Node Death Research Articles

A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks

Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities.

LPLL-LEACH: A study of a low-power and low-delay routing protocol based on LEACH

For a series of problems, such as uneven energy consumption and long packet forwarding time of a low-energy adaptive clustering hierarchy (LEACH), an efficient routing protocol with low power and low latency based on LEACH (LPLL-LEACH) is proposed in this paper. In terms of energy, first, the optimal number of cluster heads (CHs) is calculated by quantitative analysis. Second, multiple factors are considered to redefine the threshold for electing CHs. In addition, ordinary nodes decide which cluster to join according to the cost function. Moreover, a forwarding function is utilized to determine the optimal next hop CH to forward the data. Last, the ordinary nodes are allowed to directly communicate with the base station (BS). In regard to latency, a hybrid mechanism of carrier sense multiple access and time division multiple access (CSMA_TDMA) is applied to minimize the time delay of data transmission. MATLAB is used to analyze the latency and energy of LPLL-LEACH and to compare it with LEACH, LEACH-C, ECH and EE-LEACH. The experimental results show that the time of all node deaths extended by 107%, 45%, 31.8%, and 8.8%. Similarly, the latency is greatly reduced, which improves the efficiency of data transmission.

IACRA: Lifetime Optimization by Invulnerability-Aware Clustering Routing Algorithm Using Game-Theoretic Approach for Wsns.

Energy limitation is one of the intrinsic shortcomings of wireless sensor networks (WSNs), although it has been widely applied in disaster response, battlefield surveillance, wildfire monitoring, radioactivity detection, etc. Due to the large amount of energy consumed for data transmission, how to prolong the network lifespan by designing various hierarchical routing protocols has attracted more and more attention. As a result, numerous achievements have emerged successively. However, these presented mechanisms can rarely guarantee the satisfactory quality of service (QoS), while lowering the energy cost level of WSNs. Meanwhile, invulnerability is undoubtedly an excellent quantitative index to assess QoS. Therefore, it is critical to develop a practical routing method to optimize network lifetime by considering both invulnerability and energy efficiency. Game theory is suitable for such a critical problem as it can be used in node or at network level to encourage the decision-making capabilities of WSNs. In this paper, a novel invulnerability-aware clustering routing algorithm (IACRA) using game-theoretic method is proposed to solve the predicament. The core features of the addressed game-theory-based routing protocol include integral invulnerability awareness, optimal cluster head selection in hierarchical routing, distance-aware cluster head discovery, and cluster rotation update mechanism for lifetime optimization. Particularly, the integral network invulnerability based on weighted fusion is constructed for further defining the profit model by combining the invulnerability indicators used to evaluate the local and whole network. Meanwhile, the optimal probability function of every node elected as CH in per cluster is established through the game between invulnerability and node energy consumption. In addition, the cluster update mechanism base on cluster rotation is proposed to avoid the rapid death of nodes with large energy consumption for maximizing network lifetime. The experimental results indicated a significant improvement in energy balance as well as in invulnerability compared with the other three kinds of well-known clustering routing protocols including GEEC (Game-theory-based energy efficient clustering routing protocol), HGTD (Hybrid, game-theory-based distributed clustering protocol), and EEGC (Efficient energy-aware and game-theory-based clustering protocol). Concretely, at the 400 communication rounds, the invulnerability of IACRA was higher than that of GEEC, HGTD, and EEGC by 77.56%, 29.45% and 15.90%, respectively, and the average residual energy of IACRA was 8.61%, 18.35% and 6.36% larger than that of GEEC, HGTD, and EEGC, respectively. Based on these results, the proposed protocol can be utilized to increase the capability of WSNs against deterioration of QoS and energy constraints.

