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Erschienen in:
Wireless Sensor Network: Applications, Challenges, and Algorithms Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

11. Qualitative Survey on Sensor Node Deployment, Load Balancing and Energy Utilization in Sensor Network

verfasst von : Ayan Kumar Panja, Arka Ghosh

Erschienen in: Nature Inspired Computing for Wireless Sensor Networks

Verlag: Springer Singapore

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Abstract

Sensor Network is typically event-based systems. A wireless sensor network consists of many sink nodes to which it subscribes to and streams by expressing interest or queries submitted by various applications by users or organizations in general. As the sensors are battery-operated devices energy plays prime criteria in the sustainability of the network. If the size of the sensor tree pertaining to a network increases then the number of slots required for the scheduling transmission also increases. Sensors are deployed to cover various target points according to the application need; hence the proper deployment of the coverage or data gathering nodes is essential to increase the lifetime of the network. Proper deployment of coverage nodes plays a key role in load balancing and formation of the optimal subtree along with its respective base station and relay sensors. Various stochastic, deterministic, as well as heuristic-based algorithms incorporating optimization techniques to perform the node distribution has been developed and researched over the years. Researchers have also developed variegated models with bio-inspired algorithms like genetic algorithm, PSO algorithm, etc. to tackle some of the crucial problems of WSN. The paper provides a survey of some of the models and algorithms used for sensor node distribution, data aggregation, and discuss the various issues related to load balancing—advantages and disadvantages according to various applications.

