The Internet of Things (IoT) has been emerged as one of the most optimistic solution that reducing the gap between physical and cyber world. In IoT, sensors and actuators gather the sensory information; communicate with each other to fulfill application requirements. As sensor nodes are running on batteries, which cannot be replaced, so the development of the green model in IoT for shifting the real world data into the cyber world is challenging task. As IoT is deployed in large area and become more complex, so wireless sensor network current techniques cannot be applied in it directly. To achieve energy efficient network in the field of Internet of Thing, this paper addresses these challenging issues by introducing an efficient deployment scheme. This paper contributes: (1) A hierarchical cluster model for IoT network deployment; (2) An optimal Relay node election technique with respect to node density in the network and their deployment strategy by using their communication area as threshold distance for next selection within the cluster; (3) An efficient energy routing algorithm to meet the requirement of proposed model. The analysis of experimental results show that proposed method is more efficient in terms of energy consumption and suits IoT requirements.
SethiP. and SarangiS.R., Internet of things: Architectures, protocols, and applications, Journal of Electrical and Computer Engineering (2017).
2.
JelicicV., MagnoM., BrunelliD., PaciG. and BeniniL., Context-adaptive multimodal wireless sensor network for energy-efficient gas monitoring, IEEE Sensors Journal13(1) (2013), 328–338.
3.
HuangJ., MengY., GongX., LiuY. and DuanQ., A novel deployment scheme for green Internet of Things, IEEE Internet of Things Journal1(2) (2014), 196–205.
4.
SunX. and CoyleE.J., Quantization, Channel compensation, and optimal energy allocation for estimation in sensor network, ACM Transactions on Sensor Networks (TOSN)8(2) (2012), 15.
5.
Lee YounisS. and EqarM., Effective qos-aware relay node placement algorithm for connecting disjoint wireless sensor subnetworks, IEEE Transactions on Computers60(12), 1772–1787.
6.
ShenW. and WuQ., Exploring redundancy in sensor deployment to maximize network lifetime and coverage, In Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2011 8th Annual IEEE Communications Society Conference on, IEEE2011, pp. 557–565.
7.
LiF., LuoJ., WangW. and HeY., Autonomous deployment for load balancing-surface coverage in sensor networks wireless communications, IEEE Transactions on Wireless Communications14(1) (2015), 279–293.
8.
WeiP., ChuS., WangX. and ZhouY., Deployment of areinforcement backbone network with constraints of connection and resources, In Distributed Computing Systems (ICDCS), IEEE 30th International Conference on, 2010, pp. 10–19.
9.
LiF., LuoJ., XinS.Q., WangW.P. and He LaacadY., Load balancing k-area coverage through autonomous deployment in wireless sensor networks, In Distributed Computing Systems (ICDCS), IEEE 32nd International Conference on2012, pp. 566–575.
10.
RaniS., MalhotraJ. and TalwarR., EEICCP – energy efficient protocol for wireless sensor networks, Wireless Sensor Network5(07) (2013), 127.
11.
RaniS., MalhotraJ. and TalwarJ., Energy efficient protocol for densely deployed homogeneous network, In Issues and Challenges in Intelligent Computing Techniques (ICICT), International Conference on, 2014, pp. 292–298.
12.
XuM. and LeungH., A joint fusion, power allocation and delay optimization approach for wireless sensor networks, IEEE Sensors Journal11(3) 2011, 737–744.
13.
LeeJ.S. and ChengW.L., Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication, IEEE Sensors Journal12(9) (2012), 2891–2897.
14.
MajumderR., BagG. and KimK.H., Power sharing and control in distributed generation with wireless sensor networks, IEEE Transactions on Smart Grid3(2) (2012), 618–634.
15.
YanL., PanW., LuoB., LiX. and, LiuJ., An energy-efficient MAC protocol for wireless sensor networks, In INFOCOM, Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, ProceedingsIEEE, 3 (2002), pp. 1567–1576.
16.
CardeiM., ThaiM.T., LiY. and, WuW., Energy-efficient target coverage in wireless sensor networks. In INFOCOM 2005. 24th annual joint conference of the IEEE computer and communications societies. Proceedings IEEE32005, pp. 1976–1984.
17.
LiuC. and CaoG., Distributed critical location coverage in wireless sensor networks with lifetime constraint. In INFOCOM, Proceedings IEEE, 2012, pp. 1314–1322.
18.
KapnadakV. and CoyleE., Optimal non-uniform deployment of sensors for detection in single-hop wireless sensor networks, In Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 8th Annual IEEE Communications Society Conference on, 2011, pp. 89–97.
