Concurrency and Computation: Practice and Experience

Allouch, Mariem; Kallel, Sondes; Soua, Ahmed; Shagdar, Oyunchimeg; Tohme, Samir

doi: 10.1002/cpe.6228pmid: N/A

The main goal behind radio resource management (RRM), in any conventional wireless networks, is to efficiently utilize the available resources. Critical and safety communications have very stringent quality of service requirements on reliability and availability. Consequently, the efficient use of resources becomes more compelling when it deals with this type of application. In this context, cellular vehicle‐to‐everything (C‐V2X) communications, enabled by cellular device‐to‐device (D2D), have recently drawn much attention. This is due to their applicability to road safety and traffic efficiency applications, which require information exchange among road users and road‐side entities. This emerging technology, referred hereinafter as LTE‐V or C‐V2X, supports two different communication modes: 3 and 4. Both of the two modes enable direct vehicle‐to‐vehicle (V2V) communications via PC5 interface. However, they differ on how the radio resources are allocated. Several approaches and algorithms are proposed in the literature to address the resource allocation issue. In this paper, a state of the art of the radio resource allocation strategies was made for LTE‐V technology and a qualitative description was carried out of these different schemes. We proposed a classification of these strategies into different categories. This classification identified the different strengths and weaknesses of each proposal. Up to our knowledge, several surveys concerning resource allocation are published in the context of LTE but very few of them focuses on both RRM and vehicular networks.

Ghrab, Dhouha; Jemili, Imen; Belghith, Abdelfettah; Mosbah, Mohamed

doi: 10.1002/cpe.5958pmid: N/A

As sensor nodes are power constrained, saving energy and prolonging network lifetime have been given the greatest priority in the design of routing protocols in wireless sensor networks (WSNs). In this regard, duty‐cycling is broadly utilized as an underlying MAC (Medium Access Control)‐based protocol for routing solutions. In this regard, a cross‐layer approach, integrating MAC and routing protocols, is required to achieve a trade‐off between energy efficiency and communication reliability, through adapting nodes duty‐cycle to routing decisions. Besides, with the proliferation of WSNs applications in various domains, achieving energy efficiency should not be performed while ignoring the diverse quality of service (QoS) demands of the considered applications to ensure their well functioning. In this context, we propose a context‐aware routing framework under duty‐cycled networks, a generic framework that allows to support heterogeneous applications and traffic patterns. An evaluation of the framework is also proposed in this paper. Results prove the effectiveness of context‐awareness and the cross‐layer interaction between the different modules of the framework to guarantee desired QoS while reducing energy consumption.

Yingying, Su; Lianjuan, Han; Jianan, Wang; Huimin, Wang

doi: 10.1002/cpe.5522pmid: N/A

Quality control in the production process is the core of the enterprise to ensure product quality, and quality prediction is the key link of quality control and quality management. Aiming at the quality prediction of parts in the production process, a product quality prediction model is established. In this model, Rough Set (RS), Particle Swarm Optimization (PSO), and Least Square Support Vector Machine (LSSVM) are applied to solve the problem of product quality prediction and a RS‐PSO‐LSSVM synthesis algorithm is established. First, the 5M1E analysis of production process for parts is carried out, and the index system of influencing factors is established. Based on this index system, the condition attributes and decision attributes of RS are determined, in which RS is used to the reduction to extract rules and the optimal condition attribute value is obtained, which is used as the pre‐processing of LSSVM input data. Second, in order to improve the learning and generalization ability of LSSVM, PSO is used to optimize the relevant parameters and find the optimal solution. Finally, an example is given to verify the feasibility and effectiveness of the product quality prediction model and the RS‐PSO‐LSSVM comprehensive algorithm established above, and the prediction accuracy is higher than that of the RS‐LSSVM algorithm.

Gao, Yuan; Rafi, Muhammad Adnan

doi: 10.1002/cpe.6711pmid: N/A

Graphics, uncertainty, and semantics are three approaches to building models. The combination of the three approaches is a way to develop a stronger modeling method. This article surveys the research efforts toward combining these aspects, which can be divided into two routes: One is to combine graphics and uncertainty as probabilistic graphical models and then incorporate semantics, and the other is to combine graphics and semantics and then incorporate probability to handle uncertainty. The models and methods involved in these efforts are introduced and their expressiveness, pros, and cons are discussed.

Shen, Sheng; Zhu, Tianqing; Ye, Dayong; Wang, Minghao; Zuo, Xuhan; Zhou, Andi

doi: 10.1002/cpe.5932pmid: N/A

Due to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers' capabilities and make optimal decisions on selecting the best available servers for users' tasks. We consider the process of learning servers' capabilities by users as a multiagent reinforcement learning process. The learning speed and efficiency in reinforcement learning can be improved by sharing the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by the requirement that during advising all learning agents in a reinforcement learning environment must have exactly the same actions. To address the above limitation, this article proposes a novel differentially private advising framework for multiagent reinforcement learning. Our proposed approach can significantly improve the application of conventional advising frameworks when agents have one different action. The approach can also widen the applicable field of advising and speed up reinforcement learning by triggering more potential advising processes among agents with different actions.

