Energy-Efficient Data Collection Scheme for Environmental Quality Management in Buildings

Chen, Siguang; Zhou, Jiasheng; Zheng, Xiaoyao; Ruan, Xiukai

doi:10.1109/access.2018.2873789

Cited by 11 publications

(4 citation statements)

References 30 publications

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“…Several ongoing attempts have been made to reduce energy consumption while satisfying the latency requirement. These attempts relied on different technologies such as intelligent data compression [30], [31], strategic clustering [32], [33], energy-aware routing [34], mobile fog computing to suppress energy consumption deep learning [36]. Therefore, IoT systems should monitor the average energy consumption periodically, considering all the expenditures in the system that can be empirically quantified.…”

Section: • Packet Transmission Ratiomentioning

confidence: 99%

Quantifying IoT Security Parameters: An Assessment Framework

Ebad

2023

IEEE Access

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Several attempts have been made to propose metrics that quantify the parameters of existing solutions to improve the security of IoT systems. This paper presents a framework to classify and compare these metrics based on a set of attributes that can be used to answer the research questions. Forty-six metrics from the literature were analyzed, classified, and compared according to the developed framework. They were divided into two main categories according to the metric's source, i.e., internal and external sources. Then, they were further divided into seven sub-categories: size, time, numbering/scoring, checklist, blockchain, device integration effort, and legislation. There are eight widely used metrics among the existing IoT solutions: throughput, packet loss rate, jitter, password, security transmission rate, resilience, average energy consumption, and blockchain-related metrics (i.e., technical metrics). The simulation technique is the most common validation method among the current IoT solutions. Furthermore, the results revealed a gap in four aspects of proposing metrics for measuring security parameters. These aspects include the network/transport IoT layer, blockchain, legislation, and the use of data science in simulation research methodologies.

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Section: • Packet Transmission Ratiomentioning

confidence: 99%

Quantifying IoT Security Parameters: An Assessment Framework

Ebad

2023

IEEE Access

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“…Reliable protocols work best over the physical medium that loses information and is inclined to errors [12]. The error rectification, requesting and confirmatory instruments require overhead in the information parcels and increment the allowed amount of transmission capacity required to transmit information [13,14]. The drawback of using WSN in reliable communication, there may be a network delay, traffic, battery issue and distraction with another wireless device [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

AEECA for Reliable Communication to Enhance the Network Life Time for WSN

Jayaraman¹,

Dhulipala²

2023

Intelligent Automation &Amp; Soft Computing

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“…There have been ongoing efforts to improve the energy efficiency of wireless sensor networks. Traditional solutions include intelligent data compression [8], [9], strategic clustering [10], [11], and energy-aware routing [12]. Recently, mobile fog computing technology is applied to suppress energy consumption [13].…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Minimization for Data Collection From Wirelessly-Powered IoT Sensors: Session-Specific Optimal Design With DRL

Yang

Alouini

2022

IEEE Sensors J.

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Reliable and energy-efficient data collection from resource-limited sensors is essential to the success of future Internet of Things (IoT). In this article, we study the energy consumption minimization problem during the data collection from a generic wirelessly-powered sensor. Specifically, we determine the optimal data collection parameters, in terms of charging duration and charging power as well as sensor transmission rate, in real time according to the instantaneous channel condition while satisfying a certain latency constraint. For the scenario of ideal rate adaptive transmission with linear energy harvesting, we derive closed-form expressions for optimal transmission parameters. We also establish the condition on channel quality for successful data collection under a latency constraint. For the more practical case of finite block-length transmission with non-linear energy harvesting, we develop a deep reinforcement learning (DRL) solution for efficient online implementation. We also propose an online tuning scheme to cater for model inaccuracy and environment variation. The accuracy and effectiveness of our proposed approaches are verified by comparing with benchmark schemes. Our DRL-based approach has broad applicability and can solve other real-time optimal design problems in wireless communications.

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Energy-Efficient Data Collection Scheme for Environmental Quality Management in Buildings

Cited by 11 publications

References 30 publications

Quantifying IoT Security Parameters: An Assessment Framework

Quantifying IoT Security Parameters: An Assessment Framework

AEECA for Reliable Communication to Enhance the Network Life Time for WSN

Energy Consumption Minimization for Data Collection From Wirelessly-Powered IoT Sensors: Session-Specific Optimal Design With DRL

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