The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge,” near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.

A Survey on the Edge Computing for the Internet of Things

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Reversibility and Stochastic Networks

The Internet of Vehicles (IoV) is an emerging paradigm that is driven by recent advancements in vehicular communications and networking. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications that will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend on communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and cloud computing will not be sufficient due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. EIS will provide not only low-latency content delivery and computation services but also localized data acquisition, aggregation, and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies, and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open-research problems are identified.

Mobile Edge Intelligence and Computing for the Internet of Vehicles

Mobile edge computing (MEC) yields significant paradigm shift in industrial Internet of things (IIoT), by bringing resource-rich data center near to the lightweight IIoT mobile devices (MDs). In MEC, resource allocation and network economics need to be jointly addressed to maximize system efficiency and incentivize price-driven agents, whereas this joint problem is under the locality constraints, i.e., an edge server can only serve multiple IIoT MDs in the vicinity constrained by its limited computing resource. In this paper, we investigate the joint problem of network economics and resource allocation in MEC where IIoT MDs request offloading with claimed bids and edge servers provide their limited computing service with ask prices. Particularly, we propose two double auction schemes with dynamic pricing in MEC, namely a breakeven-based double auction (BDA) and a more efficient dynamic pricing based double auction (DPDA), to determine the matched pairs between IIoT MDs and edge servers, as well as the pricing mechanisms for high system efficiency, under the locality constraints. Through theoretical analysis, both algorithms are proved to be budget-balanced, individual profit, system efficient, and truthful. Extensive simulations have been conducted to evaluate the performance of the proposed algorithms and the simulation results indicate that the proposed DPDA and BDA can significantly improve the system efficiency of MEC in IIoT.

Double Auction-Based Resource Allocation for Mobile Edge Computing in Industrial Internet of Things

This paper presents a comprehensive literature review on applications of economic and pricing models for resource management in cloud networking. To achieve sustainable profit advantage, cost reduction, and flexibility in provisioning of cloud resources, resource management in cloud networking requires adaptive and robust designs to address many issues, e.g., resource allocation, bandwidth reservation, request allocation, and workload allocation. Economic and pricing models have received a lot of attention as they can lead to desirable performance in terms of social welfare, fairness, truthfulness, profit, user satisfaction, and resource utilization. This paper reviews applications of the economic and pricing models to develop adaptive algorithms and protocols for resource management in cloud networking. Besides, we survey a variety of incentive mechanisms using the pricing strategies in sharing resources in edge computing. In addition, we consider using pricing models in cloud-based software defined wireless networking. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to cloud networking.

Resource Management in Cloud Networking Using Economic Analysis and Pricing Models: A Survey

Mobile cloud computing offers an appealing paradigm to relieve the pressure of soaring data demands and augment energy efficiency for future green networks. Cloudlets can provide available resources to nearby mobile devices with lower access overhead and energy consumption. To stimulate service provisioning by cloudlets and improve resource utilization, a feasible and efficient incentive mechanism is required to charge mobile users and reward cloudlets. Although auction has been considered as a promising form for incentive, it is challenging to design an auction mechanism that holds certain desirable properties for the cloudlet scenario. Truthfulness and system efficiency are two crucial properties in addition to computational efficiency, individual rationality and budget balance. In this paper, we first propose a feasible and truthful incentive mechanism (TIM), to coordinate the resource auction between mobile devices as service users (buyers) and cloudlets as service providers (sellers). Further, TIM is extended to a more efficient design of auction (EDA). TIM guarantees strong truthfulness for both buyers and sellers, while EDA achieves a fairly high system efficiency but only satisfies strong truthfulness for sellers. We also show the difficulties for the buyers to manipulate the resource auction in EDA and the high expected utility with truthful bidding.

Auction Mechanisms Toward Efficient Resource Sharing for Cloudlets in Mobile Cloud Computing

In the past decade, there has been ever-increasing research attention to user cooperation in the wireless communication networks. The unique challenges of wireless networks such as channel fading and variation can be addressed well by taking advantage of relaying among cooperating mobile terminals. There are many studies on cooperative communications at the physical layer to exploit spatial diversity for improving channel capacity. In recent years, user cooperation from the perspective of the medium access control (MAC) layer becomes a promising new research area. In this study, the authors present a comprehensive survey on the mainstream cooperative MAC protocols in the literature. Focusing on the contention-based solutions, the authors classify the well-known proposals according to how they address two fundamental questions for user cooperation, that is, when to cooperation and whom to cooperate with. In addition to analysing the essential features of classic cooperative MAC protocols, the authors also discuss the major research challenges and project future research directions for MAC-layer cooperation.

