This paper examines the problem of locating a mobile, nonadversarial target in an indoor environment using multiple robotic searchers. One way to formulate this problem is to assume a known environment and choose searcher paths most likely to intersect with the path taken by the target. We refer to this as the multi-robot efficient search path planning (MESPP) problem. Such path planning problems are NP-hard, and optimal solutions typically scale exponentially in the number of searchers. We present an approximation algorithm that utilizes finite-horizon planning and implicit coordination to achieve linear scalability in the number of searchers. We prove that solving the MESPP problem requires maximizing a nondecreasing, submodular objective function, which leads to theoretical bounds on the performance of our approximation algorithm. We extend our analysis by considering the scenario where searchers are

We consider the important computer security problem of outbreak detection, where we want to place sensors (monitoring stations, probes) for detecting events (computer viruses) spreading over a network. We show that such problems can be modeled by the problem of simultaneously maximizing a collection of submodular set functions. We show, how the SATURATE algorithm [3] performs nearoptimally in this setting, even if sensors can (accidentally or through adversarial manipulation) fail. 1

When monitoring spatial phenomena with wireless sensor networks, selecting the best sensor placements is a fundamental task. Not only should the sensors be informative, but they should also be able to communicate efficiently. In this article, we present a data-driven approach that addresses the three central aspects of this problem: measuring the predictive quality of a set of sensor locations (regardless of whether sensors were ever placed at these locations), predicting the communication cost involved with these placements, and designing an algorithm with provable quality guarantees that optimizes the NP-hard trade-off. Specifically, we use data from a pilot deployment to build nonparametric probabilistic models called Gaussian Processes (GPs) both for the spatial phenomena of interest and for the spatial variability of link qualities, which allows us to estimate predictive power and communication cost of unsensed locations. Surprisingly, uncertainty in the representation of link qualities plays an important role in estimating communication costs. Using these models, we present a novel, polynomial-time, data-driven algorithm, PSPIEL, which selects Sensor Placements at Informative and communication-Efficient Locations. Our approach exploits two important properties of this problem: submodularity, formalizing the intuition that adding a node to a small deployment can help more than adding a node to a large deployment; and locality, under which nodes that are far from each other provide almost independent information. Exploiting these properties, we prove strong

Abstract—We consider the problem of monitoring spatial phenomena, such as road speeds on a highway, using wireless sensors with limited battery life. A central question is to decide where to locate these sensors to best predict the phenomenon at the unsensed locations. However, given the power constraints, we also need to determine when to activate these sensors in order to maximize the performance while satisfying lifetime requirements. Traditionally, these two problems of sensor placement and scheduling have been considered separately; one first decides where to place the sensors, and then when to activate them. We present an efficient algorithm, ESPASS, that simultaneously optimizes the placement and the schedule. We prove that ESPASS provides a constant-factor approximation to the optimal solution of this NP-hard optimization problem. A salient feature of our approach is that it obtains “balanced ” schedules that perform uniformly well over time, rather than only on average. We also develop MCSPASS, an extension to our algorithm that allows for a smooth power-accuracy tradeoff. Our algorithm applies to complex settings where the sensing quality of a set of sensors is measured, e.g., in the improvement of prediction accuracy (more formally, to situations where the sensing quality function is submodular). We present extensive empirical studies on several sensing tasks, and our results show that simultaneously placing and scheduling gives drastically improved performance compared to separate placement and scheduling (e.g., a 33 % improvement in network lifetime on the traffic prediction task). Index Terms—Approximation algorithms, information theory, sensor networks, sensor placement, sensor scheduling, sensor

We present an anytime algorithm for coordinating multiple autonomous searchers to find a potentially adversarial target on a graphical representation of a physical environment. This problem is closely related to the mathematical problem of searching for an adversary on a graph. Prior methods in the literature treat multi-agent search as either a worst-case problem (i.e., clear an environment of an adversarial evader with potentially infinite speed), or an average-case problem (i.e., minimize average capture time given a model of the target’s motion). Both of these problems have been shown to be NP-hard, and optimal solutions typically scale exponentially in the number of searchers. We propose treating search as a resource allocation problem, which leads to a scalable anytime algorithm for generating schedules that clear the environment of a worst-case adversarial target and have good average-case performance considering a nonadversarial motion model. Our algorithm yields theoretically bounded average-case performance and allows for online and decentralized operation, making it applicable to real-world search tasks. We validate our proposed algorithm through a large number of experiments in simulation and with a team of robot and human searchers in an office building.

As networked systems grow and traffic patterns evolve, management applications are increasing in complexity and functionality. To address the requirements of these management applications, equipment vendors and administrators today depend on incremental solutions that increase the complexity of network elements and deployment costs for operators. Despite this increased complexity and cost, the incremental nature of these solutions still leaves a significant gap between the policy objectives of system administrators and today’s mechanisms. These challenges arise in several application contexts in different networking domains: ISPs, enterprise settings, and data centers. Much of this disconnect arises from the narrow device-centric view of current solutions. Such piecemeal solutions are inefficient: network elements duplicate tasks and some locations become overloaded. Worse still, administrators struggle to retrofit their high-level goals within device-centric configurations. This