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Summary: Designing an energy efficient and durable wireless sensor networks (WSNs) is a key challenge as it personifies potential and reactive functionalities in harsh antagonistic environment at which wired system deployment is completely infeasible. Majority of the clustering mechanisms contributed to the literature concentrated on augmenting network lifetime and energy stability. However, energy consumption incurred by cluster heads (CHs) are high and thereby results in minimized network lifetime and frequent CHs selection. In this paper, a modified whale‐dragonfly optimization algorithm and self‐adaptive cuckoo search‐based clustering strategy (MWIDOA‐SACS) is proposed for sustaining energy stability and augment network lifetime. In specific, MWIDOA‐SACS is included for exploiting the fitness values that aids in determining two optimal nodes that are selected as optimal CH and cluster router (CR) nodes in the network. In MWIDOA, the search conduct of dragon flies is completely updated through whale optimization algorithm (WOA) for preventing load balancing at CHs. It minimized the overhead of CH by adopting CHs and CR for collecting information from cluster members and transmitting the aggregated data from CHs to the base station (BS). It included self‐adaptive cuckoo search (SACS) for achieving sink mobility using radius, energy stability, received signal strength, and throughput for achieving optimal data transmission process after partitioning the network into unequal clusters. Simulation experiments of the proposed MWIDOA‐SACS confirmed better performance in terms of total residual energy by 21.28% and network lifetime by 26.32%, compared to the competitive CH selection strategies. [ABSTRACT FROM AUTHOR]

Summary: In large‐scale heterogeneous wireless sensor networks (WSNs), clustering is particularly significant for lowering sensor nodes (SNs) energy consumption and creating algorithm more energy efficient. The selection of cluster heads (CHs) is a crucial task in the clustering method. In this paper, optimised K‐means clustering algorithm and optimised K‐means based modified intelligent CH selection based on BFOA for large‐scale network (lar‐OK‐MICHB) is hybridised for CH selection process. Here, we utilised the extended capabilities of OK‐MICHB algorithm for large‐scale network. Furthermore, in many applications where energy is a primary constraint, such as military surveillance and natural disaster prediction, the stability region is also a significant factor, with a longer network lifespan being a primary requirement. In the proposed approach, only the CH selection is made after every round in place of cluster and CH change as done in conventional hierarchical algorithm. The simulation results reveal that, while keeping the distributive structure of WSNs, suggested lar‐OKMIDEEC can locate real greater leftover energy nodes for selection of CH without utilising randomise or estimated procedures. Furthermore, as compared with the multi‐level MIDEEC protocol, this offers a larger stability region with 68.96% increment, more consistent selection of CH in every round, and greater packets (i.e., in numbers) received at the base station (BS) with a longer network lifetime with 327% increment. [ABSTRACT FROM AUTHOR]

Summary: Fire is a serious threat to human life and property, and effective prediction of fire can reduce the harm of fire to human. In the research of fire alarm automation, the fire alarm system based on wireless sensor network is a common method. When using wireless sensor to monitor the fire alarm information, it is necessary to ensure the safety coverage of the network in the monitoring area. Therefore, it is very necessary to study the network coverage in the fire alarm monitoring, which can ensure the effective work of the monitoring network, and good coverage control can reduce the energy consumption of the network and extend the service life of the network. Based on this, this paper designs a network coverage control algorithm based on multiobjective genetic algorithm optimization considering the coverage and energy consumption of wireless network. In this algorithm, the probability perception model is used to design the network coverage control model for the monitoring area with different coverage requirements, taking the maximum area coverage and the minimum network energy consumption rate as the optimization goal. Then the genetic algorithm is used to solve the model. In addition, because the multiobjective genetic algorithm is easy to fall into the local optimal solution when solving the optimization problem, this paper introduces the cross and variation coefficients of self‐adaptive adjustment to improve the genetic algorithm. Through the simulation analysis, it is shown that when solving the multiobjective optimization problem, the introduction of self‐adaptive adjustment of cross and variation coefficients can well overcome the problem that genetic algorithm falls into the local optimal solution, whereas the network coverage control algorithm designed in this paper based on the multiobjective genetic algorithm optimization can achieve greater coverage and lower energy consumption. Compared with the network coverage control algorithm before optimization, the optimized algorithm improves greatly in coverage, network residual energy, and number of nodes, which can well extend the service life of wireless sensor network, and is conducive to the good application of wireless sensor in fire prediction. [ABSTRACT FROM AUTHOR]

