Prolong Network Life Research Articles

Energy-Efficient Clustering in Wireless Sensor Networks Using Grey Wolf Optimization and Enhanced CSMA/CA.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

Mechanical equipment fault diagnosis based on wireless sensor network data fusion technology

Abstract To save network energy consumption and prolong network life cycle in complex mechanical fault diagnosis, a research method of data fusion routing protocol algorithm based on wireless sensor network (WSN) is proposed. The specific content of the method is as follows: First, the low-energy adaptive clustering hierarchy algorithm is analyzed and discussed. On this basis, the prim route fusion algorithm is proposed to realize the effective utilization of energy and prolong the life of the network. Then, the WSN is abstracted as an undirected graph. From the perspective of saving the energy of the whole network, several current algorithms for building fusion trees are compared. The experimental results show that the prim algorithm consumes energy only after 700 rounds of clustering, while the leach clustering algorithm consumes energy only after 500 rounds. This shows that applying the prim algorithm can reduce the energy consumption of the whole network and prolong the life cycle of the network. However, the algorithm is carried out on the premise of uniform distribution of nodes, and there is a certain gap with the specific application of WSN in mechanical fault diagnosis. In the comparison of node energy consumption, it is found that compared with using the shortest path tree, using the central point of graph algorithm can greatly save the energy consumption of the node and has better performance. Practice has proved that this method can effectively remove redundant data information and solve the problem of unreliable data collected by a single sensor node. It is more suitable for the specific application of WSN in mechanical fault diagnosis.

Improving the Transmission Efficiency of a WSN with the IACO Algorithm

The goal of this study is to reduce the energy consumption of the sensing network and enhance the overall life cycle of the network. This study proposes a data fusion algorithm for wireless sensor networks based on improved ant colony optimization (IACO) to reduce the amount of data transmitted by wireless sensor networks (WSN). This study updates pheromones for multiple optimal routes to improve the global optimal route in search function. The algorithm proposed in this study can reduce node energy consumption, improve network load balancing and prolong network life cycle. Through data fusion, regression analysis model and information processing of each node, this study uses an improved ant colony algorithm to identify the transferals avoid superfluous energy waste caused by long-span network transferal, set the shortest route and transmit data to the central node. The algorithm proposed in this study is conducive to improving the life cycle and stable network, that is, the most suitable and effective way to improve the energy consumption rate of the sensing nodes.

Hybrid grasshopper and differential evolution algorithm for prolonging network life expectancy in wireless sensor networks (WSNs)

SummaryIn wireless sensor networks (WSNs), restricted battery power, balanced energy consumption, and collaborative data processing are considered as key challenges that need to be handled with utmost care. Clustering in WSNs is an optimal methodology for conserving energy of sensor nodes and attaining efficient data processing that attributes toward the maximization of network lifetime. An efficient cluster head (CH) selection scheme is essential for achieving superior collaborative data processing in WSNs. Swarm intelligent metaheuristic algorithm‐based CH selection approaches are identified to be better for designing energy‐efficient schemes that select optimal CHs from nodes in a fair way. In this paper, Hybrid Grasshopper and Differential Evolution‐based Optimization Algorithm (HGDEOA) is proposed for targeting on the objective of attaining energy stability and prolonging network lifetime. This HGDEOA incorporates an adaptive strategy into differential evolution (DE) for improving the global searching capability during the process of optimization. It is proposed for improving the convergence efficiency and maintaining population diversity. It also possesses the capability of enhancing the degree of convergence speed and precision in calculation. This integration of GOA into DE prevents the premature convergence of the algorithm, since the deviation amid the scaling individuals induces the population to be more randomly distributed, targeting at the retention of population diversity. The simulation results confirm that the proposed HGDEOA sustains residual energy by 19.32%, improves throughput by 16.21%, prolongs network lifetime by 18.76%, and maintains stability by18.94% when compared to the benchmarked approaches used for investigation.

