Wireless integrated network sensors

Pottie, Gregory J.; Kaiser, William J.

doi:10.1145/332833.332838

Cited by 2,595 publications

(1,149 citation statements)

References 5 publications

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“…Hence they must use low-power (and consequently, low-bandwidth) wireless communication techniques to conserve battery power. In a typical scenario, it has been estimated that 3000 instructions could be executed for the same energy cost of sending a bit 100 meters by radio [9]. This framework encourages the use of computational techniques to reduce the total communication overhead in the network.…”

Section: Overviewmentioning

confidence: 99%

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Resilient Data-Centric Storage in Wireless Ad-Hoc Sensor Networks

Ghose

Großklags

Chuang

2002

Mobile Data Management

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Abstract. Wireless sensor networks will be used in a wide range of challenging applications where numerous sensor nodes are linked to monitor and report distributed event occurrences. In contrast to traditional communication networks, the single major resource constraint in sensor networks is power, due to the limited battery life of sensor devices. It has been shown that data-centric methodologies can be used to solve this problem efficiently. In data-centric storage, a recently proposed data dissemination framework, all event data is stored by type at designated nodes in the network and can later be retrieved by distributed mobile access points in the network. In this paper we propose Resilient Data-Centric Storage (R-DCS) as a method to achieve scalability and resilience by replicating data at strategic locations in the sensor network. Through analytical results and simulations, we show that this scheme leads to significant energy savings in reasonably large-sized networks and scales well with increasing node-density and query rate. We also show that R-DCS realizes graceful performance degradation in the presence of clustered as well as isolated node failures, hence making the sensornet data robust.

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Section: Overviewmentioning

confidence: 99%

“…With improvements in sensor technology, it has become possible to build small sensor devices with relatively high computational power at low costs [9,15].…”

Section: Introductionmentioning

confidence: 99%

Resilient Data-Centric Storage in Wireless Ad-Hoc Sensor Networks

Ghose

Großklags

Chuang

2002

Mobile Data Management

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“…If the base station received perception data dispatched from each node then it will lead much more energy consumed of nodes by wasting communication bandwidth, accordingly shorten network life circle. Furthermore, communication efficiency will be decreased by adding up the probability of data impact and data packet dropout as many nodes simultaneously transmit data which increase the dispatching difficulty of data link [5][6] .…”

Section: Background Analysismentioning

confidence: 99%

Preferable Wireless Sensor Networks Algorithm Based On the Maximum Weight Value of Time

Zhang¹,

Liu²,

Zhao³

2016

Advances in Computer Science Research

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Abstract--The characteristic of Wireless Sensor Networks (WSN) is the one which supported by non-partition and noninfrastructure under large-scale self-organization and limited capacity. This kind of network system is selforganized by means of wireless communications between nodes. However, capacity within nodes is limited, thus we put forward a better method of cluster network on the basis of the maximum weight calculation. In order to make data transmission as effective as possible, we have adopted a method of broadcasting to classify node as inner-cluster node and cluster-head node, and found the longest path (also known as the critical path). Ultimately, network will find the cluster-head node of next layer quickly after the failure of one of the nodes under control, and it will make the valid node complete data transmission, achieve robustness of network, and maintain its life circle effectively.

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“…Over large distances, the propagation loss term also dominates over l. We also use the fact that the energy spent on long range transmissions (E t 1 ) is much higher than the energy spent on computations (E f ) as noted in [15]. Hence, we obtain,…”

Section: Appendix Bmentioning

confidence: 99%

A minimum cost heterogeneous sensor network with a lifetime constraint

Mhatre

Rosenberg

Kofman

et al. 2005

IEEE Trans. on Mobile Comput.

290

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Abstract-We consider a heterogeneous sensor network in which nodes are to be deployed over a unit area for the purpose of surveillance. An aircraft visits the area periodically and gathers data about the activity in the area from the sensor nodes. There are two types of nodes that are distributed over the area using two-dimensional homogeneous Poisson point processes; type 0 nodes with intensity (average number per unit area) 0 and battery energy E 0 ; and type 1 nodes with intensity 1 and battery energy E 1 . Type 0 nodes do the sensing while type 1 nodes act as the cluster heads besides doing the sensing. Nodes use multihopping to communicate with their closest cluster heads. We determine the optimum node intensities ( 0 , 1 ) and node energies (E 0 , E 1 ) that guarantee a lifetime of at least T units, while ensuring connectivity and coverage of the surveillance area with a high probability. We minimize the overall cost of the network under these constraints. Lifetime is defined as the number of successful data gathering trips (or cycles) that are possible until connectivity and/or coverage are lost. Conditions for a sharp cutoff are also taken into account, i.e., we ensure that almost all the nodes run out of energy at about the same time so that there is very little energy waste due to residual energy. We compare the results for random deployment with those of a grid deployment in which nodes are placed deterministically along grid points. We observe that in both cases 1 scales approximately as ffiffiffiffiffi 0 p. Our results can be directly extended to take into account unreliable nodes.

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Wireless integrated network sensors

Cited by 2,595 publications

References 5 publications

Resilient Data-Centric Storage in Wireless Ad-Hoc Sensor Networks

Resilient Data-Centric Storage in Wireless Ad-Hoc Sensor Networks

Preferable Wireless Sensor Networks Algorithm Based On the Maximum Weight Value of Time

A minimum cost heterogeneous sensor network with a lifetime constraint

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