Dead Nodes Research Articles

TEE-AODV Trust-based Route Selection and Improving Energy Efficiency in MANET

Mobile ad hoc networks (MANETs) are the future communication infrastructure for all devices that operate without the elaborate design found in wired infrastructure networks. Constructing a routing protocol that helps MANETs to achieve (QoS). It is quite challenging to achieve energy-efficient routing, due to its limited resources and node mobility concerns, which would impact node resource stability and lead to congestion and lower user QoS. A novel Trustworthy Energy Efficient AODV protocol has been proposed for improving path selection and locating trusted stations to maximize QoS. This approach employs location data, transmission speed, and directions to estimate the energy usage and trustworthiness of distinct nodes. The proposed TEE-AODV protocol includes two new control information namely Trust Request packet and Trust Reply. (T-RREP) AODV's packet includes a neighbor list and channel trustworthiness. The number of working neighbors in every path is counted, and this information is used to efficiently choose paths. The TEE-AODV calculates a QoS supporting metric for each detected route to identify a traffic-free path at any moment and determines whether it is a trusted route. The NS-2 simulator has been used to assess the suggested approach in terms of specific characteristics, including energy consumption (EC), (PDR), end-to-end delay (), and number of dead nodes. According to the experimental results, the suggested framework enhances reliable route selection while lowering e nd-to-end latency and improving energy efficiency. The experimental findings indicate that the suggested TEE-AODV protocol achieves high energy efficiency rate in the selection of trustworthy route and minimizing the E D .

Adaptive Mobile Recharge Scheduling With Rapid Data Sharing in Wireless Rechargeable Networks

The recent breakthrough in Wireless Power Transfer (WPT) provides a promising way to prolong network lifetime by employing a charging vehicle to replenish energy. Data transmissions from nodes typically happen in response to physical sensory events, leading to time-varying energy consumption. To improve charging efficiency, the existing schemes collect energy information by employing a data-gathering vehicle or data collection protocol. However, in duty cycle networks, these schemes either incur extra vehicles or high data collection delay. To solve this problem, we propose an mobile adaptive charging scheme with rapid data sharing (rShare), which establishes multi-layer collection trees and collects overall energy data to the vehicle. A spatial predicted active sending (SPAS) algorithm is proposed for distant nodes to actively estimate the future position and transmit their data to cover potential positions of the charging vehicle, which significantly reduces data collection delay. We also propose an estimated time of arrival (ETA)-aware scheme based on the TSP Nearest Neighbor algorithm that updates the charging path based on the collected data. Extensive simulation results demonstrate that our scheme outperforms the state-of-the-arts in terms of dead node avoidance with less communication overhead.

Multiobjective-energy centric honey badger optimization based routing for wireless body area network

Wireless Body Area Network (WBAN) is an interconnection of tiny biosensors that are organized in/on several parts of the body. The developed WBAN is used to sense and transmit health-related data over the wireless medium. Energy efficiency is the primary challenges for increasing the life expectancy of the network. To address the issue of energy efficiency, one of the essential approaches i.e., the selection of an appropriate relay node is modelled as an optimization problem. In this paper, energy efficient routing optimization using Multiobjective-Energy Centric Honey Badger Optimization (M-ECHBA) is proposed to improve life expectancy. The proposed M-ECHBA is optimized by using the energy, distance, delay and node degree. Moreover, the Time Division Multiple Access (TDMA) is used to perform the node scheduling at transmission. Therefore, the M-ECHBA method is used to discover the optimal routing path for enhancing energy efficiency while minimizing the transmission delay of WBAN. The performances of the M-ECHBA are analyzed using life expectancy, dead nodes, residual energy, delay, packets received by the Base Station (BS), Packet Loss Ratio (PLR) and routing overhead. The M-ECHBA is evaluated with some classical approaches namely SIMPLE, ATTEMPT and RE-ATTEMPT. Further, this M-ECHBA is compared with the existing routing approach Novel Energy Efficient hybrid Meta-heuristic Approach (NEEMA), hybrid Particle Swarm Optimization-Simulated Annealing (hPSO-SA), Energy Balanced Routing (EBR), Threshold-based Energy-Efficient Routing Protocol for physiological Critical Data Transmission (T-EERPDCT), Clustering and Cooperative Routing Protocol (CCRP), Intelligent-Routing Algorithm for WBANs namely I-RAW, distributed energy-efficient two-hop-based clustering and routing namely DECR and Modified Power Line System (M-POLC). The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR.

