Biblio

Cognitive Radio Networks (CRNs) promise efficient spectrum utilization by operating over the unused frequencies where Vehicular Ad-hoc Networks (VANETs) facilitate information exchanging among vehicles to avoid accidents, collisions, congestion, etc. Thus, CR enabled vehicular networks (CR-VANETs), a thriving area in wireless communication research, can be the enabler of Intelligent Transportation Systems (ITS) and autonomous driver-less vehicles. Similar to others, efficient and reliable communication in CR-VANETs is vital. Besides, security in such networks may exhibit unique characteristics for overall data transmission performance. For efficient and reliable communication, the proposed routing protocol considers the mobility patterns, spectrum availability, and trustworthiness to be the routing metrics. Hence, the protocol considers the vehicle's speed, mobility direction, inter-vehicles distance, and node's reliability to estimate the mobility patterns of a node. Besides, a trust-based reliability factor is also introduced to ensure secure communications by detecting malicious nodes or other external threats. Therefore, the proposed protocol detects malicious nodes by establishing trustworthiness among nodes and preserves security. Simulation is conducted for performance evaluation that shows the proposed routing selects the efficient routing path by discarding malicious nodes from the network and outperforms the existing routing protocols.

In smart cities, data privacy and security issues of Electronic Health Record(EHR) are grabbing importance day by day as cyber attackers have identified the weaknesses of EHR platforms. Besides, health insurance companies interacting with the EHRs play a vital role in covering the whole or a part of the financial risks of a patient. Insurance companies have specific policies for which patients have to pay them. Sometimes the insurance policies can be altered by fraudulent entities. Another problem that patients face in smart cities is when they interact with a health organization, insurance company, or others, they have to prove their identity to each of the organizations/companies separately. Health organizations or insurance companies have to ensure they know with whom they are interacting. To build a platform where a patient's personal information and insurance policy are handled securely, we introduce an application of blockchain to solve the above-mentioned issues. In this paper, we present a solution for the healthcare system that will provide patient privacy and transparency towards the insurance policies incorporating blockchain. Privacy of the patient information will be provided using cryptographic tools.

Due to environmental influence and technology limitation, a wireless sensor/sensors module can neither store or process all raw data locally nor reliably forward it to a destination in heterogeneous IoT environment. As a result, the data collected by the IoT's sensors are inherently noisy, unreliable, and may trigger many false alarms. These false or misleading data can lead to wrong decisions once the data reaches end entities. Therefore, it is highly recommended and desirable to acquire trustworthy data before data transmission, aggregation, and data storing at the end entities/cloud. In this paper, we propose an In-network Generalized Trustworthy Data Collection (IGTDC) framework for trustworthy data acquisition and faulty sensor detection in the IoT environment. The key idea of IGTDC is to allow a sensor's module to examine locally whether the raw data is trustworthy before transmitting towards upstream nodes. It further distinguishes whether the acquired data can be trusted or not before data aggregation at the sink/edge node. Besides, IGTDC helps to recognize a faulty or compromised sensor. For a reliable data collection, we use collaborative IoT technique, gate-level modeling, and programmable logic device (PLD) to ensure that the acquired data is reliable before transmitting towards upstream nodes/cloud. We use a hardware-based technique called “Gray Code” to detect a faulty sensor. Through simulations we reveal that the acquired data in IGTDC framework is reliable that can make a trustworthy data collection for event detection, and assist to distinguish a faulty sensor.