Stochastic Bat Optimization Model for Secured WSN with Energy‐Aware Quantized Indexive Clustering

SummaryThe arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS‐based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network‐long short term memory (CNN‐LSTM) is employed for the data aggregation process. The proposed HGCIO‐based CNN‐LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%.

Reducing energy consumption in IoT networks remains a critical challenge in energy-efficient clustering routing protocols (EECR-IoT). Several routing protocols have been developed to address power consumption, with cluster- based protocols emerging as particularly effective. In these protocols, cluster heads are selected to aggregate data from root nodes and transmit it to the base station, optimizing energy usage. Efficient selection of cluster heads is essential to prolong network lifetime. Our proposed protocol employs static clustering for optimal cluster head selection, ensuring effective performance in both large and small areas. To enhance communication efficiency, we divide large sensor fields into rectangular clusters, which are then grouped into zones to facilitate communication between cluster heads and the base station. EECR-IoT involves a vast network of tiny sensor nodes capable of sensing, processing, and transmitting environmental data to the base station. Energy efficiency is crucial for sustaining these networks, and our study introduces an energy-efficient clustering protocol based on the Divide and Conquer Quad Tree dynamic multi-hop LEACH algorithm. This approach optimizes energy efficiency through balanced cluster creation, thereby distributing the load among cluster heads and extending network lifetime. In addition to Euclidean distance, the protocol considers residual energy for cluster head selection. Multi-hop communication between cluster heads and the base station depends on their distance, further improving energy efficiency. Simulations demonstrate that the proposed method outperforms existing heterogeneous protocols, including LEACH-B, BPK-means, Park’s approach, and Mk-means, by up to 50% in energy savings. The protocol’s effectiveness is evaluated using performance metrics such as throughput, End-to-End delay (EED), Packet Drop Rate (PDR), and node lifespan, showcasing its superiority in enhancing network longevity and reducing energy consumption across various IoT applications.

Wireless sensor networks (WSNs) face challenges in maintaining network lifetime due to energy limitations. To optimize energy usage, techniques such as node clustering and data transmission through the shortest path are employed. However, the selected Cluster Head (CH) node eventually becomes inactive as its energy is depleted during continuous transmission to the sink. To overcome this issue, we propose the utilization of Energy-Efficient Bat-Moth Flame Optimization (EEBMFO) in WSNs, aiming to enhance network lifetime. Our strategy makes use of the echolocation signal pattern that bats use to identify prey within a certain range. In a similar manner, nodes fall into clusters within the range of the CH, and the CH is chosen by looking at the highest residual energy. In addition, we use spiral path data transmission and Moth Flame optimization to route data from the source node to the CH. By combining bat and moth flame optimizations and considering each node’s residual energy, we aim to improve the network’s lifespan. With the use of simulations and performance measures such network lifetime, throughput, latency, dependability, and network stabilization, this research provides a thorough analysis of the suggested EEBMFO approach in WSNs. When compared to current techniques, the results show a noteworthy 11–16% increase in network longevity. These findings validate the efficacy of EEBMFO in prolonging the lifespan of WSNs, offering a promising solution for energy-efficient and sustainable wireless sensor networks.

In this paper, a secure blockchain based identity authentication for end-nodes is proposed in wireless sensor networks (WSNs). Moreover, to resolve the issue of limited energy in WSNs, a mechanism of cluster head (CH) selection is also proposed. The nodes in a network are authenticated on the basis of credentials to prevent from malicious activities. The malicious nodes harm the network by providing false data to nodes. Therefore, a blockchain is integrated with the WSN to make the network more secure as it allows only authenticated nodes to become a part of the network. Moreover in a WSN, sensor nodes collect the information and send it towards CH for further processing. The CH aggregates and processes the information; however, its energy depletes rapidly due to extra workload. Therefore, the CH is replaced with the node that has the highest residual energy among all nodes. The simulation result shows the network lifetime increases after CH replacement. Moreover, it shows that he transaction cost is very low during authentication phase.KeywordsBlockchainWireless sensor networksIdentity authentication

