LAUSR

Information-centric networking (ICN) is of high interest to the Internet of Things (IoT) community, since the dissemination of massive data continuously produced by IoT devices can be easily handled by ICN’s data naming scheme and inherent multipath delivery. Providing optimal multipath-oriented transmission control is crucial for ICN-IoT data delivery, but yet remains challenging because of the randomness of request arrival, dynamic link condition, and on-path caching. More prominently, the resource limitation and scalability issues in IoT require the control scheme to be lightweight and distributed. In this paper, we propose a distributed stochastic optimization framework for multipath transmission control in ICN-IoT. The transmission control, including request scheduling and data rate regulation, is formulated as a stochastic concave optimization problem, which aims to accommodate the randomness, unpredictability, and multipath delivery of ICN-IoT and maximize the overall throughput. This problem is linearly separated into two subproblems: 1) a request scheduling problem and 2) a data rate control problem, which can be individually solved per time slot. A distributed alternating descent method (DADM) is designed to optimally control the transmission by solving the aforementioned problems at client sides. DADM enables each client to sequentially update the request schedule and rate regulation via communicating the links and providers they use, which asymptotically converges to optimality while allowing low-complexity and decentralized implementation. Validated by simulations, our DADM significantly improves throughput, delay reduction, and energy efficiency, in comparison with other state-of-the-art solutions.