List of Titles

Service deployment strategy for predictive analysis of FinTech IoT applications in edge networks

- Munusamy, Ambigavathi, Adhikari, Mainak, Balasubramanian, Venki, Khan, Mohammad, Menon, Varun, Rawat, Danda, Srirama, Satish

Authors: Munusamy, Ambigavathi , Adhikari, Mainak , Balasubramanian, Venki , Khan, Mohammad , Menon, Varun , Rawat, Danda , Srirama, Satish
Date: 2023
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 10, no. 3 (2023), p. 2131-2140
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Description: The seamless integration of sensors and smart communication technologies has led to the development of various supporting systems for financial technology (FinTech). The emergence of the next-generation Internet of Things (Nx-IoT) for FinTech applications enhances the customer satisfaction ratio. The main research challenge for FinTech applications is to analyze the incoming tasks at the edge of the networks with minimum delay and power consumption while increasing the prediction accuracy. Motivated by the above-mentioned challenge, in this article, we develop a ranked-based service deployment strategy and an artificial intelligence technique for financial data analysis at edge networks. Initially, a risk-based task classification strategy has been developed for classifying the incoming financial tasks and providing the importance to the risk-based task for meeting users' satisfaction ratio. Besides that, an efficient service deployment strategy is developed using $Hall's$ theorem to assign the ranked-based financial data to the suitable edge or cloud servers with minimum delay and power consumption. Finally, the standard support vector machines (SVMs) algorithm is used at edge networks for analyzing the financial data with higher accuracy. The experimental results demonstrate the effectiveness of the proposed strategy and SVM model at edge networks over the baseline algorithms and classification models, respectively. © 2014 IEEE.

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Edge computing for Internet of Everything : a survey

- Kong, Xiangjie, Wu, Yuhan, Wang, Hui, Xia, Feng

Authors: Kong, Xiangjie , Wu, Yuhan , Wang, Hui , Xia, Feng
Date: 2022
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 9, no. 23 (2022), p. 23472-23485
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Description: In this era of the Internet of Everything (IoE), edge computing has emerged as the critical enabling technology to solve a series of issues caused by an increasing amount of interconnected devices and large-scale data transmission. However, the deficiencies of edge computing paradigm are gradually being magnified in the context of IoE, especially in terms of service migration, security and privacy preservation, and deployment issues of edge node. These issues can not be well addressed by conventional approaches. Thanks to the rapid development of upcoming technologies, such as artificial intelligence (AI), blockchain, and microservices, novel and more effective solutions have emerged and been applied to solve existing challenges. In addition, edge computing can be deeply integrated with technologies in other domains (e.g., AI, blockchain, 6G, and digital twin) through interdisciplinary intersection and practice, releasing the potential for mutual benefit. These promising integrations need to be further explored and researched. In addition, edge computing provides strong support in applications scenarios, such as remote working, new physical retail industries, and digital advertising, which has greatly changed the way we live, work, and study. In this article, we present an up-to-date survey of the edge computing research. In addition to introducing the definition, model, and characteristics of edge computing, we discuss a set of key issues in edge computing and novel solutions supported by emerging technologies in IoE era. Furthermore, we explore the potential and promising trends from the perspective of technology integration. Finally, new application scenarios and the final form of edge computing are discussed. © 2014 IEEE.

An improved congestion-controlled routing protocol for IoT applications in extreme environments

- Adil, Muhammad, Usman, Muhammad, Jan, Mian, Abulkasim, Hussein, Farouk, Ahmed, Jin, Zhanpeng

Authors: Adil, Muhammad , Usman, Muhammad , Jan, Mian , Abulkasim, Hussein , Farouk, Ahmed , Jin, Zhanpeng
Date: 2024
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 11, no. 3 (2024), p. 3757-3767
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Description: The Internet of Things (IoT) has shown its presence in applications that require monitoring extreme environments, such as wildfires, military operations, and coastal areas, among others. In these applications, the IoT nodes are deployed in hazardous terrains where humanistic access is hard or not possible. Hence, to ensure reliable data transmission in these applications, novel routing protocols need to be designed due to the multihop nature of communication possessed by the deployed nodes. Currently, most of the routing protocols utilized by IoT nodes follow traditional approaches, which creates congestion and contention in the network. As a result, the network performance is degraded in terms of various communication metrics. To address this problem and improve the communication statistics in extreme environments, we propose a deep- Q -learning-enable-destination-sequenced distance-vector (DQL-DSDV) framework. DQL-DSDV focuses on selecting the next hop during communication. Initially, the DSDV protocol updates routing information for connected nodes. This information is subsequently utilized by the deep- Q -learning (DQL) algorithm to compute the next hop count. This computation is based on reward functions, known as Q-values, which are conceptualized as the distance between connected nodes by taking into account the traffic flow. These distinguishing operational features of DQL and DSDV ensure that DQL-DSDV minimizes the packet lost ratio, congestion, end-to-end delay, and communication cost with improved Quality of Service (QoS). During simulations, we observed significant improvement in these performance metrics, in the presence of the existing schemes. Despite that, we checked the computation complexity of the proposed approach with existing protocols, which demonstrated noteworthy outcomes just like the other metrics. © 2014 IEEE.

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Marking the pace : a blockchain-enhanced privacy-traceable strategy for federated recommender systems

- Cai, Zhen, Tang, Tao, Yu, Shuo, Xiao, Yunpeng, Xia, Feng

Authors: Cai, Zhen , Tang, Tao , Yu, Shuo , Xiao, Yunpeng , Xia, Feng
Date: 2024
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 11, no. 6 (2024), p. 10384-10397
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Description: Federated recommender systems have been crucially enhanced through data sharing and continuous model updates, attributed to the pervasive connectivity and distributed computing capabilities of Internet of Things (IoT) devices. Given the sensitivity of IoT data, transparent data processing in data sharing and model updates is paramount. However, existing methods fall short in tracing the flow of shared data and the evolution of model updates. Consequently, data sharing is vulnerable to exploitation by malicious entities, raising significant data privacy concerns, while excluding data sharing will result in suboptimal recommendations. To mitigate these concerns, we present LIBERATE, a privacy-traceable federated recommender system. We design a blockchain-based traceability mechanism, ensuring data privacy during data sharing and model updates. We further enhance privacy protection by incorporating local differential privacy in user-server communication. Extensive evaluations with the real-world data set corroborate LIBERATE's capabilities in ensuring data privacy during data sharing and model update while maintaining efficiency and performance. Results underscore blockchain-based traceability mechanism as a promising solution for privacy preserving in federated recommender systems. © 2014 IEEE.

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Joint resource allocation to minimize execution time of federated learning in cell-free massive MIMO

- Vu, Tung, Ngo, Duy, Ngo, Hien, Dao, Minh, Tran, Nguyen, Middleton, Richard

Authors: Vu, Tung , Ngo, Duy , Ngo, Hien , Dao, Minh , Tran, Nguyen , Middleton, Richard
Date: 2022
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 9, no. 21 (2022), p. 21736-21750
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Description: Due to its communication efficiency and privacy-preserving capability, federated learning (FL) has emerged as a promising framework for machine learning in 5G-and-beyond wireless networks. Of great interest is the design and optimization of new wireless network structures that support the stable and fast operation of FL. Cell-free massive multiple-input-multiple-output (CFmMIMO) turns out to be a suitable candidate, which allows each communication round in the iterative FL process to be stably executed within a large-scale coherence time. Aiming to reduce the total execution time of the FL process in CFmMIMO, this article proposes choosing only a subset of available users to participate in FL. An optimal selection of users with favorable link conditions would minimize the execution time of each communication round while limiting the total number of communication rounds required. Toward this end, we formulate a joint optimization problem of user selection, transmit power, and processing frequency, subject to a predefined minimum number of participating users to guarantee the quality of learning. We then develop a new algorithm that is proven to converge to the neighborhood of the stationary points of the formulated problem. Numerical results confirm that our proposed approach significantly reduces the FL total execution time over baseline schemes. The time reduction is more pronounced when the density of access point deployments is moderately low. © 2014 IEEE.

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