An Improved Ant Colony Optimization Algorithm: a Technique for Extending Wireless Sensor Networks Lifetime Utilization

(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 8, 2020 An Improved Ant Colony Optimization Algorithm: A Technique for Extending Wireless Sensor Networks Lifetime Utilization

Ademola P. Abidoye1, Elisha O. Ochola2, Ibidun C. Obagbuwa3, Desmond W. Govender4 Department of Information Technology, Cape Peninsula University of Technology1 Cape Town, South Africa School of Computing, University of South Africa, Pretoria, South Africa2 Department of Computer Science Education, University of KwaZulu-Natal, Pinetown, South Africa3, 4

Abstract—Wireless sensor networks (WSNs) are one of the data which may be highly valuable for many types of most essential technologies in the 21st century due to their monitoring applications. Although, VSNs developed from increase in various application areas and can be deployed in WSNs; they have extended the range of WSNs applications in areas where cable and power supply are difficult to use. health assistance, vehicular networks, Internet of Things, However, sensor nodes that form these networks are energy- detection and prediction of natural calamities, smart cities, constrained because they are powered by non-rechargeable small industrial, video surveillance for security systems such as batteries. Thus, it is imperative to design a routing protocol that pipeline monitoring, home automation, immersive is energy efficient and reliable to extend network lifetime entertainment oil, gas exploration, and real-time crop utilization. In this article, we propose an improved ant colony monitoring [2-3]. Conversely, in contrast to conventional optimization algorithm: a technique for extending wireless sensor WSNs, VSNs require more energy consumption, high networks lifetime utilization called AMACO. We present a new clustering method to avoid the overhead that is usually involved bandwidth, high packet loss rate, and more processing during the election of cluster heads in the previous approaches capability to process sensed data and deliver them to a base and energy holes within the network. Moreover, fog computing is station. Sensor nodes are usually deployed on large-scale in integrated into the scheme due to its ability to optimize the the area of interest and generate large volumes of data. limited power source of WSNs and to scale up to the However, they have many challenges in terms of data requirements of the Internet of Things applications. All the data reliability and communication due to the limited capabilities packets received by the fog nodes are transmitted to the cloud for of the individual nodes. It is expected that sensed data further analysis and storage. An improved ant colony generated by the nodes are reliably delivered at the destination optimization (ACO) algorithm is used to construct optimal paths node for processing and storage. Sensor nodes are expected to between the cluster heads and fog nodes for a reliable end-to-end remain functional for a longer period while providing a data packets delivery. The simulation results show that the continuous stream of image data. These nodes are powered by network lifetime in AMACO increased by 22.0%, 30.7%, and small batteries and their life is dependent on the amount of 32.0% in comparison with EBAR, IACO-MS, and RRDLA initial power loaded onto the batteries and to the way they are before the first node dies (FND) respectively. It increased by dissipated during network operation. A sensor node dissipates 15.2%, 18.4%, and 33.5% in comparison with EBAR, IACO-MS, energy during data acquisition, formatting, pre-processing, and RRDLA before half nodes die (HND) respectively. Finally, it and data forwarding [4]. Thus, data transmission is the main increased by 28.2%, 24.9%, and 58.9% in comparison with energy consumption of a sensor node [5]. Therefore, there is a EBAR, IACO-MS, and RRDLA before the last node dies (LND) need to minimize sensor nodes' energy consumption to extend respectively. the network lifetime. Keywords—Sensor nodes; advanced nodes; fog nodes; data A. The Significance of the Routing Protocol for Wireless centre; cloud computing; ant colony optimization; visual sensor Sensor Networks networks The nodes relay their sensed data towards the base station I. INTRODUCTION through their neighbouring nodes [6]. Data routing in WSNs is Wireless sensor networks (WSNs) are one of the new very important unlike transitional networks based on the technologies of the 21st century. A WSN is a group of following features [7-8]: spatially dispersed sensor nodes, which are interconnected • Power to the WSNs is usually provided by small through wireless communication. Sensor nodes jointly batteries. measure environmental conditions in an area of interest [1]. Recent improvements in wireless communication and camera • Traffic pattern in WSNs is many-to-one data sensors technologies have brought about the design of a new transmission. kind of sensor networks called “visual sensor networks • Scalability in WSNs to the large scale of distribution. VSNs”. These networks can provide multi-perspective visual

