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Summary: Wireless sensor networks (WSNs) represent the collection of restricted energy sensor nodes that are deployed in an area of target for gathering potential environment data for decision‐making with respect to their objective of application. However, the implementation of energy‐effective data gathering strategies in large‐scale WSNs is the most challenging due to the limited energy resources. Clustering‐based data gathering strategies are identified to be quite effective for energy saving that directly attributes to extended network lifetime. Moreover, optimal path amid the cluster head (CH) and sink node needs to be selected for sustaining energy efficiency and improving network lifespan. In this article, Hybrid Salp Swarm and Improved Whale Optimization Algorithm (HSSIWOA)‐based clustering scheme is proposed for improving the network lifetime and routing optimization with maximized energy efficacy. It integrated the exploration capability of Salp Swarm Optimization Algorithm (SSOA) with exploitation benefits of Improved Whale Optimization Algorithm (IWOA) for balancing the trade‐off between the rate of exploration and exploitation during CH selection process. It utilized the parameters of residual energy, load balance, intra‐cluster distance, inter‐cluster distance, and node centrality into account during the process of fitness evaluation. It performed well by constructing an optimized number of clusters, such that energy stability and network lifetime are maintained in the network. The experimental results of the proposed HSSIWOA scheme confirmed extended network lifetime of 21.64%, minimized energy utilization of 23.42%, and maximized throughput of 18.56%, better than the baseline approaches. [ABSTRACT FROM AUTHOR]

Summary: In wireless sensor networks (WSNs), routing based on cluster construction is highly preferred as it greatly supports reliable data communication, load balancing, and fault tolerance with extended network lifetime. In specific, metaheuristic approach‐based dynamic cluster heads (CHs) selection has the possibility of enhancing the lifespan of network and at the same time is capable in reducing the energy consumption. In this paper, hybrid grasshopper and Harris hawk optimization algorithm‐based energy efficient routing protocol (HGHHOA) is propounded for optimal CH selection. This proposed HGHHOA approach adopted a fitness function that incorporated the factors of residual energy, distance between CH and cluster members, distance between selected CHs and the sink, node centrality, and node degree into account. The fitness function values of optimality facilitate a potential CH selection with significant cost‐effective routing. It is proposed with primary objective of improving the network lifespan through optimized selection of CHs that balances the available energy in a predominant way. It is proposed with significance of handling premature convergence with minimized energy consumption and network lifetime through the possibility of establishing an ideal balance between number of alive and dead nodes. The results of this proposed HGHHOA approach with varying rounds of implementation exhibited better results in throughput and residual energy which is 23.98% and 29.21%, better than the bassline CH selection mechanisms. [ABSTRACT FROM AUTHOR]

Summary The sensor networks are the primary and essential components on which the world of Internet of Things (IoT) is built. IoT empowers smart communication, computation, and sensing capabilities. In sensor networks, the data are collected by the sensor nodes and sent to the sink along a communication path. These communication paths are collaboratively established by the nodes and the sink. By incorporating energy‐efficient data gathering techniques, the lifetime of these networks is improved. The major contribution of the study in this work is to provide a survey of various techniques for data aggregation (DA) and the employed algorithmic strategies that facilitate and influence network lifetime (NL) in these environments. DA in wireless sensor networks (WSN), IoTs, and cloud computing extend the lifetime of these networks since it enables efficient merging of traffic flows, thus reducing transmissions and energy consumption of devices. In sensor networks, data aggregation tree (DAT)‐based routing facilitates energy‐efficient routing that extends NL. NL maximization using DATs constructs DATs with optimal NL and is a known NP‐complete problem. Subsequently, the study in this work surveys the various approaches employed by researchers to construct DATs and discusses techniques for DAT scheduling. This work further explores various sensor deployment techniques and discusses real world scenario in which NL is influenced by uncertainty in communication links. Finally, the study in this survey highlights the achievements in realizing NL improvement using DAT and identifies the limitations and research challenges. [ABSTRACT FROM AUTHOR]

The study develops an oxidative kinetic model of acrylic‐urethane network during photo‐aging. The kinetic model can track chemical reaction dynamics well and also enables to estimate the network lifetime by counting scission/crosslinking events. Estimated network lifetime is semiquantitively coincided with the one derived from different statistical approach. [ABSTRACT FROM AUTHOR]

Summary: In recent years, the use of wireless sensor devices in several applications, for example, monitoring in dangerous geographical spaces and the Internet of Things, has dramatically increased. Though sensor nodes (SNs) have limited power, battery replacement is not feasible in most cases. Therefore, energy saving in wireless sensor networks (WSN) is the major concern in the design of effective transmission protocol. Clustering might lower energy usage and increase network lifetime. Routing protocol for WSN represents an engineering area that has gained considerable interest among researchers due to its rapid evolution and development. Among them, the clustering routing protocol corresponds to the most effective technique to manage the energy consumption of each SN. In this manuscript, we focus on the design of a new metaheuristic optimization‐based energy‐aware clustering with routing protocol for lifetime maximization (MOEACR‐LM) method in WSN. The purpose of the MOEACR‐LM method is to improve network efficiency via proper selection of cluster heads (CHs) and effective data transmission. Initially, a hunter–prey optimization (HPO) method‐based clustering technique is used for cluster construction and the CH selection process. Next, the clouded leopard optimization (CLO) model is used for the route selection process in WSN. The HPO and CLO models derive a fitness function involving multiple parameters for clustering and routing processes. A comprehensive experimental analysis is carried out to demonstrate the enhanced performance of the MOEACR‐LM technique. The overall comparison study pointed out the improved energy efficiency results of the MOEACR‐LM technique over other existing approaches. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of specialized sensor nodes that perform sensing services for Internet of Things devices with limited battery power. As the replacement or recharging of batteries is not possible, energy consumption becomes the most important design issue in WSNs. The energy‐efficient routing protocol gets the most priority in such energy‐constrained networks. The utilization of clustering‐based routing protocols like stable election protocol (SEP) has gained much attention as the network's lifetime is significantly improved due to the clustering of sensor nodes. Moreover, the inclusion of a secondary CH (SCH) is beneficial when the member node with the most remaining energy performs data aggregation and reduces the energy burden of the CH. This article characterizes SEP and Prolong SEP (P‐SEP) protocols and then extends them by incorporating SCH, which ensures balanced energy consumption. The performance of the proposed Extended SEP and Extended P‐SEP protocols has been analysed regarding stability period, network lifetime, energy usage and throughput. The simulation results demonstrate that the proposed protocols outperform state‐of‐the‐art protocols like P‐SEP, Modified SEP (M‐SEP) and SEP. In particular, the stability period of Extended SEP and Extended P‐SEP has improved up to 42% and 89%, respectively, compared to M‐SEP. [ABSTRACT FROM AUTHOR]

