فهرست مطالب mani zarei

The data generated in the Internet of Things (IoT) ecosystem requires continuous and timely processing. Transferring generated data to cloud data centers is costly and unsuitable for real-time applications. To increase the speed of service delivery, resources should be placed as close as possible to the user, i.e. at the edge of the network. A new paradigm called fog computing was introduced and added as a layer in the IoT architecture to meet this challenge. Fog computing provides the processing and storage of IoT data locally in the vicinity of IoT devices rather than in the cloud. Fog computing can provide less latency and better service quality for real-time applications than cloud computing. In general, there are theoretical foundations for fog computing, but the issue of locating IoT services to fog nodes remains a challenge and has attracted a great deal of research.

In this research, a conceptual computing framework based on cloud-fog control software is proposed to optimally locate IoT services. The proposed model is formulated as an autonomous planning model for managing service requests due to some constraints, considering the heterogeneity of programs and resources. To solve the problem of locating IoT services, an autonomous evolutionary approach based on enhanced learning approaches has been proposed with the aim of making maximum use of fog-based resources and improving service quality. A heterogeneous advantage operator-criterion algorithm is used as a new reinforcement learning approach aimed at maximizing long-term cumulative reward.

The results of the comparisons showed that the proposed reinforcement learning-enabled framework performs better than the advanced methods of the literature. The results of the proposed method compared to FSP-ODMA, SPP-GWO, CSA-FSPP, and GA-FSP methods indicate 4.6%, 2.4%, 3.4%, and 1.1% improvement, respectively.

Experimental studies were performed on a simulated artificial environment based on various metrics including fog usage, services performed, response time, and service delay. The proposed reinforcement learning-enabled framework outperforms the previous works and shows better scalability.Analysis of parallel heuristic algorithms to find a more accurate localization than evolutionary approaches is another aspect of future work. We intend to consider new reinforcement learning approaches such as the Asynchronous Advantage Actor Critic (A3C) algorithm along with the long-term cumulative reward maximization policy for locating services. Also, future efforts will explore reinforcement learning approaches for failure recovery towards Cloud-Fog-IoT architecture, where parallel processing architecture of IoT services can be considered in the location process.

Software-defined networking (SDN) strategy is a significant factor in developing the Internet of Things (IoT). SDN deploys a centralized controller on the gateway and outside the perception layer of the IoT network. In IoT, the increasing number of things leads to an increase in data production and growth of network traffic to access the cloud-based data center. Ensuring the quality of service (QoS) is one of the most important pillars of any network. One of the key factors in increasing the level of QoS in networks is reducing the access delay using fog computing and also service improvement for nodes with a non-uniform importance degree approach. This article presents a two-level hierarchical control model for software-defined IoT networks, including the sub-controller in IoT nodes and the main controller in the IoT gateway. By placing the main controller in the fog layer equipped with the SDN, the results of the simulation show that this approach makes the IoT network manageable in an innovative way, with a higher QoS level and with less processing cost (10%), and less access delay (20%).

This paper explains fault tolerance techniques for Quantum-dot cellular automata which offer remarkable robustness to implement QCA arithmetic circuits. It begins with a study of QCA based design. A classification for fault types is presented and some fault tolerance techniques are examined and their relevance for QCA circuits is evaluated. Finally, it is concluded that a combination of two or more hardware redundancy techniques is needed for tolerating faults in QCA circuits and systems. The proper functionality of the presented design is checked by computer simulations using the QCADesigner tool. Simulation results confirm our claims and their usefulness in designing robust digital circuits.