Department of Computer Science and Information Systems
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Item Utilizing Social Networks Data for Trust Management in a Social Internet of Things Network(ACM Digital Library, 2018) Narang, NishitSocial Internet of Things (SIoT), an amalgamation of Social Networking concepts to the Internet of Things (IoT), is a strong architectural alternative for IoT solutions. A lot of research work in SIoT has proposed the use of social networking data for community and trust management in SIoT networks. While it seems like an interesting choice, it is important to analyze the effectiveness of social networking data for application to SIoT. In this paper, we analyze the accuracy of using tie information from the Facebook Friend Graph to mimic real-world SIoT network ties. We also discuss a method for ranking the strength of ties in a SIoT network by analyzing the structure of the Facebook Friend Graph. A similar analysis can be performed on data available from other Social Networking platforms, like Twitter, LinkedIn etc.Item A Neighborhood Overlap Based Approach for Service Provider Prioritization in a Directed Social IoT Service Network(ACM Digital Library, 2020-01) Narang, NishitSocial IoT (or SIoT) is an alternate architectural pattern for IoT, which involves imparting social behavioral attributes to IoT devices. An SIoT service network uses social collaboration between IoT devices (acting as service users or service providers or both), enabling low-latency collaborative services and applications. A key challenge in implementing an SIoT service network in a multi-vendor network of heterogeneous IoT devices is the issue of Trust. The problem is in prioritization and selection of trustworthy service provider(s) in an autonomous and independent manner. In a single-vendor network, the problem is handled via proprietary methods that do not scale for multi-vendor environments. The problem is further compounded in networks having IoT devices that are constrained in computational and storage resources. In this paper, we propose the use of Neighborhood Overlap for estimating tie-strengths and the consequent prioritization of service providers based on the estimated tie-strength. We verify the relationship between neighborhood overlap and tie-strength using three publicly available datasets. While past research on neighborhood-overlap and its relationship with tie-strength focuses on undirected social networks only, we extend the definition of neighborhood-overlap for directed networks. We further prove this extension with the help of two publicly available directed social network datasets. The idea proposed in this paper is fundamental and can be extended towards defining a trust framework for SIoT.Item A hybrid trust management framework for a multi-service social IoT network(Elsevier, 2021-04) Narang, NishitSocial IoT (or SIoT) is an alternate architectural pattern for IoT, which involves IoT devices with social behavioural attributes. A SIoT-based service network makes use of social collaboration between IoT devices to enable low-latency collaborative services and applications. A key challenge in implementing a SIoT-based service network in a multi-vendor environment of heterogeneous devices is the issue of Trust. In this paper, we propose a hybrid trust management framework that makes use of Probabilistic Neighbourhood Overlap (P-NO), a method for estimating tie-strengths between nodes. The neighbourhood overlap concept is borrowed from past research in sociology and extended in our paper for directed social networks. Our proposed trust management framework is hybrid because: (1) P-NO is applied on a social graph that is generated from two types of social networks — the IoT device owners’ online social network (like Facebook) and the IoT-devices’ social network (i.e. the SIoT network). Accordingly, the approach makes use of both human intelligence and device artificial intelligence for trust management. (2) The framework uses a mix of dynamic (interaction-based) and static (graph-based) approach for trust management. It helps in limiting resource overheads of a pure dynamic approach, while still benefiting from its higher accuracy compared to a pure-static approach. We provide both theoretical and simulation-based analysis of our trust management framework. Our study shows the effectiveness of the proposed framework in handling different attack scenarios while requiring limited storage and computational resources in IoT devices.