Efficient Routing In Wireless Sensor Networks Research Articles

An optimal energy efficient routing in WSN using adaptive entropy bald eagle search optimization and density based adaptive soft clustering

Wireless Sensor Network (WSN) uses soft computing techniques to reduce task time consuming and unsolvable energy consumption problems. This study used soft-computing-based methods to demonstrate the best data transfer in WSN. Nodes in a network are initially clustered using density-based Adaptive Soft (DAS) clustering. Afterward, the cluster head (CH) is selected using a modified beetle swarm optimization technique. Distance, energy, trust, and throughput are all considered when deciding on the ideal CH. The node with the highest entropy for data transmission is then determined by calculating each node’s entropy weight values based on these factors. The CH carries out the data aggregation after the data collection from the sensor nodes. Finally, entropy value based bald eagle search (EBES) optimization with an adaptive entropy value is used to perform the finest energy efficient routing, a strategy for the best possible data transmission. The proposed approach attains improved performance than the compared existing approaches in terms of delay (6.5 ms), throughput (320.1 kbps), energy (1.92j), and packet delivery ratio (218.7%), the work provided is contrasted to the various current methods. The performance of the proposed approach is compared to existing approaches to prove its effectiveness, and it has been proven to perform better than the existing routing approaches.

Intelligent Deep Learning and Softmax Routing for Energy-Efficient Wireless Sensor Networks in Public Space Design

The increasing usage of several nodes to transfer the massive volume of data to the remotes in wireless sensor networks is a challenging task to reduce the loss. The high volumes of data transmission in wireless sensor networks (WSN) can surpass their capacity, resulting in congestion, latency issues, and packet loss. However, computational intelligence (CI) models can aid in managing and creating intelligent networks in WSN. The WSN congestion issues result in information loss and increased energy usage. CI-based models have been used to resolve this issue, reducing the latency. This paper proposes SoftMax Routing with Deep Neural Network (SRDNN) for efficient routing in WSN. This will route the data packets by choosing the high energy and lower load. It consists of two parts, such as the construction of the routing path, which determines the residual energy of the node. It is analyzed using SoftMax routing to decide whether the node is efficient in energy. The route request and reply established various paths between the source and destination. The path with minimum buffer space and maximum bandwidth is chosen in the optimal routing. The simulation results under the metrics such as energy consumption, data loss rate, throughput, and delay show the proposed model performance.

Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization‐based clustering approach and hybrid Moth‐flame cuttlefish optimization algorithm for efficient routing in wireless sensor network

SummaryThe selection of good rendezvous points (RPs) is a significant role for M2obile Sink (MS) in the wireless sensor network (WSN). For the mobile sink, the selection of RP is one of the major problems in WSN. The rendezvous points are only selected based on the local information, so the possibility of selecting an optimal sensor node as RP will be extremely low. The next problem is to find the mobile sink path which visits all the RPs. The above problem is comprehended by utilizing an optimization algorithm. In this paper, Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization based clustering approach and hybrid Moth‐flame cuttlefish optimization (MFO‐CFO) algorithm for efficient routing in WSN is proposed. Initially, the number of clusters is formed due to the Fuzzy C‐means (FCM) based Ant lion optimization (ALO) clustering approach. An Adaptive Neuro‐Fuzzy Inference System (ANFIS) based Particle swarm optimization (PSO) technique is presented for best cluster head (CH) selection. Here, residual energy (RE), node degree, and histories are considered as an input parameter to find the optimal cluster head. At last, we use hybrid MFO‐CFO techniques to find the minimum RPs which decreases energy consumption. The performance metrics of end to end (E2E) delay, energy consumption, channel load, throughput, bit error rate (BER), Network Life Time (NLT), packet delivery ratio (PDR), latency, jitter, and packet loss are computed using several nodes. The performance is compared with some existing algorithms such as Genetic Approach (GA), Fuzzy, PSO (Particle Swarm Optimization), Multiple Access Data Gathering using Mobile Sink for Path Constrained Environment (MADGAMSPCE).

Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey

Wireless Sensor Networks (WSNs) are among the most emerging technologies, thanks to their great capabilities and their ever growing range of applications. However, the lifetime of WSNs is extremely restricted due to the delimited energy capacity of their sensor nodes. This is why energy conservation is considered as the most important research concern for WSNs. Radio communication is the utmost energy consuming function in a WSN. Thus, energy efficient routing is necessitated to save energy and thus prolong the lifetime of WSNs. For this reason, numerous protocols for energy efficient routing in WSNs have been proposed. This article offers an analytical and up to date survey on the protocols of this kind. The classic and modern protocols presented are categorized, depending on i) how the network is structured, ii) how data are exchanged, iii) whether location information is or not used, and iv) whether Quality of Service (QoS) or multiple paths are or not supported. In each distinct category, protocols are both described and compared in terms of specific performance metrics, while their advantages and disadvantages are discussed. Finally, the study findings are discussed, concluding remarks are drawn, and open research issues are indicated.

