Browsing by Author "Jabraeil Jamali, Mohammad Ali"

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Citation - WoS: 52
Citation - Scopus: 56
Deep Q-Learning Technique for Offloading Offline/Online Computation in Blockchain-Enabled Green Iot-Edge Scenarios
(Mdpi, 2022) Heidari, Arash; Jamali, Mohammad Ali Jabraeil; Navimipour, Nima Jafari; Akbarpour, Shahin; Jafari Navimipour, Nima; Jabraeil Jamali, Mohammad Ali
The number of Internet of Things (IoT)-related innovations has recently increased exponentially, with numerous IoT objects being invented one after the other. Where and how many resources can be transferred to carry out tasks or applications is known as computation offloading. Transferring resource-intensive computational tasks to a different external device in the network, such as a cloud, fog, or edge platform, is the strategy used in the IoT environment. Besides, offloading is one of the key technological enablers of the IoT, as it helps overcome the resource limitations of individual objects. One of the major shortcomings of previous research is the lack of an integrated offloading framework that can operate in an offline/online environment while preserving security. This paper offers a new deep Q-learning approach to address the IoT-edge offloading enabled blockchain problem using the Markov Decision Process (MDP). There is a substantial gap in the secure online/offline offloading systems in terms of security, and no work has been published in this arena thus far. This system can be used online and offline while maintaining privacy and security. The proposed method employs the Post Decision State (PDS) mechanism in online mode. Additionally, we integrate edge/cloud platforms into IoT blockchain-enabled networks to encourage the computational potential of IoT devices. This system can enable safe and secure cloud/edge/IoT offloading by employing blockchain. In this system, the master controller, offloading decision, block size, and processing nodes may be dynamically chosen and changed to reduce device energy consumption and cost. TensorFlow and Cooja's simulation results demonstrated that the method could dramatically boost system efficiency relative to existing schemes. The findings showed that the method beats four benchmarks in terms of cost by 6.6%, computational overhead by 7.1%, energy use by 7.9%, task failure rate by 6.2%, and latency by 5.5% on average.
Citation - WoS: 34
Citation - Scopus: 32
Securing and Optimizing Iot Offloading With Blockchain and Deep Reinforcement Learning in Multi-User Environments
(Springer, 2025) Heidari, Arash; Navimipour, Nima Jafari; Jamali, Mohammad Ali Jabraeil; Akbarpour, Shahin; Jafari Navimipour, Nima; Jabraeil Jamali, Mohammad Ali
The growth of the Internet of Things (IoT)-related innovations has resulted in the invention of numerous IoT objects. However, the resource limitations of individual items remain a challenge that can be overcome through offloading. A key limitation of previous research is the absence of an integrated offloading framework that can operate securely in offline/online environments. The security and calculated online/offline offloading issues in a multi-user IoT-fog-cloud system with blockchain are investigated in this article at the same time. First, we provide a reliable access control system utilizing blockchain to enhance offloading security. This technique can guard cloud resources against unauthorized offloading practices. Next, we define a computation offloading issue by optimizing the offloading decisions, allocating computing resources and radio bandwidth, and intelligent contract use to address the computation management of authorized mobile devices. This optimization challenge focuses on the long-term system costs of latency, energy use, and intelligent contract charge among all mobile devices. We create a new Deep Reinforcement Learning (DRL) technique employing a double-dueling Q-network to address the suggested offloading problem. We provide a Markov Decision Process (MDP)-based DRL solution to the IoT offloading-enabled blockchain dilemma. The supposed system works in both online and offline settings, and when operating online, we use the Post Decision State (PDS) method. The contributions of this work include a new integrated offloading framework that can operate in offline/online environments while preserving security and a novel approach that incorporates fog platforms into IoT blockchain-enabled networks for improved system efficiency. Our method outperforms four benchmarks in cost by 5.1%, computational overhead by 4.1%, energy use by 3.3%, task failure rate by 3.6%, and latency by 3.9% on average.