The increasing number of vehicles has resulted in a tremendous growth of data in vehicular communications. Cloud-based models are inefficient for handling this large data due to high latency and bandwidth requirements. To address this, fog computing-based vehicular network models have been proposed for low latency and immediate responses. However, securing data transfer between fog servers and vehicles on public channels is essential to prevent malicious attacks. Existing authenticated key agreement schemes provide security but often incur high computational costs and vulnerability to attacks. Thus, this paper proposes a blockchain-based authenticated key agreement scheme for the fog computing-enabled vehicular network. The proposed scheme integrates blockchain into fog-based Vehicular Ad-hoc Network (VANET), where fog servers and the cloud servers serve as blockchain nodes for seamless re-authentication of a moving vehicle. For secure communication, the proposed scheme uses the Chebyshev polynomial to achieve low computational cost and establishes a common session key between cloud server, fog server, and vehicle. The formal security proof of the proposed scheme is carried out using Real-Or-Random (ROR) model. Finally, the Hyperledger Fabric and cryptographic libraries have been used for the experimental analysis to demonstrate the proposed scheme's communication and computational efficiency.

The integration of fog computing and blockchain in the Internet of Medical Things (IoMT) domain has wholly transformed the healthcare industry and e-health services. In IoMT, the patient’s personal health data is shared on an open channel that makes the IoMT system an easy target for attackers. Therefore, confidentiality and authentication are the key security requirements for an IoMT system. Although few works have been proposed to provide secure communication in conventional healthcare infrastructures, limited work has been done to achieve data confidentiality and authentication in blockchain-based IoMT under fog environment. Therefore, this work proposes a blockchain-assisted certificateless aggregate signcryption called BCSoM scheme that achieves data confidentiality and device/patient authentication. In the proposed work, first, IoMT devices generate a signcrypted text and transmit it to an aggregator that generates an aggregated signcrypted text and sends it to a receiver fog server. The receiver fog server verifies the signcrypted text through the blockchain and also performs unsigncryption to retrieve the original message. The proposed scheme is proven secure under the Discrete Logarithm (DL) and Computational Diffe–Hellman (CDH) assumptions. Finally, a thorough performance analysis using the Hyperledger Fabric platform and cryptographic libraries shows that the proposed scheme is computationally more efficient when compared to existing works.

In this proposal, the impact of dynamic farm environment due to varying vegetation density on the Received Signal Strength (RSS), coverage and energy consumption of an IoT assisted Wireless Sensor Network (IoWSN) is analyzed through measurement campaign. Experimental observations on free-space and tree Path Loss Model (PLM) based sensor node deployment strategies in a cropping period have shown network disconnectivity due to incorrect assessment of excess attenuation caused by dynamically varying vegetation height and density during the monitoring period. To address the challenge, an empirically formulated PLM is proposed to estimate the excess attenuation at different crop development stages of medium grass vegetation. Further, using the formulated PLM, a Non-dominated Sorting Genetic Algorithm (NSGA-III) multi-objective optimization is performed to generate initial node deployment with a heterogeneous transmission range. To address the issue of over-coverage, transmitter output power scheduling is performed with predefined upper limit derived from the NSGA-III optimization. The output power is dynamically scheduled during the monitoring period based on changes in the captured RSS to minimize over-coverage. Improvements in coverage, connectivity, and energy efficiency compared to existing approaches are validated through Proof of Concept.

Precision agriculture, as the future of farming technology, addresses challenges faced by farmers by data mining of information collected through IoT-enabled crop monitoring infrastructures. The identification of crop disease is one of the widely studied challenges. Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. This proposal presents a fuzzy-logic-based pest prediction mechanism assisting in beforehand preparedness for potential pest prevention. The experiments are performed for pests in rice and millet crops. The data mining over samples collected in a cropping cycle revealed the plausible correlation between temperature, relative humidity, and rainfall with pest breeding. The data collected through IoT monitoring infrastructure is analyzed for the ambient breeding condition of the pest. These conditions are then employed to design the knowledge base of the fuzzy system. More specifically, the linguistic variables of the fuzzy membership function are optimized using a genetic algorithm for close prediction of pest breeding in given environmental conditions. The proposal verified that the weather factors have a strong impact on the occurrence of pests and diseases, and the fuzzy-logic-based pest prediction through IoT application development services will help farmers to take precautionary measures beforehand.

This proposal examines the effect of grass vegetation elevation and density on path loss between sensors deployed in an IoT-enabled wireless sensor network (IoWSN) crop monitoring infrastructures. Observations via real-time measurement campaigns at different node heights and vegetation depths revealed that the sensor deployment made using free-space or tree vegetation based path loss model (PLM) experiences network disconnectivity due to variations in vegetation density in a cropping cycle. An empirical PLM is formulated to identify signal strength at different development phases of paddy and sugarcane medium grass vegetation. For this, the 2.4 GHz RF path loss coefficient (PLC) is estimated using data collected through measurement campaign over combinations of sensor height and vegetation density. Further, the formulated PLC is used to train multiple regression model to develop a generic PLM for all medium grass vegetation. Improvements in coverage and connectivity have been validated through proof of concept.

Security is the major issue in most of the realtime applications, besides there being a stringent requirement for such applications to provide high QoS. As security algorithms demand large computation time, the prevalent real-time packet scheduling algorithms tend to focus on meeting the deadline without having much concern for security requirements. In this paper, we propose a utilization based secured dynamic scheduling algorithm (u-SDSA) for real-time applications on grid which tries to meet both (security and scheduling) the above-mentioned requirements. Using grid of nodes or computing elements as the distributed framework for processing real-time packets, we try to ensure maximum guarantee ratio, maximum average security level, and optimal overall performance. To ensure optimal overall performance of the grid, our mechanism distributes the incoming packets for processing amongst different nodes based on utilization of the nodes. If the minimum security level of an incoming packet cannot be ensured, then it is forwarded to one of the adjacent nodes having the least utilization, provided the utilization of this nodeis less than the utilization threshold value (Uthresh). Using extensive simulation, we show that the proposed u-SDSA algorithm performs better than the existing algorithms.