Distributed lEdger technologies have attracted significant attention recently and blockchain, as the underlying technology of cryptocurrencies, is the focal point of this attention. Blockchain has been used in various domains, such as cloud, fog, and Edge Computing and Internet of Things (IoT). However, it faces some limitations and lacks the capability to support frequent transactions. On the other side, after cloud and fog Computing, Edge Computing serves as a key enabler for many future technologies like 5G, IoT, and vehicle-to-vehicle communications by connecting cloud Computing resources and services to the end users and extends them at the Edge of the network, but it currently confronts with challenges in decentralized management and security. Incorporating of blockchain and Edge Computing in one system can provide reliable access and control of the network, storage and computation distributed at the Edges, thus providing a large scale of network servers, data storage and validity computation near the end in a secure manner. Notwithstanding the benefits of integrated blockchain and Edge Computing systems, their scalability enhancement, self-organization, resource management, functions integration and security issues need to be addressed before widespread implementation. This paper reviews some of the studies about enabling the integrated blockchain and Edge Computing system. Several critical aspects of the integration of blockchain and Edge Computing are identified. Finally, some of the effects of this integration on the business are discussed.

The exponential growth of data and the paradigm shift towards Edge Computing have necessitated innovative approaches to energy-efficient computation, especially for resource-constrained IoT devices. Approximate Computing, a paradigm that exploits the inherent tolerance of many applications to imprecision, has been extensively explored in the context of ASICs to reduce power consumption and area overhead. However, its potential in FPGA-based devices, which offer flexibility and rapid prototyping capabilities, remains largely untapped. This paper investigates the feasibility and performance implications of approximate Computing techniques for FPGAs, with a focus on the implementation of FIR and adaptive filters as illustrative case studies. Specifically, we propose novel approximate multipliers based on the Reverse Carry Propagation (RCP) adders, which are evaluated through their integration into adaptive and finite impulse response filters. Simulation results demonstrate significant improvements in operating frequency, as well as substantial reductions in hardware area for FIR filters. While the area reduction for adaptive filters is less pronounced, the proposed multipliers still exhibit acceptable performance. Our findings highlight the potential of approximate multipliers for low-power Computing systems, particularly in Edge Computing applications.

Optimizing energy consumption in industrial robots can reduce operating costs, improve performance, and extend the life of the robot during manufacturing. In recent years, with the progress of science and technology, new technologies such as cloud Computing, big data, etc. have continuously emerged, and in particular, cloud Computing technology has been used in robot research that improves the real-time performance of the designed robot. It can also provide high energy efficiency, low cost, etc. One of the most important aspects of this technology is its use in continuous monitoring of robots' performance, which can guarantee its optimal performance. In this research, first, an overview of the methods of reducing energy consumption is presented, and then the effectiveness of using Edge Computing technology in reducing energy is analyzed. For this purpose, the use of algorithms to optimize the performance of the robot, including its trajectory and working times, is controlled by the Edge. The results of the simulations show that the energy consumption can be significantly reduced by using Edge technology.

The proliferation of new applications has led to new challenges in energy consumption, task processing, and data storage. Multi-access Edge Computing (MEC) is a new computational paradigm that can transfer workloads from users' devices to powerful servers in the same location with the least possible time and energy overhead to improve the QoS and performance measures. Since joint task offloading and resource allocation in MEC is one of the main concerns of performance-aware applications, this paper explores a fine-grained view to this problem under the dynamic and time-varying conditions of the entire system. The main goal of this paper is to reduce the normalized cost of the system, which is the weighted sum of the completion time and the consumed energy, by formulating the problem and proposing a new algorithm based on the reinforcement learning approach. The results of the simulations performed under different scenarios corresponding to the real-world systems show the improvement in the completion time and energy consumption of the tasks compared to other existing methods and leads to an average reduction of 22 and 24 percent in the scenarios related to the evaluation of the normalized cost of the system.

Edge Computing is an evolving approach for the growing Computing and networking demands from end devices and smart things. Edge Computing lets the computation to be offloaded from the cloud data centers to the network Edge for lower latency, security, and privacy preservation. Although energy efficiency in cloud data centers has been widely studied, energy efficiency in Edge Computing has been left uninvestigated. In this paper, a new adaptive and decentralized approach is proposed for more energy efficiency in Edge environments. In the proposed approach, Edge servers collaborate with each other to achieve an efficient plan. The proposed approach is adaptive, and consider workload status in local, neighboring and global areas. The results of the conducted experiments show that the proposed approach can improve energy efficiency at network Edges. e.g. by task completion rate of 100%, the proposed approach decreases energy consumption of Edge servers from 1053 Kwh to 902 Kwh.

Mobile Edge Computing (MEC) are new issues to improve latency, capacity and available resources in Mobile cloud Computing (MCC). Mobile resources, including battery and CPU, have limited capacity. So enabling computation-intensive and latency-critical applications are important issue in MEC. In this paper, we use a standard three-level system model of mobile devices, Edge and cloud, and propose two offloading and scheduling algorithms. A decision-making algorithm for offloading tasks is based on the greedy Knapsack offloading algorithm (GKOA) on the mobile device side, which selects tasks with high power consumption for offloading and it saves energy consumption of the device. On the MEC side, we also present a dynamic scheduling algorithm with fuzzy-based priority task scheduling (FPTS) for prioritizing and scheduling tasks based on two criteria. Numerical results show that our proposed work compared to other methods and reduces the waiting time, latency and system overhead. Also, provides the balance of the system with the least number of resources. And the proposed system reduces battery consumption in the smart device by up to 90%. The results show that more than 92% of tasks are executed successfully in the Edge environment.

Today, as the new generation of communication networks is implemented, we are witnessing a considerable change in IoT development and new programs in this context. Despite recent advancements in mobile networks and devices, the limitations of devices connected to this platform in terms of computational power and energy have resulted in sever challenges for running resource-intensive programs with exigent latency requirements. To address these challenges, the concept of computation offloading in Multi-access Edge Computing (MEC) has been recently developed, in which storage and computation resources are provided close to the user. However, due to the user mobility and changes in the profile of offloaded applications over time, the problem of assignment of Edge servers to users with the aim of minimizing the overall offloading latency is a complicated task. In this regard, existing mobility-aware offloading approaches are not based on fine-grain offloading and use random and unrealistic mobility models. In this article, to address the aforementioned challenges, we propose a mobility-aware fine-grain computation offloading method to minimize the overall offloading delay. In the proposed approach, the user application is divided into several components and the offloading decision is made for each component according to the mobility and specifications of user components during the time slots defined in the system. In oner hand, this latter results in more efficient offloading decision. In the other hand, it reduces the overhead of migration since the migration of a subset of program’s components imposes lower cost compared to the migration of the entire program. Moreover, we use user profile and location prediction to optimize the offloading decisions considering the underlying context over time. According to the evaluation results, it is observed that the proposed method achieves significantly better performance compared to other alternatives while the complexity of offloading decision is kept very low.