13,307 research outputs found

    Minimizing Flow Time in the Wireless Gathering Problem

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    We address the problem of efficient data gathering in a wireless network through multi-hop communication. We focus on the objective of minimizing the maximum flow time of a data packet. We prove that no polynomial time algorithm for this problem can have approximation ratio less than \Omega(m^{1/3) when mm packets have to be transmitted, unless P=NPP = NP. We then use resource augmentation to assess the performance of a FIFO-like strategy. We prove that this strategy is 5-speed optimal, i.e., its cost remains within the optimal cost if we allow the algorithm to transmit data at a speed 5 times higher than that of the optimal solution we compare to

    Networked Slepian-Wolf: theory, algorithms, and scaling laws

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    Consider a set of correlated sources located at the nodes of a network, and a set of sinks that are the destinations for some of the sources. The minimization of cost functions which are the product of a function of the rate and a function of the path weight is considered, for both the data-gathering scenario, which is relevant in sensor networks, and general traffic matrices, relevant for general networks. The minimization is achieved by jointly optimizing a) the transmission structure, which is shown to consist in general of a superposition of trees, and b) the rate allocation across the source nodes, which is done by Slepian-Wolf coding. The overall minimization can be achieved in two concatenated steps. First, the optimal transmission structure is found, which in general amounts to finding a Steiner tree, and second, the optimal rate allocation is obtained by solving an optimization problem with cost weights determined by the given optimal transmission structure, and with linear constraints given by the Slepian-Wolf rate region. For the case of data gathering, the optimal transmission structure is fully characterized and a closed-form solution for the optimal rate allocation is provided. For the general case of an arbitrary traffic matrix, the problem of finding the optimal transmission structure is NP-complete. For large networks, in some simplified scenarios, the total costs associated with Slepian-Wolf coding and explicit communication (conditional encoding based on explicitly communicated side information) are compared. Finally, the design of decentralized algorithms for the optimal rate allocation is analyzed

    Network correlated data gathering with explicit communication: NP-completeness and algorithms

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    We consider the problem of correlated data gathering by a network with a sink node and a tree-based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy-based coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NP-hard. We propose some efficient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal

    Energy-efficient task allocation for distributed applications in Wireless Sensor Networks

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    We consider the scenario of a sensing, computing and communicating infrastructure with a a programmable middleware that allows for quickly deploying different applications running on top of it so as to follow the changing ambient needs. We then face the problem of setting up the desired application in case of hundreds of nodes, which consists in identifying which actions should be performed by each of the nodes so as to satisfy the ambient needs while minimizing the application impact on the infrastructure battery lifetime. We approach the problem by considering every possible decomposition of the application's sensing and computing operations into tasks to be assigned to the each infrastructure component. The contribution of energy consumption due to the performance of each task is then considered to compute a cost function, allowing us to evaluate the viability of each deployment solution. Simulation results show that our framework results in considerable energy conservation with respect to sink-oriented or cluster-oriented deployment approaches, particularly for networks with high node densities, non-uniform energy consumption and initial energy, and complex actions

    Providing End-to-End Delay Guarantees for Multi-hop Wireless Sensor Networks over Unreliable Channels

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    Wireless sensor networks have been increasingly used for real-time surveillance over large areas. In such applications, it is important to support end-to-end delay constraints for packet deliveries even when the corresponding flows require multi-hop transmissions. In addition to delay constraints, each flow of real-time surveillance may require some guarantees on throughput of packets that meet the delay constraints. Further, as wireless sensor networks are usually deployed in challenging environments, it is important to specifically consider the effects of unreliable wireless transmissions. In this paper, we study the problem of providing end-to-end delay guarantees for multi-hop wireless networks. We propose a model that jointly considers the end-to-end delay constraints and throughput requirements of flows, the need for multi-hop transmissions, and the unreliable nature of wireless transmissions. We develop a framework for designing feasibility-optimal policies. We then demonstrate the utility of this framework by considering two types of systems: one where sensors are equipped with full-duplex radios, and the other where sensors are equipped with half-duplex radios. When sensors are equipped with full-duplex radios, we propose an online distributed scheduling policy and proves the policy is feasibility-optimal. We also provide a heuristic for systems where sensors are equipped with half-duplex radios. We show that this heuristic is still feasibility-optimal for some topologies

    Katakan tidak pada rasuah

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    Isu atau masalah rasuah menjadi topik utama sama ada di peringkat antarabangsa mahupun di peringkat dalam negara. Pertubuhan Bangsa- bangsa Bersatu menegaskan komitmen komuniti antarabangsa bertegas untuk mencegah dan mengawal rasuah melalui buku bertajuk United Nations Convention against Corruption. Hal yang sama berlaku di Malaysia. Melalui pernyataan visi oleh mantan Perdana Menteri Malaysia, Tun Dr. Mahathir bin Mohamed memberikan indikasi bahawa kerajaan Malaysia komited untuk mencapai aspirasi agar Malaysia dikenali kerana integriti dan bukannya rasuah. Justeru, tujuan penulisan bab ini adalah untuk membincangkan rasuah dari beberapa sudut termasuk perbincangan dari sudut agama Islam, faktor-faktor berlakunya gejala rasuah, dan usaha-usaha yang dijalankan di Malaysia untuk membanteras gejala rasuah. Perkara ini penting bagi mengenalpasti penjawat awam menanamkan keyakinan dalam melaksanakan tanggungjawab dengan menghindari diri daripada rasuah agar mereka sentiasa peka mengutamakan kepentingan awam
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