Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches

Rajpoot, Prince; Dwivedi, Pragya

doi:10.1007/s11276-018-1812-2

Cited by 54 publications

(16 citation statements)

References 67 publications

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“…A fuzzy, three-tier multi-hop and unequal clustering-enabled optimized routing scheme is presented to form energy-efficient networking infrastructures [32]. Multiple attributes based load balanced and optimized clustering approach is proposed to enhance the lifetime of the operational WSNs [33]. However, complexity is a common issue associated with these schemes.…”

Section: Literature Reviewmentioning

confidence: 99%

A Reliable Communication and Load Balancing Scheme for Resource-Limited Networks

et al. 2020

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Sensor-cloud infrastructure provides a storage platform for the massive sensed data, that is flexible and re-configurable, for various application areas which are monitored through the resourcelimited networks such as wireless sensor networks (WSNs), ad hoc networks, and Internet of things (IoT). Due to their overwhelming characteristics, these networks are used in different application areas to assist human beings in their daily-life activities. However, these networks have different challenging issues such as reliability in communication and processing, storage of the massive data, efficient utilization of on-board battery, maximum lifetime achievement, minimum possible average packet loss ratio, and reliable routing mechanisms. Although various communication and load balancing mechanisms have been proposed in the literature to resolve this issue, however, these schemes are either application specific or overlay complex. In this paper, a reliable communication and load balancing scheme for the resource-limited networks is presented to resolve these issues, particularly with available resources. To achieve these goals, the proposed scheme bounds every sensing device C i to compute the transmission capabilities of its neighboring devices that is residual energy E r , hop count H c , round trip time (RT T i), and processing cost. Initially, to guarantee reliable wireless communication, a source device prefers a neighboring device C i with minimum H c value over those having maximum H c values. Moreover, this scheme bounds every device C i to find four shortest & reliable paths and forward maximum packets on two of these paths preferably on the most reliable and optimal route. Therefore, unlike the traditional shortest path scheme, devices C i reside on these paths do not deplete their on-board battery more rapidly than others. To further improve the reliability of the proposed scheme, the assigned weight-age factors are fine-tuned if one or two of the neighboring devices C i consume 80% of their on-board battery, that is now maximum weight-age is assigned to the residual energy E r and minimum to H c value respectively. Simulation results show the exceptional performance of the proposed reliable communication and load balancing scheme against the field-proven schemes in terms of average packet delivery ratio, average throughput, end-to-end delay, and overall network lifetime.

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Section: Literature Reviewmentioning

confidence: 99%

A Reliable Communication and Load Balancing Scheme for Resource-Limited Networks

et al. 2020

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“…In [17], researchers presented an MLB algorithm and discussed its properties in terms of costs and gains. Their objective is to minimize the load standard deviation (LSD) to distribute the load evenly over the network.…”

Section: Related Workmentioning

confidence: 99%

“…In the second step, for every candidate edge-UE, e (17) where RBUR τ l is the estimated current load of the neighbouring cell.…”

Section: ) Operator Utility Calculationmentioning

confidence: 99%

Dynamic Mobility Load Balancing for 5G Small-Cell Networks Based on Utility Functions

et al. 2019

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Deployment of small cells was introduced to support high data rate services and expand macro cell coverage for the envisioned 5G networks. A small cell network, which has a smaller size, along with the user equipment (UE) mobility, frequently undergoes unbalanced load status. Consequently, the network performance is affected in terms of throughput, increasing handover failure rate, and possibly higher link failure rate. Hence, load balancing has become an important part of recent researches on small cell networks. Mobility Load Balancing (MLB) involves load transfer from an overloaded small cell to under-loaded neighbouring small cells for the more load-balanced network. This transfer is performed by adjusting the handover parameters of the UEs according to the load situations of the small cells in the vicinity. However, inaccurate adjustment of parameters may lead to inefficient usage of network resources or degrade the Quality of Service (QoS). In this paper, we introduce a Utility-based Mobility Load Balancing algorithm (UMLB) and a new term named load balancing efficiency factor (LBEF). The UMLB algorithm considers the operator utility and the user utility for the MLB-based handover process. While LBEF is proposed to order the overloaded cells properly for the MLB algorithm operation. The simulation results show that the UMLB minimizes standard deviation with a higher average-UE data rate when compared to existing load balancing algorithms. Therefore, a well-balanced network is achieved. INDEX TERMS Small-cell network, mobility load balancing, measurement reporting, handover, cell individual offset, throughput. KHALED M. ADDALI (M'18) received the B.S. degree in electrical engineering from Mergheb University, Khoms, Libya, in 2004, and the M.S. degree in electrical engineering from Concordia University, Montreal, Canada, in 2012. He is currently pursuing the Ph.D. degree in electrical engineering with the École de Technologie supérieure (ÉTS), Université du Québec. From 2005 to 2008, he was an Operating Engineer with General Electricity Company of Libya (GECOL), Khoms. His research interests include resource and mobility management and the development of user association and load balancing techniques for 5G small-cell networks.

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“…The meta-heuristic algorithm like ABC, when implemented as a standalone algorithm, is considered to be lacking in terms of local search [6]. At this juncture, bacterial Foraging Algorithms that could ensure maximum exploitation is required for integrating it with the standard ABC algorithm [7]. This possibility of establishing the tradeoff between exploitation and exploration is essential for optimizing the parameters like network lifetime, energy consumptions and delay under the event of cluster head selection in the network [8].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm for Optimized Clustering in Wireless Sensor Network

Rambabu¹,

Reddy²,

Sengathir³

2019

IJITEE

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The emerging ubiquitous nature of wireless sensor networks has made it suitable and applicable to a diversified number of vital applications that include environment surveillance, health monitoring using implantable sensors, weather forecasting and other plethora of contexts. The critical issues such as computation time, limited memory and energy are more common due to the tiny sized hundred and thousands of sensor nodes existing in the networks. In this context, the network lifetime completely depends on the potential use of available resources. The process of organizing closely located sensor nodes into clusters is convenient for effective management of cluster and improving the lifetime of the complete network. At this juncture, swarm intelligent and evolutionary algorithms the pertains to the problem of NP-complete is determined to achieve a superior optimal solution. In this paper, a Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm-based Optimized Clustering (HABC-BFA-OC) is proposed for achieving enhanced network lifetime in sensor networks. In this proposed HABC-BFA-OC technique, the benefits of Bacterial Foraging Optimization is included for improving the local search potential of ABC algorithm in order to attain maximum exploitation and exploration over the parameters considered for cluster head selection. The simulation experiments of the proposed HABC-BFA-OC technique confirmed an enhanced network lifetime with minimized energy consumptions during its investigation with a different number of sensor nodes.

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Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches

Cited by 54 publications

References 67 publications

A Reliable Communication and Load Balancing Scheme for Resource-Limited Networks

A Reliable Communication and Load Balancing Scheme for Resource-Limited Networks

Dynamic Mobility Load Balancing for 5G Small-Cell Networks Based on Utility Functions

A Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm for Optimized Clustering in Wireless Sensor Network

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