Decentralized Plan-Free Semantic-Based Service Composition in Mobile Networks

Ridhawi, Yousif Al; Karmouch, Ahmed

doi:10.1109/tsc.2013.2297114

Cited by 29 publications

(17 citation statements)

References 23 publications

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“…It should set hard and soft thresholds, so it is very complex. EEUC (Energy-Efficient Unequal Clustering) [14] adopted a multi-hop wireless communication method. The energy consumption included inter-cluster and intra-cluster.…”

Section: Teen (Threshold Sensitive Energy Efficient Sensormentioning

confidence: 99%

“…The design algorithm of swarm intelligence is inspired by the collective act of the insects (such as bees, termites, and ants) which exist in a decentralized and self-organizing system or other animal society [9][10][11][12][13][14][15]. These insects live in a hostile and dynamic environment; they survive through coordination and cooperation.…”

Section: Introductionmentioning

confidence: 99%

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A new clustering routing method based on PECE for WSN

Zhang

Wang

Song

et al. 2015

J Wireless Com Network

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A new clustering routing method based on predictive energy consumption efficiency (PECE) for a wireless sensor network (WSN) is presented in this paper. It consists of two stages: cluster formation and stable data transfer. In the cluster formation stage, we design an energy-saving clustering routing algorithm based on the node degree, the relative distance between nodes, and the rest energy of nodes. When this algorithm selects the cluster head, the node degree and the relative distance between the nodes are fully considered, so the selected cluster not only has better coverage performance but also short average distance from other member nodes in the formative cluster; therefore, the cost of communications within the clusters is small. In the stable data transfer stage, by using bee colony optimization (BCO), we design a PECE strategy for data transmission. On the basis of considering the predictive values of energy consumption, the hops, and the propagation delay on this route, this strategy gives a precise definition of the route yield by using two types of bee agent to predict the route yield of each routing path from the source node to the sink node. Through the optimization design of the algorithm, it can improve the quality of clusters, thereby increasing the overall network performance, and reduces and balances the energy consumption of whole network and prolongs the survival time of the network.

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Section: Teen (Threshold Sensitive Energy Efficient Sensormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new clustering routing method based on PECE for WSN

Zhang

Wang

Song

et al. 2015

J Wireless Com Network

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show abstract

“…So appropriate service selection and composition models have been focused in service oriented computing area. Generally, service composition mechanisms can be divided into two categories: plan based composition and semantic based composition [2]. Plan based composition is also regarded as static composition and semantic based composition is usually regarded as dynamic composition.…”

Section: Introductionmentioning

confidence: 99%

“…The server will dynamically form a composed service according to the 58 Copyright ⓒ 2017 SERSC reality scenarios. Although centralized approaches present simple solutions, they suffer from scalability, bottlenecks and single point of failure [2]. What is more serious is that a central server can't be assigned in advance in mobile networks.…”

Section: Introductionmentioning

confidence: 99%

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A QoS-Aware Decentralized Service Composition Method in Mobile Networks

Yang¹,

Li²

2017

IJUNESST

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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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Decentralized Plan-Free Semantic-Based Service Composition in Mobile Networks

Cited by 29 publications

References 23 publications

A new clustering routing method based on PECE for WSN

A new clustering routing method based on PECE for WSN

A QoS-Aware Decentralized Service Composition Method in Mobile Networks

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

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