1,277 research outputs found

    Cooperative routing in wireless ad hoc networks.

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    Cheung, Man Hon.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 89-94).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Rayleigh Fading Channels --- p.1Chapter 1.2 --- Ultra-Wideband (UWB) Communications --- p.2Chapter 1.2.1 --- Definition --- p.2Chapter 1.2.2 --- Characteristics --- p.3Chapter 1.2.3 --- UWB Signals --- p.4Chapter 1.2.4 --- Applications --- p.5Chapter 1.3 --- Cooperative Communications --- p.7Chapter 1.4 --- Outline of Thesis --- p.7Chapter 2 --- Background Study --- p.9Chapter 2.1 --- Interference-Aware Routing --- p.9Chapter 2.2 --- Routing in UWB Wireless Networks --- p.11Chapter 2.3 --- Cooperative Communications and Routing --- p.12Chapter 3 --- Cooperative Routing in Rayleigh Fading Channel --- p.15Chapter 3.1 --- System Model --- p.16Chapter 3.1.1 --- Transmitted Signal --- p.16Chapter 3.1.2 --- Received Signal and Maximal-Ratio Combining (MRC) --- p.16Chapter 3.1.3 --- Probability of Outage --- p.18Chapter 3.2 --- Cooperation Criteria and Power Distribution --- p.21Chapter 3.2.1 --- Optimal Power Distribution Ratio --- p.21Chapter 3.2.2 --- Near-Optimal Power Distribution Ratio β´ة --- p.21Chapter 3.2.3 --- Cooperation or Not? --- p.23Chapter 3.3 --- Performance Analysis and Evaluation --- p.26Chapter 3.3.1 --- 1D Poisson Random Network --- p.26Chapter 3.3.2 --- 2D Grid Network --- p.28Chapter 3.4 --- Cooperative Routing Algorithm --- p.32Chapter 3.4.1 --- Cooperative Routing Algorithm --- p.33Chapter 3.4.2 --- 2D Random Network --- p.35Chapter 4 --- UWB System Model and BER Expression --- p.37Chapter 4.1 --- Transmit Signal --- p.37Chapter 4.2 --- Channel Model --- p.39Chapter 4.3 --- Received Signal --- p.39Chapter 4.4 --- Rake Receiver with Maximal-Ratio Combining (MRC) --- p.41Chapter 4.5 --- BER in the presence of AWGN & MUI --- p.46Chapter 4.6 --- Rake Receivers --- p.47Chapter 4.7 --- Comparison of Simple Routing Algorithms in ID Network --- p.49Chapter 5 --- Interference-Aware Routing in UWB Wireless Networks --- p.57Chapter 5.1 --- Problem Formulation --- p.57Chapter 5.2 --- Optimal Interference-Aware Routing --- p.58Chapter 5.2.1 --- Link Cost --- p.58Chapter 5.2.2 --- Per-Hop BER Requirement and Scaling Effect --- p.59Chapter 5.2.3 --- Optimal Interference-Aware Routing --- p.61Chapter 5.3 --- Performance Evaluation --- p.64Chapter 6 --- Cooperative Routing in UWB Wireless Networks --- p.69Chapter 6.1 --- Two-Node Cooperative Communication --- p.69Chapter 6.1.1 --- Received Signal for Non-Cooperative Communication --- p.69Chapter 6.1.2 --- Received Signal for Two-Node Cooperative Communication --- p.70Chapter 6.1.3 --- Probability of Error --- p.71Chapter 6.2 --- Problem Formulation --- p.75Chapter 6.3 --- Cooperative Routing Algorithm --- p.77Chapter 6.4 --- Performance Evaluation --- p.80Chapter 7 --- Conclusion and Future Work --- p.85Chapter 7.1 --- Conclusion --- p.85Chapter 7.2 --- Future Work --- p.86Chapter 7.2.1 --- Distributed Algorithm --- p.87Chapter 7.2.2 --- Performance Analysis in Random Networks --- p.87Chapter 7.2.3 --- Cross-Layer Optimization --- p.87Chapter 7.2.4 --- Game Theory --- p.87Chapter 7.2.5 --- Other Variations in Cooperative Schemes --- p.88Bibliography --- p.8

    Target Tracking in Confined Environments with Uncertain Sensor Positions

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    To ensure safety in confined environments such as mines or subway tunnels, a (wireless) sensor network can be deployed to monitor various environmental conditions. One of its most important applications is to track personnel, mobile equipment and vehicles. However, the state-of-the-art algorithms assume that the positions of the sensors are perfectly known, which is not necessarily true due to imprecise placement and/or dropping of sensors. Therefore, we propose an automatic approach for simultaneous refinement of sensors' positions and target tracking. We divide the considered area in a finite number of cells, define dynamic and measurement models, and apply a discrete variant of belief propagation which can efficiently solve this high-dimensional problem, and handle all non-Gaussian uncertainties expected in this kind of environments. Finally, we use ray-tracing simulation to generate an artificial mine-like environment and generate synthetic measurement data. According to our extensive simulation study, the proposed approach performs significantly better than standard Bayesian target tracking and localization algorithms, and provides robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201

    Multi-hop Cooperative Relaying for Energy Efficient In Vivo Communications

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    This paper investigates cooperative relaying to support energy efficient in vivo communications. In such a network, the in vivo source nodes transmit their sensing information to an on-body destination node either via direct communications or by employing on-body cooperative relay nodes in order to promote energy efficiency. Two relay modes are investigated, namely single-hop and multi-hop (two-hop) relaying. In this context, the paper objective is to select the optimal transmission mode (direct, single-hop, or two-hop relaying) and relay assignment (if cooperative relaying is adopted) for each source node that results in the minimum per bit average energy consumption for the in vivo network. The problem is formulated as a binary program that can be efficiently solved using commercial optimization solvers. Numerical results demonstrate the significant improvement in energy consumption and quality-of-service (QoS) support when multi-hop communication is adopted

    A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

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    In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

    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

    Distributed cooperative data transfer for UWB adhoc network

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