19.
YanL., PanW., LuoB., LiX. and LiuJ., Modified energy-efficient protocol for wireless sensor networks in the presence of distributed optical fiber senor link, IEEE Sensors Journal11(9), 1815–1819.
20.
LiF., LuoJ., XinS.-Q., WangW.-P. and HeY., Laacad: Load balancing k-area coverage through autonomous deployment in wireless sensor networks, In Distributed Computing Systems (ICDCS), IEEE 32nd International Conference on, pp. 566–575.
21.
XuX., AnsariR. and KhokharA., Power-efficient hierarchical data aggregation using compressive sensing in WSN, In Communications (ICC), 2013 IEEE International Conference on, pp. 1769–1773.
22.
RaniS., TalwarR., MalhotraJ., AhmedS.H., SarkarM. and SongH., A novel scheme for an energy efficient Internet of Things based on wireless sensor networks, Sensors15(11) (2015), 28603–28626.
23.
XuM. and LeungH., A joint fusion, power allocation and delay optimization approach for wireless sensor networks, IEEE Sensors Journal11(3) (2011), 737–744.
24.
YangD., MisraS., FangX., XueG. and ZhangJ., Two-tiered constrained relay node placement in wireless sensor networks, Computational complexity and efficient approximations, IEEE Transactions on Mobile Computing11(8) (2012), 1399–1411.
25.
TyagiS. and KumarN., A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks, Journal of Network and Computer Applications36 (2) (2013), 623–645.
26.
HanG., JiangX., QianA., RodriguesJ.J. and ChengL., A comparative study of routing protocols of heterogeneous wireless sensor networks, The Scientific World Journal, Article ID: 415415, 11 pages. 10.1155/2014/415415
27.
ButtyánL. and SchafferP., Position-based aggregator node election in wireless sensor networks, International Journal of Distributed Sensor Networks6(1) (2010), 679205.
28.
JungS.M., HanY.J. and ChungT.M., The concentric clustering scheme for efficient energy consumption in the PEGASIS, In Advanced Communication Technology, IEEE The 9th International Conference on, 12007, pp. 260–265.
29.
SainiP. and SharmaA.K., Energy efficient scheme for clustering protocol prolonging the lifetime of heterogeneous wireless sensor networks, National Institute of Technology Jalandhar, Department of Computer Science & Engineering, International Journal of Computer Applications6(2) (2010).
30.
ShahT., JavaidN. and QureshiT.N., Energy efficient sleep awake aware (EESAA) intelligent sensor network routing protocol, In Multitopic Conference (INMIC), IEEE 15th International, 2012, pp. 317–322.
31.
SmaragdakisG., MattaI. and BestavrosA., SEP: A stable election protocol for clustered heterogeneous wireless sensor networks, Boston University Computer Science Department, 2004.
32.
MaoY. and JieW., An Energy-Efficient Unequal Clustering Mechanism for Wireless Sensor Networks, In Proceeding of International conference on Mobile Ad hoc and Sensor System Conference, 2005, p. 604.
33.
KumarD. and PatelR.B., Multi-hop data communication algorithm for clustered wireless sensor networks, International Journal of Distributed Sensor Networks7(1) (2011), 984795.
34.
YounisO. and FahmyS., HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks, IEEE Transactions on Mobile Computing3(4) (2004), 366–379.
35.
DingP., HollidayJ. and CelikA., Distributed energy-efficient hierarchical clustering for wireless sensor networks, Distributed computing in sensor systems, 2005, 466–467.
36.
LeeS., YooJ. and ChungT., Distance-based energy efficient clustering for wireless sensor networks, In Local Computer Networks, IEEE 29th Annual IEEE International Conference on2004, pp. 567–568.
37.
YueyangL., HongJ. and GuangxinY., An energy-efficient PEGASIS-based enhanced algorithm in wireless sensor networks, China Communications, 2006, 91–97.
38.
HussainS., MatinA.W.Energy efficient hierarchical cluster-based routing for wireless sensor networks, Jodrey School of Computer Science Acadia University Wolfville, Nova Scotia, Canada, Technical Report, 2005, 1–33.
39.
MahmoodD., JavaidN., MahmoodS., QureshiS., MemonA.M. and ZamanT., MODLEACH: A variant of LEACH for WSNs. In Broadband and Wireless Computing, Communication and Applications (BWCCA), IEEE Eighth International Conference on, 2013, pp. 158–163.