Summary: Providing the appropriate coverage is quite essential for the effective functioning of many applications in Wireless Sensor Networks. Therefore, the efficiency of node deployment algorithms to supply the requested coverage is of high significance. In this paper, inspired by the equilibrium of molecules, a novel node deployment algorithm, called Smart Self‐organising Node Deployment (SSND), is proposed to provide maximum coverage. Despite other proposed algorithms, which provide coverage based on the collective movement of nodes with massive energy consumption, SSND moves one sensor in every neighbourhood at each step to reduce the sensor nodes' movement and hence the energy consumption. The chosen sensor nodes at each time step are distributedly determined by an eligibility function to reduce the non‐essential movements while improving the accuracy of the reported locations of neighbours. Our extensive simulation study shows that SSND can achieve up to 30% coverage improvement compared to those of other algorithms in most scenarios and provides an adequate trade‐off between coverage and energy consumption. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) is formed with numerous communication nodes, which plays an important role in the Internet of Things (IoT). Location‐based service is critically important for WSN; however, the nonline of sight (NLOS) condition can deteriorate the positioning precision significantly. In this paper, a robust localization algorithm based on the range measurements to deal with the mixed line of sight (LOS)/NLOS environment is proposed. Firstly, a hypothesis testing based on the data deviation is used to detect the transmission condition. For the identified LOS propagation, the measured data are processed by Gaussian mixture model (GMM) to calculate the mean and standard deviation, which can update the likelihood probability and residual by data fusion. Moreover, for the identified NLOS propagation data, the mean of every Gaussian component is processed by gray Kalman filter (GKF) to discard large outliers. Finally, maximum likelihood (ML) is adopted to derive final coordinates. The simulation results have shown that the proposed algorithm has great advantages in dealing with severe NLOS interference and has higher accuracy compared with existing classic algorithms. [ABSTRACT FROM AUTHOR]

Summary: For the past 5 years, localization has become an emerging area in the field of wireless sensor networks (WSNs), which can be applicable for diverse applications, such as outdoor environments and monitoring of objects located in indoor environments. The major constraint in localization is allocating a location for each node as diverse sensor nodes are employed to retrieve the information in WSN. Recent research works in node localization have generally moved by multihop range‐free localization approaches for achieving high accuracy through reducing the localization error among the actual and estimated position of nodes. The conventional approaches are aimed to improve the localization accuracy without considering efficiency concerned with algorithm's convergence time and energy costs. The main intent of this paper is to model the WSN localization model, which focuses on the localization of anchor nodes concerning the target nodes. The multiobjective function is developed by considering the distance function, received signal strength (RSS), and energy. Here, a hybrid metaheuristic algorithm named hybrid cuckoo–red deer algorithm (HC‐RDA) with the integration of red deer algorithm (RDA) and cuckoo search algorithm (CSA) is adopted for localization of unknown nodes. The fitness function of the developed model is the minimization of the derived multiobjective function. Both simulation studies and theoretical analysis demonstrate that the proposed approach can improve localization performance with a better convergence rate, thus ensuring high localization accuracy compared to conventional metaheuristic models. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we mainly focus on the influence of network partitioning on the network and develop a new Multi‐chargers region Partition charging Scheduling Algorithm (MPSA) based on network information. By studying the influence of the different number of partitions and partition mode on the whole sensor networks, and simultaneously comparing different MCs scheduling algorithms from multiple perspectives, such as the effective energy usage efficiency and the survival rate of sensor nodes in the whole network, the superiority of Multi‐chargers region Partition charging Scheduling Algorithm (MPSA) scheduling strategy and the rationality and necessity of layered mechanism are proven. Simulations are conducted to verify the advantages of our scheme, revealing that our scheme outperforms in terms of dead node number, energy usage efficiency, and so on. [ABSTRACT FROM AUTHOR]