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• The ability of the WSNs to quickly adjust itself to Moreover, if sensor nodes are to transmit directly to the change in the topology is considered its responsiveness. cloud, the process will need a high bandwidth network and quickly dissipates sensor nodes’ energy [19]. Some • Each sensor node transmits data to its neighbouring applications such as real-time streaming, health care data, real- node. time gaming, and augmented reality are too latency-sensitive Designing an efficient, trustworthy routing protocol, and to be deployed directly to the cloud. Similarly, in large scale provisioning quality of service (QoS) for data routing in sensor networks, sensor nodes have to constantly sense and WSNs is a challenging task due to resource-constraints of transmit a huge amount of data. The processing of such data sensor nodes, dynamic nature of the networks, and mode of needs extra efforts and time, which dissipates more energy the transmission medium. Several approaches have been from the sensor nodes. In addition, sensor nodes are resource- proposed in the literature based on data routing to meet the constrained, they can neither perform complex analytical QoS requirements [9-10]. Most of the proposed routing computations nor machine learning tasks. Therefore, instead protocols for WSNs are based on the single-path routing of sensor nodes to transmit directly to the cloud, a new algorithm, where each source node transmits its sensed data to technology called fog computing can be deployed at the edge the base station through a single path. Although, single-path of the network to provide cloud services closer in a sense [20- routing approach is simple because it can be implemented 21]. The main aim of integrating fog into the WSNs is to with minimum computational complexity and scalability but improve their reliability and minimize the redundancies there is no adequate consideration of traffic load-balance or associated with data transmission to the cloud for processing. reliability along the selected paths [11-12]. It is simple Fog computing is composed of networking devices such as because paths between source nodes and the base station can gateways, routers, proxy servers, set-top boxes. These devices be constructed in a short time. Similarly, it is scalable because store frequently used the information to provide the services to as the number of sensor nodes becomes large, the method and edge users [22]. They have higher processing capability and complexity to create paths between the source nodes and the storage than typical sensor nodes. The devices can be placed base station do not change [13]. between WSNs and cloud computing to receive, process, and Conversely, a multi-path routing protocol transmits copies temporarily store the sensed data. This technique will greatly of the sensed data to the base station through different paths. conserve sensor nodes’ energy consumption because the nodes This addresses the throughput, load balancing, reliability, and will only need to transmit through a short distance. It can security challenges of the single-path routing protocol [11, efficiently reduce the amount of bandwidth that is required 14]. In the event of the main path being unavailable due to the due to its ability to minimize the needed back and forth low energy of individual sensor nodes or congestion, other communication with the cloud and the various sensors [23]. less congested paths of the network will be used for data The term “fog computing” was developed by Cisco to transmission. This increases the throughput of a network by overcome limitations in cloud computing [24-25]. It consists transmitting sensed data in parallel through many paths and of fog nodes (FNs) which provide resources at the edge of the delivering the entire data at the destination with the network. They play an important role in the overall working of expectation of achieving high throughput, reliability, and low fog computing as they aggregate the data from source nodes data packets loss rate [10]. Many multipath routing protocols for processing. They act as decentralized local access, thus have been developed in the literature to ensure load balancing, reducing the dependency on the cloud platform for analyzing congestion avoidance, fault-tolerance, and QoS [11, 15-16]. the sensed data. This new technology offers several benefits to end users including efficient network bandwidth usage, data Braided multipath routing techniques have been proposed security, fewer bottlenecks, the solving of high latency on the to relax the requirement for node disjointedness with the network, increased reliability of transmitted sensed data, and a expectation of addressing the energy issues of node disjoint higher speed of analysis [26]. Fogging can effectively string paths [17]. This scheme creates a backup node for every everything together without reducing the overall performance sensor node on the primary path. If a node in the main route of the processes or devices. Leveraging the advantages of this fails, the backup node is used to connect other nodes. new technology, we integrate fog computing into the proposed However, this routing protocol still builds around reliability scheme to address some of the constraints of WSNs. requirements only, disregarding the throughput maximization and energy efficiency. Therefore, to achieve reliable wireless Sensor nodes are resource-constrained in terms of power communications in WSNs, there is a need to have a reliable and communication bandwidth [27]. As a consequence, routing protocol. reducing the energy dissipation of an individual sensor node is a critical issue for WSNs. Ant colony optimization (ACO) Continuous developments in wireless communication and algorithm has been applied in WSNs to find optimal paths to the Internet of Things (IoT) technologies have enabled WSNs save energy consumption during transmission. However, the to transmit raw sensed data to the cloud for processing, algorithm is prone to converge at local optima. Therefore, we analysis, and storage. Cloud computing is considered as a propose an improved ACO algorithm that can be applied to promising solution to provide applications with elastic construct the optimal paths between layer 1 and layer 2 of the resources and deliver services to end-users at a low cost. proposed scheme architecture in Fig. 1 for efficient and However, cloud computing has its drawbacks and cannot reliable data transfer. solve all WSNs routing challenges [18].

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following are some of the multipath routing protocols that have been proposed for WSNs. Radi et al. [32] proposed an interference-minimized multipath routing protocol for WSNs. The scheme aims to discover a sufficient number of minimum interfering paths with high QoS between source nodes and base stations. The approach consists of three phases: i) initialization phase; ii) path establishment phase; iii) data packet transmission phase. These phases control the traffic rate of each constructed path. Performance evaluations of the proposed method show improvements in terms of packet reception ratio, packet delivery latency, and energy consumption. Fig. 1. Proposed Scheme Architecture. However, the authors did not explain in detail how the ACO is developed by Dorigo [28]. The idea is based on clusters are formed. They only use an assumption for the the natural behaviour of real ants in their colonies and to formation of the clusters. emulate the cooperative behaviour of an ant colony, which Deepa and Suguna [33] proposed Optimized QoS-based discovers the shortest path to a food source. The behaviour of Clustering with Multipath Routing Protocol (OQoS-CMRP) real ants and sensor nodes share similar attributes. Thus, the for WSNs. The authors applied improved particle swarm algorithm has been applied to solve many routing problems in optimization (PSO)-based clustering algorithm to form WSNs [29-31]. clusters within the network. Also, they used the Single Sink- B. Contributions All Destination algorithm to determine near-optimal multi-hop This research investigates existing routing protocols for routing path from the sink to sensor nodes in order to choose WSNs in terms of their reliable performance and proposes a the next-hop neighbour nodes and Round-robin Paths new routing protocol. In this paper, we present an energy- Selection algorithm is used for sending sensed data to the sink. efficient routing scheme combined with clustering and fog Performance evaluations of the proposed scheme show that it nodes technology. We develop a model to partition the performs better in terms of energy conservation, transmission network into different clusters. An algorithm is designed to delay, and communication overhead than selected related distribute evenly advanced nodes within the network and algorithms. assign only one advanced node (cluster head) to each cluster. But, in this approach, a lot of energy get consumed We developed a model based on the distance and the residual because re-clustering and re-routing happen every time, when energy of a node to associate it to a cluster head, this enables residual energy of a node goes below a particular threshold. the nodes to communicate with their associated cluster head using a single or multi-hop communication following the Sharma and Jena [34] suggested a cluster-based multipath optimal energy consumption. An improved ACO algorithm is routing protocol for WSNs (CMRP), which uses both the designed to construct optimal and efficient paths between the multipath and clustering methods to decrease the energy cluster heads and the fog nodes to minimize the total number consumption of the sensor nodes and increase data packets of broadcast transmission between layer 1 and layer 2 of the reliability. The idea centres on the reduction of individual proposed architecture. Thus extending the life cycle of the sensor nodes load by assigning more responsibility to the WSNs, and effectively improving network congestion and destination node. The performance evaluations show that minimizing the average delay. Finally, through numerous CMRP is more energy-efficient and reliable compared to performance comparisons, we show the effectiveness of our existing protocols. approach for the proposed routing in WSNs. However, the clustering scheme in the approach is not The rest of the paper is organized as follows: Section II scalable enough to facilitate cluster maintenance, therefore it presents some related work. Section III presents the can result in domino effects. assumptions and system models. The proposed scheme is Gupta and Jha [35] proposed integrated clustering and presented in Section IV. Section V presents the analysis and routing protocol for WSNs using an improved cuckoo and simulation results for different scenarios. Lastly, the harmony search based metaheuristic techniques. The approach conclusion and our future research directions are presented in uses a novel multi-objective function for uniform distribution Section VI. of cluster head nodes and a modified harmony search based routing protocol is used for routing of a data packet from II. RELATED WORK cluster heads to the sink node. The performance of the In recent years, many routing techniques have been proposed protocol is evaluated using metrics such as average proposed to minimize energy consumption in WSNs. energy consumption, number of dead nodes, number of alive Multipath routing techniques are one of the most prominent nodes, and network lifetime. The evaluation results show and efficient routing schemes developed for WSNs. By significant improvement over the state-of-art protocols. leveraging the many routing paths, multipath routing can improve the reliability for end-to-end data transmission, However, the authors failed to explain how the clusters are minimize energy consumption and transmission delay. The formed and cluster heads selected. There is a high possibility

