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Wireless sensor network

About: Wireless sensor network is a(n) research topic. Over the lifetime, 142021 publication(s) have been published within this topic receiving 2448622 citation(s). The topic is also known as: WSN & sensor grid.

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Papers
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Journal ArticleDOI: 10.1016/S1389-1286(01)00302-4
15 Mar 2002-Computer Networks
Abstract: This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.

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17,354 Citations


Journal ArticleDOI: 10.1109/MCOM.2002.1024422
Abstract: The advancement in wireless communications and electronics has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (e.g., health, military, home). For different application areas, there are different technical issues that researchers are currently resolving. The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections. This article also points out the open research issues and intends to spark new interests and developments in this field.

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Topics: Wireless sensor network (61%), Mobile wireless sensor network (59%), Intelligent sensor (56%) ...read more

13,726 Citations


Proceedings ArticleDOI: 10.1109/HICSS.2000.926982
04 Jan 2000-
Abstract: Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

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Topics: Wireless sensor network (59%), Routing protocol (59%), Mobile wireless sensor network (57%) ...read more

11,951 Citations


Open access
01 Jan 2000-
Abstract: Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have signicant impact on the overall energy dissipation of these networks. Based on our ndings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show that LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional routing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

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Topics: Wireless Routing Protocol (61%), Routing protocol (60%), Wireless sensor network (59%) ...read more

11,410 Citations


Open accessJournal ArticleDOI: 10.1109/JPROC.2006.887293
05 Mar 2007-
Abstract: This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, time-delays, and performance guarantees. An overview of basic concepts of information consensus in networks and methods of convergence and performance analysis for the algorithms are provided. Our analysis framework is based on tools from matrix theory, algebraic graph theory, and control theory. We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in small-world networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms. A brief introduction is provided on networked systems with nonlocal information flow that are considerably faster than distributed systems with lattice-type nearest neighbor interactions. Simulation results are presented that demonstrate the role of small-world effects on the speed of consensus algorithms and cooperative control of multivehicle formations

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  • Fig. 4. (a) a small-world with 300 links, (b) a regular lattice with interconnections to k = 2 nearest neighbors and 300 links, (c) a regular lattice with interconnections to k = 10 nearest neighbors and 1000 links; (d),(e),(f) the state evolution corresponding to networks in (a), (b), and (c), respectively.
    Fig. 4. (a) a small-world with 300 links, (b) a regular lattice with interconnections to k = 2 nearest neighbors and 300 links, (c) a regular lattice with interconnections to k = 10 nearest neighbors and 1000 links; (d),(e),(f) the state evolution corresponding to networks in (a), (b), and (c), respectively.
  • Fig. 2. Examples of networks with n = 20 nodes: a) a regular network with 80 links and b) a random network with 65 links.
    Fig. 2. Examples of networks with n = 20 nodes: a) a regular network with 80 links and b) a random network with 65 links.
  • Fig. 5. (a) Interconnection graph of a multi-vehicle formation and (b) the Nyquist plot.
    Fig. 5. (a) Interconnection graph of a multi-vehicle formation and (b) the Nyquist plot.
  • Table 1. Continuous-Time vs. Discrete-Time Consensus
    Table 1. Continuous-Time vs. Discrete-Time Consensus
  • Fig. 1. Two equivalent forms of consensus algorithms: (a) a network of integrator agents in which agent i receives the state xj of its neighbor, agent j, if there is a link (i, j) connecting the two nodes; and (b) the block diagram for a network of interconnected dynamic systems all with identical transfer functions P (s) = 1/s. The collective networked system has a diagonal transfer function and is a MIMO (multi-input multi-output) linear system.
    Fig. 1. Two equivalent forms of consensus algorithms: (a) a network of integrator agents in which agent i receives the state xj of its neighbor, agent j, if there is a link (i, j) connecting the two nodes; and (b) the block diagram for a network of interconnected dynamic systems all with identical transfer functions P (s) = 1/s. The collective networked system has a diagonal transfer function and is a MIMO (multi-input multi-output) linear system.
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Topics: Consensus dynamics (71%), Uniform consensus (67%), Consensus (61%) ...read more

8,696 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2022175
20214,962
20205,853
20196,917
20188,087
20178,575

Top Attributes

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Topic's top 5 most impactful authors

Paul J.M. Havinga

172 papers, 4.9K citations

Azzedine Boukerche

148 papers, 4.7K citations

Deborah Estrin

143 papers, 42.7K citations

Lei Shu

141 papers, 3.6K citations

Sajal K. Das

134 papers, 6.4K citations

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