Summary: Sensors plays an important role in day‐to‐day life as they sense and transfer data on the cloud and servers. These sensors have limited battery power due to which their optimized use is preferred with efficient energy consumption. As sensors were deployed almost everywhere under‐earth, underwater, electronic devices, and mobiles thus their enhanced performance is really important. In WSN, clustering is considered to be an essential approach that gives various advantages such as efficient energy, stability period, network lifetime, less delay, and scalability but has an issue of hot‐spot or energy‐hole problems. For this Un‐Equal Clustering is proposed where the size of clusters varies directly to the distance of the B.S. (Base Station). The main objective of Un‐Equal clustering is energy consumption, hot‐spot problem, and load balance among the cluster heads. In this paper, we have proposed an improved fuzzy‐based multi‐attributes un‐equal clustering to overcome the problem of hot‐spot problem. Earlier, researchers considered two attributes for selecting cluster heads (CHs), but later some of the MADM approaches were used that considered multiple attributes for optimal cluster head selection. In this paper, we have applied the fuzzy‐based TOPSIS method for optimized Un‐Equal clustering where the optimal node deployments with load balance among sensor nodes having optimal coverage and connectivity is considered. The results of the proposed method validate that it is one of the effective ways for selecting optimal cluster heads using fuzzy‐based multi‐attributes. Fuzzy‐based TOPSIS for cluster heads selection method shows 37% enhanced network performance with other compared existing algorithms. The performance of the proposed method is evaluated in terms of network stability, packet delivery, energy consumption, and network lifetime and F‐MAUC (Fuzzy based multi‐attributes un‐equal clustering) outperforms all other compared algorithms. [ABSTRACT FROM AUTHOR]

Summary: Underwater wireless sensor network (UWSN) is a network made up of underwater sensor nodes, anchor nodes, surface sink nodes or surface stations, and the offshore sink node. Energy consumption, limited bandwidth, propagation delay, high bit error rate, stability, scalability, and network lifetime are the key challenges related to underwater wireless sensor networks. Clustering is used to mitigate these issues. In this work, fuzzy‐based unequal clustering protocol (FBUCP) is proposed that does cluster head selection using fuzzy logic as it can deal with the uncertainties of the harsh atmosphere in the water. Cluster heads are selected using linguistic input variables like distance to the surface sink node, residual energy, and node density and linguistic output variables like cluster head advertisement radius and rank of underwater sensor nodes. Unequal clustering is used to have an unequal size of the cluster which deals with the problem of excess energy usage of the underwater sensor nodes near the surface sink node, called the hot spot problem. Data gathered by the cluster heads are transmitted to the surface sink node using neighboring cluster heads in the direction of the surface sink node. Dijkstra's shortest path algorithm is used for multi‐hop and inter‐cluster routing. The FBUCP is compared with the LEACH‐UWSN, CDBR, and FBCA protocols for underwater wireless sensor networks. A comparative analysis shows that in first node dies, the FBUCP is up to 80% better, has 64.86% more network lifetime, has 91% more number of packets transmitted to the surface sink node, and is up to 58.81% more energy efficient than LEACH‐UWSN, CDBR, and FBCA. [ABSTRACT FROM AUTHOR]

The Wireless Sensor Network (WSN) is an emerging technology with diverse applications, ranging from industrial settings to environmental monitoring, healthcare, and remote surveillance. In this paper, the focus lies on the minimization of energy consumption during packet transmission, achieved through two primary strategies: (1) Reduction of the transmission distance for packets through the implementation of re‐clustering. (2) Allocation of increased time to those child nodes which necessitate less energy for transmission. Simulation outcomes demonstrate that the proposed transmission time and re‐clustering (TTRC) algorithm effectively harnesses in excess of 99% of the total network energy, marking an improvement of 14.05% to 15.57% over existing approaches. Furthermore, the TTRC algorithm achieves a substantial enhancement in packet delivery, ranging from 32.31% to 58.54%, coupled with a latency reduction spanning from 21.47% to 32.00%. Moreover, the network's lifestime is prolonged by 28.57% to 28.99% through the application of this algorithm. [ABSTRACT FROM AUTHOR]

Summary: Many cutting‐edge studies on collaborative data gathering and charging (CDAC) assume that the mobile vehicle (MV) has enough energy to charge the sensors as well as collect data from them. The current studies also took into account that the sensors always receive full charge from the MV, resulting in a wireless rechargeable sensor network (WRSN) with very little dead period and very little data gathering latency. It is also believed that the energy consumption rates of the sensors are constant. However, in large‐scale WRSN, the aforementioned considerations are not always practical. In addition, the utilization of single base station (BS) in a large‐scale WRSN cannot guarantee improved network scalability, expedite charging decisions by minimizing the substantial overhead of the BS, and enhance the traveling distance for MV. In order to overcome the aforementioned problems, we present an effective on‐demand partial CDAC scheme using battery‐limited multiple MVs. The proposed scheme implements the CDAC process in such a way that the total dead periods of the sensors and the energy consumption of MVs are reduced. We use a multi‐objective‐based genetic algorithm (GA) to optimize the entire CDAC process. The simulation is carried out to demonstrate the usefulness and competitiveness of the proposed scheme. In comparison with existing works, the proposed work improves CDAC performance by reducing sensor dead time and energy consumption of MV. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) is a network of smart things that connects people and objects for gathering and exchanging information by means of embedded sensor. IoT‐based wireless sensor network (WSN) nodes are monitored as a resource parameter at the variety of methods, including resource calculation, energy resource, and storage resource, with multipath routing protocols necessary to enable extended network lifespan and achieve high energy utilization. The existing routing protocols did not provide optimal path from the multipath route. Hence, in this manuscript, a Hybrid Marine Predators and Border Collie Optimization algorithm is proposed for multipath routing in IoTs. The main aim of this endeavor is to "increase network lifetime." IoT is simulated initially and multipath routing initiates in internet of things network. Multipath routing is achieved by proposed Hyb‐MP‐BCOA; this is the combination of Hybrid Marine Predators Algorithm and Border Collie Optimization algorithm. The proposed Hyb‐MP‐BCOA algorithm chooses the optimum path from the multiple path obtainable for routing, depending upon fitness parameter, like context awareness, network lifetime, residual energy, trust, and delay. Based on the fitness parameter, the optimal path is selected by using Hyb‐MP‐BCOA algorithm. Finally, the proposed Hyb‐MP‐BCOA‐MR‐IoT model is applied in MATLAB, and efficacy of proposed technique is estimated by using various performance metrics, like drop, normalized network energy, lifetime of network, delay, throughput, energy consumed, packet delivery ratio, and number of sensor nodes versus aggregation level. The proposed method attains lower delay of 14.285%, 50%, 53.846%, and 57.142%, lower packet drop of 14.285%, 50%, 53.846%, and 57.142%, lower energy consumption compared with existing methods such as multiobjective sun flower optimization with gray wolf optimizer (GWO) for multipath routing in IoT (SFO‐GWO‐MR‐IoT), multiobjective Whale Lion Fireworks optimization algorithm and fitness function for multipath routing in IoT (WLFA‐MR‐IoT), multipath routing in IoT using shuffled frog leaping algorithm (SFLA‐MR‐IoT), and multipath routing in IoT using hybrid Whale Optimization Algorithm‐Moth Flame Optimization (MFO)(hyb‐WOA‐MFO‐MR‐IoT). Finally, the safe communication is attained in the IoT network. [ABSTRACT FROM AUTHOR]

