MMOHRA: Mobility aware multi-objective hybrid routing algorithm for Wireless Sensor Networks

Kulkarni, Nandkumar; Prasad, Neeli R.; Prasad, Ramjee

doi:10.1109/vitae.2014.6934460

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…Sensor nodes correspond to the selected CH. Moreover, sensor nodes communicate with their CHs or other sensors in the same cluster, whilst CHs communicate with the sink node or base station, as shown in Figure 1 [17].…”

Section: Sensor Nodes Clusteringmentioning

confidence: 99%

“…This ranking is illustrated in Table 3 for 500 sensor nodes. 𝐿𝐵 𝑖𝑗 × 𝐿𝑊 𝑗 × 𝐿𝐷𝑊 𝑖𝑗 + 𝐶𝑇 𝑖𝑗 × 𝑇𝑊 𝑗 × 𝑇𝐷𝑊 𝑖𝑗 (17) Where the objectives weights are assumed as: EW=0.23, CW=0.23, LW=0.23, SW=0.15, and TW=0.15.…”

Section: The Assumptions To Qos Proposed Modelmentioning

confidence: 99%

“…-Then, let (i) indicate the iteration number, which may be 25, 50, 75, and 100, and increases by 25 steps each time. Let (j) range from 1 to 4, indicating four algorithms Therefore, the RMDW equation for any algorithm will be presented as(17). ISSN: 2502-4752 Indonesian J Elec Eng & Comp Sci, Vol. 26, No.…”

mentioning

confidence: 99%

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Improving the quality of service in wireless sensor networks using an enhanced routing genetic protocol for four objectives

Moshref

Al-Sayyed

Al-Sharaeh

2022

IJEECS

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<span>Multi-objective algorithms are used to achieve high performance for quality of service (QoS) in wireless sensor networks (WSNs) is an important field for researchers because these algorithms improve two or more conflicting objectives and present the best trade-off between the conflicting objectives to solve multi-objective problems (MOPs). Previous research proposed an algorithm that relies on non-dominated sorting genetic algorithm 3 (NSGA-III), namely enhanced non-dominated sorting genetic routing algorithm (ENSGRA). This algorithm is used to optimise three objectives, which include number of worked sensors, energy consuming and node covering area. The fourth objective, which is node load balancing, is added in the current research. Such an addition aims to improve node distribution around cluster heads and decrease network congestion, thus decreasing energy consumption and increasing network lifetime. The ENSGRA algorithm is compared with multi-objective multi-step particle swarm optimisation (MOMSPSO), non-dominated sorting genetic algorithm 2 (NSGA-II), and NSGA-III. The proposed algorithm ENSGRA exceed to MOMSPSO, NSGA-II, and NSGA-III in the proposed QoS model in the final outcomes, as the proposed approach achieved (38%) average combination (optimisation) percentage. Which is the highest percentage over other methods.</span>

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Section: Sensor Nodes Clusteringmentioning

confidence: 99%

Section: The Assumptions To Qos Proposed Modelmentioning

confidence: 99%

See 1 more Smart Citation

Improving the quality of service in wireless sensor networks using an enhanced routing genetic protocol for four objectives

Moshref

Al-Sayyed

Al-Sharaeh

2022

IJEECS

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“…Design of routing algorithm has been proposed in [51][52][53] to optimize various conflicting objectives including energy conservation, packet delivery ratio, jitter, delay and robustness. The solution for energy conservation has also been searched in optimal cluster formation, for example in [54], a multi-objective optimization technique has been proposed to maximize energy conservation and to minimize the delay in data collection process.…”

Section: Energy Conservationmentioning

confidence: 99%

Wireless Sensor Network Optimization: Multi-Objective Paradigm

Iqbal¹,

Naeem²,

Anpalagan³

et al. 2021

Preprint

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Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planning and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

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“…The authors showed that their algorithm performed better than the Non-dominated Sorting Genetic Algorithm II (NSGA-II) [ 50 ]. Design of routing algorithm has been proposed in [ 51 – 53 ] to optimize various conflicting objectives including energy conservation, packet delivery ratio, jitter, delay and robustness. The solution for energy conservation has also been searched in optimal cluster formation, for example in [ 54 ], a multi-objective optimization technique has been proposed to maximize energy conservation and to minimize the delay in data collection process.…”

Section: Classification Of Optimization Objectivesmentioning

confidence: 99%

Wireless Sensor Network Optimization: Multi-Objective Paradigm

Iqbal

Naeem

Anpalagan

et al. 2015

Sensors

121

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Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

show abstract

MMOHRA: Mobility aware multi-objective hybrid routing algorithm for Wireless Sensor Networks

Cited by 8 publications

References 11 publications

Improving the quality of service in wireless sensor networks using an enhanced routing genetic protocol for four objectives

Improving the quality of service in wireless sensor networks using an enhanced routing genetic protocol for four objectives

Wireless Sensor Network Optimization: Multi-Objective Paradigm

Wireless Sensor Network Optimization: Multi-Objective Paradigm

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