The Internet of Vehicles (IoV) has emerged as a robust solution to improve real-time road traffic conditions, safety, and driving comfort. However, the road’s dynamic environment and network heterogeneity make communication insecure. It poses many security threats like man-in-the-middle attacks, impersonation attacks, replay attacks, etc. Therefore, the message exchanges and real identities of nodes over the IoV network need to be preserved. The existing authentication schemes are inefficient and incur computational and communication overhead. They are unsuitable for resource-constrained IoV networks due to their highly dynamic nature, open communication, and chaotic road environment. Therefore, this paper proposes an elliptic curve cryptography-based secure and efficient anonymous batch authentication scheme with conditional privacy (EABAS-CP) for the IoV environment. The EABAS-CP scheme achieves conditional privacy by tracing the malicious vehicle efficiently. The RSU’s distributed parameters generate a pseudo-identity for vehicles and unique signatures that do not require secure channels and trusted authorities (TAs) for secure communication. Furthermore, the EABAS-CP scheme supports batch signature verification in which multiple signatures can be verified simultaneously and efficiently. The Random Oracle Model (RoM) provides unforgeability against adaptive message attacks by using the Elliptic Curve Discrete Logarithm Problem (ECDLP). The AVISPA simulation tool is used to evaluate the proposed protocol for its reliability and protection from the attacks like man-in-the-middle and replay attacks. The proposed EABAS-CP scheme is also proven secure against replay, impersonation, stolen verifier, ephemeral secret leakage, and linkability attacks. The network performance of the EABAS-CP scheme has been evaluated in terms of end-to-end delay, throughput, average message delay, average message loss ratio, communication cost, and computation cost through the NS-2 simulator and compared with existing schemes to demonstrate its efficiency.

In this day and age, we are witness to ever increasing gun violence all around the world. Technology has surpassed all human beliefs where each person can be easily tracked through their mobiles or through the fortitude of CCTV cameras available all across public properties and areas. There is a need to stop gun violence to protect people’s Right to Live. There are several instances appearing in the news daily about deaths caused due to gun violence. An alarm based system can be introduced which tracks the publicly available CCTV footage to look for guns in the open and raise appropriate alarms. In order to achieve this a robust model to identify and classify firearms automatically from videos is required. The aim of this paper is to describe a YOLO-based model which is highly effective in recognizing firearms in videos and mark them in the video such that the model can be further used for further applications such as raising alarms, tracking human beings with firearms etc.

Summary To give the complete description of an environment or to take a robust decision, a number of observations are collected and combined from multiple sensor nodes. The process of combining and analyzing the observations is called multisensor data fusion. The fusion is used to produce more consistent, accurate, and useful information rather than provided by any individual sensor node. Data fusion finds wide application in many areas of such as object recognition, wireless sensor network, image processing, environment mapping, and localization. Nowadays, the Internet of Things (IoT) utilizes wireless sensor network (WSN) as a necessary platform for data sensing and communication. For efficiency, data fusion is performed on the sensed sample collected by the sensor nodes. However, fusion of the network parameters is also essential to select an appropriate sensor node for the forwarding of data. Therefore, in this paper, we propose a multisensor data fusion (MDF) strategy that performs fusion of the collected network parameters like bandwidth and centrality for the selection of an appropriate path. Extensive simulations were made, and related results showed that our proposed scheme has a better performance compared with other schemes in our simulated scenarios.