Mobile Edge Nodes Research Articles

A Proof-of-Quality-Factor (PoQF)-Based Blockchain and Edge Computing for Vehicular Message Dissemination

Blockchain applications in vehicular networks can offer many advantages, including decentralization and improved security. However, most of the consensus algorithms in blockchain are difficult to be implemented in vehicular ad hoc networks (VANETs) without the help of edge computing services. For example, the connectivity in VANET only remains for a short period of time, which is not sufficient for highly time-consuming consensus algorithms, e.g., Proof of Work, running on mobile-edge nodes (vehicles). Other consensus algorithms also have some drawbacks, e.g., Proof of Stake (PoS) is biased toward nodes with a higher amount of stakes and Proof of Elapsed Time (PoET) is not highly secure against malicious nodes. For these reasons, we propose a voting blockchain based on the Proof-of-Quality-Factor (PoQF) consensus algorithm, where the threshold number of votes is controlled by edge computing servers. Specifically, PoQF includes voting for message validation and a competitive relay selection process based on the probabilistic prediction of channel quality between the transmitter and receiver. The performance bounds of failure and latency in message validation are obtained. This article also analyzes the throughput of block generation, as well as the asymptotic latency, security, and communication complexity of PoQF. An incentive distribution mechanism to reward honest nodes and punish malicious nodes is further presented and its effectiveness against the collusion of nodes is proved using the game theory. Simulation results show that PoQF reduces failure in validation by 11% and 15% as compared to PoS and PoET, respectively, and is 68 ms faster than PoET.

A trust evaluation system based on reputation data in Mobile edge computing network

In recent days, Mobile Edge Computing (MEC) emerges as a new computing paradigm that pushes computing resources towards the edge of the Internet and close to end users and more scholars begin to carry out their different kinds of research under the framework of edge computing. Unlike cloud computing however, edge computing often lacks a centralized security mechanism, which increases the security risks of resource consumers. Therefore, to address security issues for facilitating MEC well, we focus on the establishment of an effective trust evaluation system in this paper. We presented a Reputation-based Trust Evaluation and Management (RTEM) system for a secure and efficient MEC network, wherein the edge servers are adopted to execute reputation data management for mobile edge nodes. In this system, a three-tier trust evaluation framework (identity trust, capability trust, behavior trust) was proposed to ensure the edge nodes that join the network system for service interaction are qualified, capable, and reliable. In order to update and calculate reputation data more accurately, we designed a reputation-based trust evaluation model and evaluated the effectiveness and robustness of our designed model on the OMNeT++ platform. The results show that our proposed mechanism can increase the transaction success rate of the edge network by more than two times compared with the network without trust evaluation mechanism, and perform better than the traditional subject logic (TSL) trust evalaution mechanism.

Swarm-Intelligence-Based Rendezvous Selection via Edge Computing for Mobile Sensor Networks

Mobile-edge nodes, as an efficient approach to the performance improvement of wireless sensor networks (WSNs), play an important role in edge computing. However, existing works only focus on connected networks and suffer from high calculational costs. In this article, we propose a rendezvous selection strategy for data collection of disjoint WSNs with mobile-edge nodes. The goal is to achieve full network connectivity and minimize path length. From the perspective of the application scenario, this article is distinctive in two aspects. On the one hand, it is specially designed for partitioned networks which are much more complex than conventional connected scenarios. On the other hand, this article is specially designed for delay-harsh applications rather than usual energy-oriented scenarios. From the viewpoint of the implementation method, a simplified ant colony optimization (ACO) algorithm is performed and displays two characteristics. The first one is the path segmenting mechanism, simplifying the path construction of each part and consequently reducing the computational cost. The second one is the candidate grouping mechanism, reducing the search space and accordingly speeding up the convergence speed. Simulation results demonstrate the feasibility and advantages of this approach.

