Fault-Tolerant Cluster Head Selection Using Game Theory Approach in Wireless Sensor Network

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.

An efficient cluster head selection for wireless sensor network-based smart agriculture systems

An adaptive cellular automata scheme for diagnosis of fault tolerance and connectivity preserving in wireless sensor networks

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.

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%.

SummaryEnergy consumption in wireless sensor network (WSN) is one of the important issues as tiny sensor nodes are equipped with a non‐replaceable batteries. Clustering is one of the effective means to prolong the network lifetime in WSN by reducing the number of packets to be sent from cluster heads (CHs) to base station (BS). Recently, game theory techniques have been applied in the clustering process for optimal CH election at each round. However, none of them have considered the energy parameter in their game strategy and multihop routing for data transmission. In this manuscript, an energy‐efficient game‐theoretic clustering and multi‐hop routing scheme (EGCR) has been proposed where the non‐cooperative approach of game theory is used to elect optimal CHs. In the proposed game theoretic framework, each node behaves in a selfish manner while considering the residual energy of nodes and focuses on increasing the overall lifetime of the network. Nash equilibrium is determined among a set of pure and mixed strategies, and then the expected payoff of a node is determined for Nash equilibria. Apart from the energy‐efficient clustering, the EGCR also proposes a multi‐hop routing solution for data transmission that further enhances the lifetime of whole network. In the multi‐hop routing approach, inter‐cluster and intra‐cluster routing techniques are proposed to identify optimal routes from nodes to CHs and from CHs to BS by using various factors such as node centrality, distance from the node to BS, and residual energy. The proposed algorithm is compared with traditional clustering schemes like low‐energy adaptive clustering hierarchy (LEACH) and efficient energy‐aware game theory‐based clustering (EEGC) and also with existing game theory‐based algorithms such as clustered routing for selfish sensors (CROSS), localized game theoretical clustering algorithm (LGCA), and energy‐efficient clustering algorithm based on game theory (ECAGT). Simulation experiments validate that the proposed algorithm enhances the network's lifetime up to 47.49% compared to existing clustering algorithms.

Clustering is one of the significant techniques for expanding the lifetime of networks in wireless sensor networks (WSNs). It entails combining of sensor nodes (SNs) into clusters and electing cluster heads (CHs) for each and every cluster. CH collects the information from particular cluster nodes and passes the cumulative data to the base station (BS). However, the most important requirement in WSN is to choose a suitable CH with an increased network life span. This work introduces a new CHS model in WSN. The optimal CH is elected by a new hybridized model termed as “Lion Updated Dragonfly Algorithm (LU-DA) that hybrid the concepts of Dragonfly Algorithm (DA) and Lion Algorithm (LA)”. Moreover, the optimal selection of CH is done depending upon constraints like “energy, delay, distance, security (risk) and trust (direct and indirect trust)”. This optimal CH ensures the network lifetime enhancement. At last, the superiority of the developed approach is proved on varied measures like energy and alive node analysis. Accordingly, the proposed model has accomplished higher energy of 0.55 at 1st round, whereas at the 2000th round, the normalized energy value has been dropped to 0.1.

A Wireless Sensor Network (WSN) represents a set of deployed sensors in an area to track or monitor specific activities. Sensors are utilized to gather certain data and forward it to the sink or a Base Station (BS). Sensors are operated by limited-energy batteries. Optimizing the consumed energy will prolong the WSN's lifetime. One approach to reducing the consumed energy is by applying a cluster-based routing protocol. Optimizing the Cluster Head (CH) selection represents another approach to prolonging the WSN lifetime. This paper proposes an optimization algorithm to select the CH. The residual energy, the distance between sensors, and the centrality of the sensor node are used as significant features in selecting the CHs in this paper. The proposed algorithm is based on Particle Swarm Optimization (PSO) algorithm and K-Means algorithm. This study proposed approach is called the PSO-K algorithm. It helps in reducing energy consumption and increasing the network lifetime. MATLAB is used to model and simulate this developed algorithm. the number of sensors, number of rounds, number of CHs, the first dead nodes, the half-dead nodes, and the last dead nodes are used to evaluate the WSN performance. A comparison with other closely related works such as LEACH, EAMMH, and iLEACH shows that the PSO-K algorithm is the best in energy consumption and network lifetime.