Summary: The progressive strides in the exploration of sensing technology and the potential use of electrical devices have rendered a promising technology, termed as Wireless Body Area Networks (WBANs). This technology has rendered competency to reform healthcare and making available ubiquitous health care support to remotely located patients. However, the sensor nodes which are embedded on or underneath the body of the patients to measure multifaceted physiological parameters are suffered from the limited battery constraints. In this paper, to address this issue, we propose Intelligent‐Routing Algorithm for WBANs (I‐RAW) to elongate life period of these sensor nodes. The two sinks are located on the front and back side of the patient's body that collects data from the sensor nodes which form clusters and forward the data through the cluster head (CH). We apply Tunicate Swarm Algorithm (TSA) for selection of CH which considers the essential parameters namely, residual energy, network's average energy, distance of node from the sink, path loss model, and energy consumption rate. The use of two sinks in WBAN mitigate hot‐spot problem in the network by avoiding the multi‐hop communication. The simulation results show that I‐RAW improves stability period and network operational period by 37.7% and 42.7%, respectively, as compared to Dual Sink approach using Clustering in Body area network (DSCB) protocol, respectively. Further, I‐RAW also shows supreme performance for various performance metrics as compared to other state‐of‐the‐art protocols. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network is an essential building block for Internet of Things (IoT) due to its usage for collecting and sensing data by the sensor nodes in a nearby environment. Energy efficiency is an important requirement for these networks as the sensors used are short on power and memory. In this paper, an enhanced energy‐efficient clustering protocol is proposed that works on three major phases under network setup: grid formation and grid head selection, clustering, and cluster head (CH) selection. Clustering is achieved using nature‐inspired grey wolf optimization (GWO) algorithm, and CH selection is accomplished using fuzzy inference‐based system. Simulation is performed in MATLAB software, and the protocol is evaluated in terms of network lifetime, the first node dead (FND), half node dead (HND), and last node dead (LND). Additionally, a prototype modeling of IoT using the Thingspeak IoT Platform provided by Mathworks is also integrated with the proposed protocol to demonstrate its usage in IoT applications. The simulation results indicated that the proposed scheme attained better performance in preserving energy and extending network lifetime. [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicle (UAV)‐enabled wireless sensor networks (WSNs) have evolved as the significant paradigms to communicate the information among the devices in today's scenario instantly. These technologies support for developing smart crop spraying, smart infrastructure, aerial photography and videography, emergency services, etc. in providing smart solutions. Therefore, there is a dire need to develop an energy‐efficient solution for data gathering through UAVs. In this paper, an optimum weighted probability function is proposed by considering residual node energy, node spacing, and average energy of networks. This function helps in optimizing the clustering process for three‐tier heterogeneity by minimizing the energy of the proposed network. The proposed methods achieve 29.45% and 52.48% increment in the network lifespan as compared to the existing algorithm. The reason for such a gigantic enhancement in the network lifespan is the selection of the highest capable node as a cluster head using the proposed function. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensors are considered the key elements in enabling efficient Internet of Things (IoT) networking and services. One of the key objectives in designing a WSN is increasing network lifetime by reducing the overall network energy consumption. To achieve this, several techniques have been proposed to extend the network lifetime, such as clustering. Clustering divides the network into several clusters, each with its own member nodes and a cluster‐head (CH) node. In this paper, a novel clustering mechanism is developed with the objective of extending network lifetime through load balancing in wireless sensor networks (WSNs). This is achieved by electing a new node to a provides specific tasks in the WSN to decrease energy consumption and enhance network lifetime. This node is called a relay node that is responsible of delivering the received data packets from the CHs, and then forwards them to the central node (sink). Unlike previous clustering schemes, the proposed algorithm consists of three main processes: CH selection, relay node selection, and medium access control processes. These processes depend on energy level and user's distributions. The performance of the proposed algorithm is investigated through simulation experiments in terms of network lifetime under various network parameters. The results reveal that the proposed mechanism significantly enhances network lifetime compared to previously proposed algorithms. [ABSTRACT FROM AUTHOR]

