Energy density based mobile sink trajectory in wireless sensor networks
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Sink mobility is one of the efficient solutions to sink hole or energy hole problem which is usually caused by multi-hop communication using a static sink. However, the design of an efficient path for the mobile sink is an important issue. In this paper, we propose a novel algorithms to determine rendezvous point (RP) based dynamic path for mobile sink called delay aware energy density based trajectory (DAEDT). The DAEDT designs an energy efficient delay bound path for the mobile sink. In order to balance energy consumption among the sensor nodes, the proposed algorithm selects RPs based on the energy density of the sensor nodes. The DAEDT is also presented with a detour criteria following some threshold. The algorithm is extensively simulated and the results of DAEDT are compared with some existing schemes to show its effectiveness over various performance metrics. The simulation results are further validated through hypothesis testing using ANOVA.
The first version of the paper (Nitesh and Jana 2015) appeared in the proceedings of 4th International Conference on ‘Computing, Communication and Sensor Network’ CCSN-2015, held at Kolkata during December 24–25, 2015. The authors of this paper are thankful to the anonymous reviewers for their valuable comments and suggestions which help this extension of the paper.
- Almi’Ani K, Viglas A, Libman L (2010) Energy-efficient data gathering with tour length-constrained mobile elements in wireless sensor networks. In: 2010 IEEE 35th conference on local computer networks (LCN), pp 582–589Google Scholar
- Bianchi G, Fratta L, Oliveri M (1996) Performance evaluation and enhancement of the CSMA/CA MAC protocol for 802.11 wireless LANs. In: Seventh IEEE international symposium on personal, indoor and mobile radio communications. PIMRC’96, vol 2, pp 392–396Google Scholar
- Di Francesco M, Das SK, Anastasi G (2011) Data collection in wireless sensor networks with mobile elements: a survey. ACM Trans Sens Netw (TOSN) 8(1):7Google Scholar
- Heinzelman WB (2000) Application-specific protocol architectures for wireless networks. PhD diss., Massachusetts Institute of TechnologyGoogle Scholar
- Johnson DS, McGeoch LA (2007) Experimental analysis of heuristics for the STSP. In: The traveling salesman problem and its variations. Springer, pp 369–443Google Scholar
- Kaswan A, Nitesh K, Jana PK (2016a) A routing load balanced trajectory design for mobile sink in wireless sensor networks. In: IEEE 2016 international conference on advances in computing, communications and informatics (ICACCI), pp 1669–1673Google Scholar
- Komal P, Nitesh K, Jana PK (2016) Indegree-based path design for mobile sink in wireless sensor networks. In: IEEE RAIT, 2016 3rd international conference on recent advances in information technology, pp 78–82Google Scholar
- Mishra M, Nitesh K, Jana PK (2016) A delay-bound efficient path design algorithm for mobile sink in wireless sensor networks. In: IEEE RAIT, 2016 3rd international conference on recent advances in information technology, pp 72–77Google Scholar
- Muller KE, Fetterman BA (2002) Regression and ANOVA: an integrated approach using SAS software. SAS InstituteGoogle Scholar
- Nitesh K, Jana PK (2015) Energy density based dynamic path selection for mobile sink in wireless sensor networks. In: Proceedings of international conference CCSN 2015, (IEEE Xplore), held in Kolkata, India, December 24–25Google Scholar
- Shi Yi, Hou YT (2008) Theoretical results on base station movement problem for sensor network. In: IEEE the 27th conference on computer communications, INFOCOMGoogle Scholar
- Zema NR, Mitton N, Ruggeri G (2014) Using location services to autonomously drive flying mobile sinks in wireless sensor networks. In: Ad hoc networks. Springer International Publishing, pp 180–191Google Scholar