A distributed, energy-efficient and QoI-aware framework for in-network processing

2016 IEEE Wireless Communications and Networking Conference

2016

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We propose a framework with highly reduced complexity for optimizing in-network processing (INP) for quality of information (QoI) in wireless sensor networks (WSNs). INP provides a platform by processing (e.g., fusing, aggregating or compressing) the data along the transmission routes in the sensor network. This can reduce the volume of transmitted data, therefore optimizing utilization of energy and bandwidth. However, such data processing must ensure that the end result can meet given QoI requirements. We formulate the QoI-aware INP problem as a non-linear optimization problem to identify the optimal degree of data compression at each sensor node subject to satisfying a QoI requirement for the end-user. The formulation arranges all involved sensor nodes in the tree where data is transfered and processed from nodes to their parent nodes toward the root node of the tree. Under the assumption of uniform parameter setting, we show that the processing tree can be collapsed into a linear graph where the number of nodes represents the node levels of the original processing tree. Numerical example are provided to illustrate the performance of the proposed approach.

“…Following the assumptions made in [15], let f i (δ i , y i ) and q r (δ r , y r ) be defined as follows. Let f i denote the total energy consumption of node i as…”

Section: Proposed Solutionmentioning

confidence: 99%

Optimization framework with reduced complexity for sensor networks with in-network processing

2016 IEEE Wireless Communications and Networking Conference

2016

Self Cite

Section: Introductionmentioning

confidence: 99%

“…In our early work [20], we considered configuring a network with the same goal of minimising the total energy consumption as here and proposed a heuristic approach by imposing the same QoI constraint at each node, including the sink, to provide an approximate solution. The primary objective of this paper is to propose an exact optimal solution to the optimisation problem posed in [20]. Similarly, Sharma et al [21] minimised the system energy consumption, while making a joint decision on whether and when to compress and transmit the data, by utilising the Lyapunov optimisation framework.…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimization Framework for In-Network Data Processing

IEEE/ACM Trans. Networking

2019

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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. Specifically, aggressive data aggregation consumes much energy for processing, but results in less data for transmission, thus using less energy for communications, and vice versa. 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 minimising the total energy consumption of the system, we study and formulate the trade-off problem as a nonlinear optimisation 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 optimisation problem and very hard to be solved directly. 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 computational complexity of the problem can be reduced logarithmically such that each node can readily determine its optimal reduction rate locally. We evaluate our proposed frameworks under different parameter settings and illustrate the validity and performance of the proposed techniques through extensive simulation.

“…[9] applied the network utility maximum (NUM) framework to determine the optimal compression and fusion factors for data aggregation as well as the optimal locations for performing data processing. With the goal of minimizing energy consumption in the network, [10] proposed a heuristic approach for determining the degree of data aggregation at each individual node. Similarly, Sharma et.…”

Section: Introductionmentioning

confidence: 99%

“…In common with prior work such as [11], [10] and [9], we consider computational cost to obtain optimal aggregation decisions. But we propose a novel distributed solution that efficiently achieves the optimal solution, drawing upon key results in [12].…”

Section: Introductionmentioning

confidence: 99%

QoI-aware tradeoff between communication and computation in wireless ad-hoc networks

2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

2016

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Abstract-Data aggregation techniques exploit spatial and temporal correlations among data and aggregate data into a smaller volume as a means to optimize usage of limited network resources including energy. There is a trade-off among the Quality of Information (QoI) requirement and energy consumption for computation and communication. We formulate the energy-efficient data aggregation problem as a non-linear optimization problem to optimize the trade-off and control the degree of information reduction at each node subject to given QoI requirement. Using the theory of duality optimization, we prove that under a set of reasonable cost assumptions, the optimal solution can be obtained despite non-convexity of the problem. Moreover, we propose a distributed, iterative algorithm that will converge to the optimal solution. Extensive numerical results are presented to confirm the validity of the proposed solution approach.