Summary: With the technological advancements, wireless sensor network (WSN) has played an impeccable role in monitoring the underwater applications. Underwater WSN (UWSN) is supported by WSN but subjected to data dissemination in an acoustic medium. Due to challenging conditions in underwater scenario, the limited battery resources of these sensor nodes stem to a crucial research problem that needs to address the energy‐efficient routing in UWSN. In this research work, we intend to propose an energy‐optimized cluster head (CH) selection based on enhanced remora optimization algorithm (ECERO) in UWSN. Since CH devours the maximum energy among the nodes, we perform selection of CH based on EROA while considering energy, Euclidean distance from sink, node density, network's average energy, acoustic path loss model and lastly, the adaptive quantity of CHs in the network. Further, to reduce the load on CH node, we introduce the concept of sleep scheduling among the closely located cluster nodes. The proposed work improves the performance of recently proposed EOCSR algorithm by great magnitude which claims to mitigate hot‐spot problem, but EOCSR still suffers from the same due to relaying a large magnitude of data. [ABSTRACT FROM AUTHOR]

Summary: With the advancement in recent technologies, wireless sensor networks (WSNs) have emerged to become one of the key research areas. Sensor nodes in WSN are deployed randomly to sense the events that occur in an area. Clustering of sensor nodes in a wireless sensor network will result in better energy efficiency and topology management. In many scenarios, wireless sensor networks are deployed in remote areas and in hostile environments where batteries cannot be recharged or replaceable. In real world applications, heterogeneous sensor nodes with different initial energies in a WSN are more preferable to prolong stability period, network lifetime, and throughput. The main objective of this article is to design and develop a cluster‐based energy efficient routing protocol for heterogeneous wireless sensor networks (CEER). The proposed CEER routing protocol is based on the residual energy of all the sensor nodes in each round, distance of each sensor node from the base station, weighted election probabilities and reliability of a sensor node and the network. It is found from the results that, the proposed CEER routing protocol provides better stability, network lifetime, and throughput as compared with the existing routing protocols of heterogeneous wireless sensor networks. [ABSTRACT FROM AUTHOR]

Summary: The rapid growth of the Internet of Things (IoT) creates a high requirement for data collected through wireless sensor networks (WSNs), resulting in a lot of emphasis on WSN data collecting in recent decades. However, the resource‐constrained nature of sensor devices exerts heavy constraints for acquiring the optimal network performance. To resolve this concern, this paper proposes the Chain‐based Energy‐Efficient Clustering (CBEEC) Routing Protocol that considers nodes of two heterogeneous levels of energy (normal level and advanced level). The number of advanced nodes in CBEEC is analytically determined and hence are analytically allocated. However, normal nodes are stochastically deployed in the vicinity of advanced nodes. The data transmission is performed among the advanced nodes in the form of chain and from there it is forwarded to base station (BS). Consequently, it preserves immense amount of energy for the network. The performance of CBEEC is empirically investigated with benchmarks of different performance metrics and simulation outcomes showing that the CBEEC outperforms state‐of‐the‐art routing protocols. [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: Wireless sensor networks (WSNs) plays an indispensable role in the human life by supporting a diversified number of applications that includes military, environment monitoring, manufacturing, education, agriculture, etc. However, the sensor node batteries cannot be replaced under its deployment in an unattended or remote area due to their wireless existence. Cluster‐based routing is significant in handling the issue of energy stability and network lifetime. The meta‐heuristic algorithms‐based cluster head (CH) selection is determined to be highly promising for attaining the objective of CH selection that results in acquiring an optimal network performance. In this paper, a Hybrid Grasshopper and Improved Cat Swarm Optimization Algorithm (HGICSOA)‐based clustering scheme is proposed for attaining potential CH selection and guarantee significant sink mobility‐based data transmission. The capability of GHOA that controls the rate of exploitation and exploration degree is utilized for CH selection. It specifically adopted OBL‐based GHOA for optimal CH selection based on the objective function, which is formulated using node density, residual energy, and distance between sensor node and sink. It incorporated new CSOA for mobility‐based data transmission for increasing population diversity. It also utilized the benefits of ICSOA with a predominant local search strategy for achieving better sink mobility‐based data transmission. Simulation and statistical results confirmed that the proposed HGICSOA is better in attaining maximum energy stability by 17.21% and improved network lifetime by 23.82%, compared to the benchmarked schemes used for investigation. Moreover, the prevention rate of worst sensor nodes selected as CH is improved by 21.38%, better than baseline approaches. [ABSTRACT FROM AUTHOR]

Summary: With the evolution of technology, many modern applications like habitat monitoring, environmental monitoring, disaster prediction and management, and telehealth care have been proposed on wireless sensor networks (WSNs) with Internet of Things (IoT) integration. However, the performance of these networks is restricted because of the various constraints imposed due to the participating sensor nodes, such as nonreplaceable limited power units, constrained computation, and limited storage. Power limitation is the most severe among these restrictions. Hence, the researchers have sought schemes enabling energy‐efficient network operations as the most crucial issue. A metaheuristic clustering scheme is proposed here to address this problem, which employs the differential evolution (DE) technique as a tool. The proposed scheme achieves improved network performance via the formulation of load‐balanced clusters, resulting in a more scalable and adaptable network. The proposed scheme considers multiple parameters such as nodes' energy level, degree, proximity, and population for suitable network partitioning. Through various simulation results and experimentation, it establishes its efficacy over state‐of‐the‐art schemes in respect of load‐balanced cluster formation, improved network lifetime, network resource utilization, and network throughput. The proposed scheme ensures up to 57.69%, 33.16%, and 57.74% gains in network lifetime, energy utilization, and data packet delivery under varying network configurations. Besides providing the quantitative analysis, a detailed statistical analysis has also been performed that describes the acceptability of the proposed scheme under different network configurations. [ABSTRACT FROM AUTHOR]

