Load Balanced Routing for Lifetime Maximization in Mobile Wireless Sensor Networks

Khalid, Saifullah; Masood, Ashraf; Hussain, Faisal; Abbas, Haider; Ghafoor, Abdul

doi:10.1155/2014/979086

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Thus, Quality LBR algorithm (QLBR) is an extension of the Load Balancing Routing (LBR) algorithm, which is in turn an improvement of the AODV. Concerning the AODV routing protocol that uses only the number of hop as the optimal criterion for evaluating the path and in the LBR algorithm [10], the quality of link is not taken into account. Otherwise, the QLBR algorithm integrate a multi-metric strategy for mobile sensor networks (as shown in Fig.…”

Section: A Principalmentioning

confidence: 99%

Multi Path Routing Based Energy-Efficient for Extending Mobile Wireless Sensor Networks Lifetime

Tolba¹,

Lorenz²

2019

jcm

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Energy is a challenge in wireless sensor networks because of its effects on several fundamental functions of the network, such as data collection, communication and routing. Therefore, energy efficiency is a fundamental factor determining the performance and lifetime of the network. For this, the saving energy achieved in the WSN have led to many research projects on various aspects such as energy efficiency, energy management or energy control. In order to complete the data collection process, better multipath routing, which balances the traffic load is an additional challenge for WSN. With this in mind, we are developing energy efficiency and traffic load balanced strategies for the multipath routing algorithm. We first describe the principle of the proposed algorithm based on several metrics. Simulations were performed to check the performance of the proposed algorithm. The proposed algorithm QLBR achieves an energy gain of 11% compared to the LBR algorithm and extended network lifetime by about 9%.

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Section: A Principalmentioning

confidence: 99%

Multi Path Routing Based Energy-Efficient for Extending Mobile Wireless Sensor Networks Lifetime

Tolba¹,

Lorenz²

2019

jcm

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“…In WSN, sensors communicated with each other using wireless signal and the data transmitted by a sensor will received by all neighbors, thus the communication overhead is the major consumption of sensor's energy. Data aggregation is one of the most important methods to reduce the communication overhead and many schemes had been proposed for decreasing the redundant transmissions [2,3].…”

Section: Wireless Sensor Network (Wsn)mentioning

confidence: 99%

Cluster Based Compressed Data Aggregation and Routing in WSN

Jothi¹,

Chandrasekaran²

2016

IJIES

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Due to non uniform distribution of nodes, there is an imbalance of energy consumption among the nodes in cluster-based wireless sensor networks. Based on this problem, we propose an efficient data aggregation tree for communication and routing based on the previous clustering architecture. Here based on the parameters such as Residual Energy, Node Density and Load cluster heads are elected using fuzzy logic technique. The inter-cluster routing algorithm balances the energy consumption among cluster heads by adjusting inter-cluster energy consumption. Then data compression is applied for reducing the energy consumption using data correlation model.

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“…Our contributions in this paper are energy‐awareness and load‐balancing combination and performance improvement. Note that Kacimi et al and Khalid et al proposed energy‐efficient load‐balanced protocols for wireless sensor networks (WSNs). Although there are important similarities between WSNs and ad hoc networks, there are also fundamental differences.…”

Section: Introductionmentioning

confidence: 99%

“…In our paper, we consider a more general unicast routing protocol, which can be used in the network where any node may be the destination node dynamically. While in , the destination node is the only sink that is fixed in WSNs. There are also some recent research on energy efficiency and delay efficiency in M2M networks , but most of them focused on Long Term Evolution (LTE), LTE‐A and 5G communication systems.…”

Section: Introductionmentioning

confidence: 99%

An energy‐aware load‐balanced routing protocol for ad hoc M2M communications

Zhang

Anpalagan

Guo

et al. 2015

Trans. Emerging Tel. Tech.

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Machine‐to‐machine (M2M) communications will become ubiquitous in the future Internet of Things, and it is important that current wireless networks are developed to support M2M communications. In this paper, we propose an energy‐aware load‐balanced routing protocol for ad hoc M2M communications. Routing is a challenging issue in ad hoc M2M communication networks due to a large number of machine‐type communication (MTC) devices and a lack of infrastructure. Wireless nodes that are used as MTC devices are powered by batteries only. This makes energy efficiency a major issue as each MTC device in the network acts as a router and consumes energy in the routing process. The energy consumption should be reduced to prolong the lifetime of the batteries. Most existing research on energy‐aware routing only focuses on minimising the total energy consumption from the source to the destination device, regardless of the performance degradation caused by the heavy traffic load in the network. The unbalanced traffic load distribution among devices may cause more packet loss and quick battery depletion due to the frequent usage. Unlike existing research, we classify the devices as energy‐critical and load‐critical devices and protect them from participating in the routing frequently. Simulation results demonstrate that the proposed routing protocol significantly increases the packet delivery ratio, reduces delay and prolongs the network lifetime compared to ad hoc on‐demand distance vector and dynamic source routing in the heavy load network. Energy‐aware load‐balanced can also provide better performance compared with delay‐aware MChannel and energy‐aware MChannel protocols when devices have very low energy levels in congested networks. Copyright © 2015 John Wiley & Sons, Ltd.

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Load Balanced Routing for Lifetime Maximization in Mobile Wireless Sensor Networks

Cited by 5 publications

References 23 publications

Multi Path Routing Based Energy-Efficient for Extending Mobile Wireless Sensor Networks Lifetime

Multi Path Routing Based Energy-Efficient for Extending Mobile Wireless Sensor Networks Lifetime

Cluster Based Compressed Data Aggregation and Routing in WSN

An energy‐aware load‐balanced routing protocol for ad hoc M2M communications

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