Routing in Wireless Sensor Networks Using Optimization Techniques: A Survey

Aghbari, Zaher Al; Khedr, Ahmed M.; Osamy, Walid; Arif, Ifra; Agrawal, Dharma P.

doi:10.1007/s11277-019-06993-9

Cited by 99 publications

(41 citation statements)

References 75 publications

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“…Many researchers have developed several clustering and routing approaches for enhancing the network lifetime. There are various optimization techniques proposed by the researchers to address the issues in energy consumption and network lifetime [16]. Reeta Bhardwaj et al [12] and Lipare et al presented [15] a Multi-objective algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

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Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

Samiayya

Ramalingam

2021

Preprint

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In wireless sensor network (WSN), the gateways far away from the base station (BS) uses the gateways nearer to the BS to forward the data. It causes heavy traffic to the gateways in proximity with the BS. They need to manage this heavy traffic load but it leads to additional energy consumption and reduction in network lifetime. In order to overcome these issues, loads around the gateways need to be balanced. In this paper, multi objective based spider monkey optimization (MOSMO) has been presented to balance the load and to improve the network lifetime through energy efficient routing and clustering. The objective functions such as routing fitness and clustering fitness have been considered for optimal routing and clustering. The routing fitness function is found by incorporating both the minimum distance traversed by the gateways and minimum number of the gateway hops. The clustering fitness function is the minimum fitness function of gateways. The fitness function of each gateway is computed based on both the mean load of gateways as well as the distance between gateways and BS. The performance of the proposed MOSMO based routing and clustering scheme is compared with the existing Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) based routing and clustering scheme. The QoS features such as delay, energy consumption, delivery ratio, throughput and network lifetime with various node density are analyzed. The proposed work is simulated using MATLAB. The results show that, the reduction in delay and energy consumption is about 18% and 17% respectively whereas improvement in delivery ratio, throughput and network life time is about 15%, 24% and 19% respectively when compared to the existing PSO and GWO methods.

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Section: Literature Reviewmentioning

confidence: 99%

“…As a result, gateways carry more traffic loads and the data communication consumes more percentage of energy [16], it exhausts more of their energy quickly [9]. It may cause a power gap around the BS station and may lead to the network disconnection and thereby BS may be disconnected from the remote gateway.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

Samiayya

Ramalingam

2021

Preprint

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“…Bhalaji, et al [10] details the "Performance Evaluation of Flying Wireless Network with Vanet Routing Protocol" and the Wilson, et al [11] proposes the "Improved multi objective data transmission using conventional route [12] proposes the "Multi-objective Taylor Crow Optimization Algorithm for Cluster Head Selection in Energy Aware Wireless Sensor Network" the author al et al [13] and kumar et al [14] present the survey on the "Routing in Wireless Sensor Networks Using Optimization…”

Section: Related Workmentioning

confidence: 99%

Optimized Multi-Objective Routing for Wireless Communication With Load Balancing

Sathesh¹

2019

JTCSST

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The rapid advancements in the wireless communication networks have increased the popularity of portable or mobile devices and the network framed with these mobile devices. These mobile networks framed using the volunteering portable devices are decentralized and have dynamic topologies experiencing sudden changes in the network structure. The main reason causing the topology changes are the limited energy availability of the device and their mobility. Improper trafficking of the tasks and improper selection of the portable devices causes maximum energy consumption resulting in the link failures and changes in the topology of the network. So the paper puts forward the hybridized optimization technique to handle the multi-objective problem faced by these decentralized networks. The proposed method is validated using the network simulator-2 to evince throughput, energy consumption and the network longevity achieved by the proposed method.

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“…The role of RL is to solve the problem of sequential decision tasks in different networking scenarios. There are many methods found in the literature to solve this problem by using i) Game theory [3], ii) Swarm [4], iii) a probabilistic technique [5], and many more [6][7]. However, all these approaches are associated with some advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

NetAI-Gym: Customized Environment for Network to Evaluate Agent Algorithm using Reinforcement Learning in Open-AI Gym Platform

Vidyadhar¹,

Nagaraj²,

Ashoka³

2021

IJACSA

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The growing size of the network imposes computational overhead during network route establishment using conventional approaches of the routing protocol. The alternate approach in contrast to the route table updating mechanism is the rule-based method, but this also provides a limited scope in the dynamic networks. Therefore, reinforcement learning promises a better way of finding the route, but it requires an evaluation platform to build a model synchronization between route and agent. Unfortunately, the de-facto platform for agent evaluation, namely Open-AI Gym, does not provide a suitable networking environment. Therefore, this paper aims to propose a networking environment as a novel contribution by designing a suitable customized environment for a network synchronically with Open-AI Gym. The successful deployment of the proposed network environment: NetAI-Gym provides a functional and practical result that can be used further to develop routing mechanisms based on Q-learning. The validation of the proposed NetAI-Gym is carried out with different nodes in the network regarding Episodes Vs. Reward. The experimental outcome justifies the validity of the proposed NetAI-Gym that it is suitable for solving network-related problems.

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Routing in Wireless Sensor Networks Using Optimization Techniques: A Survey

Cited by 99 publications

References 75 publications

Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

Optimized Multi-Objective Routing for Wireless Communication With Load Balancing

NetAI-Gym: Customized Environment for Network to Evaluate Agent Algorithm using Reinforcement Learning in Open-AI Gym Platform

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