WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Lifetime Maximization of IoT-Enabled Smart Grid Applications Using Error Control Strategies(Elsevier, 2024-12) Tekin, Nazli; Dedeturk, Bilge Kagan; Gungor, Vehbi CagriRecently, with the advancement of Internet of Things (IoT) technology, IoT-enabled Smart Grid (SG) applications have gained tremendous popularity. Ensuring reliable communication in IoT-based SG applications is challenging due to the harsh channel environment often encountered in the power grid. Error Control (EC) techniques have emerged as a promising solution to enhance reliability. Nevertheless, ensuring network reliability requires a substantial amount of energy consumption. In this paper, we formulate a Mixed Integer Programming (MIP) model which considers the energy dissipation of EC techniques to maximize IoT network lifetime while ensuring the desired level of IoT network reliability. We develop meta-heuristic approaches such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) to address the high computation complexity of large-scale IoT networks. Performance evaluations indicate that the EC-Node strategy, where each IoT node employs the most energy-efficient EC technique, yields a minimum of 8.9% extended lifetimes compared to the EC-Net strategies, where all IoT nodes employ the same EC method for a communication. Moreover, the PSO algorithm reduces the computational time by 77% while exhibiting a 2.69% network lifetime decrease compared to the optimal solution.Article Citation - WoS: 23Citation - Scopus: 35A Review of On-Device Machine Learning for IoT: An Energy Perspective(Elsevier, 2024-02) Tekin, Nazli; Aris, Ahmet; Acar, Abbas; Uluagac, Selcuk; Gungor, Vehbi CagriRecently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the literature to address this issue. Although there are considerable works that discuss energy-aware ML approaches for on-device implementation, there remains a gap in the literature on a comprehensive review of this subject. In this paper, we provide a review of existing studies focusing on-device ML models for IoT applications in terms of energy consumption. One of the key contributions of this study is to introduce a taxonomy to define approaches for employing energy-aware on-device ML models on IoT devices in the literature. Based on our review in this paper, our key findings are provided and the open issues that can be investigated further by other researchers are discussed. We believe that this study will be a reference for practitioners and researchers who want to employ energy-aware on-device ML models for IoT applications.