Terms Of Network Lifetime Research Articles

An energy efficient fusing data gathering protocol in wireless sensor networks

The Internet of Things (IoT) is rapidly developing as a promising technology for today’s digital world. Wireless sensor networks (WSN) are a crucial component of IoT, and one of the major problems faced is energy constraints. The clustering protocol is an effective energy saving solution. In this paper, we propose an energy efficient fusing data gathering protocol via distributed second level clustering and deep learning-based data fusion (EEFDG) for WSN. EEFDG divides the WSN into several second-level clusters by a novel distributed cluster algorithm. The transmission distance and the number of neighbors are jointly considered to select an energy-efficient parent node in each second-level cluster. A new cluster head weight function is proposed for intra-cluster parent node rotation. To minimize redundant data transmission, a multivariate convolutional neural network and long short-term memory network (MVCLNet) are designed for data fusion. MVCLNet is deployed hierarchically into the second-level clustered structure to extract features of the multi-sensor data. The EEFDG is an effective solution to the energy limitation problem of WSN. The proposed second-level clustering and data fusion algorithm based on MVCLNet typically decreases communication distances and the amount of data, thereby minimizing communication overhead and energy consumption. Simulation results show that EEFDG significantly prolongs the network lifetime in terms of the first node dead, compared to existing protocols including MR-LEACH, BPDA, CNNDA, FDEAM, DCNN, and LPLL-LEACH, achieving improvements of 181.3%, 42.2%, 48.2%, 19.6%, 62.0%, and 30.2%, respectively. The EEFDG enables highly accurate data fusion and has superior performance in terms of remaining energy, network lifetime, and data collection.

Mathematical Modeling and Statistical Analysis of Leach Algorithm Based Cluster Head Selection Approach for Wireless Sensor Networks

In this paper, we present a comprehensive design and analysis of our proposed improved fuzzy logic-based algorithm. The algorithm utilizes a fuzzy logic system to assess node attributes such as residual energy, distance to the base station, and connectivity. By considering these parameters, the algorithm dynamically evaluates the most suitable candidates for cluster head roles, effectively distributing the energy load and promoting equitable cluster formation. An extensive performance evaluation of proposed algorithm has been conducted through simulations and comparisons with the original LEACH protocol. Our results reveal that the improved fuzzy logic-based algorithm outperforms the conventional LEACH in terms of network lifetime, energy consumption, and overall network stability. In this study, we delve into the mathematical modeling and statistical analysis of the proposed algorithm, focusing on its impact on energy efficiency, network stability, and data routing in WSNs. The Improved Fuzzy Logic-Based Cluster Head Selection Algorithm employs fuzzy logic to intelligently select cluster heads, considering parameters like residual energy, distance to the base station, and historical data. We develop mathematical models to describe the algorithm's behavior and analyze its performance through extensive simulations and statistical evaluations. The algorithm demonstrates enhanced adaptability to varying network conditions and node heterogeneity, showcasing its potential to significantly extend the operational duration of WSNs in diverse deployment scenarios. In conclusion, this paper contributes a novel perspective to the domain of WSNs by introducing an advanced fuzzy logic-based cluster head selection algorithm. The algorithm enhances the foundation of the LEACH protocol, effectively addressing energy optimization concerns and promoting prolonged network lifetime. The presented design and analysis substantiate the algorithm's superiority, offering valuable insights for researchers and practitioners working on energy-efficient routing protocols and wireless sensor network management.

WOAD3QN-RP: An intelligent routing protocol in wireless sensor networks — A swarm intelligence and deep reinforcement learning based approach

