Real-time communication in wireless lossy networks

The last decades’ tremendous advances in wireless communications have been driven mainly by personal communications. Radio resource allocation mechanisms for optimizing key metrics, such as average throughput and delay, are by now rather well-developed. However, with the increased interest in wireless machine-to-machine communication, new challenges emerge, such as multi-hop connectivity, lossy and bursty links, battery-powered nodes, and changing/unknown link parameters, among others. With these challenges in mind, this thesis studies real-time communication in wireless lossy networks, and how the resulting networking primitive can be used to design networked control systems with optimal closed-loop performance. First, we study optimal forwarding of deadline-constrained traffic over multi-hop networks with losses on links described by finite-state Markov chains. We consider two problems: maximizing the probability that packets are delivered within specified deadlines; and minimizing the expected energy cost with a guaranteed probability of on-time delivery. Both problems fall into the category of Markov Decision Processes and can be studied in a general dynamic programming framework. Particular instances with Bernoulli and Gilbert-Elliot loss models, which admit insight and efficient computations, are discussed. Moreover, a number of extensions and variations of the deadline-constrained forwarding problem are investigated. These extensions include systems with unknown channel states and unknown link loss models, scenarios with multiple concurrent flows, and solutions adapted to opportunistic routing and the recent WirelessHART standard. Second, we show how the solution for the deadline-constrained forwarding problem can be used in the optimal co-design of networked control systems. Specifically, we consider the joint design of packet forwarding policies and controllers for wireless control loops where sensor data are sent to the controller over an unreliable and energy-constrained multi-hop wireless network. For fixed sampling rate of the sensor, the co-design problem separates into two well-defined and independent subproblems: transmission scheduling for maximizing the deadline-constrained reliability and optimal control under packet loss. We develop optimal and implementable solutions for these subproblems and show that the optimally co-designed system can be efficiently found. Finally, we study online shortest-path routing problems in which link delays are time-varying and modeled by random processes with initially unknown parameters. The optimal path can only be estimated by routing packets through the network and observing the realized delays. The aim is to find a routing policy that minimizes the regret (the cumulative delay difference) between the path chosen by the policy and the unknown optimal path. We formulate the problem as a combinatorial bandit optimization problem and consider several scenarios. For each scenario, we derive the tight asymptotic lower bound on the regret that has to be satisfied by any online routing policy. These bounds help us to understand the performance improvements we can expect when (i) taking routing decisions at each hop rather than at the source only, and (ii) observing per-link costs rather than aggregate path costs. Efficient algorithms are proposed and evaluated against the state-of-the-art.