A computational offloading optimization scheme based on deep reinforcement learning in perceptual network

Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms

Analysis of cross platform power data governance

With the rapid development of smart grid, how to solve the problems of professional business cooperation and information sharing, long data input time, accurate data, weak real-time, data extraction, redundant storage, low quality, privacy protection, further comprehensive management of data, mining the value of data resources has become one of the important tasks for the development of electric power enterprises. The traditional method uses edge computing for data transmission and task allocation. On this basis, we study the cross-platform power governance scheme based on edge unloading computing and deep reinforcement learning. The fi nal experimental results show that the scheme has smaller delay and lower energy consumption

An energy-efficient off-loading scheme for low latency in collaborative edge computing

Mobile terminal users applications, such as smartphones or laptops, have frequent computational task demanding but limited battery power. Edge computing is introduced to offload terminals' tasks to meet the quality of service requirements such as low delay and energy consumption. By offloading computation tasks, edge servers can enable terminals to collaboratively run the highly demanding applications in acceptable delay requirements. However, existing schemes barely consider the characteristics of the edge server, which leads to random assignment of tasks among servers and big tasks with high computational intensity (named as “big task”) may be assigned to servers with low ability. In this paper, a task is divided into several subtasks and subtasks are offloaded according to characteristics of edge servers, such as transmission distance and central processing unit (CPU) capacity. With this multi-subtasks-to-multi-servers model, an adaptive offloading scheme based on Hungarian algorithm is proposed with low complexity. Extensive simulations are conducted to show the efficiency of the scheme on reducing the offloading latency with low energy consumption

Mobile Edge Computing for 5G Internet of Things

This is the author accepted manuscript. The final version is available from CRC Press via the link in this recor

Offloading Decision Algorithm Based on Distance Weighted K-Nearest Neighbor in Power Internet of Things

With the widespread popularity of power Internet of Things (PIoT), the data collected from smart meters are growing explosively, which makes the calculation task of power data more and more complex. In order to improve computing power and maximize resource utilization, an offloading decision algorithm based on weighted K-nearest neighbor (WKNN) is proposed. It first collects the training set required by the WKNN-based algorithm, including the Received Signal Strength (RSS) required for offloading, the transmission rate, and the load balance of the Access Point (AP), and then the Euclidean distance between the training set and the sample is weighted by Gaussian function. Finally, the result with the largest K similarities in the training set is the offloading result. The simulation results show that the proposed algorithm reduces the offloading delay of the computing tasks and improves the resource utilization rate effectively when the number of meters increases in the network, which ensures that the resources of the mobile edge computing (MEC) servers in the system can be effectively and evenly utilized

Supporting UAVs with Edge Computing: A Review of Opportunities and Challenges

Over the last years, Unmanned Aerial Vehicles (UAVs) have seen significant advancements in sensor capabilities and computational abilities, allowing for efficient autonomous navigation and visual tracking applications. However, the demand for computationally complex tasks has increased faster than advances in battery technology. This opens up possibilities for improvements using edge computing. In edge computing, edge servers can achieve lower latency responses compared to traditional cloud servers through strategic geographic deployments. Furthermore, these servers can maintain superior computational performance compared to UAVs, as they are not limited by battery constraints. Combining these technologies by aiding UAVs with edge servers, research finds measurable improvements in task completion speed, energy efficiency, and reliability across multiple applications and industries. This systematic literature review aims to analyze the current state of research and collect, select, and extract the key areas where UAV activities can be supported and improved through edge computing

SAP: An IoT Application Module Placement Strategy Based on Simulated Annealing Algorithm in Edge-Cloud Computing

The Internet of Things (IoT) is rapidly growing and provides the foundation for the development of smart cities, smart home, and health care. With more and more devices connecting to the Internet, huge amounts of data are produced, creating a great challenge for data processing. Traditional cloud computing has the problems of long delays. Edge computing is an extension of cloud computing, processing data at the edge of the network can reduce the long processing delay of cloud computing. Due to the limited computing resources of edge servers, resource management of edge servers has become a critical research problem. However, the structural characteristics of the subtask chain between each pair of sensors and actuators are not considered to address the task scheduling problem in most existing research. To reduce processing latency and energy consumption of the edge-cloud system, we propose a multilayer edge computing system. The application deployed in the system is based on directed digraph. To fully use the edge servers, we proposed an application module placement strategy using Simulated Annealing module Placement (SAP) algorithm. The modules in an application are bounded to each sensor. The SAP algorithm is designed to find a module placement scheme for each sensor and to generate a module chain including the mapping of the module and servers for each sensor. Thus, the edge servers can transmit the tuples in the network with the module chain. To evaluate the efficacy of our algorithm, we simulate the strategy in iFogSim. Results show the scheme is able to achieve significant reductions in latency and energy consumption

AI and IoT Meet Mobile Machines: Towards a Smart Working Site

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)