Energy Of Sensor Nodes Research Articles

An energy efficient dead reckoning localization for mobile Underwater Acoustic Sensor Networks

A wide range of marine applications require accurate positioning information of sensor nodes in Underwater Acoustic Sensor Networks (UASNs). In large-scale UASNs, locating the accurate position of the Mobile Sensor Node (MSN) is difficult without Global Positioning System (GPS) signal. Though the prediction of active MSN location is possible by the received information, predicting the location of a dead MSN without consuming sensor node energy is a challenging task for many decades, which has not gained any practical solution. In literature, the underwater sensor nodes are localized by sending and receiving the beacons from the source to a Base Station (BS) by consuming sensors energy. In this article, an Energy-free Heuristic Neural Network (HNN) Localization approach is proposed using Deep Learning (DL) technique to locate the dead MSN in a large-scale UASN. The proposed HNN leverages the Long- and Short-Term Memory (LSTM) technique to train huge data sets, and introduces Particle Swarm Optimization (PSO) with Repeated Iterative Technique (RFT) to reduce prediction errors during dead reckoning localization. The HNN localization achieves minimum localization error and high accuracy compared to the existing DL approaches. The localization is carried out at the Bay of Bengal, the Indian Ocean, and the Arctic Ocean. The simulation results of HNN model provide a good localization accuracy of 99% at the Bay of Bengal, 98% accuracy at the Indian Ocean, and 97% accuracy at the Arctic Ocean respectively. This model also achieves less time synchronization and low communication overhead without consuming the energy of MSN.

An Enhanced Spatial Correlation Framework for Heterogenous Wireless Sensor Networks

Background: Event detection and monitoring applications involve highly populated sensor nodes in Wireless Sensor Networks (WSNs). Dense deployment of nodes leads to correlated sensor observations in the spatial and temporal domain. Most of the previous works focused on constant sensing radii for spatially correlated sensor observations. However, in real time scenario, the sensor nodes may have variable sensing coverage areas, which comprise a Heterogeneous WSN. background: Spatial correlation model discussed in prior literature focused on Homogeneous sensing of sensor nodes. But, real time scenario the condition changes due to interferences obstructing sensing areas. Also, different manufacturers may provide different specifications for sensing areas, thus resulting into Heterogeneous sensing. Objective: To address this issue, we present an Enhanced Weighted Spatial Correlation Model for Heterogeneous sensor nodes in WSNs. Method: The mathematical framework considers the spatial coordinates of sensor nodes, the distances between the sensor nodes, and their sensing coverage. Furthermore, the correlation coefficient is calculated in terms of overlapping areas for randomly deployed nodes. Performance of the correlation model is evaluated and analyzed in terms of event distortion function. In addition to this, a macro and micro-zone concept is introduced, wherein sensor information is weighted for better event estimation at the sink node. Moreover, dynamic weighing of nodes like Inverse, Shepard’s and Gaussian distance weighing algorithms are simulated and analyzed for minimal event distortion. Over and above, the system performance is evaluated for different approaches considering reporting nodes with and without clustering of sensor nodes for macro and micro-zone concept. Simulation results for the Enhanced Weighted Spatial Correlation Model developed are obtained using MATLAB software. method: In order to evaluate the performance of the enhanced correlation model considering Macro and Micro-zone concept, simulations are carried out inMATLAB. Simulations are performed for ‚ trials and averaging of the values are finally used for analysis of results. Results: The comparative study shows an improved system performance in terms of minimal distortion obtained for non-clustered nodes; thereby reducing the computational complexity of cluster formation. Furthermore, the dynamic weighing algorithms outperform the existing fixed weighing algorithms for the correlation model with the lowest distortion function. Conclusion: Moreover, in the above algorithms, the event distortion gradually decreases and later becomes constant with the increase in the number of representative nodes. Hence, it illustrates that minimal distortion can be achieved by activating lesser number of representative nodes, thereby preserving the energy of other sensor nodes and increasing the lifetime of WSNs.

Research on the Characteristics and Application of Two-Degree-of-Freedom Diagonal Beam Piezoelectric Vibration Energy Harvester.