Big Data Cleaning Based on Mobile Edge Computing in Industrial Sensor-Cloud

With the advent of 5G, the industrial Internet of Things has developed rapidly. The industrial sensor-cloud system (SCS) has also received widespread attention. In the future, a large number of integrated sensors that simultaneously collect multifeature data will be added to industrial SCS. However, the collected big data are not trustworthy due to the harsh environment of the sensor. If the data collected at the bottom networks are directly uploaded to the cloud for processing, the query and data mining results will be inaccurate, which will seriously affect the judgment and feedback of the cloud. The traditional method of relying on sensor nodes for data cleaning is insufficient to deal with big data, whereas edge computing provides a good solution. In this article, a new data cleaning method is proposed based on the mobile edge node during data collection. An angle-based outlier detection method is applied at the edge node to obtain the training data of the cleaning model, which is then established through support vector machine. Besides, online learning is adopted for model optimization. Experimental results show that multidimensional data cleaning based on mobile edge nodes improves the efficiency of data cleaning while maintaining data reliability and integrity, and greatly reduces the bandwidth and energy consumption of the industrial SCS.

A Novel Mobile Edge Network Architecture with Joint Caching-Delivering and Horizontal Cooperation

Mobile edge caching/computing (MEC) has been emerging as a promising paradigm to provide ultra-high rate, ultra-reliable, and/or low-latency communications in future wireless networks. In this paper, we introduce a novel MEC network architecture that leverages the optimal joint caching-delivering with horizontal cooperation among mobile edge nodes (MENs). To that end, we first formulate the content-access delay minimization problem by jointly optimizing the content caching and delivering decisions under various network constraints (e.g., network topology, storage capacity and users' demands at each MEN). However, the strongly mutual dependency between the decisions makes the problem a nested dual optimization that is proved to be NP-hard. To deal with it, we propose a novel transformation method to transform the nested dual problem to an equivalent mixed-integer nonlinear programming (MINLP) optimization problem. Then, we design a centralized solution using an improved branch-and-bound algorithm with the interior-point method to find the joint caching and delivering policy which is within 1 percent of the optimal solution. Since the centralized solution requires the full network topology and information from all MENs, to make our solution scalable, we develop a distributed algorithm which allows each MEN to make its own decisions based on its local observations. Extensive simulations demonstrate that the proposed solutions can reduce the total average delay for the whole network up to 40 percent compared with other current caching policies. Furthermore, the proposed solutions also increase the cache hit ratio for the network up to 4 times, thereby dramatically reducing the traffic load on the backhaul network.

Distributed Deep Learning at the Edge: A Novel Proactive and Cooperative Caching Framework for Mobile Edge Networks

We propose two novel proactive cooperative caching approaches using deep learning (DL) to predict users’ content demand in a mobile edge caching network. In the first approach, a content server (CS) takes responsibilities to collect information from all mobile edge nodes (MENs) in the network and then performs the proposed DL algorithm to predict the content demand for the whole network. However, such a centralized approach may disclose the private information because MENs have to share their local users’ data with the CS. Thus, in the second approach, we propose a novel distributed deep learning (DDL)-based framework. The DDL allows MENs in the network to collaborate and exchange information to reduce the error of content demand prediction without revealing the private information of mobile users. Through simulation results, we show that our proposed approaches can enhance the accuracy by reducing the root mean squared error (RMSE) up to 33.7% and reduce the service delay by 47.4% compared with other machine learning algorithms.

MTES: An Intelligent Trust Evaluation Scheme in Sensor-Cloud-Enabled Industrial Internet of Things