Optimization of Heterogeneous Clustering Routing Protocol for Internet of Things in Wireless Sensor Networks

Wireless sensor network technology is widely used in various modern scenarios, and various industries have higher and higher requirements for the performance indicators of wireless sensor networks. A reasonable and effective layout of wireless sensor networks is conducive to the monitoring of environmental quality, various transactions, and status and transmits a large number of sensing data to the data aggregation center for processing and analysis. However, the operation and development of traditional wireless sensor networks are extremely dependent on the energy supply of the network. When the corresponding supply energy is limited, the operation life of the corresponding wireless sensor network will be greatly reduced. Based on the above situation, this paper proposes a nonuniform clustering routing protocol optimization algorithm from the energy loss of cluster head and clustering form algorithm in wireless sensor networks. At the level of cluster head calculation in wireless sensor networks, firstly, based on the adaptive estimation clustering algorithm, the core density is used as the estimation element to calculate the cluster head radius of wireless sensor networks. At the same time, this paper creatively proposes a fuzzy logic algorithm to further solve the uncertainty of cluster head selection, integrate the residual energy of cluster head nodes, and finally complete the reasonable distribution of cluster heads and realize the balance of node energy consumption. In order to further reduce the algorithm overhead of transmission between cluster heads and realize energy optimization, an intercluster routing optimization algorithm based on the ant colony algorithm is proposed. The pheromone is updated and disturbed by introducing chaotic mapping to ensure the optimal solution of the algorithm, and the optimal path is selected from the perspective of energy dispersion coefficient and distance coefficient, so as to optimize the energy consumption between cluster heads. The experimental results show that compared with the traditional algorithm, the proposed nonuniform clustering routing protocol optimization algorithm prolongs the corresponding life cycle by 75% and reduces the total network energy consumption by about 20%. Therefore, the algorithm achieves the purpose of optimizing network energy consumption and prolonging network life to a certain extent and has certain practical value.

Multi-Criterion Partial Clustering Algorithm for Wireless Sensor Networks

Cluster architectures are an effective approach for organizing sensor networks to balance the load and prolong network life. To cluster wireless sensor networks, this paper proposes an energy-efficient distributed algorithm. This algorithm uses two techniques (partial clustering and multi-criterion cluster formation) for efficient use of the sensor nodes’ energy. When a header expends a certain amount of power, it only notifies the nodes in its cluster that new clustering is required in the next round. Therefore, in contrast to previous studies that performed complete clustering, clustering in the present work is performed partially, which considerably reduces the clustering overhead. In addition, a multi-criterion score is calculated for each node. In this algorithm, a node with the highest remaining energy and score is a more suitable candidate to be selected as the head of the cluster. In addition, a regular node becomes the member of the cluster with the highest score in its vicinity. The experiments reveal the superiority of the proposed algorithm over other simulated algorithms in terms of energy savings and network lifetime.

A prediction model for effective data aggregation materials and fault node classification in WSN

With the integration of wireless technology with an embedded technology called Wireless Sensor Network (WSN), the concept of WSN has become even more advanced. WSN is used for monitoring various environmental factors such as pollution, weather, humidity and temperature. It consists of a variety of sensitive microelectronic components for broadcasting and receiving data. The main limitation of WSN is network lifetime due to its battery-operated resources. The key solution to prolong network life is the data aggregation approach. Data aggregation is a process by which the redundant data collected from all the sensor nodes is removed from further processes like routing. Faults present in Sensor Nodes considerably affect the efficiency of data aggregation. There are two ways to improve the process of data aggregation: Early Prediction and Faulty Data Classification. In this work, a novel LSTM based data prediction and classification algorithm is proposed for efficient data aggregation. The algorithm is combined with AdaBoost classifier to increase Classification accuracy. Experimental results show that the proposed approach outperforms other algorithms in terms of Faulty Node Detection, Data Quality, Minimized Energy Consumption, Overhead Time and Live Node Rate etc. Compared to other methods, the proposed approach achieves 5.72% of increased Faulty Node Detection rate, 31% increased Data Quality, 8.2% of Minimized Energy Consumption and 36.5 % increased Live Node Rate.

