Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments Because of its multilayer structure, deep learning is also appropriate for the edge computing environment Therefore, in this article, we first introduce deep learning for IoTs into the edge computing environment Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing In the performance evaluation, we test the performance of executing multiple deep learning tasks in an edge computing environment with our strategy The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT

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Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Electromagnetic Metamaterials: Transmission Line Theory and Microwave Applications. By Christophe Caloz and Tatsuo Itoh.

Space-air-ground integrated network (SAGIN), as an integration of satellite systems, aerial networks, and terrestrial communications, has been becoming an emerging architecture and attracted intensive research interest during the past years. Besides bringing significant benefits for various practical services and applications, SAGIN is also facing many unprecedented challenges due to its specific characteristics, such as heterogeneity, self-organization, and time-variability. Compared to traditional ground or satellite networks, SAGIN is affected by the limited and unbalanced network resources in all three network segments, so that it is difficult to obtain the best performances for traffic delivery. Therefore, the system integration, protocol optimization, resource management, and allocation in SAGIN is of great significance. To the best of our knowledge, we are the first to present the state-of-the-art of the SAGIN since existing survey papers focused on either only one single network segment in space or air, or the integration of space-ground, neglecting the integration of all the three network segments. In light of this, we present in this paper a comprehensive review of recent research works concerning SAGIN from network design and resource allocation to performance analysis and optimization. After discussing several existing network architectures, we also point out some technology challenges and future directions.

Space-Air-Ground Integrated Network: A Survey

By offloading the computation tasks of the mobile devices (MDs) to the edge server, mobile-edge computing (MEC) provides a new paradigm to meet the increasing computation demands from mobile applications. However, existing mobile-edge computation offloading (MECO) research only took the resource allocation between the MDs and the MEC servers into consideration, and ignored the huge computation resources in the centralized cloud computing center. Moreover, current MEC hosted networks mostly adopt the networking technology integrating cellular and backbone networks, which have the shortcomings of single access mode, high congestion, high latency, and high energy consumption. Toward this end, we introduce hybrid fiber–wireless (FiWi) networks to provide supports for the coexistence of centralized cloud and multiaccess edge computing, and present an architecture by adopting the FiWi access networks. The problem of cloud-MEC collaborative computation offloading is studied, and two schemes are proposed as our solutions, i.e., an approximation collaborative computation offloading scheme, and a game-theoretic collaborative computation offloading scheme. Numerical results corroborate that our solutions not only achieve better offloading performance than the available MECO schemes but also scale well with the increasing number of computation tasks.

Collaborative Computation Offloading for Multiaccess Edge Computing Over Fiber–Wireless Networks

The emergence of massive Internet of Things (IoT) mobile devices (MDs) and the deployment of ultradense 5G cells have promoted the evolution of IoT toward ultradense IoT networks. In order to meet the diverse quality-of-service and quality of experience demands from the ever-increasing IoT applications, the ultradense IoT networks face unprecedented challenges. Among them, a fundamental one is how to address the conflict between the resource-hungry IoT mobile applications and the resource-constrained IoT MDs. By offloading the IoT MDs’ computation tasks to the edge servers deployed at the radio access infrastructures, including macro base station (MBS) and small cells, mobile-edge computation offloading (MECO) provides us a promising solution. However, note that available MECO research mostly focused on single-tier base station scenario and computation offloading between the MDs and the edge server connected to the MBS. Little works can be found on performing MECO in ultradense IoT networks, i.e., a multiuser ultradense edge server scenario. Toward this end, we provide this paper to study the MECO problem in ultradense IoT networks, and propose a two-tier game-theoretic greedy offloading scheme as our solution. Extensive numerical results corroborate the superior performance of conducting computation offloading among multiple edge servers in ultradense IoT networks.

