Optimal Sink Placement in Machine to Machine over Long-Term Evolution Networks

• Main Authors: Yen-Hsiung Tseng, 曾彥雄
• Other Authors: Ren-Hung Hwang
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/89214017715837763700

碩士 === 國立中正大學 === 資訊工程研究所 === 103 === Internet of Things (IoT) aims to connect all things, such as smart meters, sensors, etc., globally to the Internet such that intelligent applications and decisions can be deployed and made. In order to achieve Internet connectivity, several protocols have been proposed by the International Engineering Task Force (IETF), such as 6LowPAN and Routing Protocol for Low Power and Lossy Network (RPL). Besides, 3GPP has proposed Machine Type Communication (MTC) for connecting machines to the Internet through cellular networks. Thus, in this thesis, we consider the wireless sensor networks adopting the RPL as the routing protocol and connecting to the Internet through cellular network interface. We assume there are two types of sensors, namely routers (relay nodes) and sensors. Only routers can relay packets and are equipped with cellular network interface. The sink placement problem, that is, how to select routers as sink nodes to relay packets to the cellular network, becomes a new challenge problem. In this thesis, we aim to prolong the lifetime of sensor network by determining the optimal sink positions. We propose a Genetic Algorithm based strategy to dynamically change the positions of sink nodes over time. The strategy considers 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. Performance of the proposed algorithm is evaluated via simulations. Our simulation results show that rapidly changing positions of sink nodes to even the energy distribution prolongs the network lifetime the best in low sink relocation overhead situation. However, with higher sink relocation overhead, moderate amount of sink relocations optimizes the lifetime better. Hence, the discussion of optimizing the number of sink relocations could be made in the future.