"Balls into Bins" - A Simple and Tight Analysis
RANDOM '98 Proceedings of the Second International Workshop on Randomization and Approximation Techniques in Computer Science
Minimizing broadcast latency and redundancy in ad hoc networks
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Data-gathering wireless sensor networks: organization and capacity
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
Capacity bounds for ad hoc and hybrid wireless networks
ACM SIGCOMM Computer Communication Review
Broadcast capacity in multihop wireless networks
Proceedings of the 12th annual international conference on Mobile computing and networking
The worst-case capacity of wireless sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Multicast capacity for large scale wireless ad hoc networks
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Capacity of a wireless ad hoc network with infrastructure
Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
The multicast capacity of large multihop wireless networks
Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
Multicast capacity for hybrid wireless networks
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
MotionCast: on the capacity and delay tradeoffs
Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Data collection capacity of random-deployed wireless sensor networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Complexity of Data Collection, Aggregation, and Selection for Wireless Sensor Networks
IEEE Transactions on Computers
Minimum data aggregation time problem in wireless sensor networks
MSN'05 Proceedings of the First international conference on Mobile Ad-hoc and Sensor Networks
Delay efficient data gathering in sensor networks
MSN'05 Proceedings of the First international conference on Mobile Ad-hoc and Sensor Networks
The capacity of wireless networks
IEEE Transactions on Information Theory
A deterministic approach to throughput scaling in wireless networks
IEEE Transactions on Information Theory
On the scaling laws of dense wireless sensor networks: the data gathering channel
IEEE Transactions on Information Theory
Computing and communicating functions over sensor networks
IEEE Journal on Selected Areas in Communications
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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 $$\Uptheta\left({\frac{n}{\log n}}W\right)$$ .