Mobile-Edge Computation Offloading for Ultradense IoT Networks

Recently, the rapid advance of vehicular networks has led to the emergence of diverse delay-sensitive vehicular applications such as automatic driving, auto navigation. Note that existing resource-constrained vehicles cannot adequately meet these demands on low / ultra-low latency. By offloading parts of the vehicles’ compute-intensive tasks to the edge servers in proximity, mobile edge computing is envisioned as a promising paradigm, giving rise to the vehicular edge computing networks (VECNs). However, most existing works on task offloading in VECNs did not take the load balancing of the computation resources at the edge servers into account. To address these issues and given the high dynamics of vehicular networks, we introduce fiber-wireless (FiWi) technology to enhance VECNs, due to its advantages on centralized network management and supporting multiple communication techniques. Aiming to minimize the processing delay of the vehicles’ computation tasks, we propose a software-defined networking (SDN) based load-balancing task offloading scheme in FiWi enhanced VECNs, where SDN is introduced to provide supports for the centralized network and vehicle information management. Extensive analysis and numerical results corroborate that our proposed load-balancing scheme can achieve superior performance on processing delay reduction by utilizing the edge servers’ computation resources more efficiently.

Task Offloading in Vehicular Edge Computing Networks: A Load-Balancing Solution

With the explosive growth of diverse Internet of Things (IoT) applications, mobile edge computing (MEC) has been brought to settle the conflict between computation-intensive applications and resource-limited IoT mobile devices (IMDs). Note that the assistance of unmanned aerial vehicles (UAVs) is of great importance in providing reliable connectivity in areas with limited or no available communication infrastructure. To cope with the surging demands for Big Data processing from UAV-aided IoT applications, combining UAV-aided communication and MEC has been envisioned to be a promising paradigm, which gives rise to the so-called UAV-enhanced edge. In consideration of IMDs’ limited battery capacity and UAV energy budget, in this article we study the energy reduction problem in UAV-enhanced edge by smartly making offloading decisions, allocating transmitted bits in both uplink and downlink, as well as designing UAV trajectory. This joint optimization problem is formulated as a mix-integer nonconvex optimization problem, and an alternative optimization algorithm based on block coordinate descent and successive convex approximation techniques is proposed as our solution. Extensive numerical results demonstrate that the overall energy consumption for accomplishing the tasks can be effectively reduced by adopting our joint optimization scheme, and the necessity of task offloading, UAV trajectory design, and bit allocation during transmission is validated.

UAV-Enhanced Intelligent Offloading for Internet of Things at the Edge

The ultra-dense network (UDN) is envisioned to be an enabling and highly promising technology to enhance spatial multiplexing and network capacity in future 5G networks. Moreover, to address the conflict between computation-intensive applications and resource-constrained IoT mobile devices (MDs), multi-access mobile edge computing (MA-MEC), which provides the IoT MDs with cloud capabilities at the edge of radio access networks, has been proposed. UDN and MA-MEC are regarded as two distinct but complementary enabling technologies for 5G IoT applications. Over the past several years, lots of research on mobile edge computation offloading (MECO) -- the key technique in MA-MEC -- has emerged. However, it is noticed that all these works focused on the single-tier base station scenario and computation offloading between the MD and the MEC server connected to the macro base station, and few works can be found on the problem of computation offloading for MA-MEC in UDN (i.e., a multi-user ultra-dense MEC server scenario). Toward this end, we study in this article the MECO problem in UDN and propose a heuristic greedy offloading scheme as our solution. Extensive numerical results and comparisons demonstrate the necessity for and superior performance of conducting computation offloading over multiple MEC servers.

Hongzhi Guo

Papers

Collaborative Computation Offloading for Multiaccess Edge Computing Over Fiber–Wireless Networks

Mobile-Edge Computation Offloading for Ultradense IoT Networks

Task Offloading in Vehicular Edge Computing Networks: A Load-Balancing Solution

UAV-Enhanced Intelligent Offloading for Internet of Things at the Edge

Computation Offloading for Multi-Access Mobile Edge Computing in Ultra-Dense Networks