To overcome high periodic maintenance requirements, difficult replacement, and large application limitations of wireless sensor nodes powered by chemical batteries during the vibration control process of stiffened plates, a two-degree-of-freedom diagonal beam piezoelectric vibration energy harvester was proposed. Multidimensional energy harvesting and broadband work are integrated into one structure through the combined action of oblique angle, mass blocks, and piezoelectric beam. The mechanical model of the beam is established for theoretical analysis; the output characteristics of the structure are analyzed by finite element simulation; a piezoelectric energy harvesting experimental bench is built. The results show that: The structure has a wider harvesting band, multi-order resonant frequency, multi-dimensional energy harvesting, and higher output voltage and power than the traditional cantilever structures. The output performance of the specimens with 45° oblique angle, 5 g:5 g mass ratio, and 0.2 mm thickness of piezoelectric substrate is good in the frequency band of 10~40 Hz. When the excitation frequency is 28 Hz, the output voltage of the sextuple array structure reaches 19.20 V and the output power reaches 7.37 mW. The field experiments show that the harvester array can meet the requirements of providing auxiliary energy for wireless sensor nodes in the process of active vibration control of stiffened plates.

ReLeC: A Reinforcement Learning-Based Clustering-Enhanced Protocol for Efficient Energy Optimization in Wireless Sensor Networks

Wireless sensor networks (WSNs) are a widely studied area in the field of networked embedded computing. They are made up of several sensor nodes, which keep track of a variety of physical and environmental parameters, like temperature and humidity. The nodes are autonomous, self-configuring, and wireless. A significant problem in WSNs is that sensors in these networks consume a lot of energy. Energy consumption is a big issue when it comes to the deployment of sensor networks. The reason for this is the cost of operating a sensor node and the cost incurred due to energy consumption. Energy optimization is based on intelligent energy management. This paper presents a reinforcement learning-based and clustering-enhanced method. Reinforcement learning is a set of algorithms inspired by operant conditioning in animal behavior, and clustering-based methods have been extensively used for devising energy-efficient protocols. The proposed method is able to plan and schedule the nodes to ensure an extended network lifetime. In this work, we aim to assess and increase the efficiency of power consumption and reduce sensor node energy loss. The simulation results prove that the presented protocol effectively reduces the energy consumption of sensor nodes and ensures a prolonged lifetime of the sensor network.

Aggregation Technique Using Dynamic Cross-Propagation Clustering Algorithm in Wireless Body Sensor Networks

Wireless body sensor networks (WBSNs) are characterized by a large number of battery-powered wireless sensor nodes, and the most challenging aspects of WBSNs are sensor node energy consumption, delay, and security (communication and data) while maintaining regular wireless sensor network (WSN) capabilities. Data aggregation, as a common procedure in data gathering applications, can waste a lot of energy since sensor nodes may stay in the listen to state even when they are not receiving data during the data collection process. In this research work, introducing the Self-Executing-Dynamic Cross-Propagation Clustering (SE-DCPC) algorithm helps to improve the node energy consumption positively by turning nodes to the accessible state when not in use and waking them up when necessary. The algorithm is energy-based and uses a self-executing-based dynamic cross-propagation clustering system to send/receive scheduling data in the WSN platform. The energy level of the nodes is the most essential component in constructing network communication, in contrast to earlier clustering algorithms. The purpose of this proposed algorithm is to enhance the traditional notion of the clustering algorithm (location-based clustering), leading to the primary goal of enhancing the permanence of the wireless sensor network, which is to conserve network coverage, using self-executing DCPC clustering technology for location and power. The result of performance analysis of the SE-DCPC is achieved by simulation using two different communication processes of clustering and intelligence decision-making methods. The numerical results shows that SE-DCPC can effectively handle and maintain a high rate of network node energy consumption. The simulation result shows that the proposed approaches securely obtain the high throughput and very minimal delay at the client side, compared to existing clustering algorithm approaches. This SE-DCPC algorithm increased by 21.89% the communication medium lifetime and by 37% the energy consumption and reduced by 23.27% the overhead compared with existing clustering algorithms.