A Study on Multi-Hop Routing Scheme for Wireless Sensor Networks

Wireless sensor networks (WSN) play an important role in IoT (Internet of Things) as an interconnecting infrastructure. Working with a limited energy source, the vital challenge for WSN is to prolong the network lifetime as an important performance metric. Furthermore, the limitations of regular transmission technologies create localized network areas of a multi-hop fashion form that adds more constraints to enhance the network performance. Hence, the clustering strategies initially have solved these problems and received the attention of many studies, an approach using unequal clustering strategy has yielded some positive results since consumed energy gaps are avoided in regions near base stations. However, the routing strategy among cluster heads in multi-hop wireless networks is still a big challenge because of its inefficiency in energy consumption aspects. Therefore, in this paper, we propose a novel method that combining an unequal clustering problem and a simple multi-hop routing to prolong network life. The numerical results show that the proposed solution is more effective than other models in recent studies

A hybrid fuzzy weighted centroid and extreme learning machine with crow‐particle optimization approach for solving localization problem in wireless sensor networks

SummaryThe sensor nodes localization is very advantageous in wireless sensor network (WSN). This allows effective data transfer between the sensor node networks. Therefore, it saves energy and prolongs network life. Here, a hybrid FWCELM‐CPO method is proposed for solving the sensor node localization problem in WSN. The proposed hybrid method is executed in MATLAB, and the performance is analyzed with different existing algorithms like centroid, fuzzy centroid, and ELM. The simulation results show that the proposed FWCELM‐CPO method attains the higher detection rate of 14.117%, 5.435%, and 11.494%, higher segmentation accuracy of 9.556%, 26.41%, and 16%, lower execution time of 66.667%, 75%, and 70.37%, higher segmented region of 65.957%, 20%, and 44.444%, and higher precision of 34.72%, 18.29%, and 8.78% compared to the existing algorithms. The simulation results demonstrate that the proposed method can be able to find the optimal global solutions efficiently with accurately.

An Energy Efficient Clustering Algorithm in Wireless Sensor Networks for Internet of Things Applications

Internet of things (IoT) integrates sensor, radio frequency identification, network and cloud computing technology into a large-scale monitoring area. Wireless sensor network (WSN), as the most suitable infrastructure for the Internet of Things, how to reduce its energy consumption has been a research hotspot. In hierarchical networks, cluster head nodes need to take on more energy consumption tasks. Therefore, reasonable cluster head selection scheme and normal node joining cluster mechanism become particularly important. This paper proposes a new clustering method to save energy and prolong network life. Firstly, in the cluster head selection phase, the influence of energy threshold, residual energy, node density and communication factor is considered. Secondly, in the ordinary node joining cluster phase, the residual energy and distance factor are considered to form the non-uniform cluster set of the network. Simulation results show that the algorithm has good performance in reducing and balancing energy consumption.

Cloud Computing Demand Elasticity Algorithm based on Ant Colony Algorithm

Background: In the recent era of technology, the traditional Ant Colony Algorithm (ACO) is insufficient in solving the problem of network congestion and load balance, and network utilization. Methods: This paper proposes an improved ant colony algorithm, which considers the price factor based on the theory of elasticity of demand. The price factor is denominated in the impact on the network load which means indirect control of network load, congestion or auxiliary solution to calculate the idle resources caused by the low network utilization and reduced profits. Results: Experimental results show that the improved algorithm can balance the overall network load, extend the life of path by nearly 3 hours, greatly reduce the risk of network paralysis, and increase the profit of the manufacturer by 300 million Yuan. Conclusion: Furthermore, the results show that the improved method has great application value in improving network efficiency, balancing network load, prolonging network life and increasing network operating profit.

Tree‐Based Multihop Routing Method for Energy Efficiency of Wireless Sensor Networks

In wireless sensor networks (WSNs), due to the limited energy of sensor nodes, how to design efficient hierarchical routing algorithms to balance network resources and extend network life is an important problem to be solved. Aiming at the problems such as random selection of cluster head, redundancy of working node, and construction of cluster head transmission path, which affect network energy consumption, this paper proposes a multihop routing algorithm based on path tree (MHRA‐PT) to optimize the network energy. Firstly, some nodes are those close to the base station and have large remaining energy which are selected to construct a cluster head set. Then, after clustering, each cluster is divided into different regions, and in each region, nodes with residual energy greater than the average residual energy of the cluster are selected as a working node. Finally, the cluster heads are sorted according to their distance from base station, and the next hop node is selected for each cluster head in turn until a path tree rooted at base station is formed completely, leading to data transmission from working node to base station. Simulation results show that the proposed algorithm can effectively reduce network energy consumption, balance network resources, and prolong network life cycle.

