Wireless Sensor Network Distributed (9 results)
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Taschenbuch. Condition: Neu. An Improved Energy-Efficient Distributed Clustering Protocol | For Heterogeneous Wireless Sensor Network | Shalini Sahay (u. a.) | Taschenbuch | 72 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202552417 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 2…2848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
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Taschenbuch. Condition: Neu. In-network processing in Wireless Sensor Networks | Novel approaches to distributed data aggregation and compression | Massimo Vecchio | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639039993 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrü…ck, mail[at]preigu[dot]de | Anbieter: preigu.
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Language: English
Published by VDM Verlag Dr. Müller, VDM Verlag Dr. Müller E.K., 2008
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Taschenbuch. Condition: Neu. Neuware - Time synchronization is an important aspect in wireless sensor networks, especially in distributed measurement system. IEEE 1588 is a standard for precise clock synchronization for network measurement and control systems in LAN environment. In the thesis, we present a time synchronization m…echanism, named Hierarchy Reference Broadcast Synchronization (HRBS), for WSN based on IEEE 802.15.4 standard. The proposed system architecture is a hybrid of PTP protocol and HRBS algorithm. It works in three steps. The Internet server receives the UTC time from GPS. PTP synchronization is used to translate its clock value to the server s clock by other devices in LAN. One of the synchronized devices is connect to the sensor node in the wireless sensor network. The synchronized device sent its time stamp to this sensor node. We use HRBS protocol to synchronize all sensor nodes clock in the wireless sensor network. Our implement is using the Linux PCs and Chipcon CC2430. In the finally, we evaluate the PTP and the HRBS time synchronization accuracy and performance. We develop into a wireless sensor network base on the distributed measurement system under precise time protocol.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Establishment of communication with sensor device and enhancement in Cluster Head selection method in distributed environment is presented in this book. The cluster head selection in wireless sensor networks is critical process. The ener…gy consumed in this process degrades the performance and stability of wireless sensor network. To enhance the performance, efficient technique is required. We introduced two new parameters in our algorithm, to make the cluster head selection process more balanced and efficient, by reducing the probability of advanced node to become cluster head when their residual energy is less than or equal to normal node. Here advanced nodes are higher energy nodes as compared to normal nodes. This scheme improves the lifetime and stability of sensor network.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Wireless sensor networks are currently an active research area mainly due to the potential of their applications. However, their deployment in large scale still requires solutions to a number of technical challenges that stem primarily f…rom the features of the sensor nodes such as limited computational power, reduced communication bandwidth and small storage capacity. Indeed, sensor nodes are typically powered by batteries with limited capacity which do not guarantee an attractive lifetime of the nodes, unless adequate power saving policies are undertaken. In this book some novel approaches to distributed data aggregation and compression are proposed, inheriting techniques from the computational intelligence world, with the final aim of minimizing the energy consumption of the communication unit. The content should help shed some light on this new and exciting environment, and should be especially useful for sensor networking and embedded systems professionals including development engineers, researchers, system architects, in a wide variety of companies from the defense industry to the home computing and electronics industry.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Source: Wikipedia. Pages: 49. Chapters: Distributed source coding, ZigBee, Sensor web, DASH7, ZigBee specification, List of wireless sensor nodes, Network coding, Z-Wave, Location estimation in sensor networks, ANT, Multiple Access with…Collision Avoidance for Wireless, TinyOS, Smartdust, Topology control, Dynamic Source Routing, Ad hoc On-Demand Distance Vector Routing, Dust Networks, Daintree Networks, Contiki, PowWow, Digi International, Visual sensor network, WirelessHART, Sun SPOT, Virtual Sensor Networks, International Conference on Information Processing in Sensor Networks, ERIKA Enterprise, Sensor grid, Key distribution in wireless sensor networks, TakaTuka, Isa100.11a, OCARI, Conference on Embedded Networked Sensor Systems, OSIAN, NesC, Wide Area Tracking System, ANT+, Wireless Identification and Sensing Platform, Low Energy Adaptive Clustering Hierarchy, Modulo-N code, Nano-RK, TSMP, European Conference on Wireless Sensor Networks, DISCUS, Secure Data Aggregation in WSN, Cooperative Positioning for Vehicular Networks, LiteOS, NeuRFon, Hogthrob. Excerpt: Distributed source coding (DSC) is an important problem in information theory and communication. DSC problems regard the compression of multiple correlated information sources that do not communicate with each other. By modeling the correlation between multiple sources at the decoder side together with channel codes, DSC is able to shift the computational complexity from encoder side to decoder side, therefore provide appropriate frameworks for applications with complexity-constrained sender, such as sensor networks and video/multimedia compression (see distributed video coding). One of the main properties of distributed source coding is that the computational burden in encoders is shifted to the joint decoder. In 1973, David Slepian and Jack Keil Wolf proposed the information theoretical lossless compression bound on distributed compression of two statistically dependent i.i.d. sources X and Y . After that, this bound was extended to cases with more than two sources by Thomas M. Cover in 1975 , while the theoretical results on lossy compression case are presented by Aaron D. Wyner and Jacob Ziv in 1976 . Although the theorems on DSC were proposed on 1970s, it was after about 30 years that attempts were started for practical techniques, based on the idea that DSC is closely related to channel coding proposed in 1974 by Aaron D. Wyner . The asymmetric DSC problem was addressed by S. S. Pradhan and K. Ramchandran in 1999, which focused on statistically dependent binary and Guassian sources and used scalar and trellis coset constructions to solve the problem . They further extended the work into symmetric DSC case lately . Syndrome decoding technology was first used in distributed source coding by the DISCUS system of SS Pradhan and K Ramachandran (Distributed Source Coding Using Syndromes). They compress binary block data from one source into syndromes and transmit data from the other source uncompressed as side information. This kind of DSC scheme achieves asymmetric.