Li, Xuejun; Gong, Lina; Liu, Xiao; Jiang, Frank; Shi, Wenyu; Fan, Lingmin; Gao, Han; Li, Rui; Xu, Jia

doi: 10.1002/cpe.6068pmid: N/A

The “last mile” problem in logistics is challenging due to its low efficiency and high cost. To address this problem, Unmanned Aerial Vehicle (UAV) delivery such as drone delivery has been proposed and widely accepted as a promising solution. However, currently most of the existing UAV delivery systems are based on Cloud Computing which cannot efficiently meet the requirements of many real‐time services in UAV delivery systems. Meanwhile, the security issues in UAV delivery systems also raise critical concerns due to the existence of multiple participants (such as the sender, middler, and receiver) who may not maintain a mutual trust relationship among them. How to secure the UAV delivery process in such an untrusted environment is still a challenging issue. In this paper, we propose a Mobile Edge Computing (MEC) and blockchain‐based UAV delivery system to resolve the “last mile” problem in logistics. Specifically, based on the MEC architecture, the blockchain nodes are deployed on the edge nodes to facilitate and secure the UAV delivery process. To verify the effectiveness of our proposed solution, a MEC‐based UAV delivery system prototype with a private blockchain on the Ethereum platform is implemented. Through the security analysis and performance evaluation, it is proven that our proposed solution can effectively solve the “last mile” problem and address the security issues in UAV delivery systems.

doi: 10.1002/cpe.6392pmid: N/A

Xu, Fang; Xiao, Nan; Deng, Min; Xie, Yong; Xiong, Zenggang; Xu, Qiong

doi: 10.1002/cpe.5524pmid: N/A

Mobile social networks (MSNs) are developed from mobile ad hoc networks. Nodes in such networks usually have social characteristics. In recent years, researchers are trying to use the social characteristics of the network to propose new data forwarding metrics, so as to design more efficient routing algorithms. However, most of the proposed algorithms only consider local context information, which leads to the performance of the routing is not optimized enough. In this paper, we introduce two key metrics, namely, social relationship and social activity. The metrics will be used to search the best data forwarding nodes to improve the probability of data delivery. We propose a prediction‐based social‐aware opportunistic routing (PSOR). In the proposed method, node's social profiles are used to search relay candidates set, and the discrete‐time semi‐Markov prediction model is used to find the probability distribution of node transition between communities. Many simulation experiments based on real traces show that the proposed PSOR algorithm is more efficient to maximize the packet delivery probability than other state‐of‐the‐art algorithms.

Xu, Gang; Dong, Qianqian; Li, Min

doi: 10.1002/cpe.5521pmid: N/A

A production process parameter optimization method based on feature extraction for manifold learning is proposed to achieve precise optimization of steel anomaly data of different grades in the same series and to improve the quality of industrial products. First, the appropriate neighboring samples are found in the state of the sample point and the next state to form the neighborhood matrix. Then, the manifold hidden inside the data is extracted, ie, the evolution trend of the process parameters between different brands. At the same time, a monitoring model is built with the training data based on the support vector data description (SVDD). If an outlier is detected, it will be projected onto the manifold to obtain the adjustment values. Thus, the outlier can return to the normal state. The Swiss roll and actual production data of interstitial‐free (IF) steels are employed to verify the effectiveness of the proposed method. The results show that the new method considers the continuity of process parameters of different product grades in the production process and uses data to extract the potential manifold, ie, using the evolution trend of process parameters among different product grades to achieve the optimization of the process parameter. The proposed method provides a new process parameter optimization method for the actual production process.

Chetouane, Ameni; Mabrouk, Sabra; Jemili, Imen; Mosbah, Mohamed

doi: 10.1002/cpe.5983pmid: N/A

Due to the increasing number of vehicles in circulation in different urban cities, several automatic traffic monitoring systems have been developed. In particular, traffic monitoring systems using roadside cameras are becoming extensively deployed, as they offer imperative technological advantages compared with other traffic monitoring systems. Vehicle detection and traffic congestion classification are two main steps for video‐based traffic congestion detection systems; the associated methods have a deep impact on the performance of the whole system. In this paper, we investigate four selected vehicle detection methods namely Gaussian Mixture Model (GMM), GMM‐Kalman filter, Optical Flow, and ACF object detector in two contexts: urban and highway. Three traffic congestion classification methods are also studied. The comparative study of the different methods allows us to choose the most appropriate ones to be integrated in the framework proposed to solve the traffic issues in the bridge of Bizerte.