Comparison of LEACH and PEGASIS Hierarchical Routing Protocols in WSN

<p>Wireless Sensor Networks is a group of sensor nodes dispatched in a geographical area for a defined objective. These sensor nodes are characterized by limited capacity of communicating, computing and especially of energy. The performance of these WSN is resting on a good routing protocol, hence the need to choose the routing protocol able to satisfy the wsn's objectives, and to satisfy the common challenge to prolong network life time.</p><p>Several routing concepts have been proposed for the WSN, hierarchical routing is one of the most used concepts. It is divided into 3 types: cluster based routing, grid based routing and chain based protocol. In this paper, we are interested to Study, analyse and compare two popular routing protocols for Wireless sensor networks (WSNs), Low-Energy Adaptive Clustering Hierarchy (LEACH) using clusters based concept and Power-Efficient Gathering in Sensor Information System (PEGASIS) with chain based concept. The both protocols are simulated with Matlab simulator, in order to evaluate its performances against the different users and the WSNs objectives defined.</p>

Routing Protocol Design for Underwater Optical Wireless Sensor Networks: A Multiagent Reinforcement Learning Approach

Underwater optical wireless sensor networks (UOWSNs) have been attracting many interests for the advantages of high transmission rate, ultrawide bandwidth, and low latency. However, due to limited energy resources and highly dynamic topology caused by the water flow movement, it is challenging to provide a low-consumption and reliable routing in UOWSNs. To tackle this issue, in this article, we propose an efficient routing protocol based on multiagent reinforcement learning, termed as DMARL, for UOWSNs. The network is first modeled as a distributed multiagent system, and residual energy and link quality are considered into the routing protocol design to improve the adaptation to a dynamic environment and the support of prolonging network life. Additionally, two optimization strategies are proposed to accelerate the convergence of the reinforcement learning algorithm. On the basis, a reward mechanism is provided for the distributed system. The simulation results show that the DMARL-based routing protocol has low energy consumption and high packet delivery ratio (over 90%), and it is suitable for networks where the average number of neighbor nodes is less than 14.

RETRACTED ARTICLE: An intelligent cross layer security based fuzzy trust calculation mechanism (CLS-FTCM) for securing wireless sensor network (WSN)

Wireless sensor networks (WSNs) have a significant number of sensing nodes with computing and communication resources for data transmission. Many essential aspects such as computational power, storage capability, and energy consumption must be taken into account to use WSNs for data transmission. Previous research work developed a cross layer security based intrusion detection system (CLS-IDS) framework that has improved network performance, minimal energy consumption and to prolong network life, meeting QoS requirements. However, this CLS-IDS method cannot detect the faults happening at various places at the same time. So, this research work proposes the cross layer security based fuzzy trust calculation mechanism (CLS-FTCM) and also least overhead monitoring for WSNs by means of memory and energy demands to resolve the above issue. This cross-layer security was designed with the aid of a fuzzy logic-based calculation method for a protocol called cross-layer protocol, uses multiple parameters extracted by the exchange of inter-layer information in order to alleviate the effects of security hazards to the network of wireless sensors. In order to identify malicious nodes on the network, the fault monitoring system is carried out using enhanced convolutional neural network (ECNN) classifier and the confidential value is determined for the trust values. The proposed cross layer security based fuzzy trust mechanism (CLS-FLTCM) is highly efficient technique to ensure optimum safety in wireless sensor environment when compared to the previous techniques. The simulation results conducted using the NS-2 simulator and testing the safety performance of the proposed system demonstrates that this approach is better than the current detection precision techniques.

A new transmission strategy to achieve energy balance and efficiency in wireless sensor networks

Energy balancing and energy efficiency are very important in prolong network life. In wireless sensor networks, cooperative MIMO technology has become a research hotspot in recent years. The appropriate cooperative nodes can transmit data efficiently. Meanwhile, energy harvesting pays attention to the transmission process of network also. This paper presents the selection of cooperative nodes and cluster nodes, which offers energy balance. The node is not only the transmitter of information, but also the transmitter of energy. In addition, the paper also focuses on how to obtain the optimal energy efficiency with proposed resource allocation algorithm. Simulation results show that when cluster nodes and the cooperative nodes are more balanced, the selection algorithm realises the energy balance. The energy efficiency increases rapidly with the amount of transmitted power, and tends to be stable when it reaches 3 dBm. The cooperative MIMO technology is adopted to obtain higher network utility.

