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Distributed Optimization Framework for In-Network Data Processing

handle: 10044/1/74634
In-Network Processing (INP) is an effective way to aggregate and process data from different sources and forward the aggregated data to other nodes for further processing until it reaches the end user. There is a trade-off between energy consumption for processing data and communication energy spent on transferring the data. An essential requirement in the INP process is to ensure that the user expectation of quality of information (QoI) is delivered during the process. Using wireless sensor networks for illustration and with the aim of minimizing the total energy consumption of the system, we study and formulate the trade-off problem as a nonlinear optimization problem where the goal is to determine the optimal data reduction rate, while satisfying the QoI required by the user. The formulated problem is a Signomial Programming (SP) problem, which is a non-convex optimization problem. We propose two solution frameworks. First, we introduce an equivalent problem which is still SP and non-convex as the original one, but we prove that the strong duality property holds, and propose an efficient distributed algorithm to obtain the optimal data reduction rates, while delivering the required QoI. The second framework applies to the system with identical nodes and parameter settings. In such cases, we prove that the complexity of the problem can be reduced logarithmically. We evaluate our proposed frameworks under different parameter settings and illustrate the validity and performance of the proposed techniques through extensive simulation.
- United States Department of the Interior United States
- United States Army Research Laboratory United States
- Imperial College London United Kingdom
- United States Army Research Laboratory United States
Optimization, Technology, Distributed databases, non-convex optimization, data aggregation, Theory & Methods, Hardware & Architecture, 0805 Distributed Computing, Engineering, Computer Science, Theory & Methods, 1005 Communications Technologies, Cloud computing, Computer Science, Hardware & Architecture, energy efficiency, trade-off, Science & Technology, Engineering, Electrical & Electronic, In-network processing, Wireless sensor networks, 004, 620, Data aggregation, Energy consumption, 0906 Electrical and Electronic Engineering, Computer Science, Telecommunications, Electrical & Electronic, Networking & Telecommunications, quality of information, distributed optimization, data reduction rate
Optimization, Technology, Distributed databases, non-convex optimization, data aggregation, Theory & Methods, Hardware & Architecture, 0805 Distributed Computing, Engineering, Computer Science, Theory & Methods, 1005 Communications Technologies, Cloud computing, Computer Science, Hardware & Architecture, energy efficiency, trade-off, Science & Technology, Engineering, Electrical & Electronic, In-network processing, Wireless sensor networks, 004, 620, Data aggregation, Energy consumption, 0906 Electrical and Electronic Engineering, Computer Science, Telecommunications, Electrical & Electronic, Networking & Telecommunications, quality of information, distributed optimization, data reduction rate
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).8 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