427 | Page www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 8, 2020 that cluster heads are selected from one side of the network transmission. Finally, the cluster maintenance phase is used to and thus results in energy holes. uniformly distribute the traffic load. The performance evaluation results show that the scheme solves hot spot An energy-efficient algorithm for reliable routing of problems, efficiently balances the energy consumption among WSNs based on distributed learning automaton (RRDLA) all sensor nodes, and prolongs network lifetime. algorithm is proposed in [11]. The author models the scheme as a multi-constrained optimal path problem. The scheme However, this approach requires a significant amount of exploits the features of distributed learning automaton (DLA) overhead energy. to determine the smallest number of sensor nodes to maintain Li et al. [39] proposed an energy-efficient load balancing the desired QoS requirements. The scheme considered many ant-based routing algorithm (EBAR) for WSNs. The approach QoS routing metrics which include end-to-end reliability, adopted the following methods - a pseudo-random path throughput, and delay to select an optimal path in the network. discovery algorithm and an improved pheromone trail The evaluation results show that the proposed scheme algorithm. The pseudo-random path discovery algorithm performs better than related proposed algorithms in terms of based on a greedy algorithm is used to optimize the route energy efficiency, throughput, and end-to-end delay. establishment and the pheromone trail is used to balance the An improved ant colony optimization-based approach with energy consumption of the sensor nodes. The proposed a mobile sink (IACO-MS) is proposed to solve the energy hole scheme exploits an energy-based opportunistic broadcast problem in WSNs [36]. The approach divided the network into scheme to minimize the energy consumption of sensor nodes different clusters and each cluster has only one cluster head caused by the control overhead. The authors used metrics such (CH). The conventional ACO algorithms construct paths as energy efficiency, energy consumption, and predicted between the CHs while the mobile sink node finds an optimal network lifetime to evaluate the proposed scheme. The mobility path to communicate with CHs under the improved performance results show that EBAR performs better ACO algorithm. The authors claimed that the sink mobility compared to selected related work. approach can be used to solve many routing problems in However, the approach is based on static network and WSNs such as hot spots problem, reduction in energy cannot be applied in a scenario with multiple base stations. consumption of the whole network, and improve the network in terms of transmission latency and network throughput III. ASSUMPTIONS AND SYSTEM MODELS compared to conventional routing algorithms. In this section, we present the assumptions and system However, the approach is compared with only one models for the proposed scheme in detail. approach and it may not be efficient if it is compared with two or three existing approaches. A. Assumptions • Krishnan et al. [37] stated that existing clustering with The initial energy of all sensor nodes is equal. static sink approaches for WSNs creates an energy hole • Each sensor node in the network is both a transmitting problem and untimely death of sensor nodes leads to data node and a source node. packets loss. The authors proposed a novel dynamic clustering approach with PSO-based mobile data collectors for • Data centres in cloud computing are final destinations information gathering to solve these problems. The for sensed data. performance evaluations show that the proposed scheme • The network topology is static after the nodes have been performs better in terms of reducing energy consumption for deployed in the network area. sensor nodes, improving throughput, and extending the network lifetime compared with the existing schemes. • Data centres power sources are unlimited. However, this approach is only implemented on a small • The coordinates of the sensor nodes are known. number of sensor nodes. It may not be energy-efficient if the network size is large. • The medium access control (MAC) layer provides the facility to determine link quality such as the packet Arjunan and Sujatha [38] proposed a lifetime reception ratio (PRR). maximization of WSNs using fuzzy-based unequal clustering and ACO based routing hybrid protocol. The protocol consists • Every node knows the PRR of its neighbouring nodes. of three phases: CH phase, inter-cluster routing phase, and B. Network Model cluster maintenance phase, and it aims to eliminate hot spot problems and prolong the network lifetime. The Fuzzy logic In this paper, N sensor nodes are randomly distributed in a part of the approach chooses CHs efficiently and splits the M x M network area as shown in Fig. 2. All sensor nodes network into different clusters based on the distance to its deployed in a target area can be represented as a set of nodes { } neighbouring nodes, residual energy, node degree, node , … , , where denotes node for all = 1,2, … . . , . centrality, and distance to the base station (BS). Thereafter, it Every node has a sensing range ( ) that allows it to monitor the푛1 events푛푣 in the area푛푖 of deployment and푖 a transmission푖 range푣 uses the ACO algorithm based on a hybrid routing protocol to 푆 construct optimal paths between the CHs and BS. The authors ( ) which allows it to communicate푅 with other nodes within employed a threshold concept to transmit or intimate sudden the network such that the nodes , , and . changes in the environment in addition to periodic data 푟 푛푖 푛푗 ∊ 푁 푛푖 ≠ 푛푗

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and the energy consumed by a node to receive a of sensed data from a node is expressed as follows. 푞 − 푏푖푡 ( ) = (4)

퐸푅푥where푞 푞 ∗ 퐸푒푙푒푐 is the electronic energy that depends on factors such as the spreading of the signal, modulation, and 푒푙푒푐 digital coding.퐸 Friss free space is denoted as and denotes multi-path fading which depends on the 푓푠 transmitter amplifier model, is the distance between휀 the 푎푚푝 nodes휖 . 푑 The threshold distance value is determined as follows.

