Minimum latency data aggregation in the physical interference model
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Delay-bounded and energy-efficient data aggregation
Wireless Communications & Mobile Computing
Systematic selection of cluster heads for data collection
Journal of Network and Computer Applications
Efficient scheduling for periodic aggregation queries in multihop sensor networks
IEEE/ACM Transactions on Networking (TON)
Minimum latency data aggregation in the physical interference model
Computer Communications
Delay-efficient data aggregation scheduling in duty-cycled wireless sensor networks
Proceedings of the 2012 ACM Research in Applied Computation Symposium
A hybrid method of CSMA/CA and TDMA for real-time data aggregation in wireless sensor networks
Computer Communications
Secure and energy-efficient data aggregation for wireless sensor networks
International Journal of Mobile Network Design and Innovation
MC-MLAS: Multi-channel Minimum Latency Aggregation Scheduling in Wireless Sensor Networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
A load balancing data aggregation scheme for grid-based wireless sensor networks
International Journal of Ad Hoc and Ubiquitous Computing
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Data aggregation is a key functionality in wireless sensor networks (WSNs). This paper focuses on data aggregation scheduling problem to minimize the delay (or latency). We propose an efficient distributed algorithm that produces a collision-free schedule for data aggregation in WSNs. We theoretically prove that the delay of the aggregation schedule generated by our algorithm is at most 16R+\Delta -14 time slots. Here, R is the network radius and \Delta is the maximum node degree in the communication graph of the original network. Our algorithm significantly improves the previously known best data aggregation algorithm with an upper bound of delay of 24D+6\Delta +16 time slots, where D is the network diameter (note that D can be as large as 2R). We conduct extensive simulations to study the practical performances of our proposed data aggregation algorithm. Our simulation results corroborate our theoretical results and show that our algorithms perform better in practice. We prove that the overall lower bound of delay for data aggregation under any interference model is {\rm max} \{\log n, R\}, where n is the network size. We provide an example to show that the lower bound is (approximately) tight under the protocol interference model when r_I=r, where r_I is the interference range and r is the transmission range. We also derive the lower bound of delay under the protocol interference model when r