Energy Efficient Spiking Deep Residual Network and Binary Horse Herd Optimization Espoused clustering Protocol for Wireless Sensor Networks

Nowadays, Wireless Sensor Networks (WSNs) plays several important application fields, specially monitoring events without any human interference. The sensor nodes (SNs) have lesser life time in wireless sensor network owing to its continual sensing with battery drains very rapidly. As a result of the heavy traffic, sensors nearby quickly expire and energy hole issues start to appear. To overcome these issues, an Energy Efficient Spiking Deep Residual Network and Binary Horse Herd Optimization Espoused Clustering Protocol for Wireless Sensor Networks (EE-SDRN-BHHO-CP-WSN) is proposed in this manuscript for sustaining the energy efficiency in the wireless sensor network. One of the main issues in wireless sensor network assisted applications is making use of the available energy. An optimum path selection is a key process of saving energy from sensor nodes to the sink. Spiking Deep Residual Network (SDRN) and Binary Horse Herd Optimization (BHHO) optimize energy efficiency in WSN. The cluster head (CH) is designated by effectual fitness function generated by multi-objectives. It benefits in low power consuming and decreases a count of idle sensor nodes. After CH selection, Binary horse herd optimization algorithm is considered for optimal route selection and data transmission by sink node. The EE-SDRN-BHHO-CP-WSN method is implemented in MATLAB and the efficiency of the proposed method is analyzed with different metrics, like network lifetime, number of alive nodes, number of dead nodes, throughput, energy consumption, packet delivery ratio (PDR). The proposed EE-SDRN-BHHO-CP-WSN method attains 32.35 %, 42.34 %, 49.27 % higher network lifetime, 26.34 %, 31.94 % and 39.35 % higher number of alive nodes and 46.26 %, 38.64 % and 27.83 % lower number of dead nodes compared with other existing models, such as Multi-objective CH based Energy-aware Optimized Routing Algorithm in WSN (MCH-EOR-WSN), Energy efficient CH selection utilizing improved Sparrow Search Algorithm in WSN (EECH-ISSA-WSN), and New Energy Aware CH Selection Algorithm for WSN (NEA-CHSA-WSN respectively).

Cluster Head Selection Using Multi-Dilation Convolutional Neural Network Optimized with BCMO for IoT Networks

Generally, more Internet of Things (IoT) based sensor networks are used for transportation and traffic signals in a smart city. The need of IoT based networks are increasing day by day. The main issue of IoT networks is poor energy optimization. Offering energy optimization in the IoT will present a number of difficulties. The researchers try to extend the network lifetime by reducing energy consumption. Clustering is a good solution to reduce the consumption of energy in a network. In IoT along wireless sensor networks (WSNs), a cluster is a group of nodes. Using these clusters, Cluster Head (CH) is formed. CH collects information from other nodes and is connected to the Base Station (BS). In this paper, Cluster head selection using a Multi-Dilation Convolutional Neural Network optimized with Balancing Composite Motion Optimization (BCMO) is proposed. The major goal of this work is to increase the normalized energy and optimize the weight, temperature, delay, distance between all nodes, and weight. In this paper, the Multi-Dilation Convolutional Neural Network optimized with Balancing Composite Motion optimization is proposed for energy optimization (CHS-MDCNN-BCMO-IoTN). The efficiency of the proposed CHS-MDCNN-BCMO-IoTN approach is compared with other existing methods in terms of normalized energy, load, temperature, and number of cluster head formations. This CHS-MDCNN-BCMO-IoTN method attains 31.45%, 30.98% and 27.24% highest number of alive nodes, 26.16%, 27.23% and 33.62% lower number of dead nodes when compared with existing methods.