Systematic Literature Review on Energy Efficient Routing Schemes in WSN – A Survey

In wireless sensor networks (WSN), routing is quite challenging area of research where packets are forwarded through multiple nodes to the base station. The packet being sent over the network should be shared in an energy efficient manner. It also considers the residual power of battery to enhance the network life time. Existing energy efficient routing solutions and surveys are presented but still there is a need for Systematic Literature Review (SLR) to identify the valid problems. This paper performs SLR for energy efficiency routing with 172 papers at initial stage. Next, 50 papers are shortlisted after filtration based on quality valuation and selection criteria by ensuring relevance with energy efficiency. Initially, we present literature that includes schemes for threshold sensitive, adaptive periodic threshold sensitive, power efficient, hybrid energy efficient distribution and low energy adaptive mechanisms. Result of systematic review reveals that consumption of energy is the most fundamental issue in WSN however, is not noticed by the researchers and practitioners where as it can contribute for the improvement of the energy efficiency. It also elaborates the weaknesses of the existing approaches which make them inappropriate for energy efficient routing in WSN.

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

SummaryThe problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system lifetime.

Real Time Meteorological Solar Data Based Modified Solar Aware LEACH

In hostile and non-accessible remote area, energy conservation plays vital role in the performance of Wireless Sensor Network (WSN). Shorter life of battery operated sensors tends to lower lifespan of the WSN which further degrades the dense network performance. In this paper, we propose the modified solar aware LEACH for efficient routing in WSN to maximize the network lifespan. This proposed scheme uses real time solar meteorological data for the implementation of solar aware LEACH (sLEACH), advance solar aware LEACH (A-sLEACH), improved solar aware LEACH (IS-LEACH).The proposed algorithm is simulated using MATLAB and performance is evaluated on the basis of data throughput, energy consumption and network lifetime which show improved performance than existing techniques.

OTSA: Optimized Time Synchronization Approach for Delay-based Energy Efficient Routing in WSN

Time Synchronization is one of the problems and still ignored problem in area of wireless sensor network (WSN). After reviewing the existing literatures, it is found that there are few studies that combinely address the problem of energy conservation, clustering, routing along with minimizing the errors due to time synchronization in sensor network. Therefore, this manuscript presents a delay-based routing which considers the propagation delay to formulate a mechanism for delay compensation in large scale wireless sensor network. The prime goal of this technique is to jointly address energy problems, time synchronization, and routing in wireless sensor network. The outcome of the proposed study was found to posse’s minimized communication overhead, minimized synchronization errors, lower energy consumption, and reduced processing time when compared with the existing standards of time synchronization techniques.

Energy Hole Minimization with Field Division for Energy Efficient Routing in WSNs

We analyze performance of famous cluster based routing protocols and identify the factors affecting energy consumption in wireless sensor networks (WSNs). From theoretical and experimental analysis, it is observed that communication distance and cluster node density are the major sources in the formation of energy and coverage holes. To overcome these deficiencies, we propose a new hybrid approach of static clustering and dynamic selection of cluster heads. We also conduct a comprehensive energy consumption analysis of our technique with selected existing ones. Simulation results show that the proposed technique is relatively better in terms of energy holes minimization and network lifetime prolongation.

Coverage and Connectivity Aware Neural Network Based Energy Efficient Routing in Wireless Sensor Networks

There are many challenges when designing and deploying wireless sensor networks (WSNs). One of the key challenges is how to make full use of the limited energy to prolong the lifetime of the network, because energy is a valuable resource in WSNs. The status of energy consumption should be continuously monitored after network deployment. In this paper, we propose coverage and connectivity aware neural network based energy efficient routing in WSN with the objective of maximizing the network lifetime. In the proposed scheme, the problem is formulated as linear programming (LP) with coverage and connectivity aware constraints. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage and connectivity aware routing with data transmission. The proposed scheme is compared with existing schemes with respect to the parameters such as number of alive nodes, packet delivery fraction, and node residual energy. The simulation results show that the proposed scheme can be used in wide area of applications in WSNs.