Upper bounds on the lifetime of sensor networks

Bhardwaj, Manish; Garnett, Timothy; Chandrakasan, Anantha P.

doi:10.1109/icc.2001.937346

Cited by 532 publications

(397 citation statements)

References 8 publications

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“…It has been noted in [7] and [12] that, for very short range communication (up to 10m), l is often comparable to r k . For such cases, it is not possible to simplify the expression in (21) any further.…”

Section: Solutions For the Two Deployment Scenarios 41 Random Deploymentioning

confidence: 98%

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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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Section: Solutions For the Two Deployment Scenarios 41 Random Deploymentioning

confidence: 98%

“…In [7], Bhardwaj et al provide loose bounds on the lifetime of a sensor network. In [20], the authors provide upper bounds on the lifetime of a sensor network by taking into account all the possible collaborative data gathering strategies over all the possible network routes.…”

Section: Related Workmentioning

confidence: 99%

A minimum cost heterogeneous sensor network with a lifetime constraint

Mhatre

Rosenberg

Kofman

et al. 2005

IEEE Trans. on Mobile Comput.

292

View full text Add to dashboard Cite

show abstract

“…As already stated, the fluid-flow based technique used in (Bhardwaj and Chandrakasan, 2002;Bhardwaj et al, 2001) is closest to our approach-in contrast to our emphasis on finding capacity bounds that are representative of different actual network deployments realized from a common underlying distribution, (Bhardwaj and Chandrakasan, 2002;Bhardwaj et al, 2001) considers lifetime bounds for a specific instance of sensor network deployment. (Bhardwaj and Chandrakasan, 2002) further determined lifetime bounds in the presence of a) traffic aggregation (where intermediate nodes would compress the incoming data), and b) multiple locations (where a sensor node could be located at multiple discrete points with different probabilities).…”

Section: Related Workmentioning

confidence: 99%

An Efficient and Robust Computational Framework for Studying Lifetime and Information Capacity in Sensor Networks

2005

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Abstract. In this paper we investigate the expected lifetime and information capacity, defined as the maximum amount of data (bits) transferred before the first sensor node death due to energy depletion, of a data-gathering wireless sensor network. We develop a fluid-flow based computational framework that extends the existing approach, which requires precise knowledge of the layout/deployment of the network, i.e., exact sensor positions. Our method, on the other hand, views a specific network deployment as a particular instance (sample path) from an underlying distribution of sensor node layouts and sensor data rates. To compute the expected information capacity under this distribution-based viewpoint, we model parameters such as the node density, the energy density and the sensed data rate as continuous spatial functions. This continuous-space flow model is then discretized into grids and solved using a linear programming approach. Numerical studies show that this model produces very accurate results, compared to averaging over results from random instances of deployment, with significantly less computation. Moreover, we develop a robust version of the linear program, which generates robust solutions that apply not just to a specific deployment, but also to topologies that are appropriately perturbed versions. This is especially important for a network designer studying the fundamental lifetime limit of a family of network layouts, since the lifetime of specific network deployment instances may differ appreciably. As an example of this model's use, we determine the optimal node distribution for a linear network and study the properties of optimal routing that maximizes the lifetime of the network.

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“…! CF 5 : Maximum connections per relay: once this threshold is reached, we add an extra cost c 5 to avoid setting additional paths through it. This factor extends the life of overloaded relay nodes by making them less favorable.…”

Section: Routing Approachmentioning

confidence: 99%

Energy-aware management for cluster-based sensor networks

2003

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Networking unattended sensors is expected to have a significant impact on the efficiency of many military and civil applications. Sensors in such systems are typically disposable and expected to last until their energy drains. Therefore, energy is a very scarce resource for such sensor systems and has to be managed wisely in order to extend the life of the sensors for the duration of a particular mission. In this paper, we present a novel approach for energy-aware management of sensor networks that maximizes the lifetime of the sensors while achieving acceptable performance for sensed data delivery. The approach is to dynamically set routes and arbitrate medium access in order to minimize energy consumption and maximize sensor life. The approach calls for network clustering and assigns a less-energy-constrained gateway node that acts as a cluster manager. Based on energy usage at every sensor node and changes in the mission and the environment, the gateway sets routes for sensor data, monitors latency throughout the cluster, and arbitrates medium access among sensors. We also describe a time-based Medium Access Control (MAC) protocol and discuss algorithms for assigning time slots for the communicating sensor nodes. Simulation results show an order of magnitude enhancement in the time to network partitioning, 11% enhancement in network lifetime predictability, and 14% enhancement in average energy consumed per packet.

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Upper bounds on the lifetime of sensor networks

Cited by 532 publications

References 8 publications

A minimum cost heterogeneous sensor network with a lifetime constraint

A minimum cost heterogeneous sensor network with a lifetime constraint

An Efficient and Robust Computational Framework for Studying Lifetime and Information Capacity in Sensor Networks

Energy-aware management for cluster-based sensor networks

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