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Outline

Title
Abstract
Figures
Background
Data Reporting Model in the Network
Mac Protocol
Related Work
Lifetime Analysis for Non-Clustered Networks
Lifetime Analysis for Single-Hop Networks
Lifetime Analysis in Multi-Hop Networks
Effect of Mac Protocol
Asymptotic Analysis
Lifetime Analysis for Clustered Networks
Lifetime Analysis for Multi-Hop Networks
Numerical Results
Traffic Load Analysis
Simulation Results
Conclusions and Future Works
References

Lifetime analysis of randomly deployed wireless sensor networks

Profile image of Moslem NooriMoslem Noori

2008

https://doi.org/10.7939/R3-8P4T-2218
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89 pages

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Abstract

Due to the limited energy resource in the network nodes, lifetime is a considerable issue in wireless sensor networks. When the network is randomly deployed, network lifetime exhibits a stochastic behaviour which may be strengthened by a random traffic generation scheme. This dissertation mainly deals with the lifetime study of such networks where a probabilistic approach is proposed to model the network lifetime. First, we perform the study for non-clustered networks and propose an optimal node distribution to prolong the network lifetime. Then, the analysis is extended to clustered networks resulting in a condition which determines whether clustering in the network is beneficial or not. Our results on the lifetime study are helpful in the network design step to adjust the network parameters appropriately. Moreover, we investigate the traffic load distribution over the nodes in a randomly deployed linear network. The outcome of this traffic study specifies where the network hot spot is located.

Figures (24)
Figure 1.1: Mica mote. nodes far from the sink die earlier than the other nodes. In a multi-hop network, however,
Figure 1.1: Mica mote. nodes far from the sink die earlier than the other nodes. In a multi-hop network, however,
Figure 2.1: A randomly deployed network over a circular area.
Figure 2.1: A randomly deployed network over a circular area.
where |-| denotes the integer ceiling. To ease the notations, let us assume that R is ar [a a (oe, ee <n
where |-| denotes the integer ceiling. To ease the notations, let us assume that R is ar [a a (oe, ee <n
Figure 3.2: Probability of achieving the lifetime threshold vs. the ratio of dead nodes for single-hop networks deployed over different area shapes. achieving the desired lifetime.
Figure 3.2: Probability of achieving the lifetime threshold vs. the ratio of dead nodes for single-hop networks deployed over different area shapes. achieving the desired lifetime.
Figure 3.4: Probability of achieving the lifetime threshold vs. the ratio of dead nodes in a multi-hop network
Figure 3.4: Probability of achieving the lifetime threshold vs. the ratio of dead nodes in a multi-hop network
earlier. As a consequence, the optimal density can be obtained for each secto1 Figure 3.6: An arbitrary network with non-centered sink. the sector, the average traffic load can be determined similar to the approach that we tool
earlier. As a consequence, the optimal density can be obtained for each secto1 Figure 3.6: An arbitrary network with non-centered sink. the sector, the average traffic load can be determined similar to the approach that we tool
Figure 4.1: Sensor nodes deployed randomly within a cluster.
Figure 4.1: Sensor nodes deployed randomly within a cluster.
Figure 4.3: Comparison of the number of alive nodes versus time between periodic and adaptive clustering.
Figure 4.3: Comparison of the number of alive nodes versus time between periodic and adaptive clustering.
Figure 4.7: Effect of the data aggregation ratio.
Figure 4.7: Effect of the data aggregation ratio.
Figure 5.2: Traffic load distribution (V = 15, 30, 60).
Figure 5.2: Traffic load distribution (V = 15, 30, 60).
Figure 5.3: Traffic load distribution (V = 1000).
Figure 5.3: Traffic load distribution (V = 1000).
Figure 5.4: Linearly increasing distribution of the nodes over the network.
Figure 5.4: Linearly increasing distribution of the nodes over the network.

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Moslem Noori

2008

In event-driven wireless sensor networks, the network lifetime has a random nature due to the randomness of data reporting. The lifetime is even more nondeterministic when sensors are also deployed randomly. The lifetime of such a network is influenced by node deployment, initial energy of sensors, packet generation model and the number of sensors. This work quantifies the effect of these parameters on the lifetime of randomly deployed event-driven networks. First, the lifetime of individual sensors are studied. Then, an analytical expression is obtained for the complementary cumulative density function of the network lifetime. Such an analysis can be used for choosing the network parameter and efficiently optimizing the network lifetime. The results of this work are obtained for both multi- hop and single-hop wireless sensor networks and are verified with computer simulation. The approaches of this paper are shown to be applicable to more general cases.

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