An Overview of Internet of Vehicles Yang, Fangchun; Wang, Shangguang; Li, Jinglin ...
China communications,
10/2014, Volume:
11, Issue:
10
Journal Article
Peer reviewed
The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks into the lnternet of Vehicles （IoV）. With the rapid development of computation and communication ...technologies, loV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. This paper proposes an abstract network model of the IoV, discusses the technologies required to create the IoV, presents different applications based on certain currently existing technologies, provides several open research challenges and describes essential future research in the area of loV.
Vehicular edge computing (VEC) is a promising paradigm to offload resource-intensive tasks at the network edge. Owing to time-sensitive and computation-intensive vehicular applications and high ...mobility scenarios, cost-efficient task offloading in the vehicular environment is still a challenging problem. In this paper, we study the partial task offloading problem in vehicular edge computing in an urban scenario. Where the vehicle computes some part of a task locally, and offload the remaining task to a nearby vehicle and to VEC server subject to the maximum tolerable delay and vehicle’s stay time. To make it cost-efficient, including the cost of the required communication and computing resources, we consider to fully exploit the vehicular available resources. We estimate the transmission rates for the vehicle to vehicle and vehicle to infrastructure communication based on practical assumptions. Moreover, we present a mobility-aware partial task offloading algorithm, taking into account the task allocation ratio among the three parts given by the communication environment conditions. Simulation results validate the efficient performance of the proposed scheme that not only enhances the exploitation of vehicular computation resources but also minimizes the overall system cost in comparison to baseline schemes.
Quality of Services play an increasingly important role during the procedure of Cloud-based web service composition for seamless and dynamic integration of business applications. However, as ...Cloud-based web services (CWSs) proliferate, it becomes difficult to facilitate service composition quickly in Cloud computing environment. In this paper, based on the notion of Skyline, we propose a fast CWS composition approach. This approach adopts Skyline operator to prune redundant CWS candidates and then employs Particle Swarm Optimization to select CWS from amount of candidates for composing single service into a more powerful composite service. Based on a real dataset, we conduct an experiment to evaluate our proposed approach. Experimental results show that our proposed approach is effective and efficient for CWS composition.
Full text
Available for:
CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In automotive adhesive defect detection, manual inspection suffers from low efficiency and blind spots in human vision, which affects the performance of parts. Therefore, automated detection methods ...are particularly important. To address the issue of adhesive defects significantly impacting production during automated gluing processes, we propose an adhesive defect detection method for automotive applications based on the improved YOLOv8 (named YOLOv8n-SSE). First, we used the SSE (skip squeeze and excitation) attention mechanism in the backbone part to dynamically adjust the importance of different channels in our model and allow our model to selectively focus on important features. Then, the original bounding box loss function is replaced by the WIoU loss function. Experimental results demonstrate that this method improves the mAP50 of the original YOLOv8n by 3.25% and achieves an average detection speed of 7.9ms per image, equivalent to 126.58 frames per second (FPS), meeting the real-time defect detection requirements.
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Quality of service (QoS) guarantee is an important component of service recommendation. Generally, some QoS values of a service are unknown to its users who has never invoked it before, and therefore ...the accurate prediction of unknown QoS values is significant for the successful deployment of web service-based applications. Collaborative filtering is an important method for predicting missing values, and has thus been widely adopted in the prediction of unknown QoS values. However, collaborative filtering originated from the processing of subjective data, such as movie scores. The QoS data of web services are usually objective, meaning that existing collaborative filtering-based approaches are not always applicable for unknown QoS values. Based on real world web service QoS data and a number of experiments, in this paper, we determine some important characteristics of objective QoS datasets that have never been found before. We propose a prediction algorithm to realize these characteristics, allowing the unknown QoS values to be predicted accurately. Experimental results show that the proposed algorithm predicts unknown web service QoS values more accurately than other existing approaches.