As an enabler for smart industrial Internet of Things (IoT), sensor cloud facilitates data collection, processing, analysis, storage, and sharing on demand. However, compromised or malicious sensor nodes may cause the collected data to be invalid or even endanger the normal operation of an entire IoT system. Therefore, designing an effective mechanism to ensure the trustworthiness of sensor nodes is a critical issue. However, existing cloud computing models cannot provide direct and effective management for the sensor nodes. Meanwhile, the insufficient computation and storage ability of sensor nodes makes them incapable of performing complex intelligent algorithms. To this end, mobile edge nodes with relatively strong computation and storage ability are exploited to provide intelligent trust evaluation and management for sensor nodes. In this article, a mobile edge computing-based intelligent trust evaluation scheme is proposed to comprehensively evaluate the trustworthiness of sensor nodes using probabilistic graphical model. The proposed mechanism evaluates the trustworthiness of sensor nodes from data collection and communication behavior. Moreover, the moving path for the edge nodes is scheduled to improve the probability of direct trust evaluation and decrease the moving distance. An approximation algorithm with provable performance is designed. Extensive experiments validate that our method can effectively ensure the trustworthiness of sensor nodes and decrease the energy consumption.

Energy Balanced Dispatch of Mobile Edge Nodes for Confident Information Coverage Hole Repairing in IoT

The promising Internet of Things (IoT) provides a powerful platform for practical smart applications. The limited resources of the IoT nodes as well as the emerged coverage holes pose a great challenge on the quality of service of the IoT. Mobile edge computing (MEC), which can improve the IoT nodes energy consumption efficiency and optimize the utilization effectiveness of the limited resources, provides a novel view for coping with the challenge. Based on the MEC, this paper focuses on how to solve the problem of dispatch of mobile edge nodes for confident information coverage holes repairing (DMEN-CICHR) with the goal of maximizing the network lifetime and guaranteeing the network connectivity. To deal with the DMEN-CICHR problem, we develop an energy-balanced and obstacle-adaptive mobile edge node dispatch algorithm called EBOADMEN-CICHR, which restricts the mobile edge nodes from moving too long distance by setting a bound for each CIC hole and repeatedly updating the bound by a competition mechanism. To guarantee the network connectivity, the EBOADMEN-CICHR recursively performs breadth first search on a constructed undirected graph to find all disconnected subgraphs and then dispatches some mobile edge nodes to connect those disconnected subgraphs until the whole network is connected. A number of experiments emulating the realistic scenarios in radiological pollution monitoring in uranium tailings are executed to verify the effectiveness of the proposed EBOADMEN-CICHR solution. Experimental results show the EBOADMEN-CICHR can perform better than other peer methods in term of higher energy efficiency and longer network lifetime.

Decentralized and Revised Content-Centric Networking-Based Service Deployment and Discovery Platform in Mobile Edge Computing for IoT Devices

Mobile edge computing (MEC) is used to offload services (tasks) from cloud computing in order to deliver those services to mobile Internet of Things (IoT) devices near mobile edge nodes. However, even though there are advantages to MEC, we face many significant problems, such as how a service provider (SP) deploys requested services efficiently on a destination MEC node, and how to discover existing services in neighboring MEC nodes to save edge resources. In this paper, we present a decentralized and revised content-centric networking (CCN)-based MEC service deployment/discovery protocol and platform. We organized a gateway in every area according to a three-tiered hierarchical MEC network topology to reduce computing overhead at the centralized controller. We revised CCN to introduce a protocol to help SP deploy their service on MEC node and assist MEC node discover services in neighboring nodes. By using our proposed protocol, MEC nodes can deploy or discover the requested service instances in the proximity of IoT devices to reduce transmission delay. We also present a mathematical model to calculate the round trip time to guarantee quality of service. Numerical experiments measure the performance of our proposed method with various mobile IoT device services. The results show that the proposed service deployment protocol and platform reduce the average service delay by up to 50% compared to legacy cloud. In addition, the proposed method outperforms the legacy protocol of the MEC environment in service discovery time.

