Distributed Security Architecture Based on Blockchain for Connected Health: Architecture, Challenges, and Approaches

As an important technology for Internet of Things, wireless sensor networks have been receiving widespread attention in recent years. Traditional wireless sensor network deploys resources for specific applications, which causes the problem of low resource utilization. Additionally, the energy consumption brought by the diversification of application requirements has also raised the burden on sensor nodes. Therefore, based on the introduction of wireless energy transfer technology, this paper proposes a resource allocation strategy for virtualized wireless sensor networks. Specifically, physical resources are pooled by the the wireless sensor network service provider. Then, virtual sensor networks are built through network slicing technology to provide one-to-one services based on the application requirements and the current status of the physical sensor nodes. Furthermore, in order to minimize the overall network energy consumption, a system-friendly resource allocation strategy is proposed to optimize the jointly configurations of sensing frequency, time slot and transmission power. Simulation results validate that the proposed strategy can not only effectively save the network energy, but also meet the diverse personalized needs of applications.

“…}} is a reasonable solution which satisfied (7) to meet minimum energy consumption of system whose target data volume is K j=1 D r i,j . Now, the energy consumed by whole system is…”

Section: Dynamic Resource Allocation Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resource Allocation in Wireless Powered Virtualized Sensor Networks

Zhong

2020

“…If all ϕ j,b is equal to zero, then perform task Q i on the local or local server. If tasks are processed on a remote server, the decision on task assignment satisfies formula (9), which ensures that each task is scheduled on only one server.…”

Section: ) Edge Collaboration Modelmentioning

confidence: 99%

“…MEC has gradually moved cloud computing applications, data and services from centralized nodes to the network edge. Unlike traditional cloud computing, MEC enables data processing at the data source [9]. According to the report, by the end of 2022, the MEC market will reach 13.137 billion, with a compound annual growth rate of 10.7% [10], [11].…”

mentioning

confidence: 99%

Load Optimization Based on Edge Collaboration in Software Defined Ultra-Dense Networks

Yang

Zhang

2020

With the intelligence of user equipment and the popularization of emerging applications such as unmanned driving and face recognition, more and more computationally intensive and delay-sensitive tasks have been generated. As a new network paradigm, ultra-dense networks can greatly improve user access capabilities by deploying dense base stations (BSs). Edge computing can effectively guarantee the low-latency requirements of users in ultra-dense networks. However, the heterogeneity of servers, the distributed resources, and the dynamic energy consumption of mobile devices in ultra-dense networks make it extremely difficult for users to offload and load balance among servers. This paper applies the idea of software defined network to proposes an edge collaboration architecture to achieve resource sharing and efficient offloading of tasks based on the characteristics of global perception. In particular, considering the high load of the local server and the idle resources of the remote server, the best offloading strategy for users is obtained game theory. Simulation results show that the performance is improved by about 30% compared to the traditional local processing, edge offload and local edge random offload schemes. INDEX TERMS Software defined network, ultra dense network, load balancing, edge collaboration. I. INTRODUCTION

“…In order to meet the above challenges, the emerging mobile edge computing (MEC) has been widely concerned by academia and industry [6]- [8]. MEC implements the MEC servers at the edge of wireless network, which can push the computing, communication, storage and other capabilities closer to the user devices [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Multimedia Task Assignment and Computing Offloading for Mobile Edge Computing Networks

Sun

Wei

et al. 2020

With the rapid development of 5G technology in recent years, multimedia communication services, such as live online and short video, have received wide attention and become the important means of people's daily social intercourse. However, the rapid growth of multimedia communication demands pose greater challenges to both the wireless network communication capacity and the network processing capacity. Mobile Edge Computing (MEC) is widely regarded as a promising technology to cope with the above challenges. To satisfy the growing demands and improve the quality of experience for users, it is in urge need to seek the effective and efficient task assignment and computing offloading strategy for MEC networks. In this paper, we focused on the multimedia services which need to be processed, uploaded and shared in the network and research the long-term task assignment and resource coordination problem. We formulate the optimization problem as a stochastic optimization problem with the aim of the minimizing the timeaverage energy consumption of the system. By using the Lyapunov optimization technique, we decompose the original problem into several subproblems which can be solved with current system information and low computational complexities. On this basis, we propose an online energy-efficient task assignment and computing offloading strategy to adaptively decide the task assignment, coordinate and optimize the wireless and computation resource allocation by taking the dynamic wireless condition and service delay constraints into consideration. Extensive simulation results show that our proposed algorithm can achieve considerate energy consumption and delay performances under different conditions. INDEX TERMS Multimedia transmission, task assignment, computing offloading, Lyapunov optimization, delay constraints.