Summary: Internet of things (IoT) applications based on wireless sensor networks (WSNs) have recently gained vast momentum. These applications vary from health care, smart cities, and military applications to environmental monitoring and disaster prevention. As a result, energy consumption and network lifetime have become the most critical research area of WSNs. Through energy‐efficient routing protocols, it is possible to reduce energy consumption and extend the network lifetime for WSNs. Using hybrid routing protocols that incorporate multiple transmission methods is an effective way to improve network performance. This paper proposes modulated R‐SEP (MR‐SEP) for large‐scale WSN‐based IoT applications. MR‐SEP is based on the well‐known stable election protocol (SEP). MR‐SEP defines three initial energy levels for the nodes to improve the network energy distribution and establishes multi‐hop communication between the cluster heads (CHs) and the base station (BS) through relay nodes (RNs) to reduce the energy consumption of the nodes to reach the BS. In addition, MR‐SEP reduces the replacement frequency of CHs, which helps increase network lifetime and decrease power consumption. Simulation results show that MR‐SEP outperforms SEP, LEACH, and DEEC protocols by 70.2%, 71.58%, and 74.3%, respectively, in terms of lifetime and by 86.53%, 86.68%, and 86.93% in terms of throughput. [ABSTRACT FROM AUTHOR]

Summary: In recent trends, wireless sensor network (WSN) is often used in harsh environments. When WSNs work, they need to deploy a large number of sensor nodes in the monitoring area. Due to the limited resources carried by the sensor nodes, the monitoring process of WSN will be limited by the energy of the sensor nodes. An energy‐efficient clustering algorithm combining interval type‐2 fuzzy logic and dual‐super‐cluster‐head (IT2FL‐DSCH) mechanism is proposed, aiming at reducing and balancing the energy consumption of sensor nodes. Considering the four fuzzy parameters, that is, cluster heads' (CHs') battery power, CHs' centrality, base station's (BS's) mobility, and the distance from CHs to BS, interval type‐2 fuzzy logic (IT2FL) is used to select the appropriate super cluster head (SCH). And SCH is the only way to communicate with the mobile BS, thus effectively using bandwidth. Then, the DSCH mechanism to balance the energy consumption of SCH is introduced in detail. In addition, we utilize mathematical derivation to obtain the supremum of the energy consumption of the SCH algorithm under different network distribution. Finally, the MATLAB simulation results show that this protocol outperforms low energy adaptive clustering hierarchy (LEACH), LEACH‐centralized (LEACH‐C), distributed energy efficient clustering (DEEC), WSNs using deep‐Q‐networks (DQN), and two‐tier distributed fuzzy logic based protocol (TTDFP) in terms of sensor nodes energy saving, network stability, and network lifetime. [ABSTRACT FROM AUTHOR]

Summary: Internet of Underwater Things (IoUT) comprises various resource‐constrained sensor nodes; therefore, the routing followed in acoustic medium underwater should be energy efficient to preserve their energies. Due to large area covered in the scenarios of underwater, the multi‐hop communication leads to energy hole problem. Therefore, Energy hole mitigation through Optimized Cluster Head (CH) selection and Strategic Routing (EOCSR) in IoUT is proposed in this paper. The proposed work not only optimizes CH selection using Tunicate Swarm Algorithm but also incorporates strategic routing to address energy hole problem. The simulation results show that EOCSR improves stability and lifetime of network by 16.8% and 17.7%, as compared with recently proposed Moth Flame Optimization‐based routing method. [ABSTRACT FROM AUTHOR]

Summary: In the modern world, it will be necessary to deploy a large number of sensor devices to sense everything around us in order to detect changes, risks, and hazards and to mitigate them. This increasing number of sensor devices represents an essential data provider in the Internet of Things (IoT). The devices generate and transmit a huge amounts of data which requires a large amount of storage and high processing power to come real‐time processing and speed up the network. It also leads to an increase in high energy consumption. Thus, it is important to remove redundant data to reduce the data transmission before sending it to the gateway while maintaining a good level of data quality. In this paper, a distributed energy‐efficient data reduction (DEDaR) approach based on prediction and compression to minimize the data transmission in IoT Networks is proposed. The DEDaR is used in periods to make decision. In each period, the autoregressive prediction (ARP) is used to predict the data of the next period and make a decision on whether to send the data of the current period to the gateway or not. In the case of data transmission, the redundant data are eliminated using an efficient compression approach based on adaptive piecewise constant approximation (APCA), symbolic aggregate approximation (SAX), and finally fixed code dictionary (FCD) based on Huffman encoding. The simulation results based on real‐sensed data show that the proposed DEDaR approach outperforms the other recent methods in terms of data reduction percentage, transmitted data size, energy consumption, and data accuracy. [ABSTRACT FROM AUTHOR]

Summary: During the past few years, wireless sensor networks (WSNs) have created a substantial interest because of their capabilities namely, self‐healing and self‐organizing under restricted network resources. Numerous WSN‐based routing algorithms are implemented to perform well, but the energy consumption of every data packet and information loss is more. Due to the power restriction in the sensor nodes, energy consumption is regarded as the major issue in networks that need to be minimized to improve the network lifetime. Hence, to conquer these consequences, this paper aims to propose an adaptive spider monkey optimization (ASMO)‐based effective optimal routing path from various sensor nodes with enhanced network lifetime. Here an ASMO is employed in selecting an optimal routing path. In addition to this, the proposed ASMO algorithm is capable of selecting energy‐aware routes by energy consumption. It also chooses the routes with minimum end‐to‐end delay using the bare least hop count criteria, thus enhancing the network life span. The results of the proposed method are compared with other methods such as DMEERP, EGSMRP, EEIABC, and RACO. The results reveal that the proposed approach consumed less amount of energy; hence, network lifetime is more when compared with other approaches. [ABSTRACT FROM AUTHOR]

Summary: In recent years, the WSN are emerging swiftly since it finds applications in various domains including weather monitoring, attack detection, industrial monitoring, monitoring of submarine organisms, patient monitoring as well as the monitoring of ecological disorders. But WSN is also influenced by various other factors like network lifetime and energy consumption. It is necessary to provide an energy effective protocol to conquer certain troubles that includes packet delivery ratio, network lifetime, residual energy as well as effective routing in WSN. Therefore, this article aims to propose a novel protocol to enhance the energy efficiency of the network thereby providing an optimal routing path. This can be achieved by selecting an optimal cluster head that maintains communication between the base station and the sensor node. In this article, a novel multi‐objective moth swarm based sailfish (MOMS‐SF) technique is employed in selecting an optimal cluster head. The proposed MOMS‐SF technique enhances the network lifetime and minimizes the energy consumption of the network. Finally, the evaluation results are conducted to determine the network performances of the proposed MOMS‐SF approach. Also, a comparative analysis is carried out and the graphical analyzes for various parameters are made for various approaches to determine the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