Blockchain and IoT-Based Cognitive Edge Framework for Sharing Economy Services in a Smart City

In this paper, we propose a Blockchain-based infrastructure to support security- and privacy-oriented spatio-temporal smart contract services for the sustainable Internet of Things (IoT)-enabled sharing economy in mega smart cities. The infrastructure leverages cognitive fog nodes at the edge to host and process offloaded geo-tagged multimedia payload and transactions from a mobile edge and IoT nodes, uses AI for processing and extracting significant event information, produces semantic digital analytics, and saves results in Blockchain and decentralized cloud repositories to facilitate sharing economy services. The framework offers a sustainable incentive mechanism, which can potentially support secure smart city services, such as sharing economy, smart contracts, and cyber-physical interaction with Blockchain and IoT. Our unique contribution is justified by detailed system design and implementation of the framework.

Secure Content Delivery With Edge Nodes to Save Caching Resources for Mobile Users in Green Cities

To save energy during content delivery in green cities, caching contents on edge nodes that are placed near mobile social users has been advocated recently. However, how to allocate the limited caching resources with secure content delivery becomes a new challenge. Therefore, in this paper, we present a novel theoretical model to deliver secure content with edge nodes in order to save energy for green cities. First, we present a reverse auction game to encourage edge nodes to cooperatively provide caching services with incentives. With the model, mobile users can determine the candidate of edge node to cache content based on the interaction between mobile users and edge nodes. Second, a trust management method is designed to evaluate the reliability of the selected candidate of edge node by considering the direct trust evaluation. Finally, extensive simulations show that the proposal can save energy with a secure content delivery where both the delay to obtain the content and the caching ratio can be improved compared with the conventional methods.

모바일 엣지 컴퓨팅 환경에서의 개인화 서비스 추천

모바일 엣지 컴퓨팅은 폭증하는 모바일 트래픽에 대응하고 다양한 요구사항을 만족시키는 서비스를 제공하기 위해 모바일 엣지 노드에서 다양한 기능을 직접 제공하는 기술이다. 예를 들어 모바일 트래픽 경감을 위한 캐싱이나, 위험감지 서비스 제공을 위한 비디오 분석 등이 모바일 엣지 노드에서 수행될 수 있다. 지금까지 개인화된 서비스를 추천하는 방법이나 구조 등에 대한 많은 연구가 있었지만, 모바일 엣지 컴퓨팅의 특성을 고려한 연구는 없었다. 개인화된 서비스를 제공하기 위해서는 사용자의 컨텍스트 정보를 획득하는 것이 중요하다. 기존 서버단 중심의 개인화된 서비스 모델은 모바일 엣지 컴퓨팅에 적용될 경우 컨텍스트 고립 문제와 프라이버시 이슈를 더욱 심화시킬 수 있다. 모바일 엣지 노드는 컨텍스트 수집이 용이하다는 이점을 가진다. 모바일 엣지 컴퓨팅 환경에서의 또 하나의 주목할 만한 특징은 사용자와 어플리케이션의 상호 연동이 매우 유동적이라는 점이다. 본 논문에서는 모바일 엣지 컴퓨팅의 특징을 반영한 로컬 서비스 추천 플랫폼 구조를 제시하고 컨텍스트 고립 문제와 프라이버시 이슈를 완화할 수 있는 개인화된 서비스 제공 방법을 제시한다. Mobile Edge Computing(MEC) is a emerging technology to cope with mobile traffic explosion and to provide a variety of services having specific requirements by means of running some functions at mobile edge nodes directly. For instance, caching function can be executed in order to offload mobile traffics, and safety services using real time video analytics can be delivered to users. So far, a myriad of methods and architectures for personalized service recommendation have been proposed, but there is no study on the subject which takes unique characteristics of mobile edge computing into account. To provide personalized services, acquiring users' context is of great significance. If the conventional personalized service model, which is server-side oriented, is applied to the mobile edge computing scheme, it may cause context isolation and privacy issues more severely. There are some advantages at mobile edge node with respect to context acquisition. Another notable characteristic at MEC scheme is that interaction between users and applications is very dynamic due to temporal relation. This paper proposes the local service recommendation platform architecture which encompasses these characteristics, and also discusses the personalized service recommendation mechanism to be able to mitigate context isolation problem and privacy issues.