Energy Efficiency Optimization in Wireless Sensor Network Using Proposed Load Balancing Approach

In today’s sensor network research, numerous technologies are used for the enhancement of earlier studies that focused on cost-effectiveness in addition to time-saving and novel approaches. This survey presents complete details about those earlier models and their research gaps. In general, clustering is focused on managing the energy factors in wireless sensor networks (WSNs). In this study, we primarily concentrated on multihop routing in a clustering environment. Our study was classified according to cluster-related parameters and properties and is subdivided into three approach categories: (1) parameter-based, (2) optimization-based, and (3) methodology-based. In the entire category, several techniques were identified, and the concept, parameters, advantages, and disadvantages are elaborated. Based on this attempt, we provide useful information to the audience to be used while they investigate their research ideas and to develop a novel model in order to overcome the drawbacks that are present in the WSN-based clustering models.

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“…Kaur discussed different difficulties related to clustering and various strategies or procedures created to overcome these difficulties [54].…”

Section: Clustering-based Routing Protocolsmentioning

confidence: 99%

Optimization of Routing-Based Clustering Approaches in Wireless Sensor Network: Review and Open Research Issues

et al. 2020

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“…Earlier, some of the techniques influenced by machine learning are already suggested for intrusion detection entail prolog of anomaly or unexpected, unreliable studies to the network and emulating a mugging situation. At this point, the data is categorized into two classes corresponding to most studies that may remain in these two areas however the measurements which are sporadic and also uncommon due to presumed attacks are considered as anomalies or intrusions [25,26,27]. Detection of outliers and unreliable measurements can be achieved by various machine learning algorithms, such as supervised, unsupervised and reinforcement learning algorithms by studying renowned malicious activities and vulnerabilities, numerous attacks and intrusions can be detected.…”

Section: Anomaly Intrusion Detection and Securitymentioning

confidence: 99%

“…The QoS enhancements guarantee of real-time events and data with high priority delivery [25]. Few of current efforts on using machine learning schemes to attain exact data integrity and QoS metrics determine various benefits such are specified below  Use of machine learning approaches can eliminate the need for flow-aware and stream-aware management techniques, as they can be trained to recognize different types of streams automatically.…”

Section: Quality Of Service Enhancement and Data Integritymentioning

confidence: 99%

A Study of Machine Learning in Wireless Sensor Network

Khan¹,

Samad²

2017

ijcna

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-Within this Paper, a concept of machine learning strategies suggested. In this investigation to address the design issues in WSNs is introduced. As can be viewed within this paper, countless endeavors have induced up to now; several layout issues in wireless sensor networks have been remedied employing numerous machine learning strategies. Utilizing machine learning based algorithms in WSNs need to deem numerous constraints, for instance, minimal sources of the network application that really needs distinct events to be tracked as well as other operational and non-operational aspects.

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“…A Wireless Sensor Network (WSN) is a congregation of nodes of imited capabilities disseminated in bulk to observe a phenomenon of interest [1,2,3]. We apply WSNs in a variety of applications: agricultural, medical, environmental, etc.…”

Section: Introductionmentioning

confidence: 99%

MA-LEACH: Energy Efficient Routing Protocol for WSNs using Particle Swarm Optimization and Mobile Aggregator

Elsayed¹,

Sharaf²

2018

ijcna

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-Routing protocols for wireless sensor networks pay a great attention to the limited resources of the nodes. As battery lifetime is a major concern, we consider our proposed model as an effort to prolong the network lifetime in harmony with WSNs' constraints. In this paper, we study LEACH routing protocol and its performance and propose an extension to it, MA-LEACH. We introduce a mobile aggregator (MA) which is a gadget adopted to mitigate the overhead on the cluster heads (CHs). In addition, we optimize the trajectory using particle swarm optimization (PSO). Hence, we adapt the TSP problem to our protocol to determine the optimal trajectory that a mobile aggregator could travel to visit every cluster head in the network. We simulate the proposed protocol in MATLAB and the results reveal that it outperforms LEACH in network lifetime and energy consumption. Also, we compare our findings with a recent extension to the LEACH called LEACH with fuzzy descriptors and with clustered heterogeneous sensor networks (CHSNs) with a mobile sink. The simulation results show that MA-LEACH surpasses LEACH with fuzzy descriptor and CHSNs.

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Energy Efficiency Optimization in Wireless Sensor Network Using Proposed Load Balancing Approach

Abstract: -Advancement in MEMS technology

Cited by 7 publications

References 22 publications

Optimization of Routing-Based Clustering Approaches in Wireless Sensor Network: Review and Open Research Issues

Optimization of Routing-Based Clustering Approaches in Wireless Sensor Network: Review and Open Research Issues

A Study of Machine Learning in Wireless Sensor Network

MA-LEACH: Energy Efficient Routing Protocol for WSNs using Particle Swarm Optimization and Mobile Aggregator

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