A staggering number of applications rely on the network architecture to carry out their tasks, which has led to a fast growth in wireless sensor networks (WSN). The possibility of harmful activity and data theft is growing as a result of the growth in devices and data. Thus, the network’s regular users have an impact on legitimate data delivery, which lowers customer happiness and worsens network standards. The data have been saved using a variety of security procedures that have been developed in past research studies. However, harmful activity continues to engage in its illegal operations despite their efforts to safeguard data transmission in the network. As a result, a number of recent research projects have concentrated on predicting innovative techniques and processes to offer security in WSN. In comparison to existing methods, this work attempted to offer an effective tighter security for WSN and suggested an ML‐Based Secured Routing Protocol (MLSRP) for WSN with improved energy efficiency and overall performance. Energy efficiency is the main requirement of WSNs, hence a clustered network is proposed where the data are routed through the cluster head nodes. In this paper, a multicriteria based decision‐making (MCDM) model is used by the MLSRP to perform data routing, clustering, and cluster head election while also analyzing a number of network characteristics that might affect the quality of a node, route, and data. In NS2 software, the suggested framework is put into practice and simulated. The results are then validated to gauge performance. The observed quantitative results reveal that the proposed MLSRP method attains an improved network lifetime by 5% and network throughput of 6%. It reduces energy consumption by 40%, curtails overhead to 37%, and minimizes end‐to‐end delay by 30% than the other conventional methods. The suggested framework performs better than others when its total performance is compared to that of older methods.

Wireless sensor network (WSN) communication has gathered a lot of attention of research scholars due to its various features such as high wireless data transmission. A large number of techniques have been developed till now in order to achieve an energy efficient network. The clustering and cluster head selection is the major and difficult task to perform in a network. LEACH serves as a basic for rest of the energy efficient clustering protocols. This study considers the LEACH-Mobile Fuzzy (LEACH-MF) as base for developing the proposed work. Fuzzy Inference System (FIS) with LEACH along with threshold based data transmission concept is developed in this work. The major objective of this work is to utilize the allotted energy to sensor nodes in an effective way. The proposed model is parted in two forms i.e. Modified Parameter-LEACH-MF (MP-LEACH-MF) and Limited Communication-LEACH-MF (LC-LEACH-MF). LC-LEACH-MF is a reactive protocol whereas the former one is periodic. In order to assure the performance efficiency of the proposed work, the parameters such as Packet Delivery Ratio (PDR), Last Node Dead (LND), Half Node Dead (HND), First Node Dead (FND), Energy Consumption of the network are evaluated and along with this a comparison analysis has been done with traditional LEACH, LEACH–Mobile (LEACH-M), LEACH-MF. After analyzing the obtained results it is concluded that the LC-LEACH-MF outnumbers the rest of the traditional energy efficient clustering techniques.

Multi-objective cluster head using self-attention based progressive generative adversarial network for secured data aggregation

A UAV-assisted CH election framework for secure data collection in wireless sensor networks

Wireless Sensor Network (WSN) includes of many nodes by restricted energy resources. Energy efficiency and harvested energy are major important issues in the WSN. Studies recently conducted have demonstrated that clustering is an effective way to increase energy efficiency. Energy Harvesting- Wireless Sensor Network (EH-WSN) is a flexible strategy for even clustering and Cluster Head (CH) selection is helpful to maximize network constancy and energy efficiency. In this paper, Energy Harvesting Possibilistic Fuzzy C-Means (EHFPCM) clustering is introduced to improve harvested energy usage by maintaining the consistency, connectivity, and balancing of harvested energy consumption in EH-WSN. It is based on Data Transmission (DT) and Cluster Establishment (CE). During CE, PFCM clustering is introduced for cluster formation. PFCM clustering divides the network into clusters. Each area forms a group and chooses one or more CH based on the multi-criteria like energy, distance to neighbors, distance to the Base Station (BS), and network load. In a cluster, the Fuzzy Election Based Optimization Algorithm (FEBOA) selects the CH according to the multi-criteria. It desires to receive packets from Cluster Member (CM), aggregate the received packets, and subsequently forward it to DT. DT, every CM wakes up during its designated working time and transmits the data it has gathered to the CH in the cluster. Lastly, measures such as Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, and average delay for transmission are used to measure the results of routing protocols.

