Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms

Tripathi, Arpit; Gupta, Pulkit; Trivedi, Aditya; Kala, Rahul

doi:10.4018/978-1-4666-2047-6.ch008

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…Providing constant data to the sink with continuous correspondence is a major task in executing extensive scale sensor systems. A great deal of research in information routing [3], [4], data compression [7] and in-network data aggregation [5], [6] has been done in WSNs amid late years. The problem of efficient data dissemination has been tried to solve to certain extent by incorporating Cooperative Caching in Wireless Sensor Networks.…”

Section: Cooperative Data Cachingmentioning

confidence: 99%

A Cooperative Cache Management Scheme for IEEE802.15.4 based Wireless Sensor Networks

Charan

Usmani

Paulus

et al. 2018

IJECE

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Wireless Sensor Networks (WSNs) based on the IEEE 802.15.4 MAC and PHY layer standards is a recent trend in the market. It has gained tremendous attention due to its low energy consumption characteristics and low data rates. However, for larger networks minimizing energy consumption is still an issue because of the dissemination of large overheads throughout the network. This consumption of energy can be reduced by incorporating a novel cooperative caching scheme to minimize overheads and to serve data with minimal latency and thereby reduce the energy consumption. This paper explores the possibilities to enhance the energy efficiency by incorporating a cooperative caching strategy.

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Section: Cooperative Data Cachingmentioning

confidence: 99%

A Cooperative Cache Management Scheme for IEEE802.15.4 based Wireless Sensor Networks

Charan

Usmani

Paulus

et al. 2018

IJECE

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“…A GA places the WSN nodes for disjoint clustering and minimizes the number of hops between sensor and nodes to have efficient intra-cluster communication. Tripathi et al [110] Bi-objective GA and Genetic Programming (GP) GP improves the deployment, while GA optimizes the node placement for maximum coverage in WSN. Pandey et al [111] MOGA GA with greedy algorithm and 'binary integer linear programming' (BILP)…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

Genetic algorithms in wireless networking: techniques, applications, and issues

et al. 2016

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Abstract-In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorithms (GAs) provide a well-established framework for implementing artificial intelligence tasks such as classification, learning, and optimization. GAs are well-known for their remarkable generality and versatility, and have been applied in a wide variety of settings in wireless networks. In this paper, we provide a comprehensive survey of the applications of GAs in wireless networks. We provide both an exposition of common GA models and configuration and provide a broad ranging survey of GA techniques in wireless networks. We also point out open research issues and define potential future work. While various surveys on GAs exist in literature, our paper is the first paper, to the best of our knowledge, which focuses on their application in wireless networks.

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“…In [13] a hybrid approach for WSN node placement is presented. The approach combines a GP method with a GA.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of a Genetic Programming Approach to Generate Wireless Sensor Network Applications

Heimfarth¹,

Araujo

Oliveira

et al. 2014

2014 IEEE 28th International Conference on Advanced Information Networking and Applications

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This article presents a systematic evaluation of a framework based on Genetic Programming (GP) which aims the automatic generation of Wireless Sensor Network (WSN) applications. Developing WSN applications poses a challenge due to massive distribution of the network nodes. The automatic generation of applications reduces drastically costs, since the manual development is a laborious process. In our approach, the user describes the desired global behavior as a fitness function which guides the evolution of the application by the GP. A scripting language based on events and actions is used to represent the WSN behavior and the GP generates programs in this language. In order to evaluate the framework, a problem of multiple events detection is introduced. Several problem instances were used to appraise the performance of our method under different parameters. Results evidence the feasibility of our approach for the proposed problem, highlighting the challenges posed by the large search space and the dead end routing problem.

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Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms

Cited by 5 publications

References 18 publications

A Cooperative Cache Management Scheme for IEEE802.15.4 based Wireless Sensor Networks

A Cooperative Cache Management Scheme for IEEE802.15.4 based Wireless Sensor Networks

Genetic algorithms in wireless networking: techniques, applications, and issues

Evaluation of a Genetic Programming Approach to Generate Wireless Sensor Network Applications

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