Energy‐Efficient Routing Algorithm for Optimizing Network Performance in Underwater Data Transmission Using Gray Wolf Optimization Algorithm

Due to the aquatic nature of communication in the underwater world, the underwater acoustic sensor network (UASN) is commonly used. However, it has inherent limitations, such as limited bandwidth, high transmission energy, long propagation delays, void regions, and expensive battery replacement. Improving network lifetime (NL) is the primary objective since replacing batteries in UWSN is very expensive and challenging. NL is improved by having a high packet delivery ratio (PDR), reduced dead nodes, and reduced energy consumption (EC). If two more node batteries are depleted, they become dead nodes, causing partitions on the network and resulting in a void region problem. Void regions occur when a node has no forwarder node to forward data packets toward the sink node. Void nodes affect the routing techniques’ overall performance regarding end‐to‐end delay (EED), data loss, and EC. So, the primary objective of this work is to avoid void regions. For the same, this paper proposes a void hole detection algorithm. The algorithm selects the best next hop node based on the fitness function calculated by the gray wolf optimization (GWO) algorithm, considering only the vertical directions despite horizontal directions, further reducing the EED. The proposed approach is simulated using MATLAB, and the evaluation is based on data broadcast copies, PDR, EC, dead node number (DNN), average operational time (AOT), NL, and EED. The paper has presented a comparison with weighting depth and forwarding area division depth‐based routing (WDFAD‐DBR) routing protocol for underwater acoustic sensor network (UASN) and energy and depth variance‐based opportunistic void avoidance scheme (EDOVS) for UASN. WDFAD‐DBR avoids void holes by selecting forwarding nodes and taking the weighting sum of depth differences of two hops; in comparison, EDOVS considers not only the depth parameters but also the normalized residual energy. The proposed paper contributes to developing an energy‐efficient routing algorithm that removes void nodes by selecting the appropriate forwarder node in void regions based on the GWO algorithm. The proposed work increases the network lifetime by avoiding void regions and balancing the EC. The simulation results show that the proposed algorithm gains more than 20% PDR, less EC, and 60% less broadcasted copies of data packets and has more NL than the WDFAD‐DBR, and 10% improved PDR and lesser broadcasted copies were sent than EDOVS along with the enhanced NL, by varying the transmission range the proposed algorithm showing the better performance in terms of EC, PDR, and DNN along with the values of 61.6, 0.97, and 35 (scenario node 60 and transmission range 600 m) and 64.1, 0.89, and 25, respectively, by variable network size.

Routing Methods For Data Transmission In Large Networks: A Review

A wireless sensor network (WSN) is a network consisting of miniaturized smart sensors communicating the information gathered or collected from a monitored environment via a wireless link. The sensors are capable of sensing the events within their environment, process the data, and transmit the data to the base station (BS). The entire processing of data and subsequent transmission to BS requires high energy consumption. The operation of WSN is limited by repeated dead nodes, which results in energy depletion. Hence, to prolong the life-span of the network, several routing protocols have been developed. However, the effectiveness of these protocols has not been well examined in terms of increasing node population for a given WSN field or area. Therefore, in this work the effect of increasing node density on cluster based energy-efficient routing protocols in wireless sensor network was analysed. The implemented routing protocols were Low Energy Adaptive Clustering Hierarchy LEACH, stable election protocol (SEP), and zone-SEP (Z-SEP). The dimension of the WSN was 100 × 100 square metre area with varying number of sensor nodes: 50, 60, 70, 80, 90, and 100. The results of the simulation conducted in MATLAB revealed that increasing node density resulted in increased alive node and throughput (measures in terms of transmitted number of packets). On the contrary, the protocol with the best performance was ZSEP.

Energy efficient gateway based routing with maximized node coverage in a UAV assisted wireless sensor network.

