Center for Applied Research in Information Technology

Recent Projects

Blocking negative influential node set in social networks: from host perspective

Principal Investigators: Harneet Kaur, Jing (Selena) He

Abstract: Nowadays, social networks are considered as the very important medium for the spreading of information, innovations, ideas and influences among individuals. Viral marketing is a most prominent marketing strategy using word-of-mouth advertising in social networks. The key problem with the viral marketing is to find the set of influential users or seeds, who, when convinced to adopt an innovation or idea, shall influence other users in the network, leading to a large number of adoptions. In this study, we study the competitive viral marketing problem from host perspective, where the host of the social network (such as Facebook, Twitter, etc.) sells the viral marketing campaigns to its customers and keeps control of the allocation of seeds. Seeds are allocated by creating ‘bang for the buck’ for each company. In this paper, we first propose a new influence diffusion model considering both negative and positive influences. Subsequently, we propose a novel optimization problem, named Blocking Negative Influential Node Set (BNINS) selection problem, to identify the positive node set (seeds) such that the number of negatively activated nodes is minimised for all competitors. Finally, we proposed a greedy algorithm called BNINS-GREEDY to solve BNINS and conducted comprehensive experiments and simulations to validate the proposed method. The results show that, for random graphs, on average, BNINS-GREEDY blocks the negative influence 17.22 per cent more than the most related work called Competitive Linear Directed Acyclic Graph. Moreover, on the real Epinions dataset, BNINS-GREEDY achieves 7.6 per cent more positive influence propagation than Competitive Linear Directed Acyclic Graph. Copyright © 2015 John Wiley & Sons, Ltd.

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A New Comprehensive RSU Installation Strategy for Cost-Efficient VANET Deployment

Principal Investigators: Donghyun Kim, Yesenia Velasco, Wei Wang, R. Uma

Project Summary: Drug treatment courts (DTC) have been identified as an effective alternative to incarceration for defendants with substance use/abuse issues. DTC programs offer individuals the opportunity to receive the treatment needed to help break the cycle of addiction and criminal involvement. While extant literature has identified various “best practices” for DTCs, it is important for DTC programs to uncover what works for their participants. To this end, the current project involves partnering with the New Hanover Drug Treatment Court to conduct qualitative interviews with past and present participants. Specifically, this project seeks to develop an understanding of participants’ perceptions and attitudes regarding the effectiveness of the NHC DTC. These data will provide the NHC DTC will valuable information that will assist in program modifications and improvements.

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Security in Fog Computing through Encryption

Principal Investigators: Akhilesh Vishwanath, Ramya Peruri, Jing (Selena) He

Abstract: Cloud computing is considered as one of the most exciting technology because of its flexibility and scalability. The main problem that occurs in cloud is security. To overcome the problems or issues of security, a new technique called fog-computing is evolved. As there are security issues in fog even after getting the encrypted data from cloud, we implemented the process of encryption using AES algorithm to check how it works for the fog. So far, to our analysis AES algorithm is the most secured process of encryption for security. Three datasets of different types are considered and applied the analysed encryption technique over those datasets. On validation, entire data over datasets is being accurately encrypted and decrypted back as well. We took android mobile as an edge device and deployed the encryption over datasets into it. Further, performance of encryption is evaluated over selected datasets for accuracy if the entire data is correctly encrypted and decrypted along with the time, User load, Response time, Memory Utilization over file size. Further best and worst cases among the datasets are analysed thereby evaluating the suitability of AES in fog.

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