An Optimal Balanced Energy Harvesting Algorithm for Maximizing Two-Way Relaying D2D Communication Data Rate

Salim, Mahmoud M.; El-Sayed, Hesham; Elaziz, Mohamed Abd; Fouda, Mostafa M.; Abdalzaher, Mohamed S.

doi:10.1109/access.2022.3216775

Cited by 23 publications

(6 citation statements)

References 71 publications

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“…SS can be involved in a smart garage, individuals' monitoring, the effective interconnection between outdoor lighting, building, lighting, heating, cooling power systems, security surveillance, physical and access systems [40], [57], [58], [59]. Interestingly, long-term evolution (LTE) can also play a significant role in the SS when D2D communication is utilized as extensively studied in [60], [61], [62], and [63].…”

Section: Common Security Methodologiesmentioning

confidence: 99%

Toward Secured IoT-Based Smart Systems Using Machine Learning

et al. 2023

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Machine learning (ML) and the internet of things (IoT) are among the most booming research directions. Smart cities, smart campuses (SCs), smart homes, smart cars, early warning systems (EWSs), etc.; or it could be called ''Smart x'' systems are implemented using ML and IoT. Those systems will alter how various world entities communicate with one another. This paper spots light on the significant roles of the IoT in SS. Also, it focuses on the importance of ML in IoT-based SS. Besides, an overview of smartness and IoT is presented. Then, this paper offers ML benchmarking along with a taxonomy that categorizes the ML models into linear and non-linear ones depending on the problem type (classification or regression). Afterward, the commonly utilized evaluation metrics are provided. In addition, this paper considers the trust techniques used for mitigating different security aspects in IoT networks, which play a crucial part in regulating the new era of communication. Moreover, two case studies devoting ML for IoT-based SS, namely IoT-based SC and IoT-based EWS, are considered for data collection and manipulation with guided research directions. Finally, the paper presents effective recommendations of ML's significant roles in SC and earthquake EWS for interested scholars.INDEX TERMS Machine learning, Internet of Things, smart systems, security.

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Section: Common Security Methodologiesmentioning

confidence: 99%

Toward Secured IoT-Based Smart Systems Using Machine Learning

et al. 2023

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“…The work in [29] presents a two-way relaying model that maximizes the data rate of D2D links and preserves QoS for all users in the network. The best relay node is selected to optimize the data rate, and the relay can harvest energy from the attached solar panel as well as the received radio frequency signal.…”

Section: Related Workmentioning

confidence: 99%

An Iterative-Based Optimum Power and Resource Allocation in Application-Dependent Scenarios for One-to-One D2D Communication

Raghu,

Kiran

2024

IEEE Access

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Efficient and timely sharing of critical information is crucial for Public Safety (PS) communications, which can be fulfilled using one of the cutting-edge technologies, Device-to-device (D2D) communication. During an emergency, the PS applications should be prioritized over other applications, ensuring the emergency messages reach the first responders in time. Due to its inherent characteristics, the evolved Node Base station will not prioritize or categorize the D2D communication based on its application type, thus treating all applications equally. Further, D2D communication introduces significant interference to cellular users and vice-versa while sharing resources, and it is vital to reduce the impact of these interferences to ensure the Quality of Service for all users in the network. Hence, this article proposes a novel interference management approach to increase the overall sum rate of the system. In addition, the proposed approach also allows more D2D communication in general, particularly PS applicationbased D2D communication, to be active in the network. As the formulated problem is a Mixed-Integer Non-Linear Programming (MINLP) type of problem, it is split into two sub-problems, namely, Iterative Resource Allocation and Sharing and Iterative Power Optimization to achieve a polynomial time complexity. The theoretical proofs adequately explain the algorithm's time complexity and convergence property. The simulation results show that the proposed system enhances the overall sum rate by allowing more active PS D2D applications in the network.INDEX TERMS D2D communication, power optimization, interference management, iterative algorithm, one-to-one resource allocation.Recent research explores the possibility of sharing the Resource Blocks (RBs) (the smallest chunk of the spectrum) of Cellular User Equipment (CUEs) with D2D User Equipment (DUE) in underlay in-band communication to significantly enhance spectrum efficiency [4]. The different resource-sharing strategies in practice are one-to-one, oneto-many, and many-to-many. In the one-to-one technique,

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“…With the proliferation of intelligent devices and the growth of high-bandwidth applications, heterogeneous networks have become an effective solution for addressing the increased surge in data traffic [8,9]. Heterogeneous networks consist of different types of base stations, including micro base stations, macro base stations, and small-cell base stations, each differing in cache capacity, coverage area, and service modes [10].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching

Sun,

Chen

2024

Electronics

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With the popularity of smart devices and the growth of high-bandwidth applications, the wireless industry is facing an increased surge in data traffic. This challenge highlights the limitations of traditional edge-caching solutions, especially in terms of content-caching effectiveness and network-communication latency. To address this problem, we investigated efficient caching strategies in heterogeneous network environments. The caching decision process becomes more complex due to the heterogeneity of the network environment, as well as due to the diversity of user behaviors and content requests. To address the problem of increased system latency due to the dynamically changing nature of content popularity and limited cache capacity, we propose a novel content placement strategy, the long-short-term-memory–content-population-prediction model, to capture the correlation of request patterns between different contents and the periodicity in the time domain, in order to improve the accuracy of the prediction of content popularity. Then, to address the heterogeneity of heterogeneous network environments, we propose an efficient content delivery strategy: the multi-intelligent critical collaborative caching policy. This strategy models the edge-caching problem in heterogeneous scenarios as a Markov decision process using multi-base-station-environment information. In order to fully utilize the multi-intelligence information, we have improved the actor–critic approach by integrating the attention mechanism into a neural network. Whereas the actor network is responsible for making decisions based on local information, the critic network evaluates and enhances the actor’s performance. We conducted extensive simulations, and the results showed that the Long Short Term Memory content population prediction model was more advantageous, in terms of content-popularity-prediction accuracy, with a 28.61% improvement in prediction error, compared to several other existing methods. The proposed multi-intelligence actor–critic collaborative caching policy algorithm improved the cache-hit-rate metric by up to 32.3% and reduced the system latency by 1.6%, demonstrating the feasibility and effectiveness of the algorithm.

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An Optimal Balanced Energy Harvesting Algorithm for Maximizing Two-Way Relaying D2D Communication Data Rate

Cited by 23 publications

References 71 publications

Toward Secured IoT-Based Smart Systems Using Machine Learning

Toward Secured IoT-Based Smart Systems Using Machine Learning

An Iterative-Based Optimum Power and Resource Allocation in Application-Dependent Scenarios for One-to-One D2D Communication

Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching

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