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

Nazemi, Sepideh; Leung, Kin K.; Swami, Ananthram

doi:10.1109/wcnc.2016.7564856

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Nazemi et al [ 31 ] suggest a framework for flow optimization in sensor networks. They consider it as a nonlinear optimization problem and then suggest a decomposition of a transmission tree into a linear graph, which significantly decreases problem complexity.…”

Section: State Of the Artmentioning

confidence: 99%

Emerging Complexity in Distributed Intelligent Systems

Guleva

Shikov

Bochenina

et al. 2020

Entropy

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Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and artificial intelligence agents (algorithms) interact, exchanging data and decisions and learning how to evolve toward a better quality of solutions. The networked dynamics of distributed natural and artificial intelligence agents leads to emerging complexity different from the ones observed before. In this study, we review and systematize different approaches in the distributed intelligence field, including the quantum domain. A definition and mathematical model of DIS (as a new class of systems) and its components, including a general model of DIS dynamics, are introduced. In particular, the suggested new model of DIS contains both natural (humans) and artificial (computer programs, chatbots, etc.) intelligence agents, which take into account their interactions and communications. We present the case study of domain-oriented DIS based on different agents’ classes and show that DIS dynamics shows complexity effects observed in other well-studied complex systems. We examine our model by means of the platform of personal self-adaptive educational assistants (avatars), especially designed in our University. Avatars interact with each other and with their owners. Our experiment allows finding an answer to the vital question: How quickly will DIS adapt to owners’ preferences so that they are satisfied? We introduce and examine in detail learning time as a function of network topology. We have shown that DIS has an intrinsic source of complexity that needs to be addressed while developing predictable and trustworthy systems of natural and artificial intelligence agents. Remarkably, our research and findings promoted the improvement of the educational process at our university in the presence of COVID-19 pandemic conditions.

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Section: State Of the Artmentioning

confidence: 99%

Emerging Complexity in Distributed Intelligent Systems

Guleva

Shikov

Bochenina

et al. 2020

Entropy

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“…While data aggregation helps optimise the usage of network Parts of the results in this paper were presented at IEEE Wireless Communications and Networking Conference, 2016 [31] and IEEE Personal, Indoor, and Mobile Radio Communications conferences, 2016 [32].…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimization Framework for In-Network Data Processing

Nazemi

Leung

Swami

2019

IEEE/ACM Trans. Networking

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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.

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Asymptotic Cost Estimation for Scheduling Data Aggregation Trees in Sensor Networks

Kale

Nene

2019

2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

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

Cited by 3 publications

References 19 publications

Emerging Complexity in Distributed Intelligent Systems

Emerging Complexity in Distributed Intelligent Systems

Distributed Optimization Framework for In-Network Data Processing

Asymptotic Cost Estimation for Scheduling Data Aggregation Trees in Sensor Networks

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