Summary: The technological progression in the area of Wireless Body Area Network (WBAN) has made it possible to design various sensing modules operating for detecting the multitudinous physical attributes of a patient's body. However, the limited battery of these sensing devices has restricted the scope for WBAN. Hence, it is imperative to design an energy efficient algorithm to combat the gigantic energy consumption of these sensor nodes. Therefore, in this paper, we propose a Novel Energy Efficient hybrid Meta‐Heuristic Approach (NEEMA) for WBAN. We adopt this hybrid approach by using Tunicate Swarm Algorithm (TSA) and Genetic Algorithm (GA); we name it as T‐GA, to deliver the high convergence and large exploitation and exploration capabilities. We follow the clustering approach by selecting the Cluster Head (CH) with the help of the fitness function of T‐GA. We consider the novel parameters for selecting the CH that helps in the overall energy preservation of sensor nodes. Our proposed work focuses on multi‐hop communication among the patients on whose body, the body sensor nodes are installed, so that the data through the multi‐hop reach to the healthcare. The Relay Head (RH) node is used for forwarding the data to next RH and hence to sink. RH selection follows the same method of selection as that of CH. The experimental outcomes of NEEMA outperform the state‐of‐the‐art algorithms and prove to be highly beneficial for various WBAN applications. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are one of the basic building blocks of Internet of Things (IoT) systems. However, the wireless sensing nodes in WSNs suffer from energy constraint issues because the replacement/recharging of the batteries of the nodes tends to be difficult. Furthermore, a number of realistic IoT scenarios, such as habitat and battlefield monitoring, contain mobile sensing elements, which makes the energy issues more critical. This research paper focuses on realistic WSN scenarios that involve mobile sensing elements with the aim of mitigating the attendant energy constraint issues using the concept of radio‐frequency (RF) energy extraction. The proposed technique incorporates a cluster head election workflow for WSNs that includes mobile sensing elements capable of RF energy harvesting. The extensive simulation analysis demonstrated the higher efficacy of the proposed technique compared with the existing techniques in terms of residual energy, number of functional nodes, and network lifetime, with approximately 50% of the nodes found to be functional at the 4000th, 5000th, and 6000th rounds for the proposed technique with initial energies of 0.25, 0.5 and 1 J, respectively. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) consist of nodes. Issues involved with the use of a sensor network are energy‐saving and the effective use of energy. Clustering in WSNs is a proven technique for energy optimization. The relationship between sensor nodes and cluster heads (CHs) has only been considered prior to cluster‐based routing protocols. However, most clustering algorithms have failed to address the routing overhead and the energy consumption rate between CH nodes and SINK. In this paper, a new framework named as EEC‐MA‐PSOGA ‐ an energy‐efficient (EE) intra‐cluster mobile agent (INC‐MA)‐based particle swarm optimization–genetic algorithm (PSO‐GA), has been presented to initiate the distance communication and place the SINK optimally in WSNs. The cluster members send the collected data towards their respective CHs for aggregation. To find the sink's best position, the PSO‐GA‐based location estimation algorithm is initiated based on the network structure. The limited capability of WSNs makes them more vulnerable to attackers. The prevention mechanism must be less complex to ensure the fair operations in the network. Here, the attack detection ability of the proposed solution has been tested against clone attack. With varied communication range, simulation time, and sensor nodes, the tests are conducted extensively on various scenarios of WSNs. When compared with the prior mechanisms, the framework provides better results and better prevention with less overhead by analyzing the experimental results such as energy consumption, network lifetime, and throughput. [ABSTRACT FROM AUTHOR]

The open communication medium of the Internet of Things (IoT) is more vulnerable to security attacks. As the IoT environment consists of distributed power limited units, the routing protocol used for distributed routing should be light‐weighted compared to other centralized networks. In this situation, complex security algorithms and routing mechanisms affect the generic data communications in IoT platforms. To handle this problem, this proposed system develops a cooperative and feedback‐based trustable energy‐efficient routing protocol (CFTEERP). This protocol calculates local trust value (LTV) and global trust value (GTV) of each node using node attributes and K‐means‐based feedback evaluation procedures. The K‐means clustering algorithm leaves out the distorted node routing metrics and misbehaving node metrics for all channels. This proposed CFTEERP uses the nearest secure node costs to increase the network lifetime without selecting the nearest nodes for routing the data. In this work, secure routing is initiated using multipath routing strategy that analyses LTV, GTV, next trustable node, average throughput, energy consumption, average packet delivery ratio (PDR) and traffic various metrics of entire IoT communication. The technical aspects of proposed system are implemented to solve different existing techniques' limitations. In the comparative experiment, the proposed method provides 90% of PDR and a minimal energy consumption rate of 25% lesser than the existing systems against different malicious attacks. [ABSTRACT FROM AUTHOR]

Summary: The sensors deployed inside a wireless sensor network (WSN) have limited energy sources, significantly impacting the network throughput. This article's research objective is to increase network lifetime by developing an energy‐efficient layered‐based routing algorithm for WSNs using the grey wolf optimization (LBR‐GWO) algorithm. In this, the grey wolf's leadership hierarchy has followed, which improves the network's energy capability. The entire region of the deployed nodes divides into four layers. In these nodes, layer one is chosen as cluster heads. If more than two nodes are present in layer one, then the cluster head is selected based on the game theory model; otherwise, the decision is made based on the node's residual energy. While the existing algorithm has several complex control parameter points, the current algorithm has fewer complex parameters. Therefore, in comparison to other algorithms, this algorithm is easy to apply in cluster‐based sensor networks. Simulation findings prove the LBR‐GWO algorithm supremacy for balancing energy consumption across the nodes and improving the network's lifetime compared to the LEACH, HEED, and PSO protocols. [ABSTRACT FROM AUTHOR]

Summary: In Sensor Networks (SNs), Network Lifetime (NL) enhancement using Data Aggregation Trees (DATs) is an extensively studied NP complete problem. The study in this paper investigates the existing DAT scheduling heuristics and proposes a metric termed as Brevis Factor ρ to evaluate the merit of the employed heuristics. Then, the ρ metric is applied to the state‐of‐art DAT scheduling heuristics which are typically based on Residual Energy (RE) parameter of the sensor nodes. Further, this paper proposes a new heuristics parameter for DAT scheduling based on the Number of Neighboring nodes (NN). Performance of NN‐based heuristics is evaluated with rigorous simulations, and the results demonstrate improvement in NL as compared with the state‐of‐art. The values of ρ are observed for the existing RE‐based heuristics as well as the proposed NN‐based heuristics. The results demonstrate how ρ metric values evaluate the DAT scheduling heuristics. In addition, the results facilitate to understand the trade‐off associated with the overheads to reconstruct DATs while improving NL. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a new paradigm that connects physical and virtual objects from various domains such as home automation, industrial processes, human health, and monitoring. IoT sensors receive information from their environment and forward it to their neighboring nodes. However, the large amounts of exchanged data are vulnerable to attacks that reduce the network performance. Most of the previous security methods for IoT have neglected the energy consumption of IoT, thereby affecting the performance and reducing the network lifetime. This paper presents a new multistep routing protocol based on cellular learning automata. The network lifetime is improved by a performance‐based adaptive reward and fine parameters. Nodes can vote on the reliability of their neighbors, achieving network reliability and a reasonable level of security. Overall, the proposed method balances the security and reliability with the energy consumption of the network. [ABSTRACT FROM AUTHOR]