Delay-aware relay node selection for cluster-based wireless sensor networks

Maximizing the network lifetime through balancing energy and lowering the delay is a primary design goal of wireless sensor networks (WSNs). In WSNs, the sensor nodes task is to acquire the data from the area or point of interest and transmit it to the sink node via relay nodes timely. Relay nodes must dissipate some energy to perform this task. So, the relay nodes and cluster heads face the challenge of energy balancing. Hence, choosing the optimal cluster heads and relay nodes is necessary to balance the energy among the sensor nodes. In this context, this paper presents a novel clustering algorithm to balance energy by considering different metrics of WSNs, including distance, energy, link lifetime, and delay characteristics—using a relay node, etc.In this paper, a Hybrid Heuristic Data Aggregation (HHDAP) Protocol is used by combining particle swarm optimization (PSO) and improved Ant colony optimization (ACO).Through this HHDAP algorithm, the relay nodes are chosen based on a sensor node's distance, energy, link lifetime, and delay metrics. This work is simulated using NS2, and the performance of proposed and existing works are evaluated. The results show the performance improvements of the proposed work over the existing algorithms in terms of different quality of service metrics.

An Energy Efficient and Reliable Multipath Transmission Strategy for Mobile Wireless Sensor Networks.

Multipath data transmission is a key problem that needs to be solved urgently in wireless sensor networks. In this paper, sensor node failure, link failure, energy exhaustion, and external interference affect the stability and reliability of network data transmission. A multipath transmission strategy for wireless sensor networks based on improved shuffled frog leaping algorithm is proposed. A mathematical model of multipath transmission in wireless sensor networks is established. In the shuffled frog leaping algorithm, combined with the transition probability in the particle swarm optimization algorithm, random individuals in the subgroup are introduced to assist the search when updating the frog individual position, which improves the algorithm's ability to jump out of the local optimum and improves the quality of the optimization algorithm solution. The model is applied to multipath transmission in wireless sensor networks. Then, the shuffled frog leaping algorithm is used to update, divide, and reorganize the sensor nodes to select the optimal node to establish the optimal transmission path and improve the stability and reliability of the network. Simulation experiments show that the algorithm in this paper can ensure the reliability of data transmission, reduce the network packet loss rate and network energy consumption, and reduce the average delay of data transmission.

Adaptive clustering routing protocol for underwater sensor networks

In underwater sensor networks, the limited energy of sensor nodes and the difficulty of replacing the power supply means that one faulty node will severely affect the lifetime of the entire network. The design of energy-saving and efficient routing protocols is the key to prolonging the network lifetime. This paper describes an adaptive clustering routing protocol for underwater sensor networks based on multi-agent reinforcement learning. The proposed protocol models the network as a multi-agent system, and allows the nodes to select the global optimal route collaboratively through reinforcement learning. To reduce the probability of hotspots generation, an adaptive cluster head selection algorithm that does not incur any additional communication overhead and does not require consensus from surrounding nodes is proposed, which enables nodes to autonomously decide whether they can act as cluster heads using routing and environment information. Additionally, a biased reward function is designed to feedback the effect of the adaptive cluster head selection algorithm on the routing performance and to encourage the nodes to select the cluster heads as relays. Simulation results show that the proposed adaptive clustering routing protocol achieves higher routing efficiency, lower energy consumption, and longer network lifetime than existing approaches.

Routing Protocol for Wireless Sensor Networks Based on Archimedes Optimization Algorithm

One of the key design problems of wireless sensor networks is to reduce energy consumption and improve node survival. Because the battery in sensor nodes is difficult to replace and the energy is limited, the design of energy-saving routing protocol becomes very important. Aiming at the problems of limiting node energy and shortening network lifetime in wireless sensor networks, a WSNs routing protocol based on improved archimedes optimization algorithm (IAOAR) is proposed in this paper. Firstly, in the cluster establishment phase, the protocol defines different fitness functions according to the energy, number and distance of sensor nodes to select the appropriate initial cluster. Then the fitness value of the node is calculated, and the weight is dynamically updated according to the number of iterations to improve the optimization ability of AOA algorithm. Virtual force is introduced to adjust the selected cluster head position to ensure the selection of optimal cluster heads, reduce the transmission energy consumption of common sensor nodes. In the data transmission stage, combined with the improved ant colony algorithm, the shortest transmission path to the sink node is constructed to reduce the energy consumption of long-distance data transmission at the cluster head. The simulation results showed that compared with LEACH, E-LEACH, LEACH-ANT and MAXLEACH protocols, the network life cycle of the protocol proposed in this paper was improved by 140.55%, 107.09%, 99.24% and 96.76% respectively. It effectively reduced the speed of node death, balances the network energy consumption and prolonged the network lifetime.