An Energy Aware Grid-Based Clustering Power Efficient Data Aggregation Protocol for Wireless Sensor Networks

A wireless sensor network (WSN) is made up of multiple sensors deployed in a specific sensing area to identify the occurrence of events and quickly transmit useful information back to the base station (BS). In WSNs, schemes to reduce energy consumption are an important topic of research. A well-designed data transmission scheme can effectively extend the lifetime of a network. In this paper, we propose an energy aware grid-based clustering power efficient data aggregation protocol (GB-PEDAP) for WSNs. The proposed scheme has a two-layer architecture: the inner layer and the outer layer. The inner layer uses direct transmission to collect the data of the cluster (cell), and the outer layer uses a tree structure transmission to collect the data of the cluster head (cell head). In our simulations, the number of rounds executed by GB-PEDAP was approximately 1.2 rounds of TBEEP, 1.3 rounds of GSTEB, and 1.5 rounds of PEDAP. With the initial energy, 0.25 J, the execution rounds of the first node death for GB-PEDAP, TBEEP, GSTEB, and PEDAP were 751, 572, 486, and 339, respectively. The proposed GB-PEDAP can evenly disperse the energy consumption of sensors to avoid the rapid death of sensors, extending the lifetime of a WSN.

Routing Protocol for Wireless Sensor Networks Based on Archimedes Optimization Algorithm

One of the key design problems of wireless sensor networks is to reduce energy consumption and improve node survival. Because the battery in sensor nodes is difficult to replace and the energy is limited, the design of energy-saving routing protocol becomes very important. Aiming at the problems of limiting node energy and shortening network lifetime in wireless sensor networks, a WSNs routing protocol based on improved archimedes optimization algorithm (IAOAR) is proposed in this paper. Firstly, in the cluster establishment phase, the protocol defines different fitness functions according to the energy, number and distance of sensor nodes to select the appropriate initial cluster. Then the fitness value of the node is calculated, and the weight is dynamically updated according to the number of iterations to improve the optimization ability of AOA algorithm. Virtual force is introduced to adjust the selected cluster head position to ensure the selection of optimal cluster heads, reduce the transmission energy consumption of common sensor nodes. In the data transmission stage, combined with the improved ant colony algorithm, the shortest transmission path to the sink node is constructed to reduce the energy consumption of long-distance data transmission at the cluster head. The simulation results showed that compared with LEACH, E-LEACH, LEACH-ANT and MAXLEACH protocols, the network life cycle of the protocol proposed in this paper was improved by 140.55%, 107.09%, 99.24% and 96.76% respectively. It effectively reduced the speed of node death, balances the network energy consumption and prolonged the network lifetime.

Performance Analysis of Wireless Sensor Network by Variation in Cluster Head Selection through mLEACH-CS Algorithm

Abstract: The energy depletion at the nodes due to large volume of data transmission and data reception for large scale Wireless Sensor Networks (WSN) is one of the important areas of concern. Unbalanced energy consumption due to data traffic overhead at the nodes near the base station (BS) results in early node death and reduction in network lifetime. To balance the load at the wireless nodes during clustering, a novel hybrid clustering and routing protocol has been proposed based on mLEACH (modified Low-Energy Adaptive Clustering Hierarchy) and Cuckoo Search (CS) algorithm. Initially, mLEACH protocol is utilized to solve a novel equation designed to select the cluster heads by rotation at every round of data transmission based on the maximum permissible cluster heads in the network. In the second phase, CS algorithm is implemented for uniform distribution of the cluster heads in the network for efficient multi-hop data routing in the network based on several weighted factors. The performance of the proposed algorithm is compared with other algorithms in literature in terms of energy consumption and network lifetime for different positions of the base station and varied number of cluster heads in the model application area. The proposed mLEACH-CS algorithm shows a significant improvement by 60% considering 300 nodes in the WSN architecture while it decreased to 40% for denser network architecture. Optimal choice of the number and position of the cluster heads in certain network architecture has proved beneficial for designing an energy efficient wireless network with improved network lifetime.

DDQN with Prioritized Experience Replay-Based Optimized Geographical Routing Protocol of Considering Link Stability and Energy Prediction for UANET.

