Lower bounds on communication complexity in distributed computer networks
Journal of the ACM (JACM)
Impact of interference on multi-hop wireless network performance
Proceedings of the 9th annual international conference on Mobile computing and networking
Computing throughput capacity for realistic wireless multihop networks
Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems
A survey on clustering algorithms for wireless sensor networks
Computer Communications
IEEE Transactions on Mobile Computing
Scheduling for information gathering on sensor network
Wireless Networks
Throughput-optimal configuration of fixed wireless networks
IEEE/ACM Transactions on Networking (TON)
Minimum-latency aggregation scheduling in multihop wireless networks
Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing
An improved approximation algorithm for data aggregation in multi-hop wireless sensor networks
Proceedings of the 2nd ACM international workshop on Foundations of wireless ad hoc and sensor networking and computing
What is the right model for wireless channel interference?
IEEE Transactions on Wireless Communications
Efficient algorithms to solve a class of resource allocation problems in large wireless networks
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
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
A Survey on Clustering Algorithms for Wireless Sensor Networks
NBIS '10 Proceedings of the 2010 13th International Conference on Network-Based Information Systems
Engineering wireless mesh networks: joint scheduling, routing, power control, and rate adaptation
IEEE/ACM Transactions on Networking (TON)
Minimum data aggregation time problem in wireless sensor networks
MSN'05 Proceedings of the First international conference on Mobile Ad-hoc and Sensor Networks
Optimally fast data gathering in sensor networks
MFCS'06 Proceedings of the 31st international conference on Mathematical Foundations of Computer Science
The capacity of wireless networks
IEEE Transactions on Information Theory
Fast Distributed Algorithms for Computing Separable Functions
IEEE Transactions on Information Theory
Lower bounds on data collection time in sensory networks
IEEE Journal on Selected Areas in Communications
Computing and communicating functions over sensor networks
IEEE Journal on Selected Areas in Communications
IEEE/ACM Transactions on Networking (TON)
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Many applications require the sink to compute a function of the data collected by the sensors. Instead of sending all the data to the sink, the intermediate nodes could process the data they receive to significantly reduce the volume of traffic transmitted: this is known as in-network computation. Instead of focusing on asymptotic results for large networks as is the current practice, we are interested in explicitly computing the maximum achievable throughput of a given network when the sink is interested in the first M statistical moments of the collected data. Here, the kth statistical moment is defined as the expectation of the kth power of the data. Flow models have been routinely used in multihop wireless networks when there is no in-network computation, and they are typically tractable for relatively large networks. However, deriving such models is not obvious when in-network computation is allowed. We develop a discrete-time model for the real-time network operation and perform two transformations to obtain a flow model that keeps the essence of in-network computation. This gives an upper bound on the maximum achievable throughput. To show its tightness, we derive a numerical lower bound by computing a solution to the discrete-time model based on the solution to the flow model. This lower bound turns out to be close to the upper bound, proving that the flow model is an excellent approximation to the discrete-time model. We then provide several engineering insights on these networks.