Summary: Energy‐efficient clustering and routing are two well‐known optimization problems, mainly employed to achieve energy efficiency and maximum network lifetime in wireless sensor networks (WSNs). The clustering and routing processes can be considered as an NP‐hard problem, and metaheuristic algorithms can be applied to resolve it. In this paper, a dynamic clustering and process protocol based on multi‐objective particle swarm optimization with Levy distribution (MOPSO‐L) algorithm. Since the parameters in WSN are related to one another, multi‐objective parameters should be included in the process of cluster head selection and routing. The proposed MOPSO‐L technique is presented for organizing the clusters and CH chosen by merging consolidated and shared models. The MOPSO‐L algorithm incorporates the benefits of PSO algorithm along with the merits of Levy distribution to escape from trapping into local optima. The presented model undergoes comparison with existing techniques under three different scenarios based on the location of the BS with respect to average energy consumption, number of data transmission, and network lifetime. The experimental outcome reveals that the proposed model attains extended network lifetime as well as efficient energy over its comparatives. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSN) encompasses millions of nodes that work jointly, senses environmental information, and transmits to the Base Station (BS). "Energy hole" or "hot‐spot" is a problem in which sensor nodes near to the BS deceasetoo early which creates the partitioning of the network. This is due to the reason of the difference in the consumption of energy by the nodes in WSN which reduces the lifespan of the sensor network. The distribution of the cluster heads and the load balancing are the major pitfalls. In this paper to deal with the statedpitfalls, a hybrid of fuzzy logic technique and harmony search algorithm is proposed to lengthen the lifetime of the network. Unequal clustering is a technique proposed by the researchers to handle the hot‐spot issue, hence it is also included. The proposed algorithm forms unequal clusters. To prove the efficiency of the proposed algorithm, some well‐known unequal clustering algorithms and harmony search based algorithmare used to compare the proposed algorithm under different network settings. In all the settings, better performance is achieved by the proposed protocol. It prolongs the lifespan of the network as compared to the different algorithms proposed so far. [ABSTRACT FROM AUTHOR]

Summary: Designing an efficient, short, and secure authentication algorithm for resource‐constrained sensor nodes of wireless sensor networks (WSNs) is a challenging task. Authentication in WSNs is mainly performed by the digital signature algorithm. In this paper, we propose an efficient, short, and secure pairing computation‐free ID‐based authentication algorithm (signature scheme) ESS‐IBAA for WSNs which completely follows the rule of identity‐based cryptosystem. Identity‐based schemes, unlike traditional public‐key infrastructure (PKI)‐based schemes, remove the need for public‐key certificates for public‐key validation. It also removes extra costs associated with the public‐key certificate and traffic management. Further, due to the requirement of low power and fast authentication, bilinear pairing computation‐free identity‐based signature schemes are applicable in WSNs. Keeping this in mind, ESS‐IBAA scheme is proposed, which is pairing computation free and uses a general cryptographic hash function in the place of a costly map‐to‐point hash function. The proposed scheme ESS‐IBAA is secure against existential forgery on adaptive chosen message and ID attack in the random oracle model under the hardness of the elliptic curve discrete logarithm problem (ECDLP). Moreover, comparative performance analysis shows that the proposed scheme ESS‐IBAA is much more efficient in both communication cost and computation cost from the existing related schemes. [ABSTRACT FROM AUTHOR]

Summary: The widely deployed wireless sensor networks, incorporated with the capabilities of sensing, processing, and communicating, require more effective routing to maintain the balance between the performance parameters and to be prevented from the security attacks. The cryptographic methods used were effective but consumed more energy on the authentication, the encryption, and the decryption. So the routing addressing the security problems, with the reduced energy consumption, latency, and PDR losses, maximizing the network lifetime is proposed in the paper. This multiobjective routing problem of the sensor networks is handled using the ant colony routing techniques, taking into consideration the remaining energy of the nodes, the distance between the nodes, and the trust credentials of the nodes, to reduce the energy consumption and enhance the lifetime of the sensor networks. The further performance evaluation using the network simulator‐2 reveals the efficiency and the quality of the service of proposed routing technique over existing techniques on the grounds of energy utilization, PDR, lifetime of sensor networks, and the security enhancement rate. [ABSTRACT FROM AUTHOR]