0 푑 = 1 (5) 휀푓푠 2

0 휖푎푚푝 Fig. 2. Network Model. D.푑 Sensor� N�ode Coverage Area A sensor node can only sense the environment and MAC sub-layer is part of the Data Link layer in WSN's perceive the event if and only if the event is within its protocol stack. The energy consumption of sensor nodes is transmission range as shown in Fig. 3. A target node is said to greatly affected by the MAC protocol which controls the node be covered if it is contained in the sensing area ( ) of a radio functionalities [40]. The reliability of links quality is sensor node. The network area is partitioned into 25 grids and obtained using the PRR. is an area of a circle = . The probability of푆퐴 target We model the routing problem as an undirected graph ( ) detection in a grid of the network2 area is expressed as 푆퐴 푆퐴 ᴨ푟 = ( , ), where is a set of sensor nodes and denotes the 퐵= . Thus, the probability ( ) for detecting a target by set of two-way edges that link two sensor nodes such that each 푃 푆퐴 at퐵 least 2a sensor node in the network푇 area is expressed as link퐺 is푁 contained퐿 in푁 for all , . 퐿 푃follows.푀 푃

We assume that퐺 nodes 푤 and푖 푤푗 ∊ 퐿 can communicate with = 1 (1 ) (6) each other if and only if the distance between them is less than 푁 푖 푗 푇 where denotes퐵 an average transmission range of a sensor or equal to the sensor node 푛transmission푛 range. The Euclidean 푃 − − 푃 node, denotes maximum transmission range of a sensor distance = between two nodes is expressed as , node, and 푅 is a small increment in . 푚푎푥 follows. 푛푖 푛푗 푅 푑 퐷 The probability푑푟 that a sensor node푟 senses an event = ( ) + ( ) (1) within its transmission range can be expressed as: 푖 2 2 푛 퐷 where� 푥푖 −( 푥푗, ) is 푦푖the− 푦coordinate푗 of node such that ( ) = + + 10 (7) = ( , ), = ( , ). 푟 푖 푖 푥 푦 푖 푃퐵 푟 푃 �푋휎 훾 훷푙표푔10 �푅� � 푛푖 Node푥푖 푦푖 is푛 a푗 neighbour푥푗 푦푗 of node if it can be expressed as follows 푛푗 푛푖 = such that ( , ) , (2)

C.푛푗 Energy� 푛푗 Model 푛푖 푛푗 ∈ 푁 퐷 ≤ 푟� Each sensor node in a WSN has a radio communication subsystem consisting of transmitter/receiver electronics, antennae, and an amplifier [40]. We adopt the radio energy model presented in [41]. The energy of the transmitter can be determined by these equations based on their transmission distance with a neighbouring node or a cluster head.퐸푇푥 We assumed two models as shown in Equation (3) for data packets transmission. A free space model is used if the distance between two communicating nodes is less than a threshold distance value , otherwise, a multi-path fading model is used to compute the energy consumption of the node. The models are presented푑0 as follows.

+ , if d < d ( , ) = (3) Fig. 3. Sensing Area for a Sensor Node Coverage. + 2 , if d d 푞 ∗ 퐸푒푙푒푐 푞 ∗ 휀푓푠 ∗ 푑 0 퐸푇푥 푙 푑 � 4 푞 ∗ 퐸푒푙푒푐 푞 ∗ 휖푎푚푝 ∗ 푑 ≥ 0

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where connotes a zero-mean, Gaussian random variable nodes (NA). These nodes are given specific responsibility to (measured in decibels) with standard deviation σ (in decibels), perform within the network. Each sensor node monitors its is a random푋휎 variable that connotes Rayleigh fading to model area, obtain sensed data, and transmits it to a neighbouring the multiple path effects in propagation path and denotes node so as to achieve a global detection objective. path훾 loss exponent [42]. Thus, ( ) can be expressed as Advanced nodes are included in the architecture to avoid follows. 훷 퐵 overhead during cluster formation and support various types 푃 푟 ( ) = of nodes for improving routing capability. The advanced nodes are responsible to receive, aggregate, and retransmit 퐵 2 (8) sensed data received from the normal sensor nodes. They have 푃 푟 2 푦 ∞ ∞ 1 푥 2 푦 �−� � 2�� 푟 �−� � �� 2훻 more processing power and energy source than normal sensor 2 2휎 2 ∫0 ∫10훷푙표푔10�푅�−푦 �2휋휎 푒 훻 푒 푑푥푑푦 nodes. = 푟 2 (9) 푦 ∞ 10훷푙표푔10�푅�−푦 푦 �−� � 2�� Layer 2–Fog computing: Layer 2 consists of fog nodes 2훻 0 2 with high computing, storage, and network connectivity. They ∫Applying퐶 � the휎 numerical� 훻 푒 integration푑푦 of the Gauss–Laguerre receive data from advanced nodes, aggregate, analyse the data ( ) , the detection probability can be expressed as: so that only the essential data gets forwarded further to the ∞ −푥 cloud, and decreases the bandwidth used [43]. Besides, ∫0 푒 푓 푥 푑푥 ( ) = 푟 (10) sensitive data can be processed at these fog nodes. 10훷푙표푔10�푅�−훻√2 Layer 3–Cloud computing: This is the uppermost part of 퐵 휎 푃 Thus,푟 퐶the� probability that� a sensor node detects a target our architecture. Pre-processed data is transmitted from fog node placed at a particular point of the network area nodes to the cloud for extensive processing and analysis using denoted as can be expressed as: 2 cloud computing platforms and storage is done in data centres. 푀 The cloud can process and store a large amount of data being ( ) = 퐴 ( ) (11) transmitted from the lower layers. Many security issues such 2 푅푚푎푥 as data integrity, data authentication, privacy are addressed at 퐵 푟=0 퐵 푃 Substituting푟 퐴 ∫ (10)푃 푟into∗ 휋푟푑푟(11), the probability that a sensor this layer [44]. node sensed a target in the specified area of the network can be expressed as follows. A. Clustering Wireless sensors networks are usually composed of a large ( ) = 푟 (12) number of low-cost sensor nodes connected through a wireless 2 푅푚푎푥 10훷푙표푔10�푅�−훻√2 network that sense data to be relayed to the data centres 퐵 푟=0 E.푃 Coverage푟 퐴 ∫ Rate퐶 C�omputation휎 � ∗ 휋푟푑푟 through multi-hop wireless transmission. Many methods have been proposed in the literature to reduce the traffic into the A target node B at point , is within the network. Clustering algorithms have been used to minimize transmission radius of the node , if its sensing rate ( , B) 퐵 퐵 communication distance among the nodes to conserve their is within the coverage of as: 푔�푥 푦 � limited energy. It involves dividing the network into different 푛푖 푟푡 푛푖 1, ( , B) 푖 groups (clusters) and select a node as the group leader (cluster ( , B) = 푛 (13) head) for each cluster in the network. 0, ( , B) 푖 푡 푖 퐷 푛 ≤ 푟 Our main aim for clustering is to minimize each sensor 푟 푛where �( , B)푖 denotes the distance between the target 퐷 푛 ≥ 푟 node transmission distance to save its energy. In addition, it node and node . The probability that the target node can eliminates energy holes problems within the network. be covered퐷 by푛 nodes푖 in is presented as follows. 푛푖 퐵 B. Setup Phase ( , ) = [1 ( , )] (14) 푁 During the setup phase, sensor nodes are randomly 푟푡 푁Thus,퐵 the∏ 푛coverage푖∈푁 − 푟 푡rate푛푖 퐵( ) of the target area can be distributed into the network area as shown in Fig. 2. Each computed as follows. node exchanges its information with neighbouring nodes. 푡 ( , ) 퐶 Unlike in previous approaches, in which normal nodes are = (15) selected as CHs using various algorithms. In our scheme, ∑퐵∈푈 푟푡 푁 퐵 advanced nodes (NA) are added to the network and they 푡 퐶 푈 IV. OUR PROPOSED AMACO SCHEME automatically become cluster heads (CHs) due to their higher The architecture of the proposed scheme is composed of specifications in terms of energy source, transmission range, three layers: Layer 1- WSNs, layer 2- fog nodes, and layer 3- and computational power than normal nodes. Algorithm 1 is remote cloud storage as shown in Fig. 1. used to evenly distribute the NA and the desired number A_N represented as K of NA in the network is determined as Layer 1-WSNs: This layer consists of various types of follows. sensor nodes connected through wireless technologies and are randomly distributed in the network area. The layer consists of = (16) two types of nodes: normal sensor nodes (NS) and advanced 푁 푝푒푟 퐴 ��푁 ∗ 푁�

