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

Blockchain-based mobile edge computing framework for secure therapy applications

Mobile edge computing (MEC) is being introduced and leveraged in many domains, but few studies have addressed MEC for secure in-home therapy management. To this end, this paper presents an in-home therapy management framework, which leverages the IoT nodes and the blockchain-based decentralized MEC paradigm to support low-latency, secure, anonymous, and always-available spatiotemporal multimedia therapeutic data communication within an on-demand data-sharing scenario. To the best of our knowledge, this non-invasive, MEC-based IoT therapy platform is first done by our group. This platform can provide a full-body joint range of motion data for physically challenged individuals in a decentralized manner. With MEC, the framework can provide therapy diagnostic and analytical data on demand to a large portion of humanity who are either born with disabilities or became disabled due to accidents, war-time injuries, or old age. For security, the framework uses blockchain–Tor-based distributed transactions to preserve the therapeutic data privacy, ownership, generation, storage, and sharing. Our initial test results from a complete implementation of the framework show that it can support a sufficiently large number of users without considerable increase in mean processing time

Mobile edge computing in wireless communication networks: design and optimization

This dissertation studies the design and optimization of applying mobile edge computing (MEC) in three kinds of advanced wireless networks, which is motivated by three non-trivial but not thoroughly studied topics in the existing MEC-related literature. First, we study the application of MEC in wireless powered cooperation-assisted systems. The technology of wireless power transfer (WPT) used at the access point (AP) is capable of providing sustainable energy supply for resource-limited user equipment (UEs) to support computation offloading, but also introduces the double-near-far effect into wireless powered communication networks (WPCNs). By leveraging cooperation among near-far users, the system performance can be highly improved through effectively suppressing the double-near-far effect in WPCNs. Then, we consider the application of MEC in the unmanned aerial vehicle (UAV)-assisted relaying systems to make better use of the flexible features of UAV as well as its computing resources. The adopted UAV not only acts as an MEC server to help compute UEs' offloaded tasks but also a relay to forward UEs' offloaded tasks to the AP, thus such kind of cooperation between the UAV and the AP can take the advantages of both sides so as to improve the system performance. Last, heterogeneous cellular networks (HetNets) with the coexistence of MEC and central cloud computing (CCC) are studied to show the complementary and promotional effects between MEC and CCC. The small base stations (SBSs) empowered by edge clouds offer limited edge computing services for UEs, whereas the macro base station (MBS) provides high-performance CCC services for UEs via restricted multiple-input multiple-output (MIMO) backhauls to their associated SBSs. With further considering the case with massive MIMO backhauls, the system performance can be further improved while significantly reducing the computational complexity. In the aforementioned three advanced MEC systems, we mainly focus on minimizing the energy consumption of the systems subject to proper latency constraints, due to the fact that energy consumption and latency are regarded as two important metrics for measuring the performance of MEC-related works. Effective optimization algorithms are proposed to solve the corresponding energy minimization problems, which are further validated by numerical results

Situation-aware Edge Computing

Future wireless networks must cope with an increasing amount of data that needs to be transmitted to or from mobile devices. Furthermore, novel applications, e.g., augmented reality games or autonomous driving, require low latency and high bandwidth at the same time. To address these challenges, the paradigm of edge computing has been proposed. It brings computing closer to the users and takes advantage of the capabilities of telecommunication infrastructures, e.g., cellular base stations or wireless access points, but also of end user devices such as smartphones, wearables, and embedded systems. However, edge computing introduces its own challenges, e.g., economic and business-related questions or device mobility. Being aware of the current situation, i.e., the domain-specific interpretation of environmental information, makes it possible to develop approaches targeting these challenges. In this thesis, the novel concept of situation-aware edge computing is presented. It is divided into three areas: situation-aware infrastructure edge computing, situation-aware device edge computing, and situation-aware embedded edge computing. Therefore, the concepts of situation and situation-awareness are introduced. Furthermore, challenges are identified for each area, and corresponding solutions are presented. In the area of situation-aware infrastructure edge computing, economic and business-related challenges are addressed, since companies offering services and infrastructure edge computing facilities have to find agreements regarding the prices for allowing others to use them. In the area of situation-aware device edge computing, the main challenge is to find suitable nodes that can execute a service and to predict a node’s connection in the near future. Finally, to enable situation-aware embedded edge computing, two novel programming and data analysis approaches are presented that allow programmers to develop situation-aware applications. To show the feasibility, applicability, and importance of situation-aware edge computing, two case studies are presented. The first case study shows how situation-aware edge computing can provide services for emergency response applications, while the second case study presents an approach where network transitions can be implemented in a situation-aware manner

Edge/Fog Computing Technologies for IoT Infrastructure

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies