25,793 research outputs found

    Worst-case performance of a mobile sensor network under individual sensor failure

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    In this paper, we consider the problem of worst-case performance by a mobile sensor network (MSN) when some of the nodes in the network fail. We formulate the problem as a game in which some subset of the nodes act in an adversarial manner, choosing their motion strategies to maximally degrade overall performance of the network as a whole. We restrict our attention in the present paper to a target detection problem in which the goal is to minimize the probability of missed detection. We use a partitioned cost function that is minimized when each sensor executes a motion strategy given by Lloyd's algorithm (i.e., each agent moves toward the centroid of its Voronoi partition at each time instant), and when the probability of missed detection for each functioning sensor increases with the distance between sensor and target for correctly functioning sensors; adversarial nodes in the network are unable to detect the target, and move to maximally increase the probability of missed detection by the properly functioning sensors. We pose the problem as a multi-stage decision process, and use forward dynamic programming over a finite horizon to numerically compute optimal strategies for the adversaries. We compare the resulting strategies to a greedy algorithm, providing both system trajectories and evolution of the probability of missed detection during execution.Ope

    Jointly Optimizing Placement and Inference for Beacon-based Localization

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    The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and Systems (IROS

    Bioans: bio-inspired ambient intelligence protocol for wireless sensor networks

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    This paper describes the BioANS (Bio-inspired Autonomic Networked Services) protocol that uses a novel utility-based service selection mechanism to drive autonomicity in sensor networks. Due to the increase in complexity of sensor network applications, self-configuration abilities, in terms of service discovery and automatic negotiation, have become core requirements. Further, as such systems are highly dynamic due to mobility and/or unreliability; runtime self-optimisation and self-healing is required. However the mechanism to implement this must be lightweight due to the sensor nodes being low in resources, and scalable as some applications can require thousands of nodes. BioANS incorporates some characteristics of natural emergent systems and these contribute to its overall stability whilst it remains simple and efficient. We show that not only does the BioANS protocol implement autonomicity in allowing a dynamic network of sensors to continue to function under demanding circumstances, but that the overheads incurred are reasonable. Moreover, state-flapping between requester and provider, message loss and randomness are not only tolerated but utilised to advantage in the new protocol

    Approximation Algorithm for Line Segment Coverage for Wireless Sensor Network

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    The coverage problem in wireless sensor networks deals with the problem of covering a region or parts of it with sensors. In this paper, we address the problem of covering a set of line segments in sensor networks. A line segment ` is said to be covered if it intersects the sensing regions of at least one sensor distributed in that region. We show that the problem of finding the minimum number of sensors needed to cover each member in a given set of line segments in a rectangular area is NP-hard. Next, we propose a constant factor approximation algorithm for the problem of covering a set of axis-parallel line segments. We also show that a PTAS exists for this problem.Comment: 16 pages, 5 figures
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