Dynamic-Detection-Based Trajectory Planning for Autonomous Underwater Vehicle to Collect Data From Underwater Sensors

Marine science and Internet of Underwater Things applications rely significantly on collecting data from underwater sensors. Data collection using long-distance underwater acoustic communications consumes a lot of energy in underwater sensor nodes, which are powered by batteries. To achieve low-energy consumption, we can use the autonomous underwater vehicle (AUV) to move close to sensor nodes and exploit the short-range and high-rate communications. Most of the existing AUV-based data collection schemes consider the scenarios having the knowledge of node positions, where the cruising trajectory can be computed before the AUV’s departure. These schemes cannot apply to some scenarios such as turtle tracking for a certain sea area having no position information. To this end, we first propose a planning-while-detecting approach to dynamically detect the sensors on turtles and adjust the AUV cruising direction to collect data. To further improve data efficiency under the energy limit of the AUV, we group the sensors that can share the same trajectory using their detected directions. A grouping-based dynamic trajectory planning (GDTP) is then proposed to determine the next cruising direction that can visit the group of sensors having the largest amount of data and demanding the least cruising energy at the risk of detection errors. Simulation results show that GDTP achieves significantly higher data collection efficiency than the existing trajectory planning algorithms in dynamic scenarios, and as the communication range increases, it can even outperform the existing algorithms with node locations.

Sensors Energy Optimization for Renewable Energy-Based WBANs on Sporadic Elder Movements.

The world is advancing to a new era where a new concept is emerging that deals with “wirelessness”. As we know, renewable energy is the future, and this research studied the integration of both fields that results in a futuristic, powerful, and advanced model of wireless body area networks. Every new emerging technology does have some cons; in this case the issue would be the usage of excess energy by the sensors of the model. Our research is focused on solving this excessive usage of energy to promote the optimization of energy. This research work is aimed to design a power-saving protocol (PSP) for wireless body area networks (WBANs) in electronic health monitoring (EHM). Our proposed power-saving protocol (PSP) supports the early detection of suspicious signs or sporadic elder movements. The protocol focuses on solving the excessive energy consumption by the body attached to IoT devices to maximize the power efficiency (EE) of WBAN. In a WSNs network, the number of sensor nodes (SNs) interact with an aggregator and are equipped with energy harvesting capabilities. The energy optimization for the wireless sensor networks is a vital step and the methodology is completely based on renewable energy resources. Our proposed power-saving protocol is based on AI and DNN architectures with a hidden Markov model to obtain the top and bottom limits of the SN sources and a less computationally challenging suboptimal elucidation. The research also addressed many critical technical problems, such as sensor node hardware configuration and energy conservation. The study performed the simulation using the OMNET++ environment and represent through results the source rate to power critical SNs improves WBAN’s scheme performance in terms of power efficiency of Sporadic Elder Movements (SEM) during various daily operations.

An energy-balanced head nodes selection scheme for underwater mobile sensor networks

Underwater sensor networks system is a promising technology for smart ocean monitoring applications. To address the imbalance problem of network overhead and limited energy of underwater sensor nodes, this paper presents a CH–SH Selection and Sink Path-planning (CSSP) scheme to collect vast amount of data from heterogeneous sensor nodes. The scheme firstly establishes a three-layer network structure model for underwater mobile sensor networks (UMSN) based on multi-mode communication mechanism and provides energy-balanced head nodes (cluster head and sub-cluster head nodes) selection algorithm based on particle swarm iterative optimization. Then, the optimal global-path-plan of mobile sink is proposed to visit all the head nodes to collect data, reduce the multi-hop underwater acoustic transmission distance of relay nodes, and avoid the energy hole problem. Lastly, the upper-layer multi-hop network of UMSN is designed to remotely control local-path-plan over mobile sink, in order to implement joint planning of global and local paths of mobile sink. Simulation results verified that the proposed CSSP scheme outperformed 11%, 16% and 22% over three typical protocols in terms of nodes energy consumption, CSSP was 9%, 12% and 19% lower than three typical protocols in terms of SD of energy consumption, packet delivery ratio of CSSP was 8%, 10% and 12% higher than three typical protocols. The scheme could significantly balance energy and reduce packet loss rate.