Summary: Mobile wireless sensor network (MWSN) consists of several wireless mobile sensor nodes which animatedly broadcast information among themselves without dependence of base station. Because of its unique mobility and energetic environment, MWSNs are more vulnerable to many forms of assaults and network protection. Any unusual activity that threatens resource dependability, privacy, or accessibility is referred to as an interruption. A novel, dynamic energy efficient cluster‐based secure routing identifies these challenges while discovering exact routing and identifying malicious node in MWSN. Design is organized as a dynamic hierarchy in which data is disseminated without loss from source to destination. Clustering methods are used to rearrange the dynamic hierarchy in order to improve communication. Cluster heads are chosen depending on residual energy and topology change. Proposed protocol is used for securing data and routing tactic will broadcasting. Simulation of energy efficient cluster‐based secured routing protocol is carried out by NS2. This routing protocol provides enhanced energy efficiency, secured clustering, lifetime of network and reduces network overhead. [ABSTRACT FROM AUTHOR]

The recent trends in wireless network sensor technology are moving towards multimedia applications. Multimedia sensor nodes are difficult to communicate, but communication is necessary. Multimedia service offers both video monitoring and voice over wireless sensor networks. This causes severe problems with usage of data, power, bandwidth, energy usage and wireless sensor network deployment costs. The key restriction for the expansion of a network is the energy conservation. The nodes are distributed in the field to collect audio, video and density information so as to collect the information from sensors. The network of wireless sensors provides different layer nodes depending on their power level. In this work, Enhanced Greedy Forwarding with efficient Multi‐path and Dynamic Routing (EGFMDR) protocol was proposed and the results were compared with existing state‐of‐the‐art protocols like Ad‐hoc On‐demand Distance Vector and DYMO. Depending on how powerful they are, multi‐stage architectures were employed in various areas such as observation, military, media and traffic surveillance. They retain the effectiveness of wireless sensor network in multimedia applications. Service quality protocol parameters were analysed, and from the simulation results attained, the proposed EGFMDR protocol displays reduced end‐to‐end delay, lesser packet loss ratio and increased energy efficiency. [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 (WSN) is a key technology that can support Industrial Revolution 4.0. The sensor nodes (SNs) are the important component of WSN. Recharge or repair of batteries of SNs is impossible, and thus, energy is the most valuable resource of WSN. Over the years, many methods have been proposed to conserve the precious energy resource of WSN. The most important concern in WSN is how much of the energy of the SNs can be saved, which can contribute to the better network life of WSN. WSN‐assisted IoT output is reduced when a wide region of the network is deployed. Energy‐efficient routing is one of the ways to reduce power consumption in WSNs. Therefore, a novel energy‐efficient two‐stage routing protocol (EETSP) has been proposed to reduce the energy used in WSN and extend the stability period as well as the network lifetime of WSN. Through this protocol, the number of packets communicated to the base station (BS) is significantly improved, and the cluster heads (CHs) and secondary cluster heads (SCHs) reduce the energy utilization in the network. SCH works as a backup CH. EETSP consists of two stages. The selection of CH and SCH is made in the primary stage, based on the input parameters. Intercluster routing and intracluster routing are performed in the second stage. The improvements of EETSP are examined against LEACH and Z‐SEP, and obtained results indicate that the EETSP is better in terms of stability period, throughput and network lifetime. [ABSTRACT FROM AUTHOR]

Summary: Mobile Wireless Sensor Networks (MWSNs) have emerged as a new evolution trend in Wireless Sensor Networks (WSNs) technology as a result of the growth and popularization of mobile terminal technology. WSNs' feature spectrum is greatly expanded by the mobility of nodes introduced by MWSNs. MWSN is a network with lots of sensors that are initially distributed randomly and collaborate to gather, process, and transmit information around targets to the sink node. MWSN is an overgrowth and emerging technology with significant applications that provide free moving for sensors and adaptable communicating with each other without fixed infrastructure. Because of their small size, they have limited radio capability, storage, and power. Therefore, MWSNs are utilized to improve network lifetime, energy expenditure, and channel capacity than fixed WSNs. Many routing protocols have recently been introduced to perform advances in the field of energy expenditure for MWSN data collection. This paper introduces a survey on hierarchical‐based routing protocols for MWSNs. The survey outlines the classification of the routing techniques in the aspect of mobility. The comparisons between Mobile Sensor Node (MSN) protocols, Mobile Sink (MS) protocols, and protocols for mobile of both sink and sensor node together are introduced individually. Moreover, the commercial software programs for MWSNs are discussed. Finally, the challenges and future trends in MWSNs are presented. [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]

Wireless sensor networks are used to track and regulate physical conditions like temperature and the environment's humidity. Wireless sensor networks with their advanced features are adopted for many real‐time applications. The limited capacity batteries usually power the sensor nodes. But the big challenge of limited battery capacity obstructs remote and inaccessible areas where their use is the most favorable. For extending the lifetime of the network, the battery should optimally utilize it for different operations. The requirement toward low complexity and low energy consumption motivate the wireless sensor networks' efficient clustering algorithm. The sensor nodes group into clusters; one sensor node is chosen as a cluster head, and communication to the sink node from the sensor nodes occurs through the cluster head (CH) node. In the proposed method, cluster heads are determined based on the sensor node's weighted metric. The sensor nodes are then self‐adaptive by making correct decisions in real‐time based on the sensed data, but detected information is often inaccurate due to some mechanical, wireless loss, and battery problems. The erroneous or irrelevant data should be overlooked to avoid unnecessary data transmission, which contributes to reducing the network's lifetime. In the neighborhood‐dependent self‐diagnosis fault detection technique, the faulty sensed data are filtered at the sensor node itself. In the data prediction algorithm, the filtered data are predicted at the cluster head. All the factors collectively contribute to enhance the network's lifetime. The lifetime improvement of the proposed approach is almost doubled compared with LEACH one time better than QLEACH and ECH, and 51% better than temporal approach. [ABSTRACT FROM AUTHOR]

