Deploying Edge Computing Nodes for Large-scale IoT:A Diversity Aware Approach

The recent advances in microelectronics and communications have led to the development of large-scale IoT networks, where tremendous sensory data is generated and needs to be processed. To support real-time processing for large-scale IoT, deploying edge servers with storage and computational capability is a promising approach. In this paper, we carefully analyze the impacting factors and key challenges for edge node deployment. We then propose a novel three-phase deployment approach which considers both traffic diversity and the wireless diversity of IoT. The proposed work aims at providing real-time processing service for the IoT network and reducing the required number of edge nodes. We conducted extensive simulation experiments, the results show that compared to the existing works that overlooked the two kinds of diversities, the proposed work greatly reduces the number of edge nodes and improves the throughput between IoT and edge nodes

From Rateless to Hopless

This paper presents a hopless networking paradigm. Incor-porating recent techniques of rateless codes, senders break packets into rateless information streams and each single stream automatically adapts to diverse channel qualities at all potential receivers, regardless of their hop distances. The receivers are capable of accumulating rateless information pieces from different senders and jointly decoding the packet, largely improving throughput. We develop a practical proto-col, called HOPE, which instantiates the hopless networking paradigm. Compared with the existing opportunistic rout-ing protocol family, HOPE best exploits the wireless channel diversity and takes full advantage of the wireless broadcast effect. HOPE incurs minimum protocol overhead and serves general networking applications. We extensively evaluate the performance of HOPE with indoor network traces col-lected from USRP N210s and Intel 5300 NICs. The results show that HOPE achieves 1.7x and 1.3x goodput gain over ExOR and MIXIT, respectively