Summary: Energy efficiency and data reliability are the crucial issues to be explored in wireless sensor networks (WSNs). A large amount of power consumption is depleted by each sensor node to transmit its sensed data which typically induces high temporal and spatial dependencies. Therefore, efficient compression approaches are designed to exploit the data correlation in both space and time so as to discard information redundancies with accurate recovery. In this paper, we propose a hybrid and adaptive spatiotemporal compression approach named Distributed Temporal Source Coding, designed to extend the lifespan of cluster based WSNs. The proposed scheme takes advantage of the performed Distributed Source Coding as an effective spatial compression algorithm and adaptively exploits the Lightweight Temporal Compression as a promising solution to reduce the temporally correlated information. The obtained results have revealed that the proposed algorithm outperforms other compression methods, promotes the network battery life, and ensures compelling data accuracy. The proposed scheme has succeeded to reduce the gap between the energy efficiency and data reliability in WSNs. [ABSTRACT FROM AUTHOR]

Summary: Wireless video sensor network (WVSN) has attracted much research interest due to its multi‐faceted potential applications in different fields. An energy‐efficient protocol is fundamental for WVSN since the network has to deal with traffic‐intensive multimedia data, such as images and videos. A few issues come in front for any such setting like energy inefficiency, throughput unfairness, and low network capacity. In many cases, we need to strike a balance between required parameters or aspects, but there is no alternative to ensuring appropriate energy efficiency in the process. Keeping this in mind, this paper proposes a holistic solution named energy‐efficient bio‐inspired Mimosa pudica (EEBIMP) algorithm. EEBIMP for WVSN can reduce the time delay and avoid unnecessary energy consumption due to clustering the network. Our proposed algorithm has been compared with one of the most recent notable solutions, like GREENNESS (Green wiReless vidEo sENsor NEtworks uSing out‐of‐band Signalling), PSO (Particle Swarm Optimization), and CUCKOO search algorithm. The result shows that EEBIMP achieved 95% of energy efficiency and throughput when compared with other algorithms of GREENNESS, PSO, and CUCKOO. [ABSTRACT FROM AUTHOR]

Summary: Energy conservation in Wireless Sensor Networks (WSNs) is essential for improving the stability of sensor nodes and network lifetime. An energy‐proficient clustering‐based routing protocol is necessary for efficient cluster formation and Cluster Head (CH) selection to sustain Quality of Service (QoS). In this paper, an Improved Bkd‐Tree‐Inspired Energy‐eFficient Clustering‐based Routing Protocol (IBkd‐Tree‐IEFCRP) is propounded as a potential strategy for guaranteeing significant cluster formation and management. It is proposed as an effective strategy in CH node allocation that handles the impacts of QoS during the process of data dissemination. This IBkd‐Tree‐IEFCRP is proposed to deal with energy saving with respect to data traffic by including a strategy of cluster formation which incorporates the benefits of partition data structure that incorporates an improved Bkd‐tree algorithm. It also concentrates on the process of CH selection by integrating a reactive approach that enhances throughput with minimized energy, delay and jitter. The simulation experiments of the proposed IBkd‐Tree‐IEFCRP confirm a better performance in improving the average throughput by 13.21%, reducing the energy consumptions by 12.96%, minimizing the delay by 11.98% and reducing the jitter by 14.24% when compared to the baseline k‐dimensional tree algorithm (kd‐tree algorithm), Weighting and Parameter Optimization‐based Energy‐Efficient Clustering Routing Protocol (WPO‐EECRP), Improved Clustering by Fast Search and Finding of Density Peaks (ICFSFDP), and Energy‐based Cluster Centered Routing Protocol (ECCRP) taken for comparison. The proposed IBkd‐Tree‐IEFCRP is also identified to reduce delay by 8.42% and jitter by 7.18% and increase throughput by 6.84% when compared to the benchmarked protocols for different number of rounds. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy logic--based jamming detection algorithm (FLJDA) is proposed to detect the presence of jamming in downstream data communication for cluster-based wireless sensor networks. The proposed FLJDA keeps an eye on the existing nodes and new node to determine their behavior by applying fuzzy logic on measured jamming detection metrics. To monitor the behavior of the nodes, the FLJDA computes the jamming detection metrics, namely, packet delivery ratio and received signal strength indicator. The major features of this paper are the following: (1) The jamming detection algorithm is specifically implemented for downstream data communication, (2) cluster head estimates jamming detection metrics for detecting the jamming unlike the existing algorithms where individual nodes explicitly collect the jamming detection metrics, and (3) the proposed algorithm optimizes the jamming detection metrics on the basis of fuzzy logic unlike the existing approaches, which uses merely jamming detection threshold alone for jamming detection. The simulation results of the proposed system provide the true detection ratio as high as 99.89%. [ABSTRACT FROM AUTHOR]