Ad-hoc wireless sensor networks face challenges of optimized node deployment for maximizing coverage and efficiently routing data to control centers in post disaster events. These challenges impact the outcome for extending the lifetime of wireless sensor networks. This study presents a uav assisted reactive zone based EHGR (energy efficient hierarchical gateway routing protocol) that is deployed in a situation where the natural calamity has caused communication and infrastructure damage to a major portion of the sensor network. EHGR is a hybrid multi layer routing protocol for large heterogeneous sensor nodes (smart nodes, basic nodes, user handheld devices etc.) EHGR is tailored to meet two important concerns for a disaster hit wsn ie. optimized deployment and energy efficient routing. The first part of EGHR focuses on maximized coverage during node deployments. Maximized coverage is an important aspect to be considered during the event of disaster since most of the nodes loose coverage and are detached from the wireless sensor network. The first part of EHGR uses state of the art game theory approach to build a model that maximizes the coverage of nodes during the deployment phase from all participating entities i.e. nodes and uavs. Rather than fixing the cluster head as is the case in traditional cluster-based approaches EHGR uses the energy centroid nodes. Energy centroid nodes evolve on the basis of aggregated energy of the zone. This approach is superior to simply electing cluster head nodes on the basis of some probability function. The nodes that fail to achieve any successful outcome from the game theory matching model fail to get any association. These nodes will use multi hop d2d relay approach to reach the energy centroid nodes. Gateway relay nodes used with the game theory approach during the deployment of the uav assisted wsn improves the overall coverage by 25% against traditional leach based hierarchical approaches. Once the optimum deployment phase is completed the routing phase is initiated. Aggregated data is sent by the energy centroid nodes from the ECN nodes to the servicing micro controller enabled un manned aerial vehicles. The routing process places partial burden of zone formation and data transmission to the control center for each phase on the servicing uavs. Energy centroid nodes engage only in the data aggregation process and transmission of data to servicing uav. Servicing-uavs reduce energy dissipated of the entire zone which result in gradual decrease of energy for the zone thus increasing the network lifetime. Node deployment phase and the routing phase of EHGR utilize the computations provide by the mirco controller enabled unmanned aerial vehicles such that the computationally intensive calculations are offloaded to the servicing uav. Experiment results indicate an increase in the first dead node report, half dead node report, and last dead node report. Network lifetime is extended to approximately 1800 rounds which is an increase by ratio of 100% against the traditional leach approach and increase by 50% percent against the latest approaches as highlighted in the literature.

Wireless sensor network routing optimization based on improved ant colony algorithm in the Internet of Things

The Internet of Things (IoT) connects devices, enabling real-time data acquisition, automation, and collaboration. Wireless sensor networks are one of the important components of the Internet of Things, consisting of many wireless sensor nodes distributed in space. These nodes can perceive environmental information and transmit it to other nodes through wireless communication. In wireless sensor network routing optimization methods, improved ant colony algorithm can be used to find the optimal routing scheme. Ant colony algorithm simulates the behavior of ants in the process of searching for food, and optimizes factors such as transfer probability and pheromone concentration to enable ants to find the shortest path. In wireless sensor networks, node positions can be used as reference nodes and anchor nodes, combined with the objective function of wireless sensor network routing optimization, and improved ant colony algorithm can be used to solve the optimal path, thus obtaining the optimal wireless sensor network routing optimization scheme. Through experimental results, it can be found that the proposed method performs well in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. These optimization results have positive implications for the sustainable development and practical application of the Internet of Things, which can improve the development of the digital economy and enhance the construction of smart cities.