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where is the percentage of NA and is the number of , , N = , , + ( ) sensor nodes. j 푝푒푟 푁 푁 퐸2�푛푖 푛푗 A� 퐸+푇푥 �푞 푑�,푛푖 푛푗,�N� 퐸 푅푥 푞 Algorithm 1. Algorithm to distribute advanced nodes evenly j within the network 퐸푇푥 �푞 푑�푛푗 A�� = 3 + , + , , N (19) Begin 2 j 2 1: Given the set of = { , , … . . , } D. Average푞퐸푒푙푒푐 Packet휀푓푠 Delay푑�푛푖 푛푗� 휀푓푠 푑�푛푗 A� 2: Initialization: ( ) = 1 ( )2 = 0; 퐾 퐴 퐴 퐴 퐴 The average packet delay ( ) is the average interval 3: Input: use Equation푁 (16)푁 to 푁compute 푁desired number of ; between data packets transmitted at the source node and 1: 푥 푘 푦 푘 푝푐푡푘 4: for = // is delivered at the destination node퐷 or N for a given period 5: ( ) = rand(1,1) * ; 푁퐴 푖 푁 of data transmission and expressed as follows.j 푛 6: ( ) = 푘rand(1,1)퐾 *퐾 ; //퐴 = of the 푗 A . 푛 푥 푘network 퐴 7: 푦Voronoi푘 (( ( ), (퐴)) 퐴 퐿푒푛푔ℎ푡 푎푛푑 푊푖푑푡ℎ = 퐾 푁 푑푒푝푎푡 푎푟푟푖푎푙 (20) ∑ ∑ 푖 8: end for 푗=1 푖=1 푛 �푡푖푗 − 푡푖푗 � 푝푐푡푘 퐾 9: Determine the푥 푘 center푦 푘 of the network 퐷 where is ∑the푗=1 푞time푗 it takes data packets to arrive at . 10: for Row = 1: the next node푎푟푟푖푎푙 media access control (MAC) layer and is 11: for C = 1: 푡푖푗 ol 푦 the time it takes the data packets to be delivered at the푑푒푝푎푡 next 10: = ( 퐿1) 0.5; 푖푗 node MAC sublayer. 푡 12: = ( 퐿푥 1) 0.5; 푥 표푙 13: 푂 ( 퐶, − ) =∗( , ); E. Data Packets End-to-End Reliability 푦 표푤 14: end푂 for 푅 − ∗ In the proposed scheme, medium access control (MAC) 푡푟 표푤 표푙 푥 푦 15: end퐶 for푅 퐶 푂 푂 layer is used to estimate the routing paths quality, that is, the 16: ( , ) ; packet reception ratio (PRR), every node knows the PRR of its End neighbouring nodes. In a multi-path transmission, the 푟푒푡푢푟푛 퐶푡푟 푅표푤 퐶표푙 probability of successful data packets delivery (DPD) to the C. Intra-Cluster Communication CH through m-hop of a path between two neighbouring Now the advanced nodes (CHs) have been distributed nodes can be expressed as follows. evenly within the network, next we divide the network into 표 = different clusters. We assume that the number of advanced ( , ) ( , (21) nodes is equal to the number of clusters. Long-distance ∀ 푛푖 푛푗 ∈표 푟 푖 푗 transmission usually dissipates the energy of a sensor node 푃퐷푅where∐ denotes푃 � 푛 the푛 data� packets delivery ratio. Based and shortens the network lifetime. Thus, a node determines the on the value of PDR and the number of data packets cluster it is to join by selecting a CH that is within its radio transmitted푃퐷푅 , we can compute the number of data packets successfully delivered to the CH as follows. transmission range in which the received signal strength (RSS) 푠푒푛푡 is strongest. A Boolean variable is used to denote whether 퐻 = 퐻푠푢푐푐푒푠푠 (22) a node is belongs to to form a cluster as follows. 푖푗 F.푠푢푐푐푒푠푠Improved 푠푒푛푡ACO Algorithm 푗 푋 퐻 퐻 ∗ 푃퐷푅 푖 1, if node 푁 close퐴 to N In this section, we use an improved ACO algorithm to = , : 1 , 1 j (17) construct an efficient and reliable path for inter-cluster 푖 A communication between the advanced nodes in layer 1 and 푖푗 0, Otherwise 푋 � ∀푖 푗 ≤ 푖 ≤ 푁 ≤ 푗 ≤ 퐾 fog nodes in layer 2 of the proposed architecture. where N denotes the number of nodes and is the number of advanced nodes. In the proposed scheme, energy Both advanced nodes and fog communicate through consumption for a sensor node to transmit to퐾 its associated wireless medium and many routing paths exist between these nodes but not all are efficient and reliable for data packet advanced node N is calculated as follows. transmission. Therefore, it is necessary to construct efficient j , N = A and reliable paths through which the advanced nodes would j transmit their aggregated data to the fog nodes for processing 퐸1�푛푖 A�+ , N , if , N < d and temporary storage. Algorithm 2 is developed to construct j 2 j (18) optimal and reliable paths for data transmission between the 푞 ∗ 퐸푒푙푒푐 + 푞 ∗ 휀푓푠 ∗ 푑�푛푖 , NA� , if푑 �푛푖 , NA� d0 advanced node and fog node . ACO algorithm has been 4 � j j used in WSNs to construct shortest paths, thus save energy 푞 ∗ 퐸푒푙푒푐 푞 ∗ 휖푎푚푝 ∗ 푑�푛푖 A� 푑�푛푖 A� ≥ 0 퐴 퐹 Moreover, if a node is far from its leader, it will transmit to consumption in푁 the network. 푁However, this algorithm converges slowly and leads to local optima. the CH through a neighbouring node . The energy consumption for data transmission can be expressed as 푗 ACO is a branch of optimization modelled algorithms follows. 푛 based on the behaviour of real ants in a colony and is a subclass of computational intelligence (IC) paradigms that aid in determining optimal solutions to optimization problems [30].