Summary: Clustering‐based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO‐CA‐OCHS) scheme is proposed to extend the lifetime. This proposed HEHO‐CA‐OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO‐CA‐OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO‐CA‐OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) include large distributed nodes in the sensing field. However, the sensor nodes may die due to energy deficiency as they are situated in a hostile environment. Therefore, an energy‐efficient WSN routing protocol is necessary in order to better accommodate the various environmental conditions. In this paper, we have proposed a new Energy‐Efficient Genetic Spider Monkey‐based Routing Protocol (EGSMRP) to improve the stability and lifetime of sensor nodes. The operation of EGSMRP is classified into two stages: (i) setup phase and (ii) steady‐state phase. In the setup phase, GSMO‐based cluster head selection procedure is done. In this phase, the base station utilizes the GSMO algorithm as a device to generate energy‐efficient clusters. Followed with this, the steady‐state phase solves the load balancing issue by utilizing the intracluster data broadcast and dual‐hop intercluster broadcasting algorithm. Thereby, the proposed EGSMRP protocol has shown the energy‐based opportunistic broadcasting with reduced control overhead. Simulation is performed in various conditions to evaluate the effectiveness of the proposed EGSMRP protocol using different metrics such as throughput, control overhead, energy consumption, end‐to‐end delay, and network lifetime. From the simulation results, it was evident that EGSMRP has achieved a higher performance compared to other traditional approaches such as EBAR, MCTRP, IEEMARP, HMCEER, and EFTETRP. [ABSTRACT FROM AUTHOR]

Summary: The technical growth in the field of the wireless sensor networks (WSNs) has resulted in the process of collecting and forwarding the massive data between the nodes, which was a major challenge to the WSNs as it is associated with greater energy loss and delay. This resulted in the establishment of a routing protocol for the optimal selection of the multipath to progress the routing in WSNs. This paper proposes an energy‐efficient routing in WSNs using the hybrid optimization algorithm, cat–salp swarm algorithm (C‐SSA), which chooses the optimal hops in progressing the routing. Initially, the cluster heads (CHs) are selected using the low‐energy adaptive clustering hierarchy (LEACH) protocol that minimizes the traffic in the network. The CHs are engaged in the multihop routing, and the selection of the optimal paths is based on the proposed hybrid optimization, which chooses the optimal hops based on the energy constraints, such as energy, delay, intercluster distance, intracluster distance, link lifetime, delay, and distance. The simulation results prove that the proposed routing protocol acquired minimal delay of 0.3165 with 50 nodes and two hops, maximal energy of 0.1521 with 50 nodes and three hops, maximal number of the alive nodes as 39 with 100 nodes and two hops, and average throughput of 0.9379 with 100 nodes and three hops. [ABSTRACT FROM AUTHOR]

Summary: Localization is one of the important requirements in wireless sensor networks for tracking and analyzing the sensor nodes. It helps in identifying the geographical area where an event occurred. The event information without its position information has no meaning. In range‐free localization techniques, DV‐hop is one of the main algorithm which estimates the position of nodes using distance vector algorithm. In this paper, a multiobjective DV‐hop localization based Non‐Sorting Genetic Algorithm‐II (NSGA‐II) is proposed in WSNs. Here, we consider six different single‐objective functions to make three multiobjective functions as the combination of two each. Localization techniques based on proposed multiobjective functions has been evaluated on the basis of average localization error and localization error variance. Simulation results demonstrate that the localization scheme based on proposed multiobjective functions can achieve good accuracy and efficiency as compared to state‐of‐the‐art single‐ and multiobjective GA DV‐hop localization scheme. [ABSTRACT FROM AUTHOR]