A Design Study on the Design of Customer Claims Management System for Qinghai Electric Power Company

Abstract This paper utilizes the SOA framework to realize the system structure design, combined with the total flow of the claim processing, the design of the acceptance and audit processing, responsibility determination and reminder management of 4 modules, constituting the customer claim management system as a whole. The improved LEACH algorithm is utilized to control the network topology, and the cluster tree combined with data fusion is used to reduce the energy loss of the nodes in the network. Based on the weighted queuing scheduling algorithm, the feedback processing module is designed to determine the grouping rules and calculation methods, and the decision tree algorithm is used to build the demand management prediction model. The accuracy of the prediction of the decision tree algorithm is verified to realize the case information management statistics. Analyze the utility of the improved LEACH algorithm and test the system’s performance. The experimental results show that the improved LEACH algorithm is more balanced in the range of [0,100] dead nodes, and the energy consumption is well balanced. The total delay is 16.13-time slots, which is the lowest delay among the four algorithms. The node death time is 1045 rounds, having the longest network life cycle. The system performance is tested using IO and memory-intensive loads. Before writing a file of 16GB, the system time responsiveness stays around 80%, and the system CPU utilization and disk IO utilization increase as the file is written. The system performance is good.

Energy efficient data transmission using multiobjective improved remora optimization algorithm for wireless sensor network with mobile sink

<span lang="EN-US">A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements. Energy consumption, data loss, and transmission delays are the major drawback of creating mobile sinks. For instance, battery life and data latency might result in node isolation, which breaks the link between nodes in the network. These issues have been avoided by means of mobile data sinks, which move between nodes with connection issues. Therefore, energy aware multiobjective improved remora optimization algorithm and multiobjective ant colony optimization (EA-MIROA-MACO) is proposed in this research to improve the WSN’s energy efficiency by eliminating node isolation issue. MIRO is utilized to pick the optimal cluster heads (CHs), while multiobjective ant colony optimization (MACO) is employed to find the path through the CHs. The EA-MIROA-MACO aims to optimize energy consumption in nodes and enhance data transmission within a WSN. The analysis of EA-MIROA-MACO’s performance is conducted by considering the number of alive along with dead nodes, average residual energy, and network lifespan. The EA-MIROA-MACO is compared with traditional approaches such as mobile sink and fuzzy based relay node routing (MSFBRR) protocol as well as hybrid neural network (HNN). The EA-MIROA-MACO demonstrates a higher number of alive nodes, specifically 192, over the MSFBRR and HNN for 2,000 rounds.</span>

Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions.

Design of ant lion optimization-based PEGASIS routing protocol for energy efficiency in networks

<p>In sensor networks the main problem facing by many researchers is regarding the energy efficiency. Different protocols are evaluated for communicating between the sensor nodes. The routing protocol when combined with evolutionary algorithms gives best optimal solution for the problem incurred in wireless sensor networks. In this paper, ant lion energy efficient-power efficient gathering in sensor information systems (ALEEPEGASIS) is used to develop the chain. This technique can achieve a global optimization solution by finding the best cluster head or the leader node for data communication. The techniques help in distributing the paths equally while the transmission of data process is performed. By performing this process, the power consumption near the sensor nodes can be reduced. The proposed technique is compared with other techniques like energy efficient PEGASIS and swarm energy efficient PEGASIS. The parameters used to compare are number of alive nodes, number of dead nodes and residual energy. The performance is observed using MATLAB simulation results.</p>

Hierarchical energy-saving routing algorithm using fuzzy logic in wireless sensor networks

Currently, sensor energy assembly in wireless sensor networks is limited, and clustering methods are not effective to improve sensor energy consumption rate. Thus, a hierarchical energy-saving routing algorithm based on fuzzy logic was constructed by considering three aspects: residual energy value, centrality, and distance value between nodes and base stations. The remaining sensor nodes selected by fuzzy logic algorithm have a longer time to live and greater residual energy than those selected by low-power adaptive clustering hierarchical protocol algorithm, fuzzy unequal clustering algorithm, and fuzzy logic cluster head election algorithm. For network life cycle, the number of rounds in which the first dead node appears, in descending order, is studied: energy-saving routing algorithm (400 rounds) > new geographic cellular structure algorithm (300 rounds) > virtual grid based dynamic routes adjustment algorithm (100 rounds). Under the same experimental round, energy-saving routing algorithm’s remaining energy curve always reaches its maximum. The energy-saving routing algorithm by fuzzy logic constructed by this research institute can significantly improve network energy utilization, which has certain reference value.

