knowledge of agents’ behaviour). Birds adapt to neighbouring behaviour and automatically form a flock Internet map 8 (source “The Opte Project”, www.opte.org ) Complex Systems: Bibliography 9 Complex Systems: Bibliography The illusion of complexity 10 Flying Robots: Beyond UAVs - Banquet speech, IEEE Radar Conference, Philadelphia, May 2016 ―Flying robots can operate in three-dimensional, indoor and outdoor environments. However, many challenges arise as we scale down the size of the robot, which is necessary for operating in cluttered environments. I will describe recent work in VIJAY KUMAR developing small, autonomous Nemirovsky Family Dean of Penn Engineering robots, and the design and algorithmic challenges in the areas of (a) control and planning, (b) state estimation and mapping, and (c) coordinating large teams of robots. I will also discuss applications to search and rescue, first response and precision farming.‖ Publications and videos are The Phlone available at kumarrobotics.org. 11 The Change of Paradigm: the Ecological Approach. Mimicking Nature • The more we study the different phenomena, the more grows our consciousness of the relationship among them, i.e. the faced problems are systemic which means they are linked and depend on each others. • We need a new vision of the problems that is based upon a deep ecological awareness. This vision looks at the world as a network of phenomena which are deeply interconnected and depend on each others. • This paradigm implies the evolution from a hierarchical- centric view to a network-centric view of the complex systems. • The network-centric approach looks at the portions of a complex system, which can be systems themselves, as ecological community of entities which are related each other by a network of interdependencies. 12 Network-centric Paradigm: Organizational Features • Organizational Scheme – It is a key concept for a systemic analysis. The scheme describes a given system by defining its configuration of relationships among its components, i.e. it provides a description of the qualitative features of that system. – There are many studies which identify the network/graphs as the scheme for the life; similar considerations can lead to assume this scheme to be applicable to complex systems, the living entities being a specific case of complex systems. • The Feedback Loop – Each network extends in many directions as consequence circular, non linear relationships are highly probable. – The feedback loop is a circular configuration of causally interconnected elements, where an initial stimulus propagates itself along the ring connections so that each element acts onto the next one, till the last element acts onto the source of the stimulus. – The consequence of this configuration of relationships (scheme) is that the first connection (input) is subjected to the effects of the last one 13 (output), which results into self-control capabilities of the whole system. Network-centric Paradigm: Key System Properties • Non-linear interaction: this can cause the rise to surprising and non-intuitive behavior on the basis of simple local co-evolution. This implies sensitivity to even small perturbations; • Decentralized control: the natural systems are not controlled centrally; there is no an “orchestra director”! • Self-organisation: natural systems can evolve over time to an attractor corresponding to a special state of the system, without the need for guidance from outside the system; • Non-equilibrium order: the natural systems, by their nature, proceed far from equilibrium. Life is at the edge of chaos. Correlation of local effects is key; • Adaptation: the dynamic systems have to continually adapt and co- evolve in a changing environment; • Collective dynamics: the ability of elements to locally influence each other, and for these effects to ripple through the system, permits continual feedback among the evolving states of the system elements. 14 Summary Summary Specialists’ Meeting Topics Definition of SWARM Complexity of a swarm Measuring complexity Intelligence of a swarm Books and papers defining the state of art Enabling algorithms Operational needs Potential systems and applications Way ahead 15 The Measures of Complexity (1/5) How many bits of information are needed to describe each pattern? The complexity of a pattern (how many bits of information are needed to describe it) isn't always obvious. The upper left panel shows 128x 128 = 16,384 squares