Abstract

In this paper, we develop techniques that create smart space in an efficient manner, wherein the efficiency is defined in terms of all-together: energy, security, delay, and cost. We design an energy-efficient smart space system using the Long-Range (LoRa) network. The system consists of various sensors that generate sensory data represented as Multi-dimensional Time Series (MTS). The sensors are connected with an Edge device and LoRa node for processing and transferring the MTS, respectively. The system first proposes a deep learning-based compression-decompression model for reducing the size of MTS at the Edge devices. Next, it uses game theory for finding a minimum-cost security mechanism to facilitate the secure transmission of MTS. Then, we formulate an optimization problem to obtain a suitable compression ratio and security mechanism to reduce the system's energy consumption, delay, and security cost. Finally, the system presents an algorithm for selecting the suitable spreading factors to transfer the compressed and secured MTS to the application server in the given time period with desired accuracy. We evaluate the proposed system over the simulation platform and demonstrate the impact of the parameters of the compression model, network, security mechanism, and the number of sensors on energy consumption, delay, cost, and system accuracy.

Department(s)

Computer Science

Comments

National Science Foundation, Grant CNS-1818942

Keywords and Phrases

communication; deep learning; internet of things; smart home

International Standard Book Number (ISBN)

978-145039077-4

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

22 Nov 2021

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