Data Processing in Cyber-Physical-Social Systems Through Edge Computing

Dautov, Rustem; Distefano, Salvatore; Bruneo, Dario; Longo, Francesco; Merlino, Giovanni; Puliafito, Antonio

doi:10.1109/access.2018.2839915

Cited by 45 publications

(40 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While in the work at hand, we focus on a framework to conduct data stream processing in the fog, Yassine et al focus on particular data analytics methods. In a very recent approach, Dautov et al discuss the clusterization of edge devices for data stream processing [9]. While the authors focus on rather powerful edge devices, this is nevertheless the approach that comes closest to the work at hand.…”

Section: Current State Of the Artmentioning

confidence: 99%

Towards a Framework for Data Stream Processing in the Fog

Hießl

Hochreiner

Schulte

2019

Informatik Spektrum

View full text Add to dashboard Cite

Section: Current State Of the Artmentioning

confidence: 99%

Towards a Framework for Data Stream Processing in the Fog

Hießl

Hochreiner

Schulte

2019

Informatik Spektrum

View full text Add to dashboard Cite

“…Based on the principles of volunteer computing and SIoT, the SSEC scheme presents a novel approach to address network latency issues at the very edge of an IoT network topology while utilizing idle resources of mobile devices. Finally, the simulation results show that the SSEC scheme has a potential power to outperform cloud-centric setups by keeping the computation locally and by involving volunteering mobile devices in clustered computation at the edge [15].…”

Section: Introductionmentioning

confidence: 99%

“…The social system through edge computing (SSEC) scheme is proposed as a new collaborative, horizontal offloading method for the distributed data processing mechanism [15]. To discover and select devices, which are able to perform an offloaded task according to specific requirements, this scheme implements the clustered edge computing paradigm.…”

Section: Introductionmentioning

confidence: 99%

“…The earlier studies [14][15][16] have attracted considerable attentions while introducing unique challenges in handling the SFIoT resource allocation problem. Therefore, our proposed scheme may look similar to the existing schemes.…”

Section: Introductionmentioning

confidence: 99%

“…Third, to reduce the computation complexity, we hierarchically implement the BVG and the NBS with cascade interactions. Most of all, the main difference between the existing schemes in [14][15][16] and our proposed scheme is a control paradigm. The principle novelty of our protocol is a judicious mixture of two game solutions, and its feasible self-adaptability in the realworld SFIoT system operations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Novel Resource Allocation Algorithms for the Social Internet of Things Based Fog Computing Paradigm

Kim

2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Social Internet of Things (SIoT) is a control paradigm by the integration of social networking concepts into the Internet of Things, and Fog Computing (FC) is an emerging technology that is aimed at moving the cloud computing facilities to the access network. Recently, the SIoT and FC models are combined by using complementary features, and a new Social Fog IoT (SFIoT) paradigm has been developed. In this paper, we design novel resource allocation algorithms for the SFIoT system. Considering the social relationship and each preference, mobile devices in the SFIoT effectively share the limited computation and communication resources of FC operators. To formulate the interaction among mobile devices and FC operator, we adopt the basic concept of two game models: voting and bargaining games. Bicooperative voting approach can make control decisions for the resource allocation method, and Nash bargaining solution is used to effectively distribute the computation resource to different application tasks. Based on the two-phase game model, the proposed scheme takes various benefits in a fair-efficient way. Through the extensive simulation experiments, we can validate the superiority of our proposed approach by the fact that it produces a mutually acceptable agreement among game players and significantly outperforms the existing protocols. Last, we point out the further challenges and future research issues about the SFIoT paradigm opportunities.

show abstract

Data agility through clustered edge computing and stream processing

Dautov

Distefano

Bruneo

et al. 2018

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

The Internet of Things is underpinned by the global penetration of network-connected smart devices continuously generating extreme amounts of raw data to be processed in a timely manner. Supported by Cloud and Fog/Edge infrastructures-on the one hand, and Big Data processing techniques-on the other, existing approaches, however, primarily adopt a vertical offloading model that is heavily dependent on the underlying network bandwidth. That is, (constrained) network communication remains the main limitation to achieve truly agile IoT data management and processing. This paper aims to bridge this gap by defining Clustered Edge Computing-a new approach to enable rapid data processing at the very edge of the IoT network by clustering edge devices into fully functional decentralized ensembles, capable of workload distribution and balancing to accomplish relatively complex computational tasks. This paper also proposes ECStream Processing that implements Clustered Edge Computing using Stream Processing techniques to enable dynamic in-memory computation close to the data source. By spreading the workload among a cluster of collocated edge devices to process data in parallel, the proposed approach aims to improve performance, thereby supporting agile data management. The experimental results confirm that such a distributed in-memory approach to data processing at the very edge of an IoT network can outperform currently adopted Cloud-enabled architectures, and has the potential to address a wide range of IoT-related data-intensive time-critical scenarios. KEYWORDS cloud computing, clustered edge computing, data agility, edge computing, internet of things, stream processing 1 INTRODUCTION AND MOTIVATION In the last decade, rapid advances in networking technologies and pervasive Internet connectivity guided the development of Information and Communication Technologies (ICT) in distributed, mobile, and embedded directions. On the one hand, supported by continuously growing datacenter infrastructures, virtualization technologies, service-oriented approaches and utility models, Cloud computing has emerged as an effective paradigm, enabling Internet-based, convenient, Quality-of-Service (QoS)-guaranteed access to a shared pool of configurable computing resources. 1 On the other hand, more and more powerful devices, either static or mobile, such as smartphones, single-board computers (eg, Raspberry Pi and Arduino Yun), or smart objects (eg, home appliances, wearables, and vehicles), have been interconnected as ''things'' through the Internet, giving rise to the Internet of Things (IoT) paradigm and corresponding services and applications. There are already tens of billions connected things, which is estimated to reach hundreds of billions in the next 5-10 years. The total amount of data produced by these objects on a daily basis, measured on a scale of Zettabytes (2 70 bytes), requires corresponding technological solutions for their efficient management, usually framed into the context of the Big Data research.

show abstract

Data Processing in Cyber-Physical-Social Systems Through Edge Computing

Cited by 45 publications

References 20 publications

Towards a Framework for Data Stream Processing in the Fog

Towards a Framework for Data Stream Processing in the Fog

Novel Resource Allocation Algorithms for the Social Internet of Things Based Fog Computing Paradigm

Data agility through clustered edge computing and stream processing

Contact Info

Product

Resources

About