Unmanned aerial vehicles (UAVs) are important equipment for efficiently executing search and rescue missions in disaster or air-crash scenarios. Each node can communicate with the others by a routing protocol in UAV ad hoc networks (UANETs). However, UAV routing protocols are faced with the challenges of high mobility and limited node energy, which hugely lead to unstable link and sparse network topology due to premature node death. Eventually, this severely affects network performance. In order to solve these problems, we proposed the deep-reinforcement-learning-based geographical routing protocol of considering link stability and energy prediction (DSEGR) for UANETs. First of all, we came up with the link stability evaluation indicator and utilized the autoregressive integrated moving average (ARIMA) model to predict the residual energy of neighbor nodes. Then, the packet forward process was modeled as a Markov Decision Process, and according to a deep double Q network with prioritized experience replay to learn the routing-decision process. Meanwhile, a reward function was designed to obtain a better convergence rate, and the analytic hierarchy process (AHP) was used to analyze the weights of the considered factors in the reward function. Finally, to verify the effectiveness of DSEGR, we conducted simulation experiments to analyze network performance. The simulation results demonstrate that our proposed routing protocol remarkably outperforms others in packet delivery ratio and has a faster convergence rate.

IMPERIALIST COMPETITIVE ALGORITHM FOR INCREASING THE LIFETIME OF WIRELESS SENSOR NETWORK

Recent years have seen the rapid growth in the applications of wireless sensor network (WSN) which is due to the advances of sensor nodes with low cost and tiny size. Despite the various potential applications of WSN, one of the key tasks in sensor network design is to make sure that the network is functional as long as possible. This paper presents an energy-efficient cluster head selection algorithm for the clustering of heterogeneous WSN, inspired by Imperialist Competitive Algorithm (ICA). In order to reduce the network energy consumption and subsequently increases the sensor network lifetime, the clustering problem is transformed into an optimization problem and the specific cost function is used to select the cluster heads in a way that the energy utilization of the network is optimized. Extensive simulation works are done based on MATLAB to test the algorithm in various network scenarios, with different network sizes and number of nodes. Simulation results have shown that the proposed algorithm is able to extend the network lifetime compared to its comparative by up to 154 percent in terms of first node death. Furthermore, choosing the optimum set of cluster heads at every round has proved that our proposed algorithm not only could reduce the network energy consumption, but also improves the total data delivery at the base station up to 59 percent compared to the well-known algorithm.

Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach.

Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other CHs are the major problems leading to higher energy consumption in IoT networks. In this paper, using fuzzy logic, bio-inspired chicken swarm optimization (CSO) and a genetic algorithm, an optimal cluster formation is presented as a Hybrid Intelligent Optimization Algorithm (HIOA) to minimize overall energy consumption in an IoT network. In HIOA, the key idea for formation of IoT nodes as clusters depends on finding chromosomes having a minimum value fitness function with relevant network parameters. The fitness function includes minimization of inter- and intra-cluster distance to reduce the interface and minimum energy consumption over communication per round. The hierarchical order classification of CSO utilizes the crossover and mutation operation of the genetic approach to increase the population diversity that ultimately solves the uneven distribution of CHs and turnout to be balanced network load. The proposed HIOA algorithm is simulated over MATLAB2019A and its performance over CSO parameters is analyzed, and it is found that the best fitness value of the proposed algorithm HIOA is obtained though setting up the parameters, number of rooster, number of hen’s and swarm updating frequency . Further, comparative results proved that HIOA is more effective than traditional bio-inspired algorithms in terms of node death percentage, average residual energy and network lifetime by 12%, 19% and 23%.

EECH/CF: An Energy-efficient Cluster Head Election and Cluster Formation Algorithms for WSNs

Background and Objective: Wireless sensor networks (WSN) consist of sensor nodes with a limited battery life and limited communication distance. The minimal energy expended by the sensor nodes and network can be achieved either by reducing the number of communications or by controlling the topology. Thus, energy consumption can be optimized by employing several techniques, such as clustering. Clustering allows the network to be divided into a set of clusters, each of which is managed by a cluster head. In a hierarchical cluster-based WSN, the cluster head receives the data from its sensor members, aggregates it and sends the result to the base station, which leads to an extra overload. So, the selection of appropriate cluster head plays a very important role in conserving the energy of the sensor nodes and extend the lifetime of the network. This paper introduces an energyefficient cluster head election and cluster formation algorithm for WSNs, called EECH/CF. To select cluster heads, our proposal election algorithm uses the initial and residual energy level of the sensor nodes, and efficiently creates the different clusters using an appropriate mechanism. Performance evaluation has shown a significant improvement in energy conservation and the network lifetime for EECH/CF in comparison to some existing clustering algorithms. Methods: Our proposal election algorithm uses the initial and residual energy level of the sensor nodes, and efficiently creates the different clusters using an appropriate mechanism. Results and Discussion: Performance evaluation has shown a significant improvement in energy conservation and the network lifetime for EECH/CF in comparison to some existing clustering algorithms. Conclusion: Results show that our clustering algorithms improve the lifetime of the network as we expected. Indeed, the delay before the death of the first node can be up to seven times longer with EECH/CF.

