Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.

This paper surveys the literature of opportunistic offloading. Opportunistic offloading refers to offloading traffic originally transmitted through the cellular network to opportunistic network, or offloading computing tasks originally executed locally to nearby devices with idle computing resources through opportunistic network. This research direction is recently emerged, and the relevant research covers the period from 2009 to date, with an explosive trend over the last four years. We provide a comprehensive review of the research field from a multidimensional view based on application goal, realizing approach, offloading direction, etc. In addition, we pinpoint the major classifications of opportunistic offloading, so as to form a hierarchical or graded classification of the existing works. Specifically, we divide opportunistic offloading into two main categories based on application goal: traffic offloading or computation offloading. Each category is further divided into two smaller categories: with and without offloading node selection, which bridges between subscriber node and the cellular network, or plays the role of computing task executor for other nodes. We elaborate, compare, and analyze the literatures in each classification from the perspectives of required information, objective, etc. We present a complete introductory guide to the researches relevant to opportunistic offloading. After summarizing the development of the research direction and offloading strategies of the current state-of-the-art, we further point out the important future research problems and directions.

To deal with the explosive traffic demand, caching at the edge of mobile networks has been proposed to improve the quality of user experience. Presently, the edge caching systems are based on the direct and simple relay transmission, which is hard to achieve high system throughput and good content service. In such situations, network coding can effectively solve these problems. One problem arises in the application of network coding: how to form ‘X’ topology network. We formulate the ‘X’ topology formation problem as a function of system throughput and content service satisfaction degree. Then We propose a coalition game theory based algorithm to solve this NP-hard problem and prove the Nash stability of our coalition formation game. We evaluate our proposed algorithm through extensive simulations in different network environments. The experiment results demonstrate that our algorithm achieves nearly optimal performance and significantly outperforms the four widely- accepted baseline algorithms.

Internet of Things (IoT) devices are increasingly growing in mobile networks with the ubiquity of various IoT services. They share the same infrastructure with smartphones while having different requirements for communication resources and security defense mechanisms. Distinguishing IoT devices from smartphones has far-reaching implications on effective network design, resource allocation scheme, pricing scheme, etc. In this article, we distinguish between 12 107 IoT devices and 12 693 smartphones in the real world via characterizing their network traffic. The IoT devices fall into five categories, namely, locating, monitoring, portable, point of sale (POS), and vehicle. We analyze the device behaviors from the network domain, physical domain, and time domain, make comparisons between each kind of IoT devices and smartphones, and design effective features based on the distinguishable network behavior characteristics at packet level, traffic level, and mobility level. Then, we train several classifiers based on our feature set to identify different kinds of mobile devices. Specifically, the accuracy of identifying IoT devices from smartphones achieves 95.86%, and the accuracies of distinguishing IoT devices in each category from smartphones are all over 95%. In the trained classifiers, feature importance verifies the discriminability of different network traffic characteristics observed in our multidomain measurement. Our study reveals the network traffic behavior characteristics for IoT devices, and successfully distinguishes them from smartphones, which paves the way for better network design, resource allocation, pricing scheme, and security defense mechanisms.

To deal with the explosive traffic demands on the overloaded cellular network, device-to-device (D2D) offloading is proposed to transmit the traffic originally delivered through cellular network by D2D communications. Current D2D offloading schemes are based on direct transmissions or simple relay-assisted transmissions, which is hard to achieve high throughput. Network coding can effectively solve the problem. However, two problems must be addressed, topology formation and resource allocation. We formulate the topology formation problem as a function on network throughput under transmission pair matching and relay selection. we adopt coalition game theory to jointly match devices to form transmission pairs and select relay to form topology. Moreover, we propose a greedy algorithm to allocate the limited spectrum resource for each transmitter to decrease the interference influence. We evaluate our proposed offloading scheme through extensive evaluations under different network environments. The result shows that our scheme can effectively improve the network throughput and enhance the offloading efficiency.