Cloud computing is becoming an important solution for providing scalable computing resources via Internet. Because there are tens of thousands of nodes in data center, the probability of server ...failures is nontrivial. Therefore, it is a critical challenge to guarantee the service reliability. Fault-tolerance strategies, such as checkpoint, are commonly employed. Because of the failure of the edge switches, the checkpoint image may become inaccessible. Therefore, current checkpoint-based fault tolerance method cannot achieve the best effect. In this paper, we propose an optimal checkpoint method with edge switch failure-aware. The edge switch failure-aware checkpoint method includes two algorithms. The first algorithm employs the data center topology and communication characteristic for checkpoint image storage server selection. The second algorithm employs the checkpoint image storage characteristic as well as the data center topology to select the recovery server. Simulation experiments are performed to demonstrate the effectiveness of the proposed method.
Identifying important nodes in complex networks is essential in disease transmission control, network attack protection, and valuable information detection. Many evaluation indicators, such as degree ...centrality, betweenness centrality, and closeness centrality, have been proposed to identify important nodes. Some researchers assign different weight to different indicator and combine them together to obtain the final evaluation results. However, the weight is usually subjectively assigned based on the researcher’s experience, which may lead to inaccurate results. In this paper, we propose an entropy-based self-adaptive node importance evaluation method to evaluate node importance objectively. Firstly, based on complex network theory, we select four indicators to reflect different characteristics of the network structure. Secondly, we calculate the weights of different indicators based on information entropy theory. Finally, based on aforesaid steps, the node importance is obtained by weighted average method. The experimental results show that our method performs better than the existing methods.
A critical research issue is to lower the energy consumption of a virtualized data center by means of virtual machine placement optimization while satisfying the resource requirements of the cloud ...services. In this paper, we focus on different existing schemes and on the energy-aware virtual machine placement optimization problem of a heterogeneous virtualized data center. We attempt to explore a better alternative approach to minimizing the energy consumption, and we observe that particle swarm optimization (PSO) has considerable potential. However, the PSO must be improved to solve an optimization problem. The improvement includes redefining the parameters and operators of the PSO, adopting an energy-aware local fitness first strategy and designing a novel coding scheme. Using the improved PSO, an optimal virtual machine replacement scheme with the lowest energy consumption can be found. Experimental results indicate that our approach significantly outperforms other approaches, and can lessen 13%-23% energy consumption in the context of this paper.
The number of mobile applications(APPs) has increased dramatically with the development of mobile Internet. It becomes challenging for users to identify these APPs they are really interested in. ...Existing mobile APP recommendation methods focus on learning users' preference and recommending high visibility APPs. However, some low visibility APPs may satisfy users and even surprise them. If those low visibility APPs have the opportunity to show to the user, they will not only improve the user's satisfaction, but also provide a fair competitive market for APP providers. Furthermore, it will improve the vitality of the APP market. To this end, we present a fairness-aware APP recommendation method named FARM. The principal study of this method emphasizes on the fairness issue during the recommendation process. In this method, APP candidates are divided into high visibility and low visibility APPs, and implement recommendation algorithm respectively. For low visibility APPs, we set a fairness factor for everyone, and use the user's latest feedback to make a dynamic adjustment. Based on the fairness factor, the recommendation is implemented by roulette-wheel. For high visibility APPs, we employ the fuzzy analytic hierarchy process to implement the recommendation. The evaluation results show that FARM outperforms baselines in terms of recommendation fairness.
Group recommendation generates a ranked list of recommendations for a group of users. Point-of-interests (POIs) group recommendation aims to suggest the most agreeable meeting places for a group of ...users. Although there are a lot of studies on group recommendation for POIs, few studies take into account the rationality of location for the whole group. In this paper, we propose a novel POI group recommendation method which factors into the rationality of the location and the intra-group influence when making group decisions. We take into account the importance of location in POI recommendations and employ distance-based pre-filtering and distance-based ranking adjustment to improve recommendation satisfaction. We have conducted extensive experimental evaluations of the proposed method via a realworld data set, which is prepared from 1 375 024 Beijing POI comment records hosted by a review website. Comprehensive experimental results show that our proposed POI group recommendation method outperforms other representative ones in terms of global satisfaction and distance satisfaction, even in the context of individual recommendation.
PDF