Algorithm for energy-efficient interaction of wireless sensor network nodes

The actual problem of developing methods of interaction in wireless sensor networks focused on energy saving is discussed. It is shown that the operation of a wireless sensor network is built taking into account compromise mechanisms that make it possible to extend the life of the network in the presence of low-power sensor nodes on which the network is built. It is concluded that it is necessary to introduce new algorithms into the operation of wireless sensor networks, which make it possible to reduce the number of operations when calculating a route, transmitting data, or other operations without losing functionality, but contributing to a reduction in energy consumption. The paper proposes one of such algorithms that develops the idea of clustering wireless sensor networks in order to reduce the power consumption of sensor nodes by transferring some of the functions to the head nodes of the clusters. Unlike the well-known adaptive clustering algorithm with low energy consumption LEACH, the proposed algorithm is based on swarm intelligence and allows choosing not only the head nodes of clusters in the current round of functioning of the wireless sensor network, but also promising nodes that become heads of clusters in subsequent rounds. If we consider that one cycle of the wireless sensor network consists of a certain predetermined number of rounds, then the procedure for searching for cluster heads can be performed not at the beginning of each round, but only at the beginning of each cycle of the wireless sensor network. It is shown that the determination of the heads of wireless sensor network clusters in the future allows to reduce the total energy consumption and thereby increase the duration of the network life cycle. The advantage of adding the bee swarm algorithm to the wireless sensor network clustering procedure is demonstrated in terms of such indicators as the time of death of the first sensor node, the dependence of the number of functioning nodes on the network operation time and the data packet delivery coefficient. The wireless sensor network clustering procedure with the addition of the bee swarm algorithm to select cluster heads for the future can be useful when deploying a wireless sensor network in real applications.

Link design for wireless optical communication network based on ant colony algorithm

With the maturation of space laser communication technologies and the development of deep-space exploration technologies, deep-space optical network (DSON) is expected to become an important foundation and inevitable development trend of future deep-space communication. According to the characteristics of DSON, we present a DSON topology structure based on flat wireless mesh network (FWMN). To improve the robustness of DSON and realize load balancing, the Routing Algorithm based on ant colony algorithm (RA-ACA) for DSON is proposed in this paper. We primarily research the unidirectional links problem and node energy consumption of DSON with RA-ACA. Instead of deleting the unidirectional links in the whole networks used in traditional ant colony algorithm (TACA), we make the best of unidirectional links, and then the performance of network will be improved. Also, in order to control the energy consumption speed of nodes and prevent premature death of nodes, we put the node energy as the routing factor in the routing, thus the lifetime of the whole network will be increased. Finally, aiming to enhance the convergence speed, we reserve the node pheromone for the subsequent path finding. Simulation results demonstrate the advantages of the proposed RA-ACA compared with TACA for DSON.

An Energy Aware Intelligent Fault Detection Scheme for IoT-Enabled WSNs

Recent years have seen an explosion in the demand for the Internet of Things (IoT). IoT makes any physical object smart by providing sensing capability. The physical objects are embedded with sensors that form a network of sensors. However, sensors are vulnerable to faults due to energy depletion, software failures, and hardware failures. The existing fault detection schemes put huge computational overhead on the battery-limited and low computational capacity sensor nodes that are subject to the premature death of the sensor nodes. Also, they suffer from poor fault detection accuracy and huge false alarm rate that significantly reduce the overall performance of the networks. In this paper, an energy-aware intelligent fault detection scheme is proposed for IoT-enabled wireless sensor networks that significantly improve fault detection accuracy and reduce false alarm rate. A novel 3-Tier hard fault detection mechanism is used for detecting hardware unit faults of the sensor nodes. Furthermore, an optimized deep learning mechanism is used for various soft fault detection that prevents premature death of sensor nodes. The paper mathematically analyses the proposed scheme in terms of energy consumption, time complexity, and message complexity. Extensive simulations show the enhanced performance of the proposed scheme compared with the state-of-the-art algorithms in terms of fault detection accuracy, false alarm rate, false-positive rate, energy consumption, and network lifetime.