Wireless Sensor Networks (WSN) are a crucial part of the Internet of Things (IoT), and research on WSN routing protocols has always been a hot topic in academia. However, traditional WSN routing protocols have limited utilization of available information during the routing decision process, leading to challenges such as insufficient adaptability to network topology changes, high communication delays, and short network lifetimes. To address these issues, this paper proposes an innovative intelligent routing algorithm WOAD3QN-RP, which cleverly integrates swarm intelligence algorithms and deep reinforcement learning. The WOAD3QN-RP not only effectively reduces delay but also balances energy consumption and flexibly adapts to changes in network topology, while simultaneously determining the optimal multi-hop path, effectively extending the lifetime of the network. Firstly, the WOAD3QN-RP algorithm employs the Whale Optimization Algorithm (WOA) to determine the optimal cluster heads (CHs). In the process of selecting CHs, the algorithm comprehensively considers key factors such as the residual energy of nodes, node distance, and communication delay, thereby significantly improving the accuracy and efficiency of CH selection, which contributes to better energy distribution and performance of the network. Secondly, in terms of multi-hop path selection, WOAD3QN-RP uses a dueling double deep Q-network (D3QN) to determine the optimal multi-hop path. Through utilizing neural networks to interact with the environment, intelligent agents are trained to learn routing policies to adapt to dynamic changes in the network topology and ensure the balance between energy consumption and multi-hop routing performance. Experimental results show that WOAD3QN-RP exhibits significant advantages over existing routing protocols in terms of network lifetime, energy efficiency, and communication delay.

Optimizing Lifetime of Internet-of-Things Networks with Dynamic Scanning

With the development of Internet-of-Things (IoT) technology, industries such as smart agriculture, smart health, smart buildings, and smart cities are attracting attention. As a core wireless communication technology, Bluetooth Low Energy (BLE) is gaining a lot of interest as a highly reliable low-power communication technology. In particular, BLE enables a connectionless mesh network that propagates data in a flooding manner using advertising channels. In this paper, we aim to optimize the energy consumption of the network by minimizing the scanning time while preserving the reliability of the network. Maximizing network lifetime requires various optimizing algorithms, including exhaustive searching and gradient descent searching. However, they are involved with excessive computational complexity and high implementation costs. To reduce computational complexity of network optimization, we mathematically model the energy consumption of BLE networks and formulate maximizing network lifetime as an optimization problem. We first present an analytical approach to solve the optimization problem and show that finding the minima from the complicated objective function of the optimization problem does not guarantee a valid solution to the problem. As a low-complexity solution, we approximate the complicated objective function into a convex form and derive a closed-form expression of the suboptimal solution. Our simulation results show that the proposed suboptimal solution provides almost equivalent performance compared to the optimal solution in terms of network lifetime. With very low computational complexity, the proposed suboptimal solution can extensively reduce implementation costs.

An energy-efficient cross-layer-based opportunistic routing protocol and partially informed sparse autoencoder for data transfer in wireless sensor network

When sensor nodes in wireless sensor networks (WSNs) have limited energy, data minimization is crucial. Usually, data communications use up energy. A sensor node's lifespan can frequently be increased by sending and receiving the proper quantity of data. The PISAE (Partially Informed Sparse Autoencoder) is a state-of-the-art unmapped neural network architecture designed to reconstruct all sensor inputs from a limited number of sensors. It is presented in this publication. Use this architecture to create a system for selecting sensors. We now propose the cross-layer based opportunistic routing protocol (CORP) as an opportunistic routing mechanism for WSNs. In order to cut down on processing time and energy consumption and increase the dependability of data transfer, the best way is selected utilizing the CORP technique, which is suggested.For energy-efficient routing based combinatorial random sampling bat optimisation (ERFN-CSSBO), we also suggest using fuzzy neural networks. This enables you to conserve energy, increase the lifespan of your wireless sensor network, and keep your energy consumption in check. With CSSBO (Combined Random Sampling Prevosti Bat Optimisation), the best path is identified by integrating features (such as distance, energy, trust, and internode measurement connection stability) from all the bats.The key challenge in WSN is choosing cluster heads (CH). K-Medoid is used to enhance sensor node clustering. Important considerations include the effect on quality of service (QoS), sensor node position, proximity, and energy state needs. This study combines Hybrid BFO (Bacterial Foraging Optimization) and HSA (Harmony Search Algorithm), two well-known optimization techniques, to pick cluster heads in wireless sensor networks that are optimal in terms of distance and energy. On-task completion. The outcomes of the simulation indicate that the proposed technique improves QoS. Endpoints, throughput (1.0 Mbps), (98.5 % of packets are forwarded), and packet loss rate (1.5 %), and other QoS factors are all included in the performance statistics plotted. It performs better than conventional routing protocols in terms of network lifetime (6100 rounds), delay at both ends (1.5 s), and energy usage (30.35 mJ).

