9,838 research outputs found

    Optimization Algorithms for Information Retrieval and Transmission in Distributed Ad Hoc Networks

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    An ad hoc network is formed by a group of self-configuring nodes, typically deployed in two or three dimensional spaces, and communicating with each other through wireless or some other media. The distinct characteristics of ad hoc networks include the lack of pre-designed infrastructure, the natural correlation between the network topology and geometry, and limited communication and computation resources. These characteristics introduce new challenges and opportunities for de- signing ad hoc network applications. This dissertation studies various optimization problems in ad hoc network information retrieval and transmission. Information stored in ad hoc networks is naturally associated with its location. To effectively retrieve such information, we study two fundamental problems, range search and object locating, from a distance sensitive point of view, where the retrieval cost depends on the distance between the user and the target information. We develop a general framework that is applicable to both problems for optimizing the storage overhead while maintaining the distance sensitive retrieval requirement. In addition, we derive a lowerbound result for the object locating problem which shows that logarithmic storage overhead is asymptotically optimal to achieve linear retrieval cost for growth bounded networks. Bandwidth is a scarce resource for wireless ad hoc networks, and its proper utilization is crucial to effective information transmission. To avoid conflict of wireless transmissions, links need to be carefully scheduled to satisfy various constraints. In this part of the study, we first consider an optimization problem of end-to-end on- demand bandwidth allocation with the single transceiver constraint. We study its complexity and present a 2-approximation algorithm. We then discuss how to estimate the end-to-end throughput under a widely adopted model for radio signal interference. A method based on identifying certain clique patterns is proposed and shown to have good practical performance

    A network-aware framework for energy-efficient data acquisition in wireless sensor networks

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    Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN

    Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

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    IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
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