Summary: Many wireless sensor‐based applications use LEACH as a preferred routing protocol owing to its unique features such as optimal sleeping time, minimum packet collisions, dynamic channel selection, and least power consumption. The traditional LEACH protocol wastes the transmission opportunities as it processes data only in an event occurring, leading to wastage of resources. To resolve this issue, this paper proposes a more robust Energy Enhanced Threshold Routing Protocol (ETH‐LEACH) for WSNs. The working of ETH‐LEACH is conceptualized in two parts. In the first part, TDMA is implemented to estimate the opportunistic paths to remove network overhead. Furthermore, in the second part, a threshold value is calculated for choosing the forwarder nodes. The proposed technique minimizes the energy usage of the sensor nodes and consequently enhances the network's lifetime by extending the duration of node death. The ETH‐LEACH protocol is contrasted with the different variants of LEACH to verify its effectiveness. The experimental results show that the proposed ETH‐LEACH protocol outperforms the traditional routing protocols. In this paper, the ETH‐LEACH protocol performs nearly 54.6% efficient than LEACH, 47.6% efficient than Q‐LEACH, 41.3% efficient than NR‐LEACH, 33.6% efficient than LEACH‐GA, and 29.7% efficient than LEACH‐POS in reducing the usage of energy in the overall simulation. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSNs), the Cluster Heads (CHs) are used for efficient data collection from the enormous number of randomly deployed sensor nodes. Since these CHs consume a massive amount of energy and degrade the lifetime of the network, thus reducing their energy consumption is the prime concern. In order to enhance the network lifetime, we are proposing the Fitness Function‐based Two‐Hop Routing (FFTHR) algorithm for inter‐cluster routing among all the CHs of the network. This algorithm depends on the residual energy of the CHs and their relative Equlidean distances from the Base Station (BS) as well as from their centroid. One of the CHs has been defined here as Central Cluster Head (CCH) based on their fitness value, and it uses single‐hop for transmitting their data packets to the BS while the remaining CHs use two‐hop. The simulation results exhibit that the FFTHR enhances the network lifetime and saves the energy consumption of the network as compared to the similar algorithm for example, REHR (Residual Energy based Hybrid Routing) and FCM (Fuzzy C‐Means). [ABSTRACT FROM AUTHOR]

Summary: In wireless sensor network (WSN), the design of routing protocols focuses mainly on the energy efficiency based on the application of the network. Over the past years, researchers developed many solutions to protract the life expectancy of the network by reducing the consumption of energy by each node. One of the main approaches is the usage of sink in mobility; this reduces the hotspot problem and energy consumption. Aiming at this problem, the paper proposes a new routing algorithm which supports the sink mobility with energy aware tree‐based sink relocation routing protocol (TBSR) using adaptive fuzzy decision‐making approach (AF‐MADM). In TBSR, a self ‐organized routing tree is formed, which reduces the consumption of energy. In the beginning, the clustering process is employed using fuzzy possibilistic C‐means (FPCM) algorithm. Further, the fuzzy analytic hierarchy process (FAHP) with multiplicative exponential weighting model is developed for optimal decision making to select the root node. Then the selected root node creates a routing tree based on the distance and energy of the sensor nodes. Then each node uses its cluster information and the distance of the neighbors to select the parent node. For relocating the sink node, the proposed protocol calculates the energy along with hop count and path capacity. These methods make TBSR an energetic and analogous protocol. Results show that the proposed TBSR improves on the energy consumption and the lifetime of the network compared with existing protocols. The simulation is carried out in OMNET++ platform. [ABSTRACT FROM AUTHOR]

Summary: With rapid increase of the advancement in wireless technologies, the world is now moving toward smart wireless devices which require the communication and collaboration of the Internet of Things (IoT). The smart devices, which called sensor nodes, enhance the use of the IoT as the essential components of today's smart world. Therefore, the wireless sensor network (WSN) is the most widely used technology enabling IoT scenarios. Using the technology, a huge volume of data needs to be tackled and transmitted carefully via the internet. Since the lifetime of the network is related to the reduced battery capacity of the connected wireless sensor nodes, extending the network lifetime is the main goal to be achieved. Clustering algorithm has a significant effect on the system lifetime for the IoT‐enabled WSN applications. In this paper, a fuzzy‐based routing protocol (FRP‐LEACH) and a cross‐layer routing protocol (CLRP‐LEACH) are proposed based on the clustering topology. The proposed algorithms are designed for the healthcare IoT applications. These algorithms offer several advanced cloud‐based services and facilities to serve patients more effectually and protect the medical and paramedical framework from pandemic illness like the ongoing COVID‐19 pandemic. To address the above‐mentioned concerns, an energy efficient routing protocol based on fuzzy logic is proposed to optimize wireless Internet‐of‐Things sensor networks performance. Simulation results are presented, and they show that the proposed algorithms outperform current cluster based‐routing protocols using a fuzzy logic algorithm in terms of generating stable clusters and extending the network lifetime. [ABSTRACT FROM AUTHOR]

Summary: Demand of cognitive radio technology and wireless sensor network is increasing in various applications. Currently, the combined cognitive wireless sensor networks have gained attraction by research community due to their extensive applications and advantages. The wireless sensor networks operate in ISM bands where managing the available spectrum is considered as a crucial task. Moreover, the sensor networks are deployed in harsh environment and equipped with limited power supply; hence, replacement of power source is not possible. Hence, efficient spectrum sensing and lifetime management are the challenging task in Cognitive Radio Sensor Networks (CRSNs). In this work, we present a combined approach to enhance the network enactment, i.e., network lifetime, energy depletion, and packet delivery with a novel spectrum sensing approach. In order to handle the issue of energy utilization, we introduce inter‐ and intra‐cluster communication model along with a clustering algorithm. Further, we present a posterior transition probability‐based model for spectrum sensing. We present an experimental study where we measure the network enactment in context of alive node, dead node, enduring energy, and packet to the base station. The experimental study shows that average spectrum sensing performance is obtained as 0.9030, 0.9188, 0.9213, 0.9355, and 0.9628 by using DE, FMODE, NSGA, ODE, and proposed approach, respectively. Experimental analysis shows that proposed approach archives better performance when compared with advanced methods. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks are significantly used for data sensing and aggregating dusts from a remote area environment in order to utilize them in a diversified number of engineering applications. The data transfer among the sensor nodes is attained through the inclusion of energy efficient routing protocols. These energy efficient routing necessitates optimal cluster head selection procedure for handling the challenge of energy consumption to extend the stability and lifetime in the sensor networks. The implementation of energy efficient routing is still complicated even when the process of clustering is enhanced through the cluster head selection. The majority of the existing cluster head selection schemes suffer from the issues of poor selection accuracy, increased computation, and duplicate nodes' selection. In this paper, hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm (HSFLBOA) for optimal cluster head selection is proposed for resolving issues that are common in cluster head selection schemes. This proposed HSFLBOA used the objective function that used the parameters of node energy, data packet transmission delay, cluster traffic density, and internode distance in the cluster. The simulation results of the proposed HSFLBOA is determined to be significant in achieving superior throughput and network energy compared to benchmarked metaheuristic optimal cluster head schemes. [ABSTRACT FROM AUTHOR]

