Leveraging fog computing and software defined systems for selective forwarding attacks detection in mobile wireless sensor networks

Yaseen, Qussai; Albalas, Firas; Jararwah, Yaser; Al‐Ayyoub, Mahmoud

doi:10.1002/ett.3183

Cited by 33 publications

(33 citation statements)

References 28 publications

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“…The issues of security and privacy in fog and mobile edge computing are also a concern that many researchers are considering in their work. The authors of related works– have briefly presented these issues and considered some counter measurements. A selective forwarding model that provides monitoring capabilities for detecting malicious nodes and tracing nodes' movements has been presented in the work of Yaseen et al The model has shown huge improvements in terms of detecting malicious nodes by leveraging the fog computing infrastructure.…”

Section: Related Workmentioning

confidence: 99%

A collaborative mobile edge computing and user solution for service composition in 5G systems

Ridhawi

Aloqaily

Kotb

et al. 2018

Trans Emerging Tel Tech

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Mobile edge computing (MEC) is an emergent technology that has revolutionized traditional cloud service solutions. Mobile edge computing extends cloud computing by providing processing, storage, and networking capabilities at the edge of the mobile network. Delay‐sensitive and context‐aware applications are able to execute within close proximity of mobile users. Additionally, today's cloud services are not tailored to user specifications, but rather diversified toward a group of users. To guarantee delivery of user‐specific services in 5G networks, service composition techniques should be incorporated. This article envisions a real‐time, context‐aware, service‐composition collaborative framework that lies at the edge of the network, comprising MEC and user devices for fast composite service delivery. The proposed solution decomposes cloud data into a set of files and services, which are then replicated to MEC nodes. Frequently requested files and services are further cached onto user mobile devices for faster access. Both MEC nodes and mobile users advertise their services onto the collaborative edge/user space, where services are delivered either composite or unrendered according to users' requests. Service composition is achieved through a learning‐based workflow‐net approach that relies on previous composition results to build service composition models to be used for new compositions. The presented solution provides guaranteed and fast delivery of the requested cloud composite services to end users while sustaining QoS requirements and load balancing among edge and mobile nodes.

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Section: Related Workmentioning

confidence: 99%

A collaborative mobile edge computing and user solution for service composition in 5G systems

Ridhawi

Aloqaily

Kotb

et al. 2018

Trans Emerging Tel Tech

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“…García‐Pérez and Merino and Farris et al investigated further into fog computing to reduce the latencies in LTE and 5G networks, respectively, where MSs can be a candidate solution for service deployment. Yaseen et al and de Brito et al discussed leveraging fog computing and SDN for the security of the mobile wireless sensor networks and smart factories.…”

Section: Related Workmentioning

confidence: 99%

Exploring microservices for enhancing internet QoS

Bhamare

Samaka

Erbad

et al. 2018

Trans Emerging Tel Tech

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With the enhancements in the field of software‐defined networking and virtualization technologies, novel networking paradigms such as network function virtualization and the Internet of Things are rapidly gaining ground. The development of Internet of Things and 5G networks and explosion in online services has resulted in an exponential growth of devices connected to the network. As a result, application service providers and Internet service providers are being confronted with the unprecedented challenge of accommodating increasing service and traffic demands from the geographically distributed users. To tackle this problem, many and ISPs, such as Netflix, Facebook, and AT&T, are increasingly adopting microservices application architecture. Despite the success of microservices in the industry, there is no specific standard or research work for service providers as guidelines, especially from the perspective of basic microservice operations. In this work, we aim to bridge this gap between the industry and the academia and discuss different microservice deployment, discovery, and communication options for service providers as a means to forming complete service chains. In addition, we address the problem of scheduling microservices across multiple clouds, including microclouds. We consider different user‐level service level agreements, such as latency and cost, while scheduling such services. We aim to reduce the overall turnaround time and costs for the deployment of complete end‐to‐end service. In this work, we present a novel affinity‐based fair weighted scheduling heuristic to solve this problem. We also compare the results of the proposed solution with standard greedy scheduling algorithms presented in the literature and observe significant improvements.

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“…In [30][31][32], the authors used the power of the fog computing to solve the problem of selective forwarding method in mobile wireless sensor networks to detect different kinds of intrusions. The authors built a model that provided a global monitoring capability based on the infrastructure of the fog computing to trace the movement of sensors and catch malicious ones.…”

Section: Related Workmentioning

confidence: 99%

Delay-aware power optimization model for mobile edge computing systems

Jararweh

Al‐Ayyoub

Al-Quraan

et al. 2017

Pers Ubiquit Comput

Self Cite

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Reducing the total power consumption and network delay are among the most interesting issues facing large-scale Mobile Cloud Computing (MCC) systems and their ability to satisfy the Service Level Agreement (SLA). Such systems utilize cloud computing infrastructure to support offloading some of user's computationally heavy tasks to the cloud's datacenters. However, the delay incurred by such offloading process lead the use of servers (called cloudlets) placed in the physical proximity of the users, creating what is known as Mobile Edge Computing (MEC). The cloudlet-based infrastructure has its challenges such as the limited capabilities of the cloudlet system (in terms of the ability to serve different request types from users in vast geographical regions). To cover the users demand for different types of services and in vast geographical regions, cloudlets cooperate among each other by passing user requests from one cloudlet to another. This cooperation affects both power consumption and delay. In this work, we present a mixed integer linear programming (MILP) optimization model for MEC systems with these two issues in mind. Specifically, we consider two types of cloudlets: local cloudlets and global cloudlets, which have higher capabilities. A user connects to a local cloudlet and sends all of its traffics to it. If the local cloudlet cannot serve the desired request, then the request is moved to another local cloudlet. If no local cloudlet can serve the request, then it is moved to a global cloudlet which can serve all service types. The process of routing requests through the hierarchical network of cloudlets increases power consumption and delay. Our model minimizes power consumption while incurring an acceptable amount of delay. We evaluate it under several realistic scenarios to show that it can indeed be used for power optimization of large-scale MEC systems without violating delay constraints.

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Leveraging fog computing and software defined systems for selective forwarding attacks detection in mobile wireless sensor networks

Cited by 33 publications

References 28 publications

A collaborative mobile edge computing and user solution for service composition in 5G systems

A collaborative mobile edge computing and user solution for service composition in 5G systems

Exploring microservices for enhancing internet QoS

Delay-aware power optimization model for mobile edge computing systems

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