Title
Capacity of data collection in randomly-deployed wireless sensor networks
Abstract
Data collection is one of the most important functions provided by wireless sensor networks. In this paper, we study theoretical limitations of data collection and data aggregation in terms of delay and capacity for a wireless sensor network where n sensors are randomly deployed. We consider different communication scenarios such as with single sink or multiple sinks, regularly-deployed or randomly-deployed sinks, with or without aggregation. For each scenario, we not only propose a data collection/aggregation method and analyze its performance in terms of delay and capacity, but also theoretically prove whether our method can achieve the optimal order (i.e., its performance is within a constant factor of the optimal). Particularly, with a single sink, the capacity of data collection is in order of $$\Uptheta(W)$$ where W is the fixed data-rate on individual links. With k regularly deployed sinks, the capacity of data collection is increased to $$\Uptheta(kW)$$ when $$k=O\left({\frac{n}{\log n}}\right)$$ or $$\Uptheta\left({\frac{n}{\log n}}W\right)$$ when $$k=\Upomega\left({\frac{n}{\log n}}\right)$$ . With k randomly deployed sinks, the capacity of data collection is between $$\Uptheta\left({\frac{k}{\log k}}W\right)$$ and $$\Uptheta(kW)$$ when $$k=O\left({\frac{n}{\log n}}\right)$$ or $$\Uptheta\left({\frac{n}{\log n}}W\right)$$ when $$k=\omega\left({\frac{n}{\log n}}\right)$$ . If each sensor can aggregate its receiving packets into a single packet to send, the capacity of data collection with a single sink is also increased to $$\Uptheta\left({\frac{n}{\log n}}W\right)$$ .
Year
DOI
Venue
2011
10.1007/s11276-010-0281-z
Wireless Networks
Keywords
DocType
Volume
Capacity,Data collection,Data aggregation,Random networks,Sensor networks
Journal
17
Issue
ISSN
Citations 
2
1022-0038
3
PageRank 
References 
Authors
0.41
27
4
Name
Order
Citations
PageRank
Siyuan Chen130.41
Yu Wang2143287.32
Xiang-Yang Li36855435.18
Xinghua Shi420919.00