Clustering is a useful mechanism in wireless sensor networks that helps to cope with scalability problems and, if combined with in-network data aggregation, may increase the energy efficiency of the network. At the same time, by assigning a special role to the cluster head nodes, clustering makes the network more vulnerable to attacks. In particular, disabling a cluster head by physical destruction or jamming may render the entire cluster inoperable temporarily until the problem is detected and a new cluster head is elected. Hence, the cluster head nodes may be attractive targets of attacks, and one would like to make it difficult for an adversary to identify them. The adversary can try to identify the cluster head nodes in various ways, including the observation of the cluster head election process itself and the analysis of the traffic patterns after the termination of the cluster head election. In this paper, we focus on the former problem, which we call the private cluster head election problem. This problem has been neglected so far, and as a consequence, existing cluster head election protocols leak too much information making the identification of the elected cluster head nodes easy even for a passive external observer. We propose the first private cluster head election protocol for wireless sensor networks that is designed to hide the identity of the elected cluster head nodes from an adversary that can observe the execution of the protocol.

Energy minimization in sensor nodes is the problem in wireless sensor networks (WSNs). The most widely accepted method to preserve energy of sensor nodes is clustering. In cluster based networks, energy consumption is higher in the nodes which are closer to the sink as compared to the nodes which are located farther away from base station (BS). Various unequal clustering algorithms were proposed in the past to correct this issue. The major drawbacks in the findings are that the nodes, which join a specific cluster head, cause overburdening of the cluster head. In this paper, we present an algorithm namely energy efficient unequal sector clustering (EUSC) using multi-sector and unequal clustering approaches which improve the network’s energy efficiency. EUSC divides the network into multiple sectors based on nodes distances from BS. Selection of cluster heads in each sector is based on nodes distances to BS, residual energy and neighbor nodes proximities in that sector. Selection of a relay cluster head for data transmission is also based on residual energy, nodes distances to BS and queue length of nodes. In this paper, we have carried out some theoretical analysis of the nodes energy consumption in each sector and derived an expression for optimal number of clusters in each sector to minimize nodes energy consumption. Various simulations were carried out with MATLAB package to differentiate the competency of the proposed EUSC algorithm with that of the existing protocols ECHA and ‘PSO based protocol’. Simulation results in different network scenarios indicate that EUSC has given much improved performance than ECHA and ‘PSO based protocol’ in terms of number of clusters formed during each round, network lifetime and energy efficiency.

Recent developments in low-power communication and signal processing technologies have led to the extensive implementation of wireless sensor networks (WSNs). In a WSN environment, cluster formation and cluster head (CH) selection consume significant energy. Typically, the CH is chosen probabilistically, without considering the real-time factors such as the remaining energy, number of clusters, distance, location, and number of functional nodes to boost network lifetime. Based on the real-time issues, different strategies must be incorporated to design a generic protocol suited for applications such as environment and health monitoring, animal tracking, and home automation. Elementary protocols such as LEACH and centralized-LEACH are well proven, but gradually limitations evolved due to increasing desire and need for proper modification over time. Since the selection of CHs has always been an important criterion for clustered networks, this paper overviews the modifications in the threshold value of CH selection in the network. With the evolution of bio-inspired algorithms, the CH selection has also been enhanced considering the behavior of the network. This paper includes a brief description of LEACH-based and bio-inspired protocols, their pros and cons, assumptions, and the criteria of CH selection. Finally, the performance factors such as longevity, scalability, and packet delivery ratio of various protocols are compared and discussed.