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The idea comes from the foraging behaviour of ants in of constructing an optimal path between the source node and their colony when searching for food. They first randomly the receiver node is made based on the probability decision explore the surrounding area and deposit a chemical substance rule in equation (25). called pheromone on their paths as they search for food, forming pheromone trails. The pheromone trails can be sensed arg max _ _ ( , ), = , ,.., by other ants in the colony and enhance the pheromone 푄푠 푎 훼 푄푠 푎 훽 (25) , > 표 deposited on the paths. They tend to choose a path marked by 푗=1 2 푚 ��휆푘푗 � ∗ �휂푘푗 � ∗ 푅 푘 푗 ⍴ ≤ ⍴ strong concentrations of pheromone. This traditional ACO 푐 � _ _ 표 algorithm does not distinguish between the different types of 퐶 ⍴ ( , ⍴) 푄푠 푎 훼 푄푠 푎 훽 , allowed data packets transmitted over the network. All data packets are = sent in the same way to a destination; this algorithm is mainly ⎧�휆푘푗 � ∗ �휂푘푗_ � ∗ 푅 푘_ 푗 ⎪ , 훼 훽 푢 designed to find the shortest path between the source nodes 푄푠 푎 푄푠 푎 푗 ∈ 퐶 푗∈푇푝푎푡ℎ 0, allowed and destination node. On the other hand, WSNs in the IoT ⎨∑ �휆푘푗 � ∗ �휂푘푗 � environment has to support multimedia data transmission, ⎪ _ 푢 where⎩ is the amount푗 ∉ of pheromone deposited on the requiring a different level of quality of service (QoS). This 푄푠 푎 path, _ represents the local heuristic value of the path work provides three different QoSs. 휆푘푗 between푄 푠the푎 sender node and the receiver node, denotes the 푘푗 Guaranteed service ( _ ): _ provides safe end-to-end QoS type,휂 α and β are control parameters used to regulate the delay guarantees. It guarantees that data packets will reach the 푠 1 푠 1 concentration of the pheromone trail and the heuristic푎 value destination node at the right푄 time푄 with zero data packets loss. respectively. The is a random variable range between 0 and 1 (i.e [0, 1]) and (0 1 ) is a given parameter. Control load-balance service ( ) : _ service is allowed for ⍴all = 1,2,3, … . , are paths that can be applied where there is a possibility that delay will happen and 표 표 푠2 푠 2 ⍴ ≤ 푞 ≤ when there is congestion in the network,푄 the 푄 can provide selected by node in the next step. _ 푗 ∈ 푢 푢 푤 a service just as if there was no congestion in the network. H. Global Pheromone updating Rule 푄푠 2 푘 Best-effort service ( _ ): This an Internet delivery service Once the communication paths between nodes have been where the network does not provide any guarantees on the constructed by the search ants, equation (22) is used to choose time delay limit the data푄푠 3 packets will be delivered in the an optimal path. network. _ = _ (26) If a link denotes a single hop between a source (an 푄푠 푎 푄푠 푎 푂 푝푎푡ℎ advanced) node and destination (fog) node, then the multi- 푍 푚푎푥 ��_푍 �푢� _ 푢 where is the path utility value. is the hop, , is푙 the∈ sum퐸 of the hops the nodes and expressed as 푄푠 푎 푄푠 푎 pheromone increment푝푎푡ℎ on the path between the advanced푘푗 node follows. �푍 �푢 훥휆 푇푝푎푡ℎ and fog node . Pheromone is updated between the nodes as = ( ) (23) follows. 푤 푘 푗 푇푝푎푡Equationℎ ∑푢= (23)1 푙푢 shows that the fewer , the nearer it is to _ the fog node. ( , ) is used to represent the relationship _ 푄푠 푎 , allowed 푝푎푡ℎ = 휛∗푍푝푎푡ℎ (27) between a node ( ) and receiver node (푇) as shown in equation 푄푠 푎 푇푝푎푡ℎ 0, allowed푢 (24). 푅 푖 푗 훥휆푘푗 � 푗 ∈ 푖 푗 where is an adjustment coefficient,푢 denotes path 1 ( ) 푗 ∉ length. Equation (24) is a pheromone update rule for the ( , ) = 푝푎푡ℎ 1 푢 푓 ( ) (24) forward ants휛 used to create the paths between푇 the advanced 푖푓0,푙 ∈ 푇 푖 nodes and the fog nodes. 푅 푖 푗 �− 푖푓 푙푢 ∈ 푇푟 푖 where ( ) and표푡 ℎ푒푟푠( ) denote forward link and reverse link I. Pheromone Deterioration respectively of the node . 푓 푟 Advanced nodes will use the optimal paths constructed to 푇 푖 푇 푖 transmit their data packets to the fog nodes. However, It is assumed that the푖 success transmission rates of all the links between the advanced nodes and fog nodes are all 100%. continuous data packets transmission along the optimal paths If the data packets transmission success rate is less than 10%, will lead to (a) congestion along the paths (b) inability to then the advanced node has low communication performance. discover other paths. These two points cannot be overlooked However, if the data packets transmission rate is more than in a dynamic network because (i) an optimal path may become 90%, then it has high communication performance. non-optimal if it is congested; (ii) It may also lead to loss of data packets due to network failure or energy holes problem. G. Local Pheromone update To overcome these challenges, pheromone control is used as a Pheromone is very important in the performance of the measure to reduce the impact of earlier experience and ACO algorithm. It is used for updating both local and global encourages the search for alternative paths that were non- trails in traditional ACO. It makes the paths visited by ants to optimal through evaporation. For a constant of pheromone depend on the objective value. Modification made to the deterioration (decay), the pheromone concentration on the traditional ACO algorithm is presented as follows. The choice path usually varies with time and expressed as⍴ follows: 푡

