As the adoption of electric vehicles (EVs) continues to rise, attention has switched to ensuring the safety of EV operations. The exponential growth in battery technology over the past several years has changed the face of energy storage and sparked a revolution in several industries. The degradation of battery insulation during regular use is a significant concern. The high voltage (HV) and current levels in HV electric vehicles pose a significant electrical threat.The advancement of electric vehicle technology has led to an increasing presence of HV electric equipment throughout the vehicle. The insulation strength and early health status detection of the batteries are essential in ensuring safety in EVs. This paper studies the different insulation detection techniques and the development of adaptive filter (AF) algorithms based on field-programmable gate arrays (FPGAs) for insulation detection. FPGAs are amongst the most accurate and fast detection techniques among all the insulation detection techniques used so far in electric vehicles. This study proposes an FPGA-based VFF-RLS algorithm for effectively implementing insulation detection in EVs. The experimental test results using FPGAs demonstrate that the proposed method can rapidly monitor changes in insulation resistance (IR). The VFFRLS-based FPGA technique works sufficiently well to reduce errors when dealing with variations in voltage and resistance conditions at the battery terminals.

Zigbee IoT devices have limited computational resources, including processing power and memory capacity. Therefore, because of their complicated computational requirements, traditional encryption techniques are inappropriate for Zigbee devices. Because of this, we proposed a novel, “lightweight encryption” method (algorithm) is based on “DNA sequences” for Zigbee devices. In the proposed way, we took advantage of the randomness of “DNA sequences” to produce a full secret key that attackers cannot crack. The DNA key encrypts the data using two operations, “substitution” and “transposition”, which are appropriate for Zigbee computation resources. Our suggested method uses the “signal-to-interference and noise ratio (SINR)”, “congestion level”, and “survival factor” for estimating the “cluster head selection factor” initially. The cluster head selection factor is used to group the network nodes using the “adaptive fuzzy c-means clustering technique”. Data packets are then encrypted using the DNA encryption method. Our proposed technique gave the best results by comparing the experimental results to other encryption algorithms and the metrics for energy consumption, such as “node remaining energy level”, key size, and encryption time.

The expansion of Zigbee-enabled IoT networks has generated significant security issues, especially around threat detection and secure key management. Using RBF and blockchain technology, this study shows a smart hybrid framework to find threats early and distribute keys safely on IoT networks enabled by Zigbee. This methodology incorporates Radial Basis Function (RBF) networks for prompt threat detection and a blockchain-based trust framework for decentralized and tamper-proof key distribution. It guarantees safe network access, comprehensive authentication, and effective key updates, reducing risks associated with IoT-related DoS attacks and Man in the Middle Attacks. The Trust-Based Security Provider (TBSP) enhances security by administering critical credentials across diverse networks. Comprehensive simulations and performance assessments illustrate the effectiveness of the framework in increasing threat detection precision, minimizing key distribution delay, and bolstering overall network security. The findings confirm its efficacy in safeguarding IoT settings from new risks while ensuring scalability and resource efficiency. We proposed an RBF-based threat detection framework for network keys using the ZBDS2023 dataset and the J48 decision tree algorithm. In conclusion, we demonstrate the security and efficiency of our proposed work.

This paper presents an efficient end-to-end communication protocol in IoT-enabled ZigBee devices and with Internet Host using 6LoWPAN protocol. Specifically, it solves the (1) The packet size problem and (2) End-to-End communication between the internet host, Gateways, and ZigBee coordinator by not only performing the neighbor discovery process but also reduces the complexity on the coordinator. This 6LoWPAN connects IP-based infrastructures with a collection of Zigbee sensor devices that provides the end-to-end communication to route the IPv6 packets into regional ZigBee Enabled IoT networks. However, packets need to be forwarded or routed with several hops. We proposed the RPL-AODV routing protocol refers to the capacity to deliver data packets from one Zigbee device to 6BR via several hops. Finally we evaluated the performance of the proposed protocol with various metrics.