A wireless sensor network (WSN) is a network comprised of a collection of small, specialized sensors with limited memory, processing capacities, and energy resources. These sensors are employed to monitor and record v...
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ISBN:
(纸本)9783031609961;9783031609978
A wireless sensor network (WSN) is a network comprised of a collection of small, specialized sensors with limited memory, processing capacities, and energy resources. These sensors are employed to monitor and record various physical characteristics of the surrounding environment, such as temperature, sound, light, pressure, pollution, among others. Localization has emerged as a prominent research area in the realm of wireless sensor networks, garnering significant interest from academia and researchers alike. This work aims to present a comprehensive survey of diverse techniques and strategies employed to determine the precise location of nodes within the network. Initially, we discuss the available classifications for localization algorithms in wireless sensor networks. Subsequently, we elucidate the techniques and algorithms utilized within each classification. Lastly, we delve into a detailed examination of the existing challenges and issues encountered in the field of wireless sensor network localization.
Imagine a coverage area with many wireless access points that cooperate to jointly serve the users, instead of creating autonomous cells. Such a cell-free network operation can potentially resolve many of the interfer...
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Imagine a coverage area with many wireless access points that cooperate to jointly serve the users, instead of creating autonomous cells. Such a cell-free network operation can potentially resolve many of the interference issues that appear in current cellular networks. This ambition was previously called Network MIMO (multiple-input multiple-output) and has recently reappeared under the name Cell-Free Massive MIMO. The main challenge is to achieve the benefits of cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to large networks with many users. We propose a new framework for scalable Cell-Free Massive MIMO systems by exploiting the dynamic cooperation cluster concept from the Network MIMO literature. We provide a novel algorithm for joint initial access, pilot assignment, and cluster formation that is proved to be scalable. Moreover, we adapt the standard channel estimation, precoding, and combining methods to become scalable. A new uplink and downlink duality is proved and used to heuristically design the precoding vectors on the basis of the combining vectors. Interestingly, the proposed scalable precoding and combining outperform conventional maximum ratio (MR) processing and also performs closely to the best unscalable alternatives.
We study cooperative sensor network localization in a realistic scenario where 1) the underlying measurement errors more probably follow a non-Gaussian distribution;2) the measurement error distribution is unknown wit...
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We study cooperative sensor network localization in a realistic scenario where 1) the underlying measurement errors more probably follow a non-Gaussian distribution;2) the measurement error distribution is unknown without conducting massive offline calibrations;and 3) non-line-of-sight identification is not performed due to the complexity constraint and/or storage limitation. The underlying measurement error distribution is approximated parametrically by a Gaussian mixture with finite number of components, and the expectation-conditional maximization (ECM) criterion is adopted to approximate the maximum-likelihood estimator of the unknown sensor positions and an extra set of Gaussian mixture model parameters. The resulting centralized ECM algorithms lead to easier inference tasks and meanwhile retain several convergence properties with a proof of the "space filling" condition. To meet the scalability requirement, we further develop two distributed ECM algorithms where an average consensus algorithm plays an important role for updating the Gaussian mixture model parameters locally. The proposed algorithms are analyzed systematically in terms of computational complexity and communication overhead. Various computer based tests are also conducted with both simulation and experimental data. The results pin down that the proposed distributedalgorithms can provide overall good performance for the assumed scenario even under model mismatch, while the existing competing algorithms either cannot work without the prior knowledge of the measurement error statistics or merely provide degraded localization performance when the measurement error is clearly non-Gaussian.
Channel assignment in multi-channel multi-radio wireless networks poses a significant challenge due to scarcity of number of channels available in the wireless spectrum. Further, additional care has to be taken to con...
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Channel assignment in multi-channel multi-radio wireless networks poses a significant challenge due to scarcity of number of channels available in the wireless spectrum. Further, additional care has to be taken to consider the interference characteristics of the nodes in the network especially when nodes are in different collision domains. This work views the problem of channel assignment in multi-channel multi-radio networks with multiple collision domains as a non-cooperative game where the objective of the players is to maximize their individual utility by minimizing its interference. Necessary and sufficient conditions are derived for the channel assignment to be a Nash Equilibrium (NE) and efficiency of the NE is analyzed by deriving the lower bound of the price of anarchy of this game. A new fairness measure in multiple collision domain context is proposed and necessary and sufficient conditions for NE outcomes to be fair are derived. The equilibrium conditions are then applied to solve the channel assignment problem by proposing three algorithms, based on perfect/imperfect information, which rely on explicit communication between the players for arriving at an NE. A no-regret learning algorithm known as Freund and Schapire Informed algorithm, which has an additional advantage of low overhead in terms of information exchange, is proposed and its convergence to the stabilizing outcomes is studied. New performance metrics are proposed and extensive simulations are done using Matlab to obtain a thorough understanding of the performance of these algorithms on various topologies with respect to these metrics. It was observed that the algorithms proposed were able to achieve good convergence to NE resulting in efficient channel assignment strategies.
We consider meshed optical transport networks having multiple levels of hierarchy whereby cross-connects in different levels perform multiplexing and demultiplexing functions at different granularities. We present a t...
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ISBN:
(纸本)0780378024
We consider meshed optical transport networks having multiple levels of hierarchy whereby cross-connects in different levels perform multiplexing and demultiplexing functions at different granularities. We present a traffic grooming approach that can be implemented in a centralized or distributed fashion, based on the novel design that takes advantage of algorithm efficiency and a simple threshold mechanism to decide when grooming is economical. Our approach allows nodes to perform grooming and degrooming automatically, which is desired in the next-generation optical network where circuits are setup and torn-down dynamically through signaling. We present several experiments using different network topologies. The results indicate that the flow pattern influences the port requirement behavior, and that the flow thickness influences the optimal value of the threshold.
The classical max-min policy has been suggested by the ATM Forum to support the available bit rate (ABR) service class. However, there are several drawbacks in adopting the max-min rate allocation policy. In particula...
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The classical max-min policy has been suggested by the ATM Forum to support the available bit rate (ABR) service class. However, there are several drawbacks in adopting the max-min rate allocation policy. In particular, the max-min policy is not able to support the minimum cell rate (MCR) requirement and the peak cell rate (PCR) constraint for each ABR connection. Furthermore, the max-min policy does not offer flexible options for network providers wishing to establish a usage-based pricing criterion. In this paper, we present a generic weight-based rate allocation policy, which generalizes the classical max-min policy by supporting the MCR/PCR for each connection. Our rate allocation policy offers a flexible usage-based pricing strategy to network providers. A centralized algorithm is presented to compute network-wide bandwidth allocation to achieve this policy. Furthermore, a simple switch algorithm using ABR flow control protocol is developed with the aim of achieving our rate allocation policy in a distributed networking environment. The effectiveness of our distributed algorithm in a local area environment is substantiated by simulation results based on the benchmark network configurations suggested by the ATM Forum.
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