Optimal selection of cluster head in wireless sensor networks using particle swarm optimization (PSO)

In a wide range of applications, such as the military, healthcare, and environmental monitoring, wireless sensor networks (WSNs) have emerged as a key player. Cluster-based WSNs are a viable method for enhancing the life of the sensor network. Choosing the proper cluster head for wireless sensor networks (WSNs) is a key undertaking that affects the network's performance. Current approaches for selecting the cluster head have a number of drawbacks, such as nodes dying too quickly, uneven energy utilization, and shorter network lifespan. Additionally, conventional techniques like fixed Cluster Head and randomized Clustering are ineffective at extending the network lifetime. In the proposed method, Particle swarm optimization was used to create an optimal cluster head selection that addresses the problem of intra-cluster communication and lowers SN energy consumption. The simulation result shows that the performance improvement of the developed algorithm PSO in terms of network lifetime is 10% against Improved Cuckoo Search Algorithm (ICSA) and 25% against Hybrid Crow Search Algorithm (HCSA), energy consumption is 15% against ICSA and 20% against HCSA, and number of alive node is 4% against ICSA and 6% against HCSA respectively. Therefore, our developed algorithm PSO outperforms ICSA and HCSA in terms of the aforementioned parameters.

Joint time and energy-optimal approach to allocate task to actors in wireless sensor actor networks

In most applications of wireless sensor actor networks (WSANs) in harsh environments, minimizing the make-span and maximizing the residual energy are of paramount importance. The majority of existing task allocation approaches is typically concerned with one of the energy savings or time constraints. These approaches do not consider the types and various features of tasks WSANs may need to perform. Moreover, the limitation on the capacity of task queues has been overlooked by these approaches, and thus may not be applicable to some types of real applications such as search and rescue missions. To this end, a novel queue length aware task allocation (QLATA) approach is proposed that considers the energy consumption as well as the make-span. QLATA is aware of task queues constraint, types of tasks, and the distribution necessities of WSANs with hybrid architecture. QLATA comprises of two protocols, namely a Make-span Calculation Protocol (MsCP) and an Energy Consumption Calculation Protocol (ECCP). Through considering both time and energy, QLATA makes a tradeoff between minimizing make-span and maximizing the residual energies of actors by solving the joint optimization for make-span and energy consumption, simultaneously. A series of extensive simulation results on typical scenarios show shorter make-span and higher remaining energy in comparison to when opportunistic load balancing (OLB), minimum make-span task allocation (MMTa), stochastic task allocation (STA), and task assignment algorithm based on quasi-newton interior point (TA-QNIP) approaches is used. It is also shown that the proposed approach performs significantly better than the four compared algorithms in terms of network lifetime.

Energy-efficient mobile sink data collection in wireless sensor networks using LEACH with rendezvous nodes

In Wireless Sensor Networks (WSNs), energy consumption management poses a formidable challenge due to the energy-intensive nature of data transmission and processing. WSNs consist of densely deployed sensor nodes, and data collection is carried out by cluster heads. To address the issue of transmitting data to the mobile sink (MS) and extend the network's lifetime, rendezvous nodes(RNs) are utilized. However, existing approaches have not sufficiently improved network longevity. To tackle this problem, a novel protocol called Energy Efficient Mobile Sink Data Collection (EEMSDC) is proposed. This protocol aims to balance energy usage, prevent sensor node overload, and facilitate data delivery to the MS through RNs. The proposed approach optimizes the MS's tour in WSNs by employing predetermined TDMA slots for efficient data transmission with RNs. The proposed protocol outperforms existing protocols such as LEACH, EAMMH, LEACH-C, MOD-LEACH, MGEAR, and TDEEC in terms of network lifetime, energy consumption, and data loss. By reducing network overhead and ensuring balanced energy consumption among sensor nodes, the proposed approach significantly extends the network's lifetime. The effectiveness of the proposed protocol is validated through mathematical analysis and simulations conducted using MATLAB 2021a. The simulations show a remarkable reduction in energy consumption by up to 68% and an impressive increase in network lifetime by 73% compared to LEACH. These findings underscore the substantial advantages of the proposed approach in enhancing the performance of WSNs.

