Adaptive In-Network Processing for Bandwidth and Energy Constrained Mission-Oriented Multihop Wireless Networks

Eswaran, Sharanya; Edwards, James; Misra, Archan; Porta, Thomas F. La

doi:10.1109/tmc.2011.169

Cited by 15 publications

(20 citation statements)

References 23 publications

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“…Furthermore, we assume that the total energy consumption of a node consists of the energy spent in receiving p R , computing p C and transmitting p T its data. Among these operations, data transmission typically uses more energy than the others [9]. Data generated in the information networks has a large amount of redundancy due to the spatial and temporal correlation among sensors.…”

Section: Problem Formulation a Scenario Qoi Metrics And Assumptmentioning

confidence: 99%

“…There may be a concern that the linear model in (5) to (7) is unable to adequately adjust to all the characteristics of communication and computation in the network (e.g., coding and processing functions); however, as a general assumption in [9], [16] we assume a linear model here. We will investigate non-linear cost models in our future work.…”

Section: Distributed Energy-efficient and Qoi-aware Approachmentioning

confidence: 99%

“…Eswaran et al [9] apply the network utility maximum (NUM) framework to determine the optimal compression and fusion factors for INP and which nodes in a path serve as the optimal places for performing the data processing. However, they do not include realistic QoI attributes in their work, as they assume a utility function to represent all of the attributes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Nazemi

Leung

Swami

2014

2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC)

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Abstract-In-network processing (INP) is a promising method that allows aggregation of data while it is being transferred along the communication paths as a means to optimize the utilization of network resources without violating the quality of information (QoI) requirements. Given the large amount of data existing in dynamic environments, the optimization of INP requires a distributed framework that can adapt easily to network changes and user requirements. In this work, we develop the principle for designing a distributed mechanism in order to determine and control INP. Specifically, the proposed framework can decide, in a distributed way, which nodes along the communication paths optimally perform INP, with consideration of operational energy consumption and QoI requirements for achieving global optimal INP. The significance of the proposed distributed method is that it requires each node to make independent decisions locally for data aggregations, thus naturally enhance robustness and efficiency against network and data load dynamics. Extensive numerical results are presented to confirm the validity of the proposed approach.

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Section: Problem Formulation a Scenario Qoi Metrics And Assumptmentioning

confidence: 99%

Section: Distributed Energy-efficient and Qoi-aware Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Nazemi

Leung

Swami

2014

2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC)

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“…al. [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.…”

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

Nazemi

Leung

Swami

2016

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

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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.

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Performance of a Partial Discrete Wavelet Transform Based Path Merging Compression Technique for Wireless Multimedia Sensor Networks

Banerjee

Chatterjee

Bit

2018

Wireless Pers Commun

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Adaptive In-Network Processing for Bandwidth and Energy Constrained Mission-Oriented Multihop Wireless Networks

Cited by 15 publications

References 23 publications

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

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

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

Performance of a Partial Discrete Wavelet Transform Based Path Merging Compression Technique for Wireless Multimedia Sensor Networks

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