Bio-inspired Algorithm for Multi-objective Optimization in Wireless Sensor Network

Raychaudhuri, Anindita; De, Debashis

doi:10.1007/978-981-15-2125-6_12

Cited by 16 publications

(9 citation statements)

References 107 publications

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“…It should be noted that most of these studies attempt to maximize data delivery rate and network lifetime and minimize packet delivery delays and energy consumption in network energies. Therefore, in this study, we compared the predictions of some previous patterns such as ZAODV, ZAG [12], Standard ZigBee, and NAR [13] in terms of network latency, delivered packages, and delivery rates on the network. To this end, we increase the number of proposals in the network to 50 in order to compare, in equal terms, the proposals with previous ones.…”

Section: Comparison Of the Suggested Methods With Previous Onesmentioning

confidence: 99%

WSN Routing Protocol Using a Multiobjective Greedy Approach

Nabavi

Eraghi

Torkestani

2021

Wireless Communications and Mobile Computing

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Due to the widespread use of communication networks and the ease of transmitting and gathering information through these networks, wireless sensor networks (WSN) have become increasingly popular. Usability in any environment without the need for environmental monitoring and engineering of these networks has led to their increasing usage in various fields. Routing information from the sensor node to sink, so that node energy is consumed uniformly and network life is not reduced, is one of the most important challenges in wireless sensor networks. Most wireless networks have no infrastructure, and embedded sensor nodes have limited power. Thus, the early termination of the wireless node’s energy based on the transmission of messages over the network can disrupt the entire network process. In this paper, the object is designed to find the optimal path in WSN based on the multiobjective greedy approach to the near optimal path. The proposed model is presented in this method to transfer sensed data of the sensor network to the base station for the desired applications. In this method, the sensor nodes are identified as adjacent nodes based on their distance. The energy of all nodes initially is approximately equal, which decreases with the transfer of information between the nodes. In this way, when a node senses a message, it checks several factors for transmitting information to its adjacent nodes and selects the node with the largest amount of factors as the next hop. The simulation results show that the energy consumption in the network grids is almost symmetrically presented, and the network lifetime is reduced with a gentle slope that provides optimum energy consumption in the networks. Also, the packet transmission delay in the network reaches 450 milliseconds for the transmission of information between 15 nodes and 650 connections. Besides, network throughput increases by approximately 97%. It also shows better performance compared to other previous methods in terms of evaluation criteria.

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Section: Comparison Of the Suggested Methods With Previous Onesmentioning

confidence: 99%

WSN Routing Protocol Using a Multiobjective Greedy Approach

Nabavi

Eraghi

Torkestani

2021

Wireless Communications and Mobile Computing

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“…The vast activity noted in the literature is a clear representation of the technical advances attained lately with bioinspired computation applied to Big Data. Indeed, manifold domains have capitalized bio-inspired computation in databased applications, including energy [198,199], transport and mobility [60], health [200], industry [201,202], agriculture [203], cyber-physical systems [20], social networks [204][205][206] or sensor networks [207], among many others.…”

Section: Critical Analysis Open Challenges and Research Directionsmentioning

confidence: 99%

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.

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“…Arash et al [30] proposed a multi-objective Bat algorithm for optimizing the cost for allocating human resources to an emergency hospital. Anindita et al [31] proposed a bio-inspired algorithm for solving different aspects of wireless sensor network (WSN) like optimal routing, clustering, dynamic allocation of motes, lifetime optimization and the energy problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Many-objective BAT algorithm

2020

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In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the issue of diversity and convergence in MaOPs, many approaches are proposed using different multi objective evolutionary algorithms (MOEAs). Moreover, to get better results, the researchers use the sets of reference points to differentiate the solutions and to model the search process, it further evaluates and selects the non-dominating solutions by using the reference set of solutions. Furthermore, this technique is used in some of the swarm-based evolutionary algorithms. In this paper, we have used some effective adaptations of bat algorithm with the previous mentioned approach to effectively handle the many objective problems. Moreover, we have called this algorithm as many objective bat algorithm (MaOBAT). This algorithm is a biologically inspired algorithm, which uses echolocation power of micro bats. Each bat represents a complete solution, which can be evaluated based on the problem specific fitness function and then based on the dominance relationship, non-dominated solutions are selected. In proposed MaOBAT, dominance rank is used as dominance relationship (dominance rank of a solution means by how many other solutions a solution dominated). In our proposed strategy, dynamically allocated set of reference points are used, allowing the algorithm to have good convergence and high diversity pareto fronts (PF). The experimental results show that the proposed algorithm has significant advantages over several state-of-the-art algorithms in terms of the quality of the solution.

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Bio-inspired Algorithm for Multi-objective Optimization in Wireless Sensor Network

Cited by 16 publications

References 107 publications

WSN Routing Protocol Using a Multiobjective Greedy Approach

WSN Routing Protocol Using a Multiobjective Greedy Approach

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Many-objective BAT algorithm

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