Reliable Data Analysis through Blockchain based Crowdsourcing in Mobile Ad-hoc Cloud

Mobile Ad-hoc Cloud (MAC) is the constellation of nearby mobile devices to serve the heavy computational needs of the resource constrained edge devices. One of the major challenges of MAC is to convince the mobile devices to offer their limited resources for the shared computational pool. Credit based rewarding system is considered as an effective way of incentivizing the arbitrary mobile devices for joining the MAC network and to earn the credits through computational crowdsourcing. The next challenge is to get the reliable computation as incentives attract the malicious devices to submit fake computational results for claiming their reward and we have used the blockchain based reputation system for identifying the malicious participants of MAC. This paper presents a malicious node identification algorithm integrated within the Iroha based permissioned blockchain. Iroha is a project of hyperledger which is focused on mobile devices and thus light-weight in nature. It is used for keeping the track of rewarding and reputation system driven by the malicious node detection algorithm. Experiments are conducted for evaluated the implemented test-bed and results show the effectiveness of algorithm in identifying the malicious devices and conducting the reliable data analysis through the blockchain based computational crowdsourcing in MAC. This is a post-peer-review, pre-copyedit version of an article published in Mobile Networks and Applications. The final authenticated version is available online at: https://link.springer.com/journal/1103

Social Relationships and Temp-Spatial Behaviors Based Community Discovery to Improve Cyber Security Practices

Cyber security significantly relies on the dynamic communities in social networks. The location-based social network (LBSN) is a new type of social system that has sprung up recently that. It turns traditional social networks into heterogeneous networks by incorporating location information, which is used as the medium between the real world and the online social networks, thus bringing new challenges to the community discovery problems. This paper proposes a LBSN homogeneous network model (LSHNM) based on the user social relations and temp-spatial behaviors to calculate the user similarity relations in multi-dimensional features and construct LBSN isomorphism network topology, which can be used to improve cyber security practices. After that non-negative matrix decomposition (NMF) is used to find communities from above isomorphism network topology. The experimental results show that the LSHNM can find more satisfactory community structures. (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other work