Wireless sensor networks (WSNs) are widely deployed to support transmissions of the huge data emitted by heterogeneous connected devices. The quality of service (QoS) and energy requirements in WSNs are dramatically increasing and becoming challenging issues. Integrating network coding, in the data transmission process, leads to reduced energy consumption and improved QoS. In this work, we study the problem of energy efficiency in multi-hop WSNs, namely wireless unsplittable multi-commodity flow with network coding (wUMCFC). We propose mixed-integer programming formulations of the wUMCFC problem, and we implement an open source solver wUMCFC based on the branch-and-price algorithm. To the best of our knowledge, this work is the first study of the wUMCFC problem by mathematical programming tools. We perform a computational study on realistic instances with an analysis of the performance of the proposed algorithm. The computational results show the efficiency of the network coding and provide important information for strategic decisions on routing policies and network management https://github.com/ lidingxu/wUMCFC.

Covering problems are well-studied in the domain of Operations Research, and, more specifically, in Location Science. When the location space is a network, the most frequent assumption is to consider the candidate facility locations, the points to be covered, or both, to be discrete sets. In this work, we study the set-covering location problem when both candidate locations and demand points are continuous sets on a network. This variant has received little attention, and the scarce existing approaches have focused on particular cases, such as tree networks and integer covering radius. Here we study the general problem and present a Mixed Integer Linear Programming formulation (MILP) for networks with edges' lengths no greater than the covering radius. The model does not lose generality, as any edge not satisfying this condition can be partitioned into subedges of appropriate lengths without changing the problem. We propose a preprocessing algorithm to reduce the size of the MILP, and devise tight big-M constants and valid inequalities to strengthen our formulations.Moreover, a second MILP is proposed, which admits edges' lengths greater than the covering radius. As opposed to existing formulations of the problem (including the first MILP proposed herein), the number of variables and constraints of this second model does not depend on the lengths of the network's edges. This second model represents a scalable approach that particularly suits real-world networks, whose edges are usually greater than the covering radius. Our computational experiments show the strengths and limitations of our exact approach on both real-world and random networks. Our formulations are also tested against an existing exact method.

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