1,341 research outputs found

    A survey of localization in wireless sensor network

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    Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

    Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

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    Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

    Mobile-Beacon Assisted Sensor Localization with Dynamic Beacon Mobility Scheduling

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    International audienceIn mobile-beacon assisted sensor localization, beacon mobility scheduling aims to determine the best beacon trajectory so that each sensor receives sufficient beacon signals with minimum delay. We propose a novel DeteRministic bEAcon Mobility Scheduling (DREAMS) algorithm, without requiring any prior knowledge of the sensory field. In this algorithm, beacon trajectory is defined as the track of depth-first traversal (DFT) of the network graph, thus deterministic. The mobile beacon performs DFT under the instruction of nearby sensors on the fly. It moves from sensor to sensor in an intelligent heuristic manner according to RSS (Received Signal Strength)-based distance measurements. We prove that DREAMS guarantees full localization (every sensor is localized) when the measurements are noise-free. Then we suggest to apply node elimination and topology control (Local Minimum Spanning Tree) to shorten beacon tour and reduce delay. Through simulation we show that DREAMS guarantees full localization even with noisy distance measurements. We evaluate its performance on localization delay and communication overhead in comparison with a previously proposed static path based scheduling method

    Optimized Cooperative Localization Technique Based on Linear Intersection over Wireless Sensor Networks

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    Localization is one of the significant techniques in wireless sensor networks. The localization approaches are different in several applications. Localization offers geographical information for managing the topology. In this paper, we propose optimized cooperative localization technique based on trilateration, multilateration and linear intersection. The approach reduces the error rates, communication cost and energy consumption for maintaining the high accuracy. Furthermore, the approach is implemented for controlling air craft system to avoid the landing and takeoff delays. To demonstrate the strength of the approach, we used network simulator ns-2 to validate the estimation errors, computational latency, energy consumption and error tolerance. Based on the simulation results, we conclude that the presented approach outperforms other existing cooperative scheduling approaches in terms of accuracy, mobility, consumed power

    beacon based context aware architecture for crowd sensing public transportation scheduling and user habits

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    Abstract: Crowd sourcing and sensing are relatively recent paradigms that, enabled by the pervasiveness of mobile devices, allow users to transparently contribute in complex problem solving. Their effectiveness depends on people voluntarism, and this could limit their adoption. Recent technologies for automating context-awareness could give a significant impulse to spread crowdsourcing paradigms. In this paper, we propose a distributed software system that exploits mobile devices to improve public transportation efficiency. It takes advantage of the large number of deployed personal mobile devices and uses them as both mule sensors, in cooperation with beacon technology for geofecing, and clients for getting information about bus positions and estimated arrival times. The paper discusses the prototype architecture, its basic application for getting dynamic bus information, and the long-term scope in supporting transportation companies and municipalities, reducing costs, improving bus lines, urban mobility and planning

    Wireless Sensor Technologies and Applications

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    Recent years have witnessed tremendous advances in the design and applications of wirelessly networked and embedded sensors. Wireless sensor nodes are typically low-cost, low-power, small devices equipped with limited sensing, data processing and wireless communication capabilities, as well as power supplies. They leverage the concept of wireless sensor networks (WSNs), in which a large (possibly huge) number of collaborative sensor nodes could be deployed. As an outcome of the convergence of micro-electro-mechanical systems (MEMS) technology, wireless communications, and digital electronics, WSNs represent a significant improvement over traditional sensors. In fact, the rapid evolution of WSN technology has accelerated the development and deployment of various novel types of wireless sensors, e.g., multimedia sensors. Fulfilling Moore’s law, wireless sensors are becoming smaller and cheaper, and at the same time more powerful and ubiquitous. [...
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