40.
MuruganathanS.D., MaD.C., BhasinR.I. and FapojuwoA.O., A centralized energy-efficient routing protocol for wireless sensor networks, IEEE Communications Magazine43(3) (2005), S8–13.
41.
YeMaoL., FaC. and HaiW., An energy efficient clustering scheme in wireless sensor networks, Ad Hoc & Sensor Wireless Networks, 2006.
42.
DehniL., KriefF. and BennaniY., Power control and clustering in wireless sensor networks, Challenges in Ad Hoc Networking, 2006, 31–40.
43.
RaniS., MalhotraJ. and TalwarR., On the development of realistic cross layer communication protocol for wireless sensor networks, Wireless Sensor Network6(05) (2014), 57.
44.
DialloC., MarotM. and BeckerM., Link quality and local load balancing routing mechanisms in wireless sensor networks, In Telecommunications (AICT), IEEE Sixth Advanced International Conference on (2010), 306–315.
45.
TangJ., ZhouZ., NiuJ. and WangQ., An energy efficient hierarchical clustering index tree for facilitating time-correlated region queries in the Internet of Things, Journal of Network and Computer Applications40 (2014), 1–11.
46.
Bara’aA.A. and KhalilE.A., A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks, Applied Soft Computing12(7) (2012), 1950–1957.
47.
HussainS., MatinA.W. and IslamO., Genetic algorithm for hierarchical wireless sensor networks, JNW2(5) (2007), 87–97.
48.
LiuJ.L. and RavishankarC.V., LEACH-GA: Genetic algorithm-based energy-efficient adaptive clustering protocol for wireless sensor networks, International Journal of Machine Learning and Computing1(1) (2011), 79.
49.
NorouziA., BabamirF.S. and ZaimA.H., A new clustering protocol for wireless sensor networks using genetic algorithm approach, Wireless Sensor Network3(11) (2011), 362.
50.
XiaX., ChenZ., LiD. and LiW., Proposal for efficient routing protocol for wireless sensor network in coal mine goaf, Wireless Personal Communications77(3) (2014), 1699–1711.
51.
KangY., HuB., DingY. and TanJ., A hybrid node scheduling approach based on energy efficient chain routing for WSN, Advances in Mechanical Engineering6 (2014), 254761.
52.
AliaO.M., A decentralized fuzzy C-means-based energy-efficient routing protocol for wireless sensor networks, The Scientific World Journal (2014), Article ID: 647281, 9 pages. 10.1155/2014/647281
53.
AhmedS.H., BoukS.H., JavaidN. and SasaseI., Combined human, antenna orientation in elevation direction and ground effect on RSSI in wireless sensor networks, In Frontiers of Information Technology (FIT), IEEE 10th International Conference on (2012), pp. 46–49.
54.
ChungY., An energy-efficient unicast routing protocol for wireless sensor Networks, Tech Int J Comput Sci Emerg Tech2 (2011), 60–64.
55.
ButtM.R., AkbarA.H., KimK.H., JavedM.M., LimC.S. and TajQ., LABILE: Link quAlity-based lexIcaL routing mEtric for reactive routing protocols in IEEE 802.15. 4 networks, The Journal of Supercomputing62(1) (2012), 84–104.
56.
MachadoK., RosárioD., CerqueiraE., LoureiroA.A., NetoA. and de SouzaJ.N., A routing protocol based on energy and link quality for internet of things applications, Sensors13(2) (2013), 1942–1964.
57.
PerkinsC., Belding-RoyerE. and DasS., Ad hoc on-demand distance vector (AODV) routing, RFC 3561, 2003. doi:.
58.
MehmoodA., KhanS., ZhangD., LloretJ. and AhmedS.H., Iotec: Iot based efficient clustering protocol for wireless sensor network, In Proceedings of the International Industrial Information Systems Conference, Chiang Mai, Thailand, 2014, pp. 21–24.
59.
ShahT., JavaidN. and QureshiT.N., Energy efficient sleep awake aware (EESAA) intelligent sensor network routing protocol, In Multitopic Conference, IEEE 15th International, 2012, pp. 317–322.
60.
AkyildizI.F., SuW., SankarasubramaniamY. and CayirciE., Wireless sensor networks: A survey, Computer Networks38(4) (2002), 393–422.
61.
HeinzelmanW.B., ChandrakasanA.P. and BalakrishnanH., An application-specific protocol architecture for wireless microsensor networks, IEEE Transactions on Wireless Communications1(4) (2002), 660–670.
