3,704 research outputs found
Evasion Paths in Mobile Sensor Networks
Suppose that ball-shaped sensors wander in a bounded domain. A sensor doesn't
know its location but does know when it overlaps a nearby sensor. We say that
an evasion path exists in this sensor network if a moving intruder can avoid
detection. In "Coordinate-free coverage in sensor networks with controlled
boundaries via homology", Vin deSilva and Robert Ghrist give a necessary
condition, depending only on the time-varying connectivity data of the sensors,
for an evasion path to exist. Using zigzag persistent homology, we provide an
equivalent condition that moreover can be computed in a streaming fashion.
However, no method with time-varying connectivity data as input can give
necessary and sufficient conditions for the existence of an evasion path.
Indeed, we show that the existence of an evasion path depends not only on the
fibrewise homotopy type of the region covered by sensors but also on its
embedding in spacetime. For planar sensors that also measure weak rotation and
distance information, we provide necessary and sufficient conditions for the
existence of an evasion path
Robust Environmental Mapping by Mobile Sensor Networks
Constructing a spatial map of environmental parameters is a crucial step to
preventing hazardous chemical leakages, forest fires, or while estimating a
spatially distributed physical quantities such as terrain elevation. Although
prior methods can do such mapping tasks efficiently via dispatching a group of
autonomous agents, they are unable to ensure satisfactory convergence to the
underlying ground truth distribution in a decentralized manner when any of the
agents fail. Since the types of agents utilized to perform such mapping are
typically inexpensive and prone to failure, this results in poor overall
mapping performance in real-world applications, which can in certain cases
endanger human safety. This paper presents a Bayesian approach for robust
spatial mapping of environmental parameters by deploying a group of mobile
robots capable of ad-hoc communication equipped with short-range sensors in the
presence of hardware failures. Our approach first utilizes a variant of the
Voronoi diagram to partition the region to be mapped into disjoint regions that
are each associated with at least one robot. These robots are then deployed in
a decentralized manner to maximize the likelihood that at least one robot
detects every target in their associated region despite a non-zero probability
of failure. A suite of simulation results is presented to demonstrate the
effectiveness and robustness of the proposed method when compared to existing
techniques.Comment: accepted to icra 201
Renyi Entropy based Target Tracking in Mobile Sensor Networks
This paper proposes an entropy based target tracking approach for mobile sensor networks. The proposed tracking algorithm runs a target state estimation stage and a motion control stage alternatively. A distributed particle filter is developed to estimate the target position in the first stage. This distributed particle filter does not require to transmit the weighted particles from one sensor node to another. Instead, a Gaussian mixture model is formulated to approximate the posterior distribution represented by the weighted particles via an EM algorithm. The EM algorithm is developed in a distributed form to compute the parameters of Gaussian mixture model via local communication, which leads to the distributed implementation of the particle filter. A flocking controller is developed to control the mobile sensor nodes to track the target in the second stage. The flocking control algorithm includes three components. Collision avoidance component is based on the design of a separation potential function. Alignment component is based on a consensus algorithm. Navigation component is based on the minimization of an quadratic Renyi entropy. The quadratic Renyi entropy of Gaussian mixture model has an analytical expression so that its optimization is feasible in mobile sensor networks. The proposed active tracking algorithm is tested in simulation. © 2011 IFAC
Performance Evaluation of IEEE 802.15.4 for Mobile Sensor Network
The IEEE 802.15.4 standard medium access control (MAC) protocol for low rate wireless personal area networks (LRWPAN) is design mainly for static sensor networks and its capability to support mobile sensor networks has not yet been established. To the best knowledge of authors, this is the first paper that evaluates the suitability of IEEE 802.15.4 MAC in mobile sensor networks environment. We evaluate the performance based on node\u27s speed and beacon order, and observe the effect on energy usage, packet delivery ratio and time required to associate with its coordinator. From the experiment we observe that the moving nodes experienced serious problems in association and synchronization and show results on energy usage, throughput , association and reassociation rate with different speeds of moving node. We also identify some key research problems that need to be addressed for successful implementations of IEEE 802.15..4 in mobile sensor networks environment
Connectivity-guaranteed and obstacle-adaptive deployment schemes for mobile sensor networks
Mobile sensors can relocate and self-deploy into a network. While focusing on the problems of coverage, existing deployment schemes largely over-simplify the conditions for network connectivity: they either assume that the communication range is large enough for sensors in geometric neighborhoods to obtain location information through local communication, or they assume a dense network that remains connected. In addition, an obstacle-free field or full knowledge of the field layout is often assumed. We present new schemes that are not governed by these assumptions, and thus adapt to a wider range of application scenarios. The schemes are designed to maximize sensing coverage and also guarantee connectivity for a network with arbitrary sensor communication/sensing ranges or node densities, at the cost of a small moving distance. The schemes do not need any knowledge of the field layout, which can be irregular and have obstacles/holes of arbitrary shape. Our first scheme is an enhanced form of the traditional virtual-force-based method, which we term the Connectivity-Preserved Virtual Force (CPVF) scheme. We show that the localized communication, which is the very reason for its simplicity, results in poor coverage in certain cases. We then describe a Floor-based scheme which overcomes the difficulties of CPVF and, as a result, significantly outperforms it and other state-of-the-art approaches. Throughout the paper our conclusions are corroborated by the results from extensive simulations
SimpleTrack:Adaptive Trajectory Compression with Deterministic Projection Matrix for Mobile Sensor Networks
Some mobile sensor network applications require the sensor nodes to transfer
their trajectories to a data sink. This paper proposes an adaptive trajectory
(lossy) compression algorithm based on compressive sensing. The algorithm has
two innovative elements. First, we propose a method to compute a deterministic
projection matrix from a learnt dictionary. Second, we propose a method for the
mobile nodes to adaptively predict the number of projections needed based on
the speed of the mobile nodes. Extensive evaluation of the proposed algorithm
using 6 datasets shows that our proposed algorithm can achieve sub-metre
accuracy. In addition, our method of computing projection matrices outperforms
two existing methods. Finally, comparison of our algorithm against a
state-of-the-art trajectory compression algorithm show that our algorithm can
reduce the error by 10-60 cm for the same compression ratio
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