AE-LEACH: An Incremental Clustering Approach for Reducing the Energy Consumption in WSN

In recent years, optimizing sensor nodes energy appears to be a strong element in improving the performance of the Wireless Sensor Network (WSN). In the focus of these different approaches, substantial outputs regarding energy optimization are revealed by applying clustering-based object tracking techniques. Clustering is achieved through an enhanced adaptive LEACH algorithm, named Advanced Efficient Low-Energy Adaptive Clustering Hierarchy (AE-LEACH) algorithm. The cluster head (CH) is adopted through firm descriptors like residual energy and reserve distance from the Base Station (BS) in the clustering process. The selected CH predicts the target trajectory using Particle Filter (PA) algorithm and selectively activates from the next round of sensor nodes to continuously track the target. The target nodes’ predictable locations are routed to the base station through the backbone that lies in the cluster head. Gini index is used to assess the energy efficiency of the clustering algorithm . Thus, the proposed performance metrics stated on network timespan, residual energy, energy consumption, and Gini index for remaining energy and achieves significantly lower Gini index of upto 78.38%, 86.11%, and 85.92% as compared to K means clustering , HEED clustering, and LEACH clustering algorithm, respectively. Moreover, time accuracy is directly proportional to the network lifetime and energy consumption.

An energy‐efficient clustering algorithm combined interval type‐2 fuzzy logic and dual‐super‐cluster‐head mechanism for homogeneous wireless sensor networks of mobile sink

SummaryIn recent trends, wireless sensor network (WSN) is often used in harsh environments. When WSNs work, they need to deploy a large number of sensor nodes in the monitoring area. Due to the limited resources carried by the sensor nodes, the monitoring process of WSN will be limited by the energy of the sensor nodes. An energy‐efficient clustering algorithm combining interval type‐2 fuzzy logic and dual‐super‐cluster‐head (IT2FL‐DSCH) mechanism is proposed, aiming at reducing and balancing the energy consumption of sensor nodes. Considering the four fuzzy parameters, that is, cluster heads' (CHs') battery power, CHs' centrality, base station's (BS's) mobility, and the distance from CHs to BS, interval type‐2 fuzzy logic (IT2FL) is used to select the appropriate super cluster head (SCH). And SCH is the only way to communicate with the mobile BS, thus effectively using bandwidth. Then, the DSCH mechanism to balance the energy consumption of SCH is introduced in detail. In addition, we utilize mathematical derivation to obtain the supremum of the energy consumption of the SCH algorithm under different network distribution. Finally, the MATLAB simulation results show that this protocol outperforms low energy adaptive clustering hierarchy (LEACH), LEACH‐centralized (LEACH‐C), distributed energy efficient clustering (DEEC), WSNs using deep‐Q‐networks (DQN), and two‐tier distributed fuzzy logic based protocol (TTDFP) in terms of sensor nodes energy saving, network stability, and network lifetime.

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.

Efficient routing and performance amelioration using Hybrid Diffusion Clustering Scheme in heterogeneous wireless sensor network

SummaryHeterogeneous wireless sensor networks (HWSNs) have a wide range of application in the contemporary world particularly in sensing and hunting of events without human interference. Due to this limitation of network resources and energy of sensor nodes, the contention of available resources for transmission will increase the energy consumption in the network and degrade the performance of Quality of Service. During dense traffic conditions, sensor nodes in close proximity to base station (BS) faces severe bottleneck resulting in congestion and energy hole problem. The efficient utilization of energy and packet loss is due to link break and by traffic analysis attack is a primary threat in HWSN applications. To sustain maximum energy in the network, two important aspects are considered, namely, flawless selection of path and concise clustering. This paper describes about a multi‐objective‐based Hybrid Diffusion Clustering Scheme (HDCS) through Jellyfish routing protocol with Time Division Multiple Access (JRP‐TDMA)‐based routing method in HWSN. Cluster head is elected based on multiple objectives such as residual energy, cost, and distance. Once CH selection is done, JRP‐TDMA based on available timeslot handpicks the best path for transmitting to BS. The proposed protocol is analyzed, simulated, and performance results are correlated with existing protocols namely, diffusion clustered routing protocol (DCRP), efficient unequal clustering (EEUC), Markov chain model‐based optimal cluster head (MOCH), and genetic algorithm (GA). The number of alive sensor nodes of proposed method has increased by 24.4% when compared with other approaches.