Clustering the sensor networks based on energy-aware affinity propagation

Due to the limited energy of wireless sensor networks (WSNs), improving energy efficiency and prolonging network lifetime are the key issues of WSNs application. In this paper, we proposed a cluster routing protocol based on energy-aware affinity propagation(CAP). The CAP protocol enhances network performance from three aspects. First, an energy-aware affinity propagation clustering algorithm is proposed, which finds the optimal network clustering topology taking into account the total network energy consumption and load balancing. Second, an inter-cluster relay selection method is proposed for CH relay selection in multi-hop routing. At last, the energy threshold re-cluster scheme is applied to avoid frequent re-clustering that consumes a large amount of energy. The simulation experiments are conducted in two sink node deployment scenarios: sink external(case 1) and central(case 2) deployment. The results indicate that our proposed CAP outperforms LEACH, LEACHC, and KCE in terms of energy efficiency and network lifetime. In case 1, the first node death of the proposed CAP algorithm occurs in 1114 rounds, which is 61.4%, 64.5%, and 33.3% longer compared to LEACH, LEACHC, and KCE, respectively. In case 2, the first node death of the CAP algorithm occurs in 1372 rounds, which is 68.8%, 31.9%, and 49.8% longer compared to LEACH, LEACHC, and KCE, respectively.

HDRM: A hole detection and recovery method in wireless sensor network

SummaryWireless sensor networks (WSNs) consist of many sensor nodes, gathering data from the environment and sending it to base station (BS). One of the major challenges in WSNs is coverage maintenance. Since batteries provide sensor nodes' energy, battery depletion leads to the death of sensor nodes; when several nodes are dead, connectivity is lost, and network coverage fails. This paper proposed a method consisting of two phases to provide and maintain coverage in WSNs. In the first phase, hole detection is performed through a simple distributed method with low overhead. Nodes explore whether there are holes around them or not. In the second phase, coverage holes are covered by mobile nodes. When the number of holes exceeds mobile nodes, the holes with the highest priority and the shortest distance to mobile nodes will be selected. The performance of the proposed method (HDRM) was compared with Dezfuli et al. method, PEAS‐LI, CACA, Dezfuli et al. method, and Verma et al. method in terms of average coverage percentage, the average number of alive nodes, energy consumption, and network lifetime. The results of the simulation reveal that the proposed method leads to a decrease in the energy consumption of network, an increase in network lifetime, and improved coverage in the network.

Echo Location Based Bat Algorithm for Energy Efficient WSN Routing

Due to the wide range of applications, Wireless Sensor Networks (WSN) are increased in day to day life and becomes popular. WSN has marked its importance in both practical and research domains. Energy is the most significant resource, the important challenge in WSN is to extend its lifetime. The energy reduction is a key to extend the network's lifetime. Clustering of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving WSN. In this paper, an energy efficient protocol is proposed using metaheuristic Echo location-based BAT algorithm (ECHO-BAT). ECHO-BAT works in two stages. First Stage clusters the sensor nodes and identifies tentative Cluster Head (CH) along with the entropy value using BAT algorithm. The second stage aims to find the nodes if any, with high residual energy within each cluster. CHs will be replaced by the member node with high residual energy with an objective to choose the CH with high energy to prolong the network's lifetime. The performance of the proposed work is compared with Low-Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Zoning Clustering Algorithm (PEZCA) and Chaotic Firefly Algorithm CH (CFACH) in terms of lifetime of network, death of first nodes, death of 125th node, death of the last node, network throughput and execution time. Simulation results show that ECHO-BAT outperforms the other methods in all the considered measures. The overall delivery ratio has also significantly optimized and improved by approximately 8%, proving the proposed approach to be an energy efficient WSN.