Wireless Sensor Networks (WSN) are becoming a viable tool for many monitoring applications. These applications may be of critical nature where the transportation of the information of events from the region of interest to some base station is crucial, where the data loss can not be tolerated. In the cluster-based two-tier WSN, where cluster-head nodes gather data from sensors in their clusters and then transmit to base station, when cluster head nodes start to die, the coverage of those clusters is lost and it leaves the region unmonitored. Even if the cluster heads are rotated and reassigned after some time, until the next rotation that cluster in question will be out of cluster head and will lose coverage. A lot of information is lost which is sensed and sent by the sensor nodes of the cluster to the dead cluster head. We propose here to select backup cluster heads (BCHs), for those cluster heads which are close to deplete their energy. The cluster head, when about to die, sends an SOS with the gathered information until then, to the respective BCH which takes over the responsibility and continues to work as a new cluster head. To evaluate the effect and results of BCH we used LEACH-C protocol and compared the data loss ratio with and without a BCH.

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

Lifetime and energy consumption are the main objectives of wireless sensor networks (WSNs). The existing methodologies have certain inhibitions which limit their applications. Clustering optimization is a major technique in any wireless sensor networks to optimize energy efficiency. Clustering technique assembles the objects of comparable shape in one shape. This technique is the excellent data assembly model for WSN, and it handles the redundant data within the network. The selection of the cluster head is an important feature in the clustering technique, then cluster heads cumulative the data and transmits to sink. Here fuzzy logic control algorithm has been proposed with hybridized particle swarm optimization algorithm and gravitational search algorithm to select cluster heads in WSN (FHPSOGSA). The algorithm is used to merge the constraints like remaining energy, node degree and distance to sink and select the best appropriate nodes as Cluster Head (CH). Simulation outcome displays that the proposed algorithm (FHPSOGSA) is establish to yield best results over other conventional algorithms.

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

In many modern situations, wireless sensor networks play a crucial role, used for monitoring things like environmental conditions, transactions, and various statuses. These networks gather a large amount of data, sending it to a central hub for analysis. However, a significant problem is that traditional wireless sensor networks heavily rely on energy, and this limits how long they can operate. This chapter develops an optimization approach for a variable clustering routing protocol to address the issue. The objective is to enhance the cluster structure in wireless sensor networks while reducing energy loss in cluster heads. The first step involves employing a dynamic estimation method for clustering to determine cluster heads, utilizing core concentration to establish the head within the cluster radius. The authors also introduce a fuzzy logic algorithm to handle uncertainties in selecting cluster heads. The residual energy of the cluster's head nodes, maintaining a balanced distribution of cluster heads, and optimizing node use of energy are all taken into account by this fuzzy logic approach. An ant colony algorithm-based technique for optimizing inter-cluster transport is presented in this outline. The primary objective is to optimize energy consumption while simultaneously reducing the data communication overhead among cluster heads. Chaotic mapping is utilized by this technique to update and perturb pheromones, ensuring optimal performance. Energy utilization among cluster heads is optimized by selecting the optimal path based on considerations of energy dispersion parameters and distance coefficients. Our experiments show that compared to traditional algorithms, Researchers provide a non-uniform clustering approach for route optimization that dramatically extends the network lifespan by 75% and lowers overall energy consumption by about 20%. This effectively optimizes network energy utilization and significantly extends the network lifetime, demonstrating the practical effectiveness of our method.

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.

In wireless sensor networks, power is the most essential resource because each sensor node has limited batteries. Many kinds of existing clustering protocols have been developed to balance and maximize lifetime of the sensor nodes in wireless sensor networks. These protocols select cluster heads periodically, and they considered only `How can we select cluster heads energy-efficiently?' or `What is the best selection of cluster heads?' without considering energy-efficient period of the cluster heads replacement. Unnecessary head selection may dissipate limited battery power of the entire sensor networks. In this paper, we present T-LEACH, which is a threshold-based cluster head replacement scheme for clustering protocols of wireless sensor networks. T-LEACH minimizes the number of cluster head selection by using threshold of residual energy. Reducing the amount of head selection and replacement cost, the lifetime of the entire networks can be extended compared with the existing clustering protocols. Our simulation results show that T-LEACH outperformed LEACH in terms of balancing energy consumption and network lifetime.