An optimized approach to enhance the network lifetime through integrated data aggregation and data dissemination in wireless sensor network

<div class="WordSection1"><p>Wireless sensor network (WSN) is an integral part of internet of things (IoT), it comprises multiple sensor nodes to sense that data for various applications; also, sensor nodes have limited energy. Hence, several researchers to improvise the network lifetime and reduce energy through machine learning approaches like clustering have used data aggregation; considering the WSN architecture and development of novel use cases and dynamic behavior, data aggregation cannot solve the problem of efficiency solely. Hence integrating data aggregation and data dissemination can provide a research scope to achieve optimal efficiency. This research work introduces an integrated-data aggregation and data dissemination (DADD) to develop an efficient WSN-based model for lifetime enhancement. Integrated-DADD follows two sub-mechanisms; the first part of the mechanism introduces an optimal clustering technique to perform the clustering and optimal parameter tuning is formulated and efficient data aggregation takes place. The second part of the integrated-DADD introduces optimal data dissemination through optimal path selection, which helps in finding the suitable path for data dissemination. Integrated-DADD is evaluated considering the parameters like energy consumption, network lifetime in terms of rounds; active node participation, and communication overhead, comparative analysis indicates that integrated-DADD outperforms the existing model.</p></div>

Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining Particle Swarm Optimization and Efficient Genetic Algorithm

Wireless Sensor Networks (WSNs) have become a crucial component of numerous applications, including the military, healthcare, and environmental monitoring. A promising approach to increasing the lifespan of the sensor network is cluster-based WSNs. In WSNs, choosing the best cluster head is a crucial task that has an impact on the network's performance and energy efficiency. There are various issues with current methods for choosing the cluster head, including nodes dying too soon, uneven energy usage, and shorter network lifetimes. Moreover, traditional methods such as Randomized Clustering and Fixed Cluster Head are not effective in prolonging the network lifetime as they do not consider the energy consumption and residual energy of nodes. In this paper, an optimal selection of cluster head is presented where we combine the Particle Swarm Optimization (PSO) and Efficient Genetic Algorithm (EGA). Firstly, PSO is used to randomly select the cluster head and update the position of each cluster. Thereafter, EGA invokes its fitness values to select the best cluster head that transmits information to the base station. The simulation result shows that the performance improvement of the proposed method PSO_EGA in terms of network lifetime is 0.10% against Improve Cuckoo Search Algorithm (ICSA) and 0.20% against Hybrid Crow Search Algorithm (HCSA), packet to cluster head is 7% against ICSA and 16% against HCSA, packet to sink is 11% against ICSA and 22% against HCSA and number of alive node is 28% against ICSA and 48% against HCSA. Therefore, our proposed method outperforms ICSA and HCSA in terms of the aforementioned parameters.

Performance Analysis of Centroid-based Routing Protocol in Wireless Sensor Networks

In this paper, a clustering-based energy-efficient routing protocol is designed to overcome the hotspot problem in Wireless Sensor networks (WSNs). We have divided the sensor nodes into several clusters, each cluster having a Cluster Head (CH) or a leader node and also implemented a centroid-based mobile sink. The sink moves to a location called “Energy Dependent Centroid Point” where the CHs need minimal energy to transmit data to the sink. We have defined this “Centroid Point” by an equation where the residual energies of CHs are used as a “weight factor” on a normal centroid equation. We have simulated the proposed algorithm and analyzed the results by varying the values of the energy weight factor in the centroid equation up to certain limits (α = 0, 1, 2, and 3). The simulation results are compared with WSN’s existing routing protocols having a mobile sink and a static sink. The comparison results show that our proposed centroid model with α = 0, where the mobile sink moves independent of the residual energies of CHs gives better performances in terms of network lifetime, energy loss, and total data packet transmission.

