MARA: Mobility-Aware Rate Adaptation for Low Power IoT Networks Using Game Theory

The rapid growth in the number of Internet of Things (IoT) devices has increased the demand for exploring high-throughput communications. Low power IoT networks perform poorly under heavy traffic due to severe congestion and high packet loss problems. Controlling the rate of traffic load is advocated as an effective way to reduce the congestion in traditional networks. However, it poses a major challenge to low power IoT networks due to the lack of infrastructure, dynamic changes of the network topology, and using multi-hop communication through unstable lossy wireless links. To overcome this problem, in this paper we propose an optimized Mobility-Aware Rate Adaptation (MARA) framework based on the game theory. We model the rate control problem as a non-cooperative game where IoT nodes compete for higher bandwidth as selfish players. Based on the Rosen's theorem for concave N-person games, we prove the existence and uniqueness of Nash equilibrium. Finding the optimal solution of the game is modeled as a nonlinear programming (NLP) problem which is solved by using Lagrange multipliers and Karush-Kuhn-Tucker (KKT) optimality conditions. MARA can effectively adapt the transmission rate of each node to the changes in the network topology, traffic dynamics, and energy resources. We implement MARA on Zolerita IoT motes and Contiki operating system to evaluate its performance. Emulation results show that MARA improves the packet delivery ratio by up to 42%, and reduces the end-to-end delay and the energy consumption by up to 32% and 30% respectively.