VANET is a scalable and unbounded network which is completely independent from the number of nodes. In VANET, communication is done between V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure). In both type of communications nodes are gathering information from other nodes or from RSU which must be trustworthy. VANETs is having different security requirement for governing proper vehicular communication. VANET are specially design for nodes having high mobility with unbounded network structure and want to communicate time critical information in a secure way. There is a two category in routing protocol which involve in this communication: Proactive and Reactive. AODV is a demand based reactive routing protocol. AODV only establish the route when any need occurs. It is having route request-response mechanism through which it send the request for finding the route and based on received response it establish the optimal path. In this paper work of Reactive routing protocol AODV is presented with the implementation methods using different simulators like SUMO, MOVE and NS2. Here AODV implementation is presented with the details comparative result analysis using different parameters like Packet Drop Rate, Throughput, Average End to End Delay, Jitter and Network Routing Load.

Vehicular Ad-hoc Networks (VANETs) is an advancement over Mobile Ad Hoc Networks (MANETs) which is mainly design for inter vehicle communication. VANET is a scalable and unbounded network which is completely independent from the number of nodes. It can be implemented for one or several cities even for whole country. VANET is a self organizing network emergent technology with a promising advantages but having high challenges in its security. VANET communication must be secure and having guarantee that transmitted message is not inserted or modified by any attackers. VANETs is having different security requirement for governing proper vehicular communication. For secure VANET communication first systems have to discover who are the attackers, their capacity and nature to spoil the network communication. In this paper we have done the analysis of different Routing protocols OLSR, DSDV, AODV and DSR in VANET. We selected AODV as a most vulnerable protocol in VANET and presented effect of Blackhole Attack using AODV Protocol with the detail comparative analysis of Packet Drop Rate, Throughput and End to End Delay Parameters.

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AI technologies have the potential to help deaf individuals communicate. Due to the complexity of sign fragmentation and the inadequacy of capturing hand gestures, the authors present a sign language recognition (SLR) system and wearable surface electromyography (sEMG) biosensing device based on a Deep SLR that converts sign language into printed message or speech, allowing people to better understand sign language and hand motions. On the forearms, two armbands containing a biosensor and multi-channel sEMG sensors are mounted to capture quite well arm and finger actions. Deep SLR was tested on an Android and iOS smartphone, and its usefulness was determined by comprehensive testing. Sign Speaker has a considerable limitation in terms of recognising two-handed signs with smartphone and smartwatch. To solve these issues, this research proposes a new real-time end-to-end SLR method. The average word error rate of continuous sentence recognition is 9.6%, and detecting signals and recognising a sentence with six sign words takes less than 0.9 s, demonstrating Deep SLR's recognition.

For secure VANET communication first system has to discover who the attackers are and how they can damage the VANET communication. Attackers disturb the communication by getting full or partial access in communication of network. Based on the participation nature of attackers we can detect the different attackers. VANET communication must be secure and must having surety that transmitted message is not inserted, deleted or modified by any attackers. Detection of such attackers will only give improvement up to some extent, but there is a need to identify a method which prevents such attackers before spreading attack in other VANET communication areas. In this paper we presented the analysis of hybrid preventive approach PAODV_RTPSN (Preventive AODV-Reactive Trusted Path based on Sequence Number). We presented the improvement of hybrid method under blackhole routing attack with the detail comparative analysis of different parameters like Packet Drop Rate, Throughput, Average End to End Delay, Jitter and Network Routing Load.