Designing routing protocols for Energy Harvesting- Wireless Sensor Network (EH-WSN) has two key challenges like improving harvested energy utilization and examination harvested energy utilization. However, the clustering and routing phases of the clustering-based routing protocols employed in EH-WSN need a lot of message overhead. Cluster Head Selection (CHS) is a crucial component of clustering that improves energy efficiency. An Enhanced Energy Harvesting Clustering (EEHC) protocol with two phases for cluster setup and data transmission is presented in this research. Sensor nodes are grouped into clusters during cluster construction, and CHS using the Gaussian Harris Hawks Optimizer (GHHO) algorithm is used for data transmission. Hawks chasing behaviours serve as the search agent in the GHHO algorithm, while prey serves as the ideal CH position. Data from each cluster node is collected by CHS, which then sends the aggregated data to the base station. Following CHS, connecting Cluster Member (CM) to a suitable CH is done using a variety of factors like energy, distance to neighbours, distance to the Base Station (BS), node density, and Signal-to-Noise Ratio (SNR). Each CM awakens during its designated working time slot during the DT stage, and transfers the data it has gathered towards the CH of the same cluster. A mobile sink is employed to enhance network performance and relay transmission distance is changed to match the amount of gathered energy. Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Network Lifetime, and average delay are metrics used to measure the results of clustering methods.

Since 2000, the usage of Wireless Sensor Networks (WSNs) in the military and civilian applications is increased rapidly. WSNs consist of low cost hardware which acts as multi-functional sensor nodes and are linked to the base station to efficiently aggregate and transmit the data. However, the limitation of sensor nodes is limited battery power. Efficient cluster head (CH) selection manages the WSNs in reducing the overall energy consumption. The cluster head combinations the data from the neighbouring nodes and transmits to the base station. The selection of cluster head in WSN is a Non-deterministic Polynomial-hard problem. In this paper, a bio inspired approach called firefly algorithm is used for selection of CH in WSNs. The synchronous firefly algorithm selects the optimal CH node among all nodes participated in the network, which also increases the performance of the network. The experimental results show that the proposed algorithm has effective CH selection procedure when compared to the other existing algorithms. The proposed algorithm reduced the packet loss ratio by 8.32% and it improved the reduction of energy consumption of overall network.

Wireless sensor networks are emerging and involved in a wide range of applications. Improving energy efficiency is a significant task in WSNs. Energy consumption is a crucial problem that affects the network lifetime due to energy limitations in sensor nodes. So, it's required to introduce more energy-efficient protocols for WSNs. Different types of energy-efficient solutions exist for WSNs. The energy consumption is optimized and controlled if clustering is an efficient data collection method. The cluster heads aggregate the data from the member nodes and transmit the aggregated data to BS (base station) either in an uncompressed or compressed manner after collecting all data by the cluster-heads (CHs). Due to the heavy inter-cluster relay and aggregation Overhead, earlier death happens to the CHs located very close to the sink. There is a need for an efficient and reliable CH selection algorithms to select reliable CHs to prevent the earlier death of CHs and frequent reselection of CHs. Therefore, in this work, a fuzzy optimal CH selection algorithm is proposed to select the optimal CHs to improve the network's lifetime. The proposed algorithm uses the following multi-objective parameters: cluster load, communication cost, neighbour density, inter and intra-cluster distance, and node energy for CH selection. The modified Emperor Penguin Optimization (EPO) is used in the proposed work to determine an optimal route for transmitting the data from CH to sink. The modified EPO algorithm helps to improve the multi-hop routing efficiency, reduces the cost of long-distance transmission, and decreases energy utilization during data transmission. The modified EPO considers the parameters like link quality factor, relative distance factor, and residual energy factor for relay selection. The proposed technique was compared against earlier methods like Z-DSS, CCR, RETOPSIS, and LEACH-FC. The various parameters like packet delivery ratio, lifetime, energy consumption, end-to-end delay, and Throughput are studied. The evaluation results prove that the proposed mechanism achieves 40% energy efficiency with 60% lesser Overhead and improves the Throughput rate by 50% than the compared methods.