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_ ( ) = _ (28) V. PERFORMANCE EVALUATION 푄푠 푎 푄푠 푎 −⍴푡 To evaluate the performance of the proposed scheme, the _ 휆푘푗 where푡 휆0 denotes푒 the initial pheromone concentration. AMACO algorithm is compared with three related algorithms If is much푄 푠less푎 than 1 (i.e 1) then. namely RRDLA, IACO-MS, and EBAR. The following 휆0 _ _ metrics are used to measure the performance of these ⍴푡 ( ) (1 ) ⍴푡 ≪ algorithms: network lifetime, the sum of energy consumption, 푄푠 푎 푄푠 푎 the average number of hops, packet delivery ratio, and success 휆푘푗 If 푡the≈ time휆0 increment− ⍴푡 is 1, then the evaporation can be rate. Network Simulator- 2 (NS-2) is used to perform the approximated as follows. experiment and 200 sensor nodes are randomly distributed in a 2 _ ( ) _ 100 * 100 m network area. We set the initial energy of each (1 ) (29) 푄푠 푎 푡+1 푄푠 푎푡 sensor node to 0.5J and other relevant parameters are presented in Table I. 휆푘푗 Thus, ←the general− ⍴ 휆 푘푗pheromone update formula can be expressed as: A. Network Lifetime _ ( ) _ _ = (1 ) + (30) The network lifetime can be defined as: 푄푠 푎 푡+1 푄푠 푎푡 푄푠 푎푡 sum of the initial energy푁 of all the sensor nodes 휆푘푗 where denotes− ⍴ 휆the푘푗 rate of훥휆 푘푗pheromone evaporation; its = 푡 _ total energy dissipated in one round value ranges between 0 and 1. The increment is the 푁 ⍴ 푠 푡 amount of pheromone deposited at time along with푄 푎 푡path The proposed scheme is implemented to determine the 푘푗 and . 휆 network lifetime as shown in Fig. 4. The network consists of 푡 푘 200 nodes randomly distributed and 5 advanced nodes are 푗 Algorithm 2. Construction of optimal path based on uniformly distributed within the network. The proposed and improved ACO algorithm three other selected schemes were implemented and run for 1200 rounds. All the schemes dissipate their energy slowly as

Begin the simulation time increases. The result shows that the Input: Advanced nodes ( ) proposed, AMACO, has more number of alive nodes than Output: Construct an optimal path between and EBAR, IACO-MS, and RRDLA. The first node dies (FND) at 퐴 푁 263rd round in RRDLA, at 268th round in IACO-MS, at 퐴 1: _ 푁 302nd round in EBAR, and 387th round in AMACO. Initialize 퐹 and 1 th th 푁푄푠 푎 Similarly, the last node dies (LND) at 391th, 714 , 683 , and 2: While termination conditions are not met do th 푘푗 951 in RRDLA, IACO-MS, EBAR, and AMACO +휆1 푎 ← 3: respectively. In all the scenarios, AMACO had the highest 4: for = 1 to number of alive nodes and better performance in the network 푎 ← 푎 5: is positioned at lifetime compared to RRDLA, IACO-MS, and EBAR. The 6: ( 푘) 퐾 퐿 reason is that during intra-cluster communication, sensor 푘 퐹 8: ; nodes are only responsible for sensing and short distance 퐹 푘 ← 퐹퐿 9: While푘 ( ) 푘 do transmission which conserve their limited energy. 10: if푅 C(←(∅)) 휑- ← ∅ then 퐿 Additionally, inter-cluster communication in AMACO only 11: Choose퐹 푘 ( ≠( 푘)퐹 from C( ( )) - to move based on CHs (advanced nodes) communication with fog nodes and the퐹 probabilistic푘 휑 ≠ transition∅ rule in푘 equation ( 25) transmit through optimal paths. 12: 퐹 {푗 ( )}; 퐹 푘 휑{ ( )}; j 13: else푘 푘 푘 푘 TABLE I. SIMULATION PARAMETERS AND THEIR VALUES 14: Back푅 to← the푅 ⋃previous퐹 푖 -휑hop ←of 휑(⋃) 퐹 푖 푘 ← 15: end if Parameters Values 16: end while 퐹 푘 Network area 100 x 100 m2 17: Compute the paths between the nodes using equation Initial energy of nodes 0.5 – 2.0J (24) Number of nodes 200 18: _ Determine the value of using equation (26) Energy consumption for sending unit of data 0.01J 19: end for 푄푠 푎 푂 Transmission range 75m 20: Update the number 푍of pheromone values using equation (27) Number of Advanced nodes 5 21: Compare the values of solutions obtained Path loss exponent ( ) 4 22: end while 0 dB to 10dB Multipath component훷 ( ) 23: Return the optimal solution Standard deviation ( ) 0 dB to 12dB 24: Choose the best solution as the output 훻 Energy consumption on휎 circuit (( ) 50 nJ/bit 25: End 푋 휎 2 Free-space model ( ) 푒푙푒푐 10 pJ/bit/m 퐸 ( ) 4 Multi-path model 휀푓푠 0.0013 pJ/bit/m Data packet size 휖푎푚푝 500 bytes

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200 C. Sum of Energy Consumption AMACO The sum energy consumption of the sensor network is 180 EBAR IACO-MS defined as the total energy consumed by each sensor node of 160 RRDLA the whole network. The lesser the sum energy consumption of

140 the network, the lesser the routing cost, thus the longer the network lifetime. Fig. 6 presents the sum of the energy 120 consumption against the number of rounds for all the