In the military battlefield, maintaining high survivability of energy constraint wireless sensor network (WSN) when some nodes are destroyed by the foe troops is one of the prominent issues for the researchers. This study proposes the fuzzy and neuro‐fuzzy based relay selection schemes for cooperative WSN which reduce the bit error rate (BER) while improving the network lifetime. Meanwhile, the proposed model maintains the uninterrupted communication between military nodes in a warfare situation. The proposed proposed strategies are compared with the maximum residual energy‐based relay selection (MRERS), minimum energy consumption‐based relay selection (MECRS), and random relay selection (RRS) strategies. The performance of the proposed and existing strategies is evaluated on the basis of BER, network lifetime, number of dead nodes, and average energy of nodes in the network. The simulation results demonstrate that proposed schemes has 13.19–19.28% improvement in BER than the existing strategies. Moreover, 76.00–90.88% network lifetime improves when compared with the MECRS and RRS strategies. However, 6–7.76% is less than MRERS strategy. Furthermore, the results show that network survivability and average network energy is also better when the number of dead nodes in the network increases. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) comprises of numerous sensors deployed either directly or randomly in the region of interest. Due to the limited power of the sensors, these networks are energy‐constrained and thus need efficient power utilisation. Efficient clustering and cluster head (CH) selection ensures balanced energy distribution to the sensors within the WSN and hence prolong the network lifetime. This study proposes the method for evaluating the threshold for the CH selection after each round, which increases the network lifetime and throughput significantly. The threshold for CH selection is modified considering the normalised first‐order and second‐order statistical parameters, such as mean average low‐energy adaptive clustering hierarchy (AvgLEACH) and variance (VarLEACH) of the overall network energy. These methods have been formulated after studying the effect of the number of working nodes in each round on the threshold value selection. Apart from including energy parameter to the threshold equation, the methods of VarLEACH and AvgLEACH are augmented with a residual energy parameter that is local to the nodes and named as VarRLEACH and AvgRLEACH. The simulation results comparing all the methods suggest that the proposed method AvgRLEACH outperforms LEACH by a factor of 1.5 in delivering data to the base station and outlives the network driven by LEACH protocol by 30–40%. [ABSTRACT FROM AUTHOR]

This Letter proposes a stochastic model of the wearable sensor nodes of a wireless body area network (WBAN) based on Markov decision process (MDP). The proposed technique tackles the problem of maximising the likelihood of data transmission while minimising the computational cost and energy. Suitable transmission strategies subject to the energy level of the nodes and channel conditions are needed to be investigated to strike a balance between network lifetime and packet delivery ratio. MDP formulation of the problem is solved offline to find out the optimal transmission strategies for the representative input conditions. The results of this offline computation approach can directly map to optimal transmission policies for intra-WBAN communications. The experimental results demonstrate the robustness of the proposed scheme in handling both favourable and unfavourable operating conditions. [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]

Summary: Wireless sensor networks (WSNs) not only have become an essential technology in the current scenario but also has been established as a core technology for many applications, like the Internet of Things (IoT). The drawback of limited energy resources in WSN creates the challenge of designing an energy‐efficient network topology and routing path. As these devices are generally not attended for any maintenance, such as battery replacement or charging, these resources need to be intelligently used. The paper represents a methodology for enhancing the lifetime of a network by using a genetic algorithm‐based clusteringand particle swarm optimization‐based routing in WSN. The best cluster head (CH) that collects the data from the remaining nodes is selected using a genetic algorithm, which considers distance and energy parameters. Further, the particle swarm optimization algorithm depends on optimal routing paths that are chosen for all relay nodes to send data to the Base Station (BS). The proposed procedure shows that the relay node supports and facilitates communication among the sink and CH, which enhances energy efficiency. Finally, the quality of service (QoS) of the WSNs is improved, due to which the system would survive longer. The results of simulated experiments show that the proposed approach significantly increases the lifetime of WSN and that energy consumption is reduced when compared with the existing protocols. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are exposed to various data‐deployment faults during the communication action. These faults may impact the behaviour of the sensors that degrade its performance and cuts its life. Therefore, we tend to implement the integration of two independent trends are self‐awareness and self‐adaptation capabilities along with two integrated adaptive filters, FIR and RLS. The proposed Autonomous Fault‐Awareness and Adaptive (AFAA) model composed of three adaptive two‐stage executed self‐awareness approach to limit the impact of such faults during the propagation process. In this paper, we introduce the operational mechanism of AFAA that manages to identify the failure and aware of the lost signal values autonomously, then filter the perceptive‐signals for eliminating the accompanied interference and gaining convergent values. It executed the incorporated autonomous model at the level of Cluster Head (CH) for independent fault‐correction using an adaptive feedback model. Compared to the state‐of‐the‐art methods, the proposed model achieved speed in fault diagnosis; also high‐accuracy rate in the prediction of the lost signal values as much as 98.63%, thus improving the percentage of performance efficiency to 3:1 times along of duty cycle. Hence, it enhanced the overall network lifetime. [ABSTRACT FROM AUTHOR]

The clustering technique is a key routing method for large‐scale wireless sensor networks (WSNs), which strongly extends the network's lifetime and scalability. The fundamental protocol of this technique, which is called low energy adaptive clustering hierarchy (LEACH), is proved as a solution that offers good energy efficiency and consequently expands the network lifetime. As the energy efficiency is deteriorated with the network size, LEACH improvements are becoming so substantial to support the scalability in the large‐scale WSNs. The authors propose a new dynamic multi‐hop clustering protocol. It exploits both the mobility and the multi‐hop communication to prolong the lifetime of the mobile WSN. The proposed protocol, called dynamic multi‐hop LEACH (DMH‐LEACH), combines the dynamic clustering, multi‐hop transmissions, and node mobility to improve the energy efficiency of LEACH. They also study the effect of the traffic and energy heterogeneity on the proposed protocol. Finally, they simulate DMH‐LEACH protocol for both homogeneous and heterogeneous scenarios. The simulation results show that the proposed protocol allows to better balance the energy consumption for all sensor nodes and considerably extends the network's lifetime. [ABSTRACT FROM AUTHOR]