A new transmission strategy to achieve energy balance and efficiency in wireless sensor networks

Energy balancing and energy efficiency are very important in prolong network life. In wireless sensor networks, cooperative MIMO technology has become a research hotspot in recent years. The appropriate cooperative nodes can transmit data efficiently. Meanwhile, energy harvesting pays attention to the transmission process of network also. This paper presents the selection of cooperative nodes and cluster nodes, which offers energy balance. The node is not only the transmitter of information, but also the transmitter of energy. In addition, the paper also focuses on how to obtain the optimal energy efficiency with proposed resource allocation algorithm. Simulation results show that when cluster nodes and the cooperative nodes are more balanced, the selection algorithm realises the energy balance. The energy efficiency increases rapidly with the amount of transmitted power, and tends to be stable when it reaches 3 dBm. The cooperative MIMO technology is adopted to obtain higher network utility.

A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network

In this work, a new hybrid clustering routing protocol is proposed to prolong network life time through detecting holes and edges nodes. The detection process attempts to generate a connected graph without any isolated nodes or clusters that have no connection with the sink node. To this end, soft clustering/estimation maximization with graph metrics, PageRank, node degree, and local cluster coefficient, has been utilized. Holes and edges detection process is performed by the sink node to reduce energy consumption of wireless sensor network nodes. The clustering process is dynamic among sensor nodes. Hybrid clustering routing protocol–hole detection converts the network into a number of rings to overcome transmission distances. We compared hybrid clustering routing protocol–hole detection with four different protocols. The accuracy of detection reached 98%. Moreover, network life time has prolonged 10%. Finally, hybrid clustering routing protocol–hole detection has eliminated the disconnectivity in the network for more than 80% of network life time.

A multiple pheromone ant colony optimization scheme for energy-efficient wireless sensor networks

Ant colony optimization (ACO) is a well-applied technique to solve the real-time problem of discovering the energy-efficient routes to transmit the sensing information to the base station (BS). Traditionally, ACO incorporated wireless sensor networks used only one pheromone, i.e., minimum distance between the sensor nodes to discover the optimum route to the BS. The authors illustrated a multiple pheromone-based ACO technique known as multiple pheromone ant colony optimization (MPACO), for instance, distance between sensing nodes, their residual energy and number of neighbor nodes to ascertain an efficient route. MPACO enables the sensing nodes to transmit the sensing data to BS over optimal routes with economical energy consumption to achieve a prolonged network life span. The comprehensive evaluation reveals that MPACO proffers 20% more network lifetime than the current existing ACO technique, i.e., improved ACO. Moreover, MPACO shows a significant improvement of 300% in network life span than another existing fuzzy-based strategy, i.e., multi-objective fuzzy clustering algorithm.

Energy-Saving Algorithm and Simulation of Wireless Sensor Networks Based on Clustering Routing Protocol

An efficient and energy-saving algorithm, K-means and FAH (KAF), has been proposed to solve the problems of node energy constraints, short network cycle and low throughput in current wireless sensor networks. Network clustering is obtained by optimizing K-means clustering. Based on FAHP (Fuzzy Analytic Hierarchy Process) method, the cluster head selection is optimized considering the factors of node energy, distance from base station and energy efficiency of nodes. Based on the factors of transmission distance, energy and hop number, multi-hop routing is constructed to effectively reduce the energy consumption of nodes in data transmission. The simulation results show that compared with other protocols, KAF algorithm has obvious advantages in reducing node energy consumption, prolonging network life cycle and increasing network throughput. And under different routing protocol, the performances of the algorithm are verified. By adjusting the size of the candidate node set selection area, the reliability of data transmission of the long-distance node is increased, and the energy consumption load of the near-distance node is reduced. At the same time, the use of opportunistic transmission strategies increases the reliability of data transmission. The simulation results show that the proposed protocol can effectively reduce the energy consumption of nodes and prolong the network life cycle.