Honesty-Based Social Technique to Enhance Cooperation in Social Internet of Things

The Social Internet of Things (SIoT) can be seen as integrating the social networking concept into the Internet of Things (IoT). Such networks enable different devices to form social relationships among themselves depending on pre-programmed rules and the preferences of their owners. When SIoT devices encounter one another on the spur of the moment, they seek out each other's assistance. The connectivity of such smart objects reveals new horizons for innovative applications empowering objects with cognizance. This enables smart objects to socialize with each other based on mutual interests and social aspects. Trust building in social networks has provided a new perspective for providing services to providers based on relationships like human ones. However, the connected IoT nodes in the community may show a lack of interest in forwarding packets in the network communication to save their resources, such as battery, energy, bandwidth, and memory. This act of selfishness can highly degrade the performance of the network. To enhance the cooperation among nodes in the network a novel technique is needed to improve the performance of the network. In this article, we address the issue of the selfishness of the nodes through the formation of a credible community based on honesty. A social process is used to form communities and select heads in these communities. The selected community heads having social attributes prove effective in determining the social behavior of the nodes as honest or selfish. Unlike other schemes, the dishonest nodes are isolated in a separate domain, and they are given several chances to rejoin the community after increasing their honesty levels. The proposed social technique was simulated using MATLAB and compared with existing schemes to show its effectiveness. Our proposed technique outperforms the existing techniques in terms of throughput, overhead, packet delivery ratio (PDR), and packet-delivery latency.

Secured Online Learning in COVID-19 Pandemic using Deep Learning Methods

With the outbreak of the global pandemic covid-19, most educational institutions across India have moved towards the usage of online teaching platforms Viz., Google Meet, Zoom, Webex, and Microsoft teams to process learning continuity. In research and development, it is observed that meager importance is given to address the issues of securing e-learning systems. Securing an e-learning system is a unique challenge faced in India as many systems are accessed and managed through the internet by numerous users distributed over diverse networks. Moreover, the online teaching platforms are open, distributed, and interactive;hence, it becomes challenging to ensure that every user has access to the correct information. Building trust will leverage the usage of online teaching systems in terms of security, usability, and protection of personal information. The key focus of this paper is to analyze the existing online and remote learning tools and identify the level of cyberattacks. This article also explores recent progress in novel ICT engineering paradigms in cyber assurance and protection. The paper proposes a cyber security framework using cutting-edge technologies like AI and Deep Learning to fight against cyber-attacks.

Trusted Compliance Enforcement Framework for Sharing Health Big Data

COVID pandemic management via contact tracing and vaccine distribution has resulted in a large volume and high velocity of Health-related data being collected and exchanged among various healthcare providers, regulatory and government agencies, and people. This unprecedented sharing of sensitive health-related Big Data has raised technical challenges of ensuring robust data exchange while adhering to security and privacy regulations. We have developed a semantically rich and trusted Compliance Enforcement Framework for sharing large velocity Health datasets. This framework, built using Semantic Web technologies, defines a Trust Score for each participant in the data exchange process and includes ontologies combined with policy reasoners that ensure data access complies with health regulations, like Health Insurance Portability and Accountability Act (HIPAA). We have validated our framework by applying it to the Centers for Disease Control and Prevention (CDC) Contact Tracing Use case by exchanging over 1 million synthetic contact tracing records. This paper presents our framework in detail, along with the validation results against Contact Tracing data exchange. This framework can be used by all entities who need to exchange high velocity-sensitive data while ensuring real-time compliance with data regulations. © 2021 IEEE.