A simplified optimization for resource management in cognitive radio network-based internet-of-things over 5G networks

With increasing evolution of applications and services in internet-of-things (IoT), there is an increasing concern of offering superior quality of service to its ever-increasing user base. This demand can be fulfilled by harnessing the potential of cognitive radio network (CRN) where better accessibility of services and resources can be achieved. However, existing review of literature shows that there are still open-end issues in this regard and hence, the proposed system offers a solution to address this problem. This paper presents a model which is capable of performing an optimization of resources when CRN is integrated in IoT using five generation (5G) network. The implementation uses analytical modeling to frame up the process of topology construction for IoT and optimizing the resources by introducing a simplified data transmission mechanism in IoT environment. The study outcome shows proposed system to excel better performance with respect to throughput and response time in comparison to existing schemes

Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThe booming of Social Internet of Things (SIoT) has witnessed the significance of graph mining and analysis for social network management. Online Social Networks (OSNs) can be efficiently managed by monitoring users behaviors within a cohesive social group represented by a maximal clique. They can further provide valued social intelligence for their users. Maximal Cliques Problem (MCP) as a fundamental problem in graph mining and analysis is to identify the maximal cliques in a graph. Existing studies on MCP mainly focus on static graphs. In this paper, we adopt the Formal Concept Analysis (FCA) theory to represent and analyze social networks. We then develop two novel formal concepts generation algorithms, termed Add-FCA and Dec-FCA, that can be applicable to OSNs for detecting the maximal cliques and characterizing the dynamic evolution process of maximal cliques in OSNs. Extensive experimental results are conducted to investigate and demonstrate the correctness and effectiveness of the proposed algorithms. The results reveal that our algorithms can efficiently capture and manage the evolutionary patterns of maximal cliques in OSNs, and a quantitative relation among them is presented. In addition, an illustrative example is presented to verify the usefulness of the proposed approach.National Natural Science Foundation of ChinaEuropean Union Horizon 2020Fundamental Research Funds for the Central Universitie

A social-driven edge computing architecture for mobile crowd sensing management

The multi-access edge computing (MEC) architectural model has fostered the development of new human-driven edge computing (HEC) frameworks that extend the coverage of traditional MEC solutions leveraging people roaming around with their devices. HEC is a well-suited architecture for human-centered technologies such as mobile crowdsensing (MCS) as it allows conveying and distributing sensing tasks at the edges of the network, also enabling (local) sensing data collection from devices. This article, through the joint use of HEC and MCS paradigms, introduces a new social-driven edge computing architecture based on incentives and centrality measures. The core idea is to add social MEC (SMEC) nodes to complement the traditional edge nodes (i.e., the main actors of the middle layer of the standard MEC architecture), acting as bridges between other devices and the cloud. The principle that underlies the SMEC selection is based on the attitude of the users in performing tasks and on their measures of centrality. In addition, we report extensive experimental results based on co-location traces and cooperativeness scores extracted from the ParticipAct living lab, a well-known MCS dataset based on data collected between 2013 and 2015 from 170 students of the University of Bologna, that show how the selection based on centrality measurements returns greater benefits than simple selection based on cooperativeness scores

A social-driven edge computing architecture for mobile crowd sensing management

The multi-access edge computing (MEC) architectural model has fostered the development of new human-driven edge computing (HEC) frameworks that extend the coverage of traditional MEC solutions leveraging people roaming around with their devices. HEC is a well-suited architecture for human-centered technologies such as mobile crowdsensing (MCS) as it allows conveying and distributing sensing tasks at the edges of the network, also enabling (local) sensing data collection from devices. This article, through the joint use of HEC and MCS paradigms, introduces a new social-driven edge computing architecture based on incentives and centrality measures. The core idea is to add social MEC (SMEC) nodes to complement the traditional edge nodes (i.e., the main actors of the middle layer of the standard MEC architecture), acting as bridges between other devices and the cloud. The principle that underlies the SMEC selection is based on the attitude of the users in performing tasks and on their measures of centrality. In addition, we report extensive experimental results based on co-location traces and cooperativeness scores extracted from the ParticipAct living lab, a well-known MCS dataset based on data collected between 2013 and 2015 from 170 students of the University of Bologna, that show how the selection based on centrality measurements returns greater benefits than simple selection based on cooperativeness scores