100 algorithms; the energy of the network increases, with the number of rounds. RRDLA consumes more energy than the 80 Nodes alive other algorithms. The reason is that the scheme adopts a multipath for data transmission and did not consider the residual 60 energy of nodes that are selected as relay nodes. There is a 40 high possibility for the nodes to transmit their data through long-distance either to their neighbouring nodes or CHs and 20 hence more energy will be consumed. Conversely, the sum of 0 energy consumption in AMACO is less than the other three 0 200 400 600 800 1000 1200 schemes the reason is that the improved ACO algorithm Number of rounds considers the dynamic optimization of the ants while taking Fig. 4. Nodes Alive Against the Number of Rounds. into account the load balance characteristics, thus it prevents the intermittent ant diffusion, and conserves the energy B. Comparison of the Network Lifetime consumption of the network, therefore achieving the purpose The network lifetime is one of the important metrics of of prolonging the network lifetime. WSNs. The energy of a sensor node is mostly consumed on data transmission during network operation. If the limited D. The Average Number of Hops energy of these nodes is completely exhausted, energy holes We define the average number of hops as the number of will be created and some nodes will not be able to links traversed by a data packet in the network. The longer the communicate with the destination nodes. This will affect the average number of hops is, the heavier the data packet traffic normal operation of the entire network. We performed various along the path is. So, there is a positive association between simulations to compare the network lifetime of the four the end-to-end delay and the average number of hops. In schemes. We varied the number of nodes in the network area Fig. 7, we can see that the performance of RRDLA algorithm from 40, 80, 120, 160, and 200 for each of the algorithms and is worst, the reason is that the algorithm uses single-hop present the simulation results in Fig. 5. We can see that the communication for data packet transmission to the base AMACO scheme has the longest network lifetime among all station. Our proposed AMACO dynamically considers both the four schemes. The reason is that the AMACO algorithm single-hop and multi-hop communication to transmit data to only chooses nodes in which their energy is above the energy the fog nodes. Each sensor node uses the energy model threshold as neighbouring nodes and avoids the low energy presented in Section 3 to transmit data to its associated cluster nodes in the process of data transmission to the CHs. Thus, the head. proposed algorithm selects the optimal and efficient paths in 120 the energy-abundant nodes. RRDLA IACO-MS

100 EBAR 300 AMACO

80 250

60 200

40 150 Sum of energy consumption (J) consumption Sum energy of 20 Network lifetime (sec) 100

RRDLA 0 50 IACO-MS 0 200 400 600 800 1000 1200 EBAR Number of rounds AMACO 0 Fig. 6. Sum of Energy Consumption Against many Rounds. 0 40 80 120 160 200 Number of nodes Fig. 5. Network Lifetime Against the Number of Nodes.

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5 VERAGE IME ER TERATION AND THE UCCESS ATE OF RRDLA TABLE II. A T P I S R SEARCHING FOR THE OPTIMAL SOLUTION OF THE ALGORITHMS IACO-MS EBAR 4 Average time for one AMACO Name of algorithms Success rate (%) iteration (ms)

AMACO 4.38 99.23 3 EBAR 6.17 93.60

IACO-MS 10.54 85.49 2 RRDLA 16.92 54.17

Average number of hops

0 0 200 400 600 800 1000 1200 Number of rounds

Fig. 7. The Average Number of Hops Against the Number of Rounds.

E. Packet Delivery Ratio

A Packet delivery ratio PDR: It can be measured as the 100 ratio of the total number of data packets delivered to the total number of data packets transmitted from the sender node to the receiver node in the network. PDR of various algorithms is 90 presented in Table II and Fig. 8. We observed that as the number of rounds increases, the PDR decrease linearly, the reason is that sensor nodes dissipate energy slowly as the 80 number of rounds increases. There is a high likelihood of energy holes to be created among the nodes. RRDLA has the

least PDR because its approach is similar to LEACH protocol. Packets delivery ratio (%) 70 The method used for CH selection incurs high overhead. RRDLA Similarly, IACO-MS algorithm is developed in a way that if a IACO-MS EBAR sensor node transmits a data packet to the base station, the AMACO 60 data pass through all the sensor nodes as it is moving to the 0 400 800 1200 base station. Therefore, the overhead in the delivery of the Number of rounds data packet to the base station increase for every sensor node Fig. 8. Packet Delivery Ratio Against the Number of Rounds. present in the network. AMACO has the highest PDR compared to the three algorithms. The reason is that it avoids VI. CONCLUSION overhead that is usually involved during cluster formation and Clustering and fog nodes integration technologies are selection of cluster heads in the previous approaches. effective techniques to improve the performance of WSNs. In F. Success Rate this paper, we presented an improved ant colony optimization routing algorithm to prolong the network lifetime of WSNs. The average time per iteration of all the schemes and the We first partition the entire network area into different success rate of searching for the optimal solution in 75 clusters. An algorithm is designed to ensure that advanced iterations of the four schemes are presented in Table II. nodes which are automatically selected as CHs are evenly AMACO algorithm has a higher data packet delivery rate distributed within the network and assign a CH to a cluster. compared to the other three schemes in computing time. The Each node belongs to a cluster based on its residual energy simulation runs for 75 random topologies to ensure and distance from the CHs. An improved ACO algorithm is consistency of the results to assess the success rate of developed to construct an optimal path between the CHs and searching for the optimal solution of all the schemes. We fog nodes for efficient and reliable data transmission. NS-2 observe that AMACO has great potential to search for the Simulator is used to measure the performance against three global optimal solution. Its success rate is more than compared related algorithms namely RRDLA, IACO-MS, and EBAR of algorithms. It can reliably and efficiently improve the using the following metrics: network lifetime, the sum of routing computation and high success rate of searching for the energy consumption, the average number of hops, packet global optimal solution. Thus, it shows that the AMACO delivery ratio, and success rate. The simulation results show algorithm is effective and reliable. that AMACO performs better in terms of energy consumption and data packet delivery than the other three algorithms.

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VII. FUTURE WORK [13] X. Liu and J. Wu, "A method for energy balance and data transmission optimal routing in wireless sensor networks," Sensors.vol. 19, no. 13, In the future, we intend to implement all the algorithms in pp. 216-228, 2019. a real test-bed using the above metrics. [14] M. Z. Hasan, H. Al-Rizzo, and F. Al-Turjman, "A survey on multipath routing protocols for QoS assurances in real-time wireless multimedia DECLARATION OF INTERESTS sensor networks," IEEE Communications Surveys & Tutorials.vol. 19, no. 3, pp. 1424-1456, 2017. The authors declare that they have no known competing [15] P. M. Chanal, M. S. Kakkasageri, A. A. Shirbur, and G. S. Kori, financial interests or personal relationships that could have "Energy aware multipath routing scheme for wireless sensor networks," appeared to influence the work reported in this article. in: Proceedings - 2017 IEEE 7th International Advance Computing Conference (IACC), pp. 313-317, 2017. AUTHORS’ CONTRIBUTIONS [16] S. 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