A Lagrangian approach to Chance Constrained Routing with Local Broadcast

Abstract

Mobile cellular networks play a pivotal role in emerging Internet of Things (IoT) applications, such as vehicular collision alerts, malfunctioning alerts in Industry-4.0 manufacturing plants, periodic distribution of coordination information for swarming robots or platooning vehicles, etc. All these applications are characterized by the need of routing messages within a given local area (geographic proximity) with constraints about both timeliness and reliability (i.e., probability of reception). This paper presents a Non-Convex Mixed-Integer Nonlinear Programming model for a routing problem with probabilistic constraints on a wireless network. We propose an exact approach consisting of a branch-and-bound framework based on a novel Lagrangian decomposition to derive lower bounds. Preliminary experimental results indicate that the proposed algorithm is competitive with state-of-the-art general-purpose solvers, and can provide better solutions than existing highly tailored ad-hoc heuristics to this problem