Residual energy-based clustering in UAV-aided wireless sensor networks for surveillance and monitoring applications

Aim: Unmanned aerial vehicle (UAV)-aided wireless sensor networks (WSNs) are effectively used for surveillance, monitoring, and rescue applications in military and commercial domains. In UAV-aided WSNs (UWSNs), efficient data gathered from sensor nodes are desired to enhance network performance. However, communication between UAV and sensor nodes is challenging due to the high mobility of the UAV and a large number of sensor nodes. Clustering in UWSNs limits the number of sensor nodes communicating with the UAV, i.e., only the cluster head in a cluster can transmit the sensed data to the UAV, which reduces collision probability. Methods: In this paper, we propose a residual energy-based clustering algorithm for sensor-to-UAV communication in UWSNs. The cluster size and the number of sensor nodes in a cluster are determined on the basis of the residual energy of the sensor nodes. The performance of the proposed algorithm is evaluated by using the MATLAB simulator and then compared with that of the conventional clustering algorithm. Results: According to our extensive simulation results, the proposed clustering scheme significantly outperforms the conventional one in terms of network lifetime and data delivery ratio. Conclusion: Hence, through our studies and simulations, it can be assured that the network lifetime of UWSNs can be prolonged and the throughput of the network can also be elevated by controlling the early death of sensor nodes due to the uneven energy consumptions. We will come up with further analysis and validation of our work in the future.

Energy efficient clustering based on fuzzy logic in heterogeneous wireless sensor networks

Wireless sensor networks (WSNs) must be decentralised and independent of wireless sensor nodes that can track physical or environmental status. The operation of a node depends on the battery's life. Therefore, the battery's life is one of the key issues that limit the wide application of WSNs. In this article, a fuzzy logic system-based energy-efficient clustering (FLEEC) in heterogeneous WSN is introduced to solve the problem of maximising the network lifetime. To be more precise, the three factors considered, node remaining energy, distance, and proximity of the neighbour nodes, are then combined with fuzzy logic to select the appropriate sensor node as cluster head (CH); thereby, balancing the energy consumption of sensor nodes and prolonging the network lifetime. Finally, the simulation results show that the FLEEC algorithm is superior to the existing algorithms in the aspects of the stable period, half node death, throughput and network lifetime.

Incremental Linear Discriminant Analysis Dimensionality Reduction and 3D Dynamic Hierarchical Clustering WSNs

Optimizing the sensor energy is one of the most important concern in Three-Dimensional (3D) Wireless Sensor Networks (WSNs). An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus, the total consumption of energy is optimal. However, the computational complexity will be increased due to data dimension, and this leads to increase in delay in network data transmission and reception. For solving the above-mentioned issues, an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis (ILDA) is proposed for 3D hierarchical clustering WSNs. The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs. This ILDA approach consists of four major steps such as data dimension reduction, distance similarity index introduction, double cluster head technique and node dormancy approach. This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head (CH) selection technique. According to node’s position and residual energy, optimal cluster-head function is generated, and every CH is elected by this formulation. For a 3D spherical structure, under the same network condition, the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering (IDHC) is compared with Distributed Energy-Efficient Clustering (DEEC), Hybrid Energy Efficient Distributed (HEED) and Stable Election Protocol (SEP) techniques. It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput, network residual energy, network lifetime and first node death round.

Combination of improved Harris’s hawk optimization with fuzzy to improve clustering in wireless sensor network

A Wireless Sensor Network (WSN) is divided into groups of sensor nodes for efficient transmission of data from the point of measuring to sink. By performing clustering, the network remains energy-efficient and stable. An intelligent mechanism is needed to cluster the sensors and find an organizer node, the cluster head. The organizer node assembles data from its constituent nodes called member nodes, finds an optimal route to the sink of the network, and transfers the same. The nomination of cluster head is crucial since energy utilization is a major challenge of sensor nodes deployed over a hostile environment. In this paper, a fuzzy-based Improved Harris’s Hawk Optimization Algorithm (IHHO) is proposed to select an able cluster head for data communication. The fuzzy inference model ponders balance energy, distance from self to sink node, and vicinity of nodes from cluster head as input factors and decides if a candidate node is eligible for becoming a cluster head. The IHHO tunes the logic into an energy-efficient network with less complexity and more ease. The novelty of the paper lies in applying the hawk-pack technique based on fuzzy rules. Simulations show that the combination of Fuzzy based IHHO reduces the death of nodes through which network lifetime is enhanced.