dc.contributor.authorZhao, Ming
dc.identifier.citationZhao, M. (2016). Energy-efficient and reliable routing techniques for machine-to-machine communications. Doctoral thesis, Nanyang Technological University, Singapore.
dc.description.abstractIn recent years, Machine-to-Machine (M2M) communications has emerged as ubiquitous communication among large number of embedded devices. M2M communications networks are typically composed of thousands of highly resource constraint devices connected with dynamic and lossy wireless links with predominantly unpredictable characteristics, categorized as Low-Power and Lossy networks (LLNs). Routing is of paramount importance for M2M communications, as the data has to be relayed via multiple hops before it reaches destination. The Internet Engineering Task Force (IETF) has standardized the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) as predominate candidate for M2M communications. In fact, the severe resource constraints and unreliable wireless medium of LLNs have driven the routing protocols towards an energy-efficient, reliable, stable, and scalable design. Power conservation and reliable data delivery are the two main primary goals to design routing protocols. Though, it is extremely challenging to achieve both simultaneously, especially for LLNs. This thesis studies, designs, implements and evaluates the performance of energy-efficient and reliable routing techniques for M2M communications. Specifically, this thesis first presents a review of the state-of-the-art of M2M communications with focus on the enabling wireless communication technologies, standardization activities, and envisioned research opportunities. Furthermore, with a comprehensive study on RPL, a framework design of RPL is proposed. Aiming to simultaneously achieve high reliability and energy conservation in LLNs, suits of routing protocols are proposed. Extensive experimental studies are conducted in NS-3 to validate the effectiveness and flexibility of proposed protocols. This thesis proposes a hybrid RPL (HyRPL) aiming to provide reliable routing support with significant reduction of routing overhead. In HyRPL, the necessity for route discovery is determined by measuring the cost of pre-established route. With the design of a route constraint, HyRPL effectively balances the trade-off between utilizing the pre-established route and on-demand route discovery in LLNs. This thesis also proposes a cluster-parent based RPL (CRPL), which effectively leverages path diversity so as to improve the reliability and reduce the number of retransmissions. With the objective to minimize the total number of transmissions, CRPL allows each node to select an optimal forwarder-set with a low-complexity algorithm. Moreover, an efficient loss recovery scheme for the detection and retransmission of lost data packets is designed to avoid duplicate transmissions. This thesis also proposes a novel energy-efficient and region-based routing protocol, called ER-RPL. In contrast to traditional routing protocols, where all the nodes are required for route discovery, ER-RPL only requires a subset of nodes. By exploiting the region feature of static M2M communication networks, ER-RPL achieves energy conservation without compromising the reliability. Both the theoretical analysis and extensive simulation studies indicate the performance superiority of proposed ER-RPL. Further enhancing the CRPL, a hybrid energy-efficient and cluster-parent-based RPL (HECRPL), which prolongs the lifetime of the network while achieving high reliability and fairness, is proposed. In addition, a novel routing metric to precisely measure the end-to-end transmission cost is also designed. At last, a distributed algorithm is proposed to select the optimal transmission power and forwarder list.en_US
dc.format.extent222 p.en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleEnergy-efficient and reliable routing techniques for machine-to-machine communicationsen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.supervisorGuan Yong Liangen_US
dc.description.degreeDOCTOR OF PHILOSOPHY (EEE)en_US

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