Prolonged Lifetime of Wireless Sensor Network by Load Distribution and Data Compression

摘要

We discuss the use of static multiple sinks in a fixed pattern or sink mobility to enhance network performance for a certain class of applications in sensor networks. A major performance bottleneck in sensor networks is energy since it is impractical to replace the batteries in embedded sensor nodes post-deployment. A significant portion of the energy expenditure is attributed to communications and, in particular, the nodes close to the sensor network gateways used for data collection typically suffer a large overhead as these nodes must relay data from the remaining network which is called bottleneck problem. As nodes die due to overhead and communication to sink becomes impossible, network fails. This can be prevented by eliminating bottleneck problem hence prolonging network lifetime. To expend energy in a distributive fashion over the entire network many solutions like clustering, positioning multiple sinks and mobile sink had been presented. But the problem remains that how appropriate location can be chosen for cluster-heads, sinks or which path should a mobile sink follow, commonly known as “Facility location problem”. Load distribution reduces the communication energy consumption at the energy constrained nodes and, thus, increase useful network lifetime. We discuss a network infrastructure based on the use of static sinks or path constrained mobile sinks solving this in a model of uniformly distributed nodes. We initially present a distributive and mathematically proven model. Model is flexible to choose between tradeoffs of load distribution and cost factor. We present methods of calculation to construct our model according to requirement considering adjustable tradeoff parameters. Finally we test the routing protocol and load distribution over the network as a proof to solution to facility location problem hence load-distributive network implying a network with a prolonged lifetime.
　　Radio transmission is most energy draining task that a node performs. Data aggregation and data compression are the widely discussed cases to save a single nodes’ lifetime by reducing data to be transmitted but as the memory and computational resources are very limited, these tasks must be performed efficaciously. Here we present a modified version of data compression algorithm presented by Francesco Marcelloni in IEEE Communications Letters 2008. The algorithm Marcelloni presented performs best on variables having a trend of reading implying having least fluctuation with previous reading but as the data fluctuates, the trend vanishes and algorithm becomes inefficacious. We add a pseudo-trend to those fluctuating data on referred algorithm and make it more efficacious on such scenarios. Our experimental results are shown in this article explaining how the existing algorithm, that is referred to, cannot perform its best at those conditions and how our modified algorithm outperforms.

目录

封面

目录

List of Tables

List of Figures

英文摘要

Chapter I: Introduction

Objective of Study

Chapter II: Theory and Literature Review

Key Issues

Routing

Reliability

Real-Time

Mobility

Voids

Security

Congestion

Node Localization

Clock Synchronization

Literatures

Chapter III: Optimizing Network for Energy Efficiency and Lifetime

III.I. Architectural Approach: Distributive Facility Location for Multiple Static Sinks or Mobile Sink

Sections

Highways

Sector

Graphical Model Representation

Node Routing

Pseudo-code:

Sink Layer Routing

Simulation Parameters, Load Distribution and Efficiency

Network Lifetime

III.II. Nodal Approach: An Efficient Lossless Data Compression Algorithm for Fluctuating Environment Variables

Decoding

Experimental Results

Chapter IV: Summary and Conclusion

参考文献

致谢