Energy efficient data collection in a delay-bound application is a challenging issue for mobile sink-based wireless sensor networks. Many researchers have proposed the concept of rendezvous points (RPs) to design the path for the mobile sink. Rendezvous points are the locations in the network where the mobile sink halts and collects data from the nearby sensor nodes. However, the selection of RPs for the design of path has a significant impact on timely data collection from the network. In this paper, we propose an efficient algorithm for selection of the RPs for efficient design of mobile sink trajectory in delay-bound applications of wireless sensor networks. The algorithm is based on a virtual path and minimum spanning tree and shown to maximize network lifetime. We perform extensive simulations on the proposed algorithm and compare results with the existing algorithms to demonstrate the efficiency of the proposed algorithm of various performance metrics. [ABSTRACT FROM AUTHOR]

This paper investigates the schemes of asymptotically optimal quantization and bit allocation for distributed estimation in wireless sensor networks in which a total bit rate constraint is imposed. Because there is no communication among all sensors, quantizers for observations need to be designed separately. The Lloyd-Max quantizer is shown to be asymptotically optimal for each sensor. Moreover, it is shown that the level number of asymptotically optimal quantization for sensors is proportional to the signal-to-noise ratio, which is determined by the variances of observation and noise. Because the original quantized minimum mean-square error estimator often corresponds to a high computational cost when a large number of sensors are active, in our work, an asymptotically equivalent algorithm of iterative quantized estimator (IQE), which enjoys a low computational cost, is proposed. In addition, an IQE algorithm can be applied to wireless sensor networks with delay or packet loss. Simulation results of this work indicate that the effectiveness of our IQE algorithm is obvious; that is, a significant improvement of estimation performance is achieved by using the optimal bit allocation when comparing with the uniform bit allocation. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we propose a distributed topology management algorithm, named T-Must, which orchestrates coalition formation game between camera and scalar sensor (SS) nodes, for use in wireless multimedia sensor networks. In the proposed solution, connectivity among the peer camera sensor (CS) nodes is maintained, and coverage is ensured between them. Only the scalar data are not sufficient to describe an event in a particular monitored area. In many cases, multimedia data (specifically, video data) are required to provide more precise information about the event. As the CS nodes, which sense and transmit multimedia data, are costlier than the SS nodes, the former are deployed in the monitored area in lesser numbers compared to the latter ones. In case of CS nodes, power consumption due to sensing is also significant, similar to power consumption for the transmission and reception of packets. Therefore, in this work, in order to increase the network lifetime, topology is controlled by forming coalition between the CS and SS nodes. Upon occurrence of an event, the SS nodes send scalar data to their associated CS nodes. If the scalar data received from SS nodes cross a preconfigured threshold, the associated CS node in the coalition starts sensing the event, captures the video data, and forwards the video data toward other coalitions or sink. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The use of multiple channels in 802.11 wireless local area networks can improve network performance. Many efforts have been done to better exploit multiple non-overlapped channels. However, the number of orthogonal channels in the Institute of Electrical and Electronics Engineers 802.11 standards is very much limited. Recent studies indicate that we can improve the full-range channel utilization and the network throughput by properly utilizing the partially overlapping channels. However, little work was focused on channel assignment for partially overlapping channels. In this paper, we investigate the problem of partially overlapping channel assignment to improve the performance of 802.11 wireless networks based on the Signal to Interference-Noise Ratio interference model. Using the Signal to Interference-Noise Ratio model, we deduce a direct relationship between maximizing system throughput and minimizing total interference when partially overlapping channels are employed. After that, we propose a greedy method to minimize the total interference for throughput maximization. We evaluate our algorithm through extensive simulations and compare its performances with those of the state-of-the-art. [ABSTRACT FROM AUTHOR]