Design of Surveillance Sensor Grids with a Lifetime Constraint

Mhatre, Vivek; Rosenberg, Catherine; Kofman, Daniel; Mazumdar, Ravi R.; Shroff, Ness B.

doi:10.1007/978-3-540-24606-0_18

Cited by 49 publications

(46 citation statements)

References 7 publications

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“…In this section, we propose polynomial-time algorithms to produce valid compact wakeup schedulings for tree and grid topologies, which are commonly used in WSNs [31][32][33][34][35].…”

Section: Compact Wakeup Scheduling Algorithmsmentioning

confidence: 99%

Interference-Free Wakeup Scheduling with Consecutive Constraints in Wireless Sensor Networks

Lou

2012

International Journal of Distributed Sensor Networks

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Wakeup scheduling has been widely used in wireless sensor networks (WSNs), for it can reduce the energy wastage caused by the idle listening state. In a traditional wakeup scheduling, sensor nodes start up numerous times in a period, thus consuming extra energy due to state transitions (e.g., from the sleep state to the active state). In this paper, we address a novel interference-free wakeup scheduling problem called compact wakeup scheduling, in which a node needs to wake up only once to communicate bidirectionally with all its neighbors. However, not all communication graphs have valid compact wakeup schedulings, and it is NP-complete to decide whether a valid compact wakeup scheduling exists for an arbitrary graph. In particular, tree and grid topologies, which are commonly used in WSNs, have valid compact wakeup schedulings. We propose polynomial-time algorithms using the optimum number of time slots in a period for trees and grid graphs. Simulations further validate our theoretical results.

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“…In this section, we propose polynomial-time algorithms to produce valid compact wakeup schedulings for tree and grid topologies, which are commonly used in WSNs [31][32][33][34][35].…”

Section: Compact Wakeup Scheduling Algorithmsmentioning

confidence: 99%

Interference-Free Wakeup Scheduling with Consecutive Constraints in Wireless Sensor Networks

Lou

2012

International Journal of Distributed Sensor Networks

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“…Regular structures provide a uniform node density so a locally optimal scheme is also globally optimal and may be deployed in a distributed manner. Regular structured networks have been employed in several theoretical network studies [14,22,23] and therefore form a basis for comparison of network capacity and protocol efficiency.…”

Section: Theoretical and Practical Significance Of Regular Topologiesmentioning

confidence: 99%

MAX: A Maximal Transmission Concurrency MAC for Wireless Networks with Regular Structure

Mangharam

Rajkumar

2006

2006 3rd International Conference on Broadband Communications, Networks and Systems

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Multi-hop wireless networks facilitate applications in metropolitan area broadband, home multimedia, surveillance and industrial control networks. Most applications require high end-to-end throughput and/or bounded delay. Current single-hop networks primarily employ random access link-layer protocols such as Carrier Sense Multiple Access (CSMA). These perform poorly in the multi-hop regime and provide no endto-end QoS guarantees. The primary causes are uncoordinated interference and unfairness in exclusive access of the shared wireless medium. Furthermore, random access schemes do not leverage spatial reuse effectively and require routes to be link-aware. MAX is a time division multiplexed resource allocation framework for multi-hop networks with practical architectures for node scheduling algorithms. MAX tiling delivers optimal end-to-end throughput across arbitrarily large regularly structured networks while maintaining bounded delay. It outperforms CSMA-based random access protocols by a factor of 5-to-8. The MAX approach provides network services including: flexible uplink and downlink bandwidth management, deterministic route admission control, and optimal gateway placement. MAX has being implemented on IEEE 802.15.3 embedded nodes and a test-bed of 50 nodes has been deployed both indoors and outdoors. Keywords Scheduling Algorithms for Embedded Wireless Networks

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“…0087266) and a grant from Purdue Research Foundation. Some of these results were presented at the First European Workshop on Wireless Sensor Networks, Berlin, Germany, January 19-21 2004, see [1].…”

Section: Acknowledgmentsmentioning

confidence: 99%

A minimum cost heterogeneous sensor network with a lifetime constraint

Mhatre

Rosenberg

Kofman

et al. 2005

IEEE Trans. on Mobile Comput.

292

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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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Design of Surveillance Sensor Grids with a Lifetime Constraint

Cited by 49 publications

References 7 publications

Interference-Free Wakeup Scheduling with Consecutive Constraints in Wireless Sensor Networks

Interference-Free Wakeup Scheduling with Consecutive Constraints in Wireless Sensor Networks

MAX: A Maximal Transmission Concurrency MAC for Wireless Networks with Regular Structure

A minimum cost heterogeneous sensor network with a lifetime constraint

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