EFFICIENT ROUTING AND OFFLOADING DESIGN IN INTERNET-OF-THINGS SYSTEMS

Abstract

One of the fundamental challenges in Internet-of-Things systems is that network environment is always changing. Conventional networking approaches do not consider the dynamic evaluation of the networks or consider the network dynamic as a mirror thing, which may not be able to work or has a low efficiency in the Internet-of-Things systems. This dissertation is uniquely built by considering the dynamic network environment and even taking advantage of the network dynamic to improve the network performances, with a focus on the routing and offloading issues. The first part is related to the routing design in the opportunistic mobile networks. The opportunistic mobile network is expected to be an intrinsic part of the Internet of Things. Devices communicate with each other autonomously without any centralized control and collaborate to gather, share, and forward information in a multi-hop manner. The main challenge in opportunistic mobile networks is due to intermittent connection and thus data is delivered through store-carry-forwarding paradigm. In this dissertation, We found an observation regarding the contact duration and proposed efficient data partitioning routing algorithms in the opportunistic mobile networks. The second part is related to the offloading issues in the Internet-of-things systems. With the surging demand on high-quality mobile services at any time, from anywhere, how to accommodate the explosive growth of traffics with/without existing network infrastructures is a fundamental issue. Specifically, We consider three different offloading problems, i.e., cellular data offloading, cloud task offloading, and mobile worker task offloading problems in vehicular networks, cloud, and crowdsourcing platforms. The common issue behind them is how to efficiently utilize the network resources in different scenarios by design efficient scheduling mechanisms. For the cellular data offloading, We explored the trade-off of cellular offloading in the vehicular network. For the cloud task offloading, We conducted the research to adjust the offloading strategies wisely so that the total offloading cost is minimized. For the worker task offloading in the smart cities, We optimized the cost-efficiency of the crowdsourcing platforms.