Comparison between graph-based and interference-based STDMA scheduling
MobiHoc '01 Proceedings of the 2nd ACM international symposium on Mobile ad hoc networking & computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Topology control meets SINR: the scheduling complexity of arbitrary topologies
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
The worst-case capacity of wireless sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Tight bounds for distributed selection
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
A measurement study of interference modeling and scheduling in low-power wireless networks
Proceedings of the 6th ACM conference on Embedded network sensor systems
Minimum-latency aggregation scheduling in multihop wireless networks
Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing
Wireless Communication Is in APX
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Oblivious interference scheduling
Proceedings of the 28th ACM symposium on Principles of distributed computing
Wireless Sensor Networks: A Networking Perspective
Wireless Sensor Networks: A Networking Perspective
Effective carrier sensing in CSMA networks under cumulative interference
INFOCOM'10 Proceedings of the 29th conference on Information communications
Minimum-latency aggregation scheduling in wireless sensor networks under physical interference model
Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
What is the use of collision detection (in wireless networks)?
DISC'10 Proceedings of the 24th international conference on Distributed computing
Complexity of Data Collection, Aggregation, and Selection for Wireless Sensor Networks
IEEE Transactions on Computers
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
Wireless connectivity and capacity
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Minimum data aggregation time problem in wireless sensor networks
MSN'05 Proceedings of the First international conference on Mobile Ad-hoc and Sensor Networks
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Minimum latency data aggregation in the physical interference model
Computer Communications
Distributed backbone structure for algorithms in the SINR model of wireless networks
DISC'12 Proceedings of the 26th international conference on Distributed Computing
Connectivity and aggregation in multihop wireless networks
Proceedings of the 2013 ACM symposium on Principles of distributed computing
Distributed deterministic broadcasting in wireless networks of weak devices
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
Improved minimum latency aggregation scheduling in wireless sensor networks under the SINR model
International Journal of Sensor Networks
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Given a set of nodes $\mathcal{V}$ , where each node has some data value, the goal of data aggregation is to compute some aggregate function in the fewest timeslots possible. Aggregate functions compute the aggregated value from the data of all nodes; common examples include maximum or average . We assume the realistic physical (SINR) interference model and no knowledge of the network structure or the number of neighbors of any node; our model also uses physical carrier sensing. We present a distributed protocol to compute an aggregate function in O (D +Δlogn ) timeslots, where D is the diameter of the network, Δ is the maximum number of neighbors within a given radius and n is the total number of nodes. Our protocol contributes an exponential improvement in running time compared to that in [18].