Dual-discriminator Conditional Generative Adversarial Network Optimized with Hybrid Momentum Search Algorithm and Giza Pyramids Construction Algorithm for Cluster-based Routing in WSN Assisted IoT

Wireless sensor network (WSN) efficiently sends and receives the data on the internet of things (IoT) environment. As a large-scale WSN's nodes are powered by batteries, it is essential to create an energy-efficient system to decrease energy consumption and increase the network's lifespan. The existing methods not present effectual cluster head (CH) selection and trust node computation. Therefore, dual-discriminator conditional generative adversarial network optimized with a hybrid Momentum search algorithm and Giza Pyramids Construction algorithm for Cluster Based Routing in WSN Assisted IoT is proposed in this manuscript, for securing data transmission by identifying the optimum CH in the network (DDcGAN-MSA-GPCA-CBR-WSN-IoT). Initially, the proposed method is acting routing process via cluster head. Therefore, Dual-Discriminator conditional Generative Adversarial Network (DDcGAN) is considered to select the CH depending on multi-objective fitness function. The multi-objective fitness function, such as energy, delay, throughput, distance among the nodes, cluster density, capacity, collision, traffic rate, and cluster density. Based on fitness function, CH is selected. After cluster head selection, a malicious node depends on three parameters: trust, delay, and distance. These three parameters are optimized by hyb MSA-GPCA for ideal trust path selection. The proposed DDcGAN-MSA-GPCA-WSN-IoT technique is activated in PYTHON and network simulator (NS2) tool. Its effectiveness is analyzed under performance metrics, such as number of alive nodes, dead nodes, delay, energy consumption, packet delivery ratio, a lifetime of sensor nodes, and total residual energy. The simulation outcomes display that the proposed method attains lower delay, higher packet delivery ratio and high network lifetime when comparing to the existing models.

Balance energy based on duty cycle method for extending wireless sensor network lifetime

Due to the limited resources acquired by the sensor, energy consumption is one of the main issues that have drawn the attention of researchers in the field of wireless sensor networks (WSN). In this paper, we propose a new protocol based on the duty cycle method and the energy threshold. The suggested protocol aims to balance the traffic among all nodes. As a result, the energy consumption in the entire network will be balanced to prevent service interruptions on a portion of the network. Furthermore, a good result has been obtained compared to other related approaches. As the energy retention was 28%, 33% higher than the compared approach in member nodes, while nearby 33% and 61% in cluster heads. Moreover, the Suggested method outperforms expectations in terms of time, since it reduces the required transfer time by 60% less than the compared approaches. Also, the first dead member node occurs in many later rounds in the suggested method while there is no cluster head node die in 2,000 rounds. Finally, our research objective was met because the energy depletion process in the nodes was nearly convergent.

Enhancing the Lifetime of WSN Using a Modified Ant Colony Optimization Algorithm

Wireless sensor networks (WSNs) have been extensively used in various fields, such as health, defense, education, and industrial applications, to collect and transmit environmental data to the base station. However, energy efficiency is a significant challenge in WSNs, as data transmission is typically limited to a single route, leading to excessive energy consumption by the nodes along that route. This can lead to a decrease in the network's overall efficiency and effectiveness. To address this issue, this study aims to extend the lifespan of WSNs by optimizing route selection based on three variables: residual node energy, distance to the base station, and number of shared neighbors. In this paper, the authors propose three systematic approaches, namely Energy-Aware ACO Routing (EACO), Cost-Effective ACO Routing (CEACO), and Cost-Efficient Node Replacement Strategies ACO (CERACO), to enhance the lifetime of WSNs. These approaches consider various factors such as cost, energy consumption, replacement, and reliability. The paper provides a practical guide for researchers and practitioners to overcome the challenges related to energy efficiency and cost-effectiveness in WSNs. Experimental results demonstrate that the first dead node occurs later with the proposed methods than with the traditional Ant Colony Optimization (ACO) algorithm.

