Secure Distributed Detection under Energy Constraint in IoT-Oriented Sensor Networks

Zhang, Guomei; Sun, Harold

doi:10.3390/s16122152

Cited by 6 publications

(19 citation statements)

References 41 publications

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“…Besides, RL has recently been regarded as a potential technique for solving MDPs efficiently (W. . In another study of Zhang and Sun (Zhang and Sun, 2016), they created a consensus transfer Q-learning algorithm with the aim of energy dispatch which shared Q-value matrices and used previous information to accelerate algorithm convergence. For dynamic economic dispatch, (Dai et al, 2020) suggested an RL method in which state-action-value function approximation was integrated with multiplier distributed optimization based on splitting.…”

Section: Reinforcement Learning and Metaheuristic Algorithmsmentioning

confidence: 99%

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Le,

Priya,

et al. 2024

Int. J. Renew. Energy Dev.

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The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.

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Section: Reinforcement Learning and Metaheuristic Algorithmsmentioning

confidence: 99%

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Le,

Priya,

et al. 2024

Int. J. Renew. Energy Dev.

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“…How to optimize these comparison thresholds is not discussed by the authors in [ 27 ]. In [ 28 ], the optimal thresholds were derived to further improve the performance. When relays are used in IoT networks with passive eavesdroppers with locations, a randomize-and-forward relay scheme has been proposed in [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

Secure Communications in CIoT Networks with a Wireless Energy Harvesting Untrusted Relay

Gao

Liao

et al. 2017

Sensors

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The Internet of Things (IoT) represents a bright prospect that a variety of common appliances can connect to one another, as well as with the rest of the Internet, to vastly improve our lives. Unique communication and security challenges have been brought out by the limited hardware, low-complexity, and severe energy constraints of IoT devices. In addition, a severe spectrum scarcity problem has also been stimulated by the use of a large number of IoT devices. In this paper, cognitive IoT (CIoT) is considered where an IoT network works as the secondary system using underlay spectrum sharing. A wireless energy harvesting (EH) node is used as a relay to improve the coverage of an IoT device. However, the relay could be a potential eavesdropper to intercept the IoT device’s messages. This paper considers the problem of secure communication between the IoT device (e.g., sensor) and a destination (e.g., controller) via the wireless EH untrusted relay. Since the destination can be equipped with adequate energy supply, secure schemes based on destination-aided jamming are proposed based on power splitting (PS) and time splitting (TS) policies, called intuitive secure schemes based on PS (Int-PS), precoded secure scheme based on PS (Pre-PS), intuitive secure scheme based on TS (Int-TS) and precoded secure scheme based on TS (Pre-TS), respectively. The secure performances of the proposed schemes are evaluated through the metric of probability of successfully secure transmission (PSST), which represents the probability that the interference constraint of the primary user is satisfied and the secrecy rate is positive. PSST is analyzed for the proposed secure schemes, and the closed form expressions of PSST for Pre-PS and Pre-TS are derived and validated through simulation results. Numerical results show that the precoded secure schemes have better PSST than the intuitive secure schemes under similar power consumption. When the secure schemes based on PS and TS polices have similar PSST, the average transmit power consumption of the secure scheme based on TS is lower. The influences of power splitting and time slitting ratios are also discussed through simulations.

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“…In particular, the authors introduce an interesting security model with a self-adaptive schema that enables a system to automatically meet the environmental parameters, hence offering the corresponding protections. A study on secure distributed detection problems under energy constraint for IoT-oriented sensor networks is proposed in [ 23 ]. In particular, authors focus on how to optimize the key thresholds for estimating the channel gain in Channel-Aware Encryption (CAE).…”

Section: Related Workmentioning

confidence: 99%

Enabling Secure XMPP Communications in Federated IoT Clouds Through XEP 0027 and SAML/SASL SSO

Celesti

Fazio

Villari

2017

Sensors

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Nowadays, in the panorama of Internet of Things (IoT), finding a right compromise between interactivity and security is not trivial at all. Currently, most of pervasive communication technologies are designed to work locally. As a consequence, the development of large-scale Internet services and applications is not so easy for IoT Cloud providers. The main issue is that both IoT architectures and services have started as simple but they are becoming more and more complex. Consequently, the web service technology is often inappropriate. Recently, many operators in both academia and industry fields are considering the possibility to adopt the eXtensible Messaging and Presence Protocol (XMPP) for the implementation of IoT Cloud communication systems. In fact, XMPP offers many advantages in term of real-time capabilities, efficient data distribution, service discovery and inter-domain communication compared to other technologies. Nevertheless, the protocol lacks of native security, data confidentiality and trustworthy federation features. In this paper, considering an XMPP-based IoT Cloud architectural model, we discuss how can be possible to enforce message signing/encryption and Single-Sign On (SSO) authentication respectively for secure inter-module and inter-domain communications in a federated environment. Experiments prove that security mechanisms introduce an acceptable overhead, considering the obvious advantages achieved in terms of data trustiness and privacy.

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Secure Distributed Detection under Energy Constraint in IoT-Oriented Sensor Networks

Cited by 6 publications

References 41 publications

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Secure Communications in CIoT Networks with a Wireless Energy Harvesting Untrusted Relay

Enabling Secure XMPP Communications in Federated IoT Clouds Through XEP 0027 and SAML/SASL SSO

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