An Improvement to Data Service in Cloud Computing with Content Sensitive Transaction Analysis and Adaptation

Lu, Chih-Wei; Hsieh, Chih-Ming; Chang, Chih‐Hung; Yang, Chao-Tung

doi:10.1109/compsacw.2013.72

Cited by 21 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MCC offers a lot of attractive features, such as parallel processing, virtualized resources, high scalability, and security. Therefore, MCC can not only provide the ability of processing computationintensive tasks, but also offers low cost infrastructure maintenance [44]. However, nowadays it is predicted that CVs may produce a large amount of data in high speeds such as the camera captured videos for driving assistance, which will make the data dramatically increase to TB/PB levels in seconds [45].…”

Section: Introductionmentioning

confidence: 99%

Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

Wang

Liu

Kim

et al. 2020

IEEE Commun. Surv. Tutorials

121

View full text Add to dashboard Cite

As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computationintensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this paper, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this paper are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges, (iv) presenting a holistic overview on the design of CV systems from both academia and industry perspectives, including activities in industry, functional requirements, service requirements, and design considerations, and (v) proposing several open research issues of designing cloud/edge/fog computing architectures for CVs.

show abstract

Section: Introductionmentioning

confidence: 99%

Architectural Design Alternatives Based on Cloud/Edge/Fog Computing for Connected Vehicles

Wang

Liu

Kim

et al. 2020

IEEE Commun. Surv. Tutorials

121

View full text Add to dashboard Cite

show abstract

“…Cloud computing is a new technology developed for large interconnected smart objects with large‐scale services, sharing among many users whereby large amounts of data generated by different devices can be stored. This technology enables pervasive, easy communication, on‐demand network access to a shared pool of computing resources with reduced infrastructure maintenance cost, minimal management effort and users’ access [5]. The advantages of cloud computing include parallel processing, large data storage, data security, backup and recovery, and data service integration.…”

Section: Introductionmentioning

confidence: 99%

Models for integrating wireless sensor networks into the Internet of Things

Abidoye

Obagbuwa

2017

IET Wireless Sensor Systems

View full text Add to dashboard Cite

Ubiquitous sensing and unique characteristics of wireless sensor networks (WSNs) have led to an increase in application areas such as smart parking, environmental monitoring, automotive industries and sports. In recent years, WSNs have gained more significance as the foundation infrastructure for the Internet of Things (IoT), which has greatly increased the number of connected objects with instantaneous communication and data processing. However, designing energy-efficient models for integrating WSNs into IoT is a challenging issue due to scalability and interoperability of IoT, and previous approaches designed for WSNs cannot be applied directly. This study proposes two energy-efficient models for WSNs in the IoT environment: (i) a service-aware clustering model where individual sensor nodes are assigned roles based on their service delivery; and (ii) an energy-aware clustering model. Performance evaluation shows better energy efficiency, end-to-end delay and network load balance of the proposed models for integrating wireless sensor networks into the IoT protocol compared with low-energy adaptive clustering hierarchy centralised protocol and fuzzy C-means clustering protocol.

show abstract

“…; punishment vehicle cost indicate vehicle delay time caused by fine service. [1] vehicle scheduling problem with time windows have been studied to optimize the objective is to reduce the gross vehicle with minimum cost; the literature [2] on the open vehicle routing problem with time windows have been studied to optimize the target vehicle to enable fixed fees and expenses and minimum vehicle; [3] in the transport vehicle minimum sum of direct costs and the shortest distance and the grid optimization target; [4] rental and variable costs to the minimum and, where available this is the unit becomes kilometers fuel consumption, improved cost vague concept to other literature, but there is a flaw, without considering the load of the vehicle is not fuel consumption while the unit is not the same, especially for large vehicle fuel consumption unit change is relatively large; [5] respectively, the distance to fuel costs and cost optimization objectives were analyzed, in order to obtain better fuel cost optimization goal to optimize the distribution network of the conclusions, but it does not take into account different models the use of different fixed costs; [6] Although the cost of the vehicle will be used for the breakdown of fixed and variable costs, but did not affect the variable costs important factor in vehicle rate analysis.…”

Section: Introductionmentioning

confidence: 99%