A Hybrid Metaheuristic Algorithm for Stop Point Selection in Wireless Rechargeable Sensor Network

A wireless rechargeable sensor network (WRSN) enables charging of rechargeable sensor nodes (RSN) wirelessly through a mobile charging vehicle (MCV). Most existing works choose the MCV’s stop point (SP) at random, the cluster’s center, or the cluster head position, all without exploring the demand from RSNs. It results in a long charging delay, a low charging throughput, frequent MCV trips, and more dead nodes. To overcome these issues, this paper proposes a hybrid metaheuristic algorithm for stop point selection (HMA-SPS) that combines the techniques of the dragonfly algorithm (DA), firefly algorithm (FA), and gray wolf optimization (GWO) algorithms. Using FA and GWO techniques, DA predicts an ideal SP using the run-time metrics of RSNs, such as energy, delay, distance, and trust factors. The simulated results demonstrate faster convergence with low delay and highlight that more RSNs can be recharged with fewer MCV visits, further enhancing energy utilization, throughput, network lifetime, and trust factor.

Energy-Efficient Improved Optimal K-Means: Dynamic Cluster Head Selection based on Delaying the First Node Death in MWSN-IoT

The Internet of Things (IoT), which attaches dynamic devices that can access the Internet to create a smart environment, is a tempting research area. IoT-based mobile wireless sensor networks (WSN-IoT) are one of the major databases from which the IoT collects data for analysis and interpretation. However, one of the critical constraints is network lifetime. Routing-based clustered protocols and cluster head (CH) selection are crucial in load balancing and sensor longevity. Yet, with clustering, sensor node mobility requires more overhead because the nodes close to the center may get far and thus become unsuitable to be a CH, whereas those far from the center may get close and become good CH candidates, influencing energy consumption. This paper suggests an energy-efficient clustering protocol with a dynamic cluster head selection considering the distance to the cluster center, remaining energy, and each node's mobility degree implementing a rotation mechanism that allows cluster members to be equally elected while prioritizing those with the minimum weight. The advised algorithm aims to delay the first node death (FND) and thus prolong the stability period and minimize energy consumption by avoiding re-clustering. The performance of the proposed protocol exceeds that achieved in our previous work, Improved OKmeans, by 11%, in first dead node lifetime maximization, 43% in throughput, and 44% in energy efficiency.

UASN‐3D: An energy efficient localization based on LEACH‐BR algorithm (EELBL‐BR)

AbstractUnderwater acoustic sensor networks (UASN) research is gaining popularity as it has various applications such as military communication, oil rig maintenance, animal survival, linking submarines to land, gathering data for water monitoring, and other commercial fields. The two most critical requirements for the application's proper operation are the accurate knowledge of sensor node locations and the efficient transmission of accurate underwater sensor node information to the base station with efficient energy consumption. The proposed energy efficient localization based on the LEACH‐beacon and reinforced node (EELBL‐BR) algorithm satisfies both the requirements in 3D‐UASN. The proposed algorithm considers the deployment and computation of accurate location of sensor nodes in the underwater environment by applying I‐LASP (improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) (Yadav N, Khilar PM. Trans Emerg Telecommun Technol. 2023;34:e4772.) for 3D environment. It performs clustering of sensor nodes for enhancing network lifetime using three different types of nodes such as beacon, reinforced, and member nodes. The proposed clustering LEACH‐BR (low‐energy adaptive clustering hierarchy‐beacon and reinforced nodes) algorithm is based upon the LEACH algorithm which provides accurate location of all the sensor nodes, improves energy consumption and reliability in the underwater environment. The result shows that the proposed algorithm EELBL‐BR, considering both beacon and reinforced nodes, provides the improvement in the number of alive nodes, reduction in the number of dead nodes, reduction in energy consumption and enhances residual energy in the UASN by 68.90%, 51.91%, 51.47%, and 68.12% respectively with respect to the number of rounds as compared to that of the existing algorithm by Rizvi et al. (Wirel Pers Commun. 2022;124(4):3725–3741.) and thus outperforms the existing algorithm (Rizvi HH, Khan SA, Enam RN. Wirel Pers Commun. 2022;124(4):3725–3741.).