Wireless sensor networks (WSN) consist of a set of sensors and collection sinks that gather and analyze environmental conditions. Spurred by the growing need for data collection and transmission, WSN research topics have gained interest in recent years. WSNs are often deployed in hostile or inaccessible locations in which sensor replacement or repair is impractical. This survey explores various research approaches and extensions to the problem, which include online routing, clustering approaches, and lifetime maximization on specially structured networks. We additionally consider the impact of having mobile and/or multiple sinks and delay-tolerant routing. Finally, we expand our analysis to examine multicriteria optimization problems, and outline future research challenges in the field.

No abstract

Time-dependent network applications, such as wireless sensor network and infrastructure optimization settings, may require dynamic flows to be transmitted according to a nonsimultaneous schedule of path-flows. We study a dynamic network flow optimization problem considering the presence of activation costs required to begin transmitting flow on an arc. This problem can be modeled as a dynamic version of the minimum-cost flow problem having arc-activation costs (MCFA). The MCFA is related but not equivalent to the fixed-charge network flow problem. We first discuss the relationship between these two problems, and show how MCFA is unique in the network flow literature. We present a mixed-integer programming (MIP) model along with a series of symmetry-breaking inequalities for solving the MCFA. As an alternative, we employ a relaxation-based algorithm that iteratively obtains upper and lower bounds via the solution of a series of smaller, more tractable MIPs. We show that this algorithm finitely terminates with an optimal MCFA solution. Finally, computational results demonstrate the efficacy of our approach compared to solving a MIP using a state-of-the-art commercial solver.

We consider a class of maximum flow problems (MFPs) having node-and arc-capacity constraints, in which each unit of flow on arc (i, j) consumes a positive amount of capacity at node i. This problem arises in wireless sensor network optimization applications, where node capacities refer to sensor energy limits. This version of the MFP is traditionally solved using linear programming due to the presence of the complicating node-capacity constraints. As an alternative scheme, we prescribe an approach for this problem based on augmenting flows along paths and cycles, showing why sending flows on augmenting cycles becomes necessary in this class of problems. Although our augmenting flow algorithm ultimately requires the solution of auxiliary linear programs, we demonstrate the computational advantages of our approach on randomly generated test instances.