Detection and partial genetic characterisation of a novel variant of Avian nephritis virus in Indian poultry flocks showing diverse clinical signs

Avian nephritis virus (ANV) infects poultry flocks worldwide, but no confirmed cases have been reported from India so far. In the current study, disease investigation was carried out in 21 broiler flocks at different parts of India with clinical signs of nephritis, uneven and stunted growth, diarrhoea, reduced body weight, and mortality up to 9.72%. Out of the 21 flocks screened, two were found positive for ANV in RT-PCR assay. BLAST analysis revealed that the ANV of Indian origin was closely related to ANV-1 strains reported from Japan, Hungary and China. However, comparison of a small portion (~12% of nucleotides, i.e. ~60 nts, common site for ANV-1 and ANV-3, position 2200–2260 of ORF 1a gene) of the Indian ANV sequence with ANV-3 sequences revealed 89–93% identities with different ANV-3 isolates. Phylogenetically, ANV-1 forms three clades, and the Indian ANV clustered under clade II. This study confirms the existence of ANV in Indian poultry flocks and is the first report on the molecular detection and genetic characterisation of ANV from India

ARPVP: Attack Resilient Position-Based VANET Protocol Using Ant Colony Optimization

Abstract The position-based routing of Vehicular Ad hoc Network (VANET) vulnerable to various security attacks because of dependency on computing, control, and communication technologies. The Internet of Things (IoT)-enabled VANET application leads to the challenges such as integrity, access control, availability, privacy protection, non-repudiation, and confidentiality. Several security solutions have been introduced for two decades in two categories as cryptography-based and trust-based. Due to the high computation complexity, cryptography-based solutions are outperformed by recent intelligent trust-based mechanisms. The trust-based techniques are lightweight and effective against the well-known security threats in VANET. The objective of this paper has to design a novel position-based routing in which the conduct of vehicles assessed to accomplish reliable VANET communications. Attack Resilient Position-based VANET Protocol (ARPVP) proposed to detect and prevent malicious vehicles in the network using the trust evaluation technique and artificial intelligence (AI). In the first phase of ARPVP, the periodic self-trust assessment algorithm has designed using various trust parameters to detect unreliable vehicles in the network. In the second phase of ARPVP, the position-based route formation algorithm has designed using the AI technique Ant Colony Optimization (ACO). ACO solves the problem of reliable route formation by neglecting the attacker's using a trust-based fitness function. The trust parameters of each vehicle as mobility, buffer occupancy, and link quality parameters had measured in both phases of ARPVP. Simulation outcomes of the proposed model outperformed state-of-art protocols in terms of average throughput, communication delay, overhead, and Packet Delivery Ratio (PDR).</jats:p

Optimal Trained Deep Neural Network for Localization in Wireless Sensor Network

Wireless Sensor Network (WSN) localization has been bloomed as an active area of research in this era. In fact, WSN differs from the traditional network in diverse aspects and therefore requires novel algorithms for addressing specific challenges like the identification of the unknown node location in hazardous environments. In this paper, a new localization model is introduced by the range-based localization approach via an optimization-assisted deep learning model. The proposed work undergoes two major phases: (a) training phase and (b) localization phase. The trained Deep Neural Network (DNN) with the measured distance-based features like the “Angle of Arrival (AoA) and Received Signal Strength Indicator (RSSI)” find out the place of the unknown node more precise. Further, to enhance the localization accuracy, the weight of DNN is tuned via a novel hybrid optimization algorithm named as Lion-Assisted Firefly Algorithm (LAFA) model. The proposed LAFA is the concept of both the Lion Algorithm (LA) and Firefly Algorithm (FF). Finally, the evaluation of the presented work is done in terms of error measures. </jats:p