Underwater wireless sensor network-based multihop data transmission using hybrid cat cheetah optimization algorithm

For the conservation and sustainable use of the oceanic environment, monitoring of underwater regions is ineluctable and is effectuated with the aid of an underwater wireless sensor network. It is accoutered with smart equipment, vehicles and sensors and utilized for the transmission of acquired data from the monitoring region and forwarded to the sink nodes (SN) where the data are retrieved. Moreover, data transmission from sensor nodes to SN is complicated by the aquatic environment's inherent complexities. To surpass those issues, the work in this article focusesto propose a Hybrid Cat Cheetah optimization algorithm (HC2OA) that purveys the energy efficient clustering based routing. The network is then partitioned into numerous clusters, each of which is led by a cluster head (CH) and comprised of many sub-clusters (CM). Based on the factors such as distance and residual energy the CH selection is optimized and collects data from the respective CMs and forwarded to the SN with a multi-hop transmission approach. The proposed HC2OA chooses the optimized multi-hop route from the CH to SN. Thus mitigates the complexities over multi-hop routing and CH selection. Simulations are effectuated in the NS2 simulator and analyzed the performance. The results of the study show that the proposed work has significant advantages over state-of-the-art works in terms of network lifetime, packet delivery ratio, and energy consumption. The energy consumption of the proposed work is 0.2 J with a packet delivery ratio is 95%.The network life time of proposed work, with respect to the coverage area around 14 km is approximately 60 h.

A Dynamic Opportunistic Routing Protocol for Asynchronous Duty-Cycled WSNs

Opportunistic routing (OR) is widely adopted in Wireless Sensor Networks (WSNs) running asynchronous duty-cycled MAC protocols. In conventional routing, where packets are forwarded along predetermined routes, the sender may wait for the receiver to wake up for a long time. To reduce the sender waiting time, the OR protocols allow nodes to select multiple neighbors as the forwarders so that the packets could be forwarded by multi-path. Thus, the forwarders selection algorithm affects network performance seriously. However, an excessive number of forwarders increases the probability that more than one forwarders wake up simultaneously. This will consume more energy since each of them will receive the packet. To address the two issues, a Dynamic Opportunistic Routing protocol using Analytical Hierarchy Process (AHP) and Fuzzy Inference System (FIS) called DORAF is proposed in this paper. DORAF is implemented in three steps. First, multiple criteria (i.e., residual energy, distance, and angle) at the network layer are defined to evaluate the nodes where the importance of these criteria is determined by AHP. Second, the pairwise comparison matrices in AHP are generated by using mathematical functions (i.e., Boltzmann function and Logistic function) and FIS. Third, each node uses AHP and FIS to prioritize its neighbors based on the criteria and selects appropriate ones as the forwarders dynamically in a distributed manner. The experimental results demonstrate that our protocol performs better than other state-of-the-art in terms of network lifetime, energy consumption, and average redundant transmissions.

Enhancing the lifetime and performance of WSNs in smart irrigation systems using cluster‐based selection protocols

AbstractThis paper presents a comparative analysis of cluster‐based selection protocols for wireless sensor networks (WSNs) in smart irrigation systems. The study evaluates the performance of stable election protocol (SEP)‐based protocols, including conventional SEP, zonal SEP (ZSEP), enhanced SEP (ESEP) and threshold‐sensitive SEP (TSEP), and distributed energy‐efficient clustering (DEEC)‐based protocols, including conventional DEEC, developed DEEC (DDEEC), enhanced DEEC (EDEEC) and threshold‐sensitive DEEC (TDEEC). Simulation results show that TSEP and ZSEP outperform other SEP‐based protocols in terms of network lifetime, stability period, number of dead nodes and throughput. EDEEC and TDEEC show superior performance compared to other DEEC‐based protocols. However, TDEEC and EDEEC have a lower performance in terms of packets sent to cluster heads (CHs) due to the three levels of energy heterogeneity, which reduce the average packets sent to CHs. Finally, a comparative analysis between the SEP‐based and DEEC‐based protocols is presented. The findings reveal that TSEP outperforms all other protocols in terms of the performance metrics examined in this paper, except for throughput, which is higher in the case of ZSEP. Overall, the proposed cluster‐based selection protocols improve the energy efficiency and lifetime of WSNs in smart irrigation systems, and the results demonstrate their effectiveness in supporting the performance of smart irrigation systems.

