Paper Info
Title
Optimal Sink Placement in M2M over LTE Networks
Abstract
Internet of Things (IoT) aims to connect all things to the Internet such that intelligent applications and decisions can be deployed and made. In order to achieve Internet connectivity, the several protocols have been proposed by the Internet Engineering Task Force (IETF), such as IPv6 over Low power WPAN (6LowPAN) and Routing Protocol for Low power and Lossy Networks (RPL), etc. Besides, the Third Generation Partnership Project (3GPP) has proposed Machine Type Communication (MTC) for connecting machines to the Internet through the cellular networks. Thus, in this paper, we consider the wireless sensor networks adopting RPL as the routing protocol and connecting to the Internet through the cellular network interface. There are two types of sensors assumed, routers (relay nodes) and sensors. Only the routers can relay packets and are equipped with the cellular network interface. The sink placement problem becomes a new challenge problem. In this paper, a Genetic Algorithm based strategy is proposed to dynamically change the positions of sink nodes over time. In this strategy, we consider the residual energy on each node and possible routing topologies to determine the optimal positions for sink nodes as well as the optimal number of sink nodes. Finally, the simulation results show that the proposed approach is able to prolong the lifetime of WSN and an adaptive strategy is needed to determine the sink relocation time interval which considers the sink relocation overhead as well as optimizing the sink node selection timely.
Year
DOI
Venue
2015
10.1109/SmartCity.2015.60
2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)
Keywords
Field
DocType
dynamic sink placement,RPL,network lifetime optimization
IPv6,Computer science,Network packet,Computer network,Network topology,Internet access,6LoWPAN,Wireless sensor network,Routing protocol,The Internet,Distributed computing
Conference
Citations 
PageRank 
References 
0
0.34
7
Authors
4
Name
Order
Citations
PageRank
Yen-Hsiung Tseng100.34
Ren-hung Hwang264369.87
Chin-Feng Lai397374.85
Cheng-Yu Hou400.34