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Locating fixed sensing devices with a mobile anchor is attractive for covering larger deployment areas. Probabilistic localization has been shown to reduce error over deterministic approaches, yet the performance sensitivity to the geometric arrangement of anchor beacon positions remains unexplored. This paper presents a detailed description of a new RSSI-based localization algorithm which uses a...
Localization is fundamental for many wireless sensor network applications. Localizing ground-based fixed nodes through an airborne mobile anchor node is particularly useful for sensors deployed from the air, yet its dynamics are not well-understood. In this work-in-progress paper, we propose a new formulation for probabilistic localization using gradient descent, and compare it with a deterministic...
Ubiquitous computing is increasingly introduced in our daily life. An emerging application is environmental monitoring in urban areas. Data gathering in such wireless sensor networks can be performed by using uncontrolled mobile sinks besides the fixed sinks in order to reduce the transmission energy. This method can be highly inefficient as the notification of presence by the mobile sink is not guaranteed...
In this paper we propose a tracking system for wireless sensor networks, which operates on accumulated ranging data from a number of anchor sensor nodes in order to infer the trace and other kinematic characteristics of a mobile target. The nonlinear nature of the ldquotracking with range-only measurementsrdquo problem yields significant challenges. In the proposed system, this problem is modeled...
Recent advance in robotics technology has led to an increasing interest in mobile sensor networks that are constructed by sensor nodes with a moving facility (mobile sensors). Until now, we have proposed an effective mobile sensor control method, named DATFM (Data Acquisition and Transmission with Fixed and Mobile Node), for sparse sensor networks. DATFM achieves the effective data acquisition in...
We derive the Neyman-Pearson error exponent for the detection of Gauss-Markov signals using randomly spaced sensors. We assume that the sensor spacings, d1,d2,..., are drawn independently from a common density fd(.), and we treat both stationary and nonstationary Markov models. Error exponents are evaluated using specialized forms of the strong law of large numbers, and are seen to take on algebraically...
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