Energy-efficient and delay-guaranteed routing algorithm for software-defined wireless sensor networks: A cooperative deep reinforcement learning approach

Wireless sensor networks (WSNs) have undergone rapid development but still suffer from energy shortage issues. Meanwhile, existing energy-efficient routing approaches lack a cross-layer cooperation mechanism and cannot offer intelligent decision-making capabilities for resource-constrained nodes. In this paper, we design an intelligent routing algorithm with adaptive duty cycling for application in software-defined WSNs (SDWSNs) to maximize the network lifetime and data transmission reliability subject to a long-term data queue stability constraint. In particular, we introduce Lyapunov optimization to decouple the multistage routing optimization problem into per-frame deterministic problems with delay guarantee. To optimize the routing algorithm and adaptively adjust the duty-cycling mechanism, we propose a cooperative deep reinforcement learning (CDRL) model. Moreover, we develop a centralized training and distributed execution framework to decouple the model training and inference processes, which provides the capability of intelligent routing decision-making for resource-constrained nodes with low computational complexity. In addition, we design a new hybrid routing metric and a unique feature construction and extraction scheme to capture fine-grained information from our hybrid routing metric. The proposed CDRL algorithm adopts a cooperative and iterative model optimization strategy to achieve highly reliable data transmission and optimal long-term network performance. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of network lifetime, packet delivery ratio, and statistical delay performance under various scenarios compared to the EBR-RL, EA-AGG, and QLRP algorithms.

NMSFRA: Heterogeneous routing protocol for balanced energy consumption in mobile wireless sensor network

Wireless sensor networks are widely used in various fields and consist of a large number of sensor nodes that are powered by batteries. As a result, energy efficiency is crucial for the effective operation of these networks. With the advancement of cross technology communication (CTC), the presence of heterogeneous wireless nodes, especially mobile nodes, in wireless sensor networks has increased, making traditional routing protocols for homogeneous wireless sensor networks inapplicable. To overcome the above issues, this paper proposes a heterogeneous wireless sensor network routing protocol for a balanced energy consumption, NMSFRA (NGO based mean shift fuzzy clustering routing algorithm). In order to mitigate the impact of uneven cluster distribution and unstable network links due to the mobility of nodes, the proposed protocol first divides the network into balanced clusters using the Mean Shift (MS) method. The Mamdani fuzzy inference system is then used to take node mobility into account. The protocol dynamically selects different parameters as inputs to the fuzzy logic, enabling it to choose cluster heads (CHs) based on the overall location of the clusters. Additionally, to extend the network lifetime, the protocol generates multi-hop forwarding paths using the Northern Goshawk Optimization (NGO) method. Finally, the paper presents experimental simulations carried out on three different network topologies and compares the results with existing protocols such as LEACH, EETPF, E-FUCA, and DE-SEP. The results show that the NMSFRA protocol extends the survival period of nodes by 235 rounds, 240 rounds, 364 rounds, and 190 rounds, on average, compared to the LEACH, EETPF, E-FUCA, and DE-SEP protocols, respectively. The network throughput also increased by 154.92%, 48.74%, 84.77%, and 106.77%, respectively. The NMSFRA protocol also showed better performance in terms of total residual energy, FND, HND, and average speed difference between nodes in the first 5 rounds. Therefore, the proposed NMSFRA protocol demonstrates superior performance in terms of network lifetime, energy utilization, throughput, and network stability.

Double Q‐learning‐based adaptive trajectory selection for energy‐efficient data collection in wireless sensor networks

SummaryThis paper proposes a novel distributed stochastic routing strategy using mobile sink based on double Q‐learning algorithm to improve the network performance in wireless sensor network with uncertain communication links. Furthermore, in order to extend network lifetime, a modified leach‐based clustering technique is proposed. To balance the energy dissipation between nodes, the selected cluster head nodes are then rotated based on the newly suggested threshold energy value. The simulation results demonstrate that the proposed algorithms outperform the QWRP, QLMS, ESRP and HACDC in terms of network lifetime by 18.33%, 35.1%, 39.7% and 44.7%, respectively. Moreover, the proposed algorithms considerably enhances the learning rate and hence reduces the data collection latency.