Efficiency of temporal sensor data compression methods to reduce LoRa-based sensor node energy consumption

PurposeMinimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node.Design/methodology/approachIn this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured.FindingsEnergy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods.Originality/valueThis paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms.

Achieving Longevity in Wireless Body Area Network by Efficient Transmission Power Control for IoMT Applications

The application of tiny body sensors to collect, process, store, analyze, and retrieve medical information from a human body is a part of the Internet of Medical Things (IoMT). IoMT helps to monitor and track human vital health parameters, predict disease, notify the patients and the health care professionals with relevant data for analyzing the problems before they become severe and for earlier invention. By 2022, more than 60 % of IoT applications will be health-related. The convergence of biomedical sensors, wireless body area networks (WBAN), Information technology, and bioinformatics will help improve the efficiency of saving human lives. In a WBAN, network longevity is challenging because of the limited supply of low power battery energy in tiny body sensor nodes. Here, we proposed an energy-efficient transmission power control (TPC) algorithmto extend the network lifetime in IoMT networks for healthcare applicationsby eliminating the transceiver overhearing problem. In TPC, human tissue resistivity properties are considered to adjust the transmission power, which reduces the communication power and extends the network lifetime. The simulation results show that network power consumption is reduced by 35%.

An Energy Efficient Clustering Protocol Using CM-YSGA Optimization Approach in WSN

Abstract: While dealing with the wireless sensor networks (WSNs), one of the greatest factors that must be taken into consideration is energy consumption of nodes. One of the most effective common way of preserving energy in sensor nodes is clustering technique in which CH selection is of great importance. In this manuscript, an improved energy efficient clustering protocol is proposed in which chaotic mapping algorithm is clubbed along with the advanced variant of Yellow Saddle Goatfish Algorithm (YSGA). The main objective of the proposed model is to reduce the energy consumption of nodes which ultimately prolongs the lifespan of wireless network. Initially, cluster are formed in the network by selecting the nodes randomly. After this, four important parameters which included, residual energy, distance to sink, average distance to CH neighbor node and delay are taken into consideration and on the basis of these factors’ fitness value is calculated by the chaotic-YSGA technique. The node whose fitness value came out to be highest of all other nodes, is selected as the CH in that particular cluster. In addition, the Huffman data compression technique is used in the proposed system to reduce energy consumption in nodes. Finally, the performance of the proposed Chaotic-YSGA model is analyzed and compared with conventional energy efficient models in MATLAB software in terms of alive node, dead nodes, throughput, residual energy, FND, HND and LND. Keywords: WSN, Energy Efficiency, CH selection, Optimization Algorithms.

Energy Balance Routing Protocol for Wireless Sensor Networks Based on Fuzzy Control Strategy

The existing routing protocols for wireless sensor networks were not reasonable in design, which limited their application. Most of the existing studies did not take into account the energy consumption of the network and the balanced use of the energy of sensor nodes, which led to the unsatisfactory application effect of wireless sensor networks in some fields. Therefore, from the perspective of energy balance in wireless sensor networks, this paper proposed a construction method of an energy balance routing protocol in wireless sensor networks based on a fuzzy control strategy. Firstly, based on the analysis of the basic composition of wireless sensor networks and the structure of sensor nodes, this paper expounded the basic process of wireless data transmission and summarized the classification and characteristics of routing protocols in wireless sensor networks from different angles. Secondly, according to the node data transmission characteristics of wireless sensor networks, the energy balance use model of sensor nodes was established, and the design method of the energy balance routing protocol based on fuzzy control strategy was proposed, and the data transmission link was optimized. Finally, through experimental comparative analysis, the results showed that the energy balanced routing protocol proposed in this paper can effectively realize the energy balanced use of the network data transmission process. Compared with other common routing protocols, the wireless sensor network routing protocol proposed in this paper can not only improve the data transmission efficiency and reduce the data redundancy but also save energy consumption and prolong the network running time. The design method of routing protocol proposed in this paper will be conducive to the optimization and application of routing protocol in wireless sensor networks and provide a theoretical basis for the related research of wireless sensor networks.