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Vorheriges Kapitel A Comprehensive Survey of Intelligent-Based Hierarchical Routing Protocols for Wireless Sensor Networks
Nächstes Kapitel Bio-inspired Algorithm for Multi-objective Optimization in Wireless Sensor Network
Literatur
1.
Das SK, Samanta S, Dey N, Kumar R (2019) Design frameworks for wireless networks. In: Lecture notes in networks and systems. Springer, Berlin. pp 1–439. ISBN: 978–981-13-9573-4
2.
Roy S et al (2016) Symmetric key encryption technique: a cellular automata based approach in wireless sensor networks. Procedia Comput Sci 78:408–414CrossRef
3.
Das SK, Tripathi S (2019) Energy efficient routing formation algorithm for hybrid ad-hoc network: a geometric programming approach. Peer-to-Peer Networking Appl 12(1):102–128CrossRef
4.
Sujoy S et al (2011) Post disaster management using delay tolerant network. In: Recent trends in wireless and mobile networks. Springer, Berlin, Heidelberg, pp 170–184
5.
Mukherjee A et al (2019) Delay tolerant network assisted flying ad-Hoc network scenario: modeling and analytical perspective. Wirel Netw 25(5):2675–2695CrossRef
6.
Mukherjee A et al (2019) A disaster management specific mobility model for flying ad-hoc network. In: Emergency and disaster management: concepts, methodologies, tools, and applications. IGI Global, pp 279–311
7.
Yadav AK, Das SK, Tripathi S (2017) EFMMRP: design of efficient fuzzy based multi-constraint multicast routing protocol for wireless ad-hoc network. Comput Netw 118:15–23CrossRef
8.
Das SK, Yadav AK, Tripathi S (2017) IE2M: design of intellectual energy efficient multicast routing protocol for ad-hoc net work. Peer-to-Peer Networking Appl 10(3):670–687CrossRef
9.
Sen BK, Khatua S, Das RK (2015) Target coverage using a collaborative platform for sensor cloud. In: 2015 IEEE international conference on advanced networks and telecommuncations systems (ANTS). IEEE
10.
Ab Aziz NAB, Mohemmed AW, Daya Sagar BS (2007) Particle swarm optimization and Voronoi diagram for wireless sensor networks coverage optimization. In: 2007 international conference on intelligent and advanced systems. IEEE
11.
Chew LP (1990) Building Voronoi diagrams for convex polygons in linear expected time
12.
Zhang Q, Huang J, Wang J, Jin C, Ye J, Zhang W, Hu J (2008) A two-phase localization algorithm for wireless sensor network. In: 2008 international conference on information and automation. IEEE, pp 59–64
13.
Dorigo M, Birattari M (2010) Ant colony optimization. Springer, US
14.
Fidanova S, Marinov P, Alba E (2012) Ant algorithm for optimal sensor deployment. In: Computational intelligence. Springer, Berlin, Heidelberg, pp 21–29CrossRef
15.
Yuce B, Packianather M, Mastrocinque E, Pham D, Lambiase A (2013) Honey bees inspired optimization method: the bees algorithm. Insects 4(4):646–662CrossRef
16.
Hajizadeh N, Jahanbazi P, Javidan R (2018) Controlled deployment in wireless sensor networks based on a novel Multi Objective Bee Swarm Optimization algorithm. In: 2018 3rd conference on swarm intelligence and evolutionary computation (CSIEC). IEEE
17.
Zuhairy RM, Al Zamil MG (2018) Energy-efficient load balancing in wireless sensor network: an application of multinomial regression analysis. Int J Distrib Sens Netw 14(3):1550147718764641CrossRef
18.
Toumpis S, Gitzenis S (2009) Load balancing in wireless sensor networks using kirchhoff’s voltage law. In: IEEE INFOCOM 2009. IEEE, pp 1656–1664
19.
Duan Q et al (2013) Minimum cost blocking problem in multi-path wireless routing protocols. IEEE Trans Comput 63(7):1765–1777MathSciNetCrossRef
20.
Gupta G, Younis M (2003) Load-balanced clustering of wireless sensornetworks. In: IEEE international conference on communications, 2003. ICC’03, vol 3. IEEE, pp 1848–1852
21.
Zhang H, Li L, Yan X-F, Li X (2011) A load-balancing clustering algorithm of WSN for data gathering. In: 2011 2nd International conference on artificial intelligence, management science and electronic commerce (AIMSEC). IEEE, pp 915–918
22.
Israr N, Awan I (2006) Multi-hop clustering algo. For load balancing in WSN. Int J Simul 8(1)
23.
Kim N, Heo J, Kim HS, Kwon WH (2008) Reconfiguration of cluster heads for load balancing in wireless sensor networks. Comput Commun 31(1):153–159CrossRef
24.
Sarobin MVR, Ganesan R (2015) Swarm intelligence in wireless sensor networks: a survey. Int J Pure Appl Math 101(5):773–807
25.
Choi M, Kim J, Yang S, Ha N, Han K (2008) Load balancing for efficient routing in wireless sensor networks. In: 2008 international multi-symposiums on computer and computational sciences. IEEE, pp 62–68
26.
Nan G-F, Li M-Q, Li J (2007) Estimation of node localization with a real-coded genetic algorithm in WSNs. In: 2007 international conference on machine learning and cybernetics. vol 2. IEEE
27.
Wang G, Cao G, Porta TL (2003) Movement-assisted sensor deployment. IEEE INFOCOM 2004 4:2469–2479CrossRef
28.
Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 2. IEEE
29.
Sim KM, Sun WH (2003) Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Trans Syst Man Cybern-Part A: Syst Hum 33(5):560–572
30.
Mnasri S, Thaljaoui A, Nasri N, Val T (2015) A genetic algorithm based approach to optimize the coverage and the localization in thewireless audio-sensors networks. In: 2015 international symposium on networks, computers and communications (ISNCC), pp 1–6
31.
Kacimi R, Dhaou R, Beylot AL (2013) Load balancing techniques for lifetime maximizing in wireless sensor networks. Ad Hoc Netw 11(8):2172–2186CrossRef
32.
Mahdavi M, Ismail M, Jumari K (2009) Load balancing in energy efficient connected coverage wireless sensor network. In: 2009 international conference on electrical engineering and informatics, vol 2. IEEE, pp 448–452
33.
Zeynali M, Khanli LM, Mollanejad A (2010) Fuzzy based approach for load balanced distributing database on sensor networks. Int J Future Gener Commun Networking 3(2)
34.
Low CP, Fang C, Ng JM, Ang YH (2007) Load-balanced clustering algorithms for wireless sensor networks. In: 2007 IEEE international conference on communications. IEEE, pp 3485–3490
35.
Dey N et al (eds) (2018) Internet of things and big data analytics toward next-generation intelligence. Springer, Berlin
36.
Binh HTT, Nguyen TH, Dey N (2018) Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural Comput Appl 30(7):2305–2317CrossRef
37.
Das SK, Tripathi S (2018) Adaptive and intelligent energy efficient routing for transparent heterogeneous ad-hoc network by fusion of game theory and linear programming. Appl Intell 48(7):1825–1845CrossRef
Metadaten
Titel
Qualitative Survey on Sensor Node Deployment, Load Balancing and Energy Utilization in Sensor Network
verfasst von
Ayan Kumar Panja
Arka Ghosh
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Nature Inspired Computing for Wireless Sensor Networks

Print ISBN: 978-981-15-2124-9

Electronic ISBN: 978-981-15-2125-6

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-2125-6_11

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