A Distributed Clustering Based Energy Management Scheme for Heterogenous Wireless Sensor Network

The Internet of Things (IoT) is an emerging paradigm that offers a wide array of benefits for real-world applications. An IoT system is supported by a network of heterogeneous sensor nodes that collect information about the environment over time. The non-rechargeable batteries and energy constraints limit the network's lifespan. For this reason, controlling energy dissipation is one of the most important considerations when designing communication protocols for sensor networks. The clustering scheme is one of the most efficient ways to help networks be more energy-efficient. However, the majorities of existing protocols do not handle energy distribution among heterogeneous sensor nodes well and are inappropriate. The purpose of this paper is to emphasize cluster head selection criteria and to propose an energy management clustering mechanism that takes heterogeneity into account. A dense deployment of heterogeneous multi-level nodes is considered in the proposed scheme to improve the nodes' stability and power efficiency. The threshold probability for selecting optimal CH nodes is determined by an energy-balanced weighted parameter. The proposed scheme is implemented on a numerical computing tool MatLab. In terms of alive nodes, dead nodes, packets sent to the base station, and processing time, the simulation provides efficient results. When compared to similar existing approaches, the proposed scheme was more successful in terms of network lifetime, stability, delay, and packet delivery.

A Centralized Energy-Efficient Clustering Protocol for Wireless Sensor Networks

Energy saving is one of the hot research topics in wireless sensor networks (WSNs), and a number of solutions have been proposed. Clustering protocols have become the widely used method to reduce energy consumption. However, the energy efficiency in existing clustering protocols is limited because of the excessive energy consumption in periodic reclustering. In this article, we propose a centralized energy-efficient clustering (CEEC) protocol, which includes two aspects of node energy balancing schemes, to enhance energy efficiency in WSNs. As for intracluster energy balancing, we employ the cluster head (CH)-rotation approach to balance the energy load among nodes in each cluster. With respect to intercluster energy balancing, we suggest four sequential algorithms to balance the energy consumption among clusters, and the main contributions are as follows: 1) a size-balanced cluster formation (SBCF) algorithm aims to partition the whole network into several small clusters of the same size; 2) a low-cost forecast-based maintenance mechanism is raised to help the base station (BS) maintain the total nodes’ energy information of each cluster in the network, and then, BS utilizes our introduced intercluster energy adjustment (ICEA) algorithm to reallocate a few cluster members in related clusters with low overhead; and 3) an energy-aware data forwarding (EDF) scheme is to maintain the nodes’ energy balance among clusters in intercluster communication. Simulation results show that our CEEC protocol outperforms the classical clustering mechanisms in terms of network lifetime, energy consumption, and network throughput.

An implementation of energy efficient fuzzy-optimized routing in wireless sensor networks using Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA)

The clustering approach can improve wireless sensor network parameters such as lifetime enhancement, load balancing, reliable communication, and fault tolerance. The Cluster head in the cluster is responsible for reliable data transmission between node and sink or base station. Selecting suitable cluster heads and establishing an optimal path for data transmission is the main objective of this research work. Fuzzy-based clustering based on cluster head selection, optimized routing using particle swarm optimization (PSO), adaptive whale optimization algorithm (AWOA) are presented in this research work. Fuzzy logic considers the parameters like the distance between base station to node, node centrality, node degree, and residual energy for cluster head selection. The optimization model obtains an optimized node for routing from the selected cluster heads. In terms of network lifetime, delay, energy consumption, packet delivery ratio, and energy efficiency, simulation analysis of the proposed model is compared to conventional routing algorithms such as bacteria foraging optimization (BFO), Tree-based data gathering (TBDG) algorithm, Immune inspired routing (IIR), Low-Energy Adaptive Clustering Hierarchy (LEACH), and Hybrid Energy-Efficient Distributed (HEED) protocol. The results demonstrate that the proposed approach outperforms existing approaches in terms of network lifetime and energy efficiency.