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Abstract-We study a decentralized detection architecture in which each of a set of sensors transmits a highly compressed summary of its observations (a binary message) to a fusion center, which then decides on one of two alternative hypotheses. In contrast to the star (or "parallel") architecture considered in most of the literature, we allow a subset of the sensors to both transmit their messages to the fusion center and to also broadcast them to the remaining sensors. We focus on the following architectural question: is there a significant performance improvement when we allow such a message broadcast? We consider the error exponent (asymptotically, in the limit of a large number of sensors) for the Neyman-Pearson formulation of the detection problem. We prove that the sharing of messages does not improve the optimal error exponent.

Abstract-A promising feature of emerging wireless sensor networks is the opportunity for each spatially-distributed node to measure its local state and transmit only information relevant to effective global decision-making. An equally important design objective, as a result of each node's finite power, is for measurement processing to satisfy explicit constraints on, or perhaps make selective use of, the distributed algorithmic resources. We formulate this multi-objective design problem within the Bayesian decentralized detection paradigm, modeling resource constraints by a directed acyclic network with low-rate, unreliable communication links. Existing team theory establishes when necessary optimality conditions reduce to a convergent iterative algorithm to be executed offline (i.e., before measurements are processed). Even so, this offline algorithm has exponential complexity in the number of nodes, and its distributed implementation assumes a fully-connected communication network. We state conditions under which the offline algorithm admits an efficient message-passing interpretation, featuring linear complexity and a natural distributed implementation. We experiment with a simulated network of binary detectors, applying the message-passing algorithm to optimize the achievable tradeoff between global detection performance and network-wide online communication. The empirical analysis also exposes a design tradeoff between constraining in-network processing to preserve resources (per online measurement) and then having to consume resources (per offline reorganization) to maintain detection performance.

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We consider the NP-hard problem of multirobot informative path planning in the presence of communication constraints, where the objective is to collect higher amounts of information of an ambient phenomenon. We propose a novel approach that uses continuous region partitioning into Voronoi components to efficiently divide an initially unknown environment among the robots based on newly discovered obstacles enabling improved load balancing between robots. Simulation results show that our proposed approach is successful in reducing the initial imbalance of the robots’ allocated free regions while ensuring close-to-reality spatial modeling within a reasonable amount of time.

Precision agriculture is the collection of hardware and software technologies that allow a farmer to make informed, differentiated decisions regarding agricultural operations such as planting, fertilizing, pest control, and harvesting. In recent years, advances in agricultural machinery and the emergence of agricultural robots continuously increased the resolution at which differentiated treatment is possible. This creates a corresponding need for information at a fine spatial and temporal resolution. Autonomous multi-robot systems (e.g., unmanned ground and aerial vehicles) are some of the most promising approaches for such information collection in open-air farms. In this paper, we survey the current state and challenges of multi-robot information gathering for precision agriculture, with a special focus on maximizing information and ensuring the security of the collected data while simultaneously keeping energy consumption in check.

Abstract. We consider a network security classification game in which a strategic defender decides whether an attacker is a strategic spy or a naive spammer based on an observed sequence of attacks on file-or mail-servers. The spammer's goal is attacking the mail-server, while the spy's goal is attacking the file-server as much as possible before detection. The defender observes for a length of time that trades-off the potential damage inflicted during the observation period with the ability to reliably classify the attacker. Through empirical analyses, we find that when the defender commits to a fixed observation window, often the spy's best response is either full-exploitation mode or full-confusion mode. This discontinuity prevents the existence of a pure Nash equilibrium in many cases. However, when the defender can condition the observation time based on the observed sequence, a Nash equilibrium often exists.