Survey on cooperative medium access control protocols

Cooperative transmission can exploit the broadcast nature of a wireless medium and leverage relay nodes to forward overheard data from the source to the destination. To optimize the performance gain, effective cooperation strategies are essential to identify the best relay(s) with a minimum overhead and enable forwarding with high success probability. In this paper, we focus on an opportunistic relaying scenario and develop two distributed cooperation strategies. Both adopt a backoff-based intergroup coordination, whereas the intragroup contention is based on either the forwarding probability or backoff timer. In particular, we employ stochastic geometry to address the impact of spatial distribution of relays. Considering a Poisson point process for random relays, we derive the probability distributions of the average received signal-to-noise ratio (SNR) and transmission success probability of potential relays. Making use of such statistics and location information, each relay can independently determine its contention parameters such as backoff time and/or a forwarding probability. We analytically evaluate the relaying performance and validate the accuracy with simulations. The results demonstrate the improvement over a pure probabilistic scheme and the gap to the upper bound of a centralized scheme with the preselected best relay.

Distributed Opportunistic Two-Hop Relaying With Backoff-Based Contention Among Spatially Random Relays

Cooperative communications offer appealing potentials to improve quality of service (QoS) for wireless networks. Many existing works on cooperative communications assume that participation in cooperative relaying is unconditional. In practice, however, due to resource consumption, it is vital to provide incentives for selfish cooperating peer nodes. In this paper, we analyze a cooperative medium access control (MAC) protocol with incentive design using a game-theoretic approach. Specifically, our analysis addresses two questions: 1) Can a cooperating agreement be reached between peer nodes, and 2) can cooperating lead to higher utility than not cooperating? We first formulate a one-stage game for the slotted-Aloha-based cooperative MAC protocol, where not cooperating is a Nash equilibrium (NE) strategy, whereas cooperating is not. To exploit cooperation gain, the one-stage game is extended to a two-stage game by incorporating an incentive mechanism that adapts channel access probabilities with tuning factors. Based on the Markov chain analysis of the system states with the repeated two-stage games, we determine valid tuning factors, guaranteeing that cooperating attains an NE and provides expected utility not less than that of the not-cooperating NE. Moreover, a special case with saturated and symmetric assumptions is investigated, and closed-form criteria for the tuning factors are derived. Finally, we compare the derived tuning factors for individual nodes with an optimal choice that is selected from the system perspective to maximize the overall system utility. The numerical results confirm our analytical conclusions and demonstrate that the tuning factors selected according to our derived criteria can achieve high utility that is slightly lower than that of the optimal choice.

Repeated Game Analysis for Cooperative MAC With Incentive Design for Wireless Networks

Due to channel fading and user mobility in wireless networks, quality-of-service (QoS) provisioning for multimedia services requires great effort. It is even more challenging to support the fast-growing multimedia services in a green manner. As a promising technique, cooperative communications make use of the broadcasting nature of the wireless medium to facilitate data transmission, and it can achieve energy saving. To support the delay-sensitive multimedia services in an energy-efficient manner, we consider a new framework in this paper where multiple source–destination pairs share a group of relays with an energy constraint. We also propose an effective uncoordinated cooperation strategy, which is based on the backoff timer. The theoretical performance bounds of the proposed strategy are derived with respect to the collision probability and the transmission success probability. As shown in the numerical and simulation results, the proposed strategy outperforms a probability-based uncoordinated strategy in terms of average packet delay, delay outage probability, and energy consumption. Further, we investigate the scalability of our proposed strategy and find that it can be deployed in a large-scale network.

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Peijian Ju

Papers

Auction Mechanisms Toward Efficient Resource Sharing for Cloudlets in Mobile Cloud Computing

Survey on cooperative medium access control protocols

Distributed Opportunistic Two-Hop Relaying With Backoff-Based Contention Among Spatially Random Relays

Repeated Game Analysis for Cooperative MAC With Incentive Design for Wireless Networks

Energy-Aware Cooperation Strategy With Uncoordinated Group Relays for Delay-Sensitive Services