Ranjit Sadakale

Bio: Ranjit Sadakale is an academic researcher from College of Engineering, Pune. The author has contributed to research in topics: Computer science & Routing protocol. The author has an hindex of 2, co-authored 9 publications receiving 14 citations. Previous affiliations of Ranjit Sadakale include Savitribai Phule Pune University & Veermata Jijabai Technological Institute.

TAD-HOC Routing Protocol for Efficient VANET and Infrastructure-Oriented Communication Network

Abstract: Intelligent Transportation System (ITS) is a critical factor for Vehicular Ad hoc Networks (VANET). Even though VANET belongs to the class of Mobile Ad hoc Network (MANET), none of the MANET routing protocol applies to VANET. VANET network is dynamic, due to increased vehicle speed and mobility. Vehicle mobility of VANET affects conventional routing algorithm performance, which deals with the dynamicity of the network node. The evaluation of the existing research stated that Ad hoc On-Demand Distance Vector (AODV) is an effective MANET protocol to adopt network changes for significant resource utilization and also provides effective adaptation in the network change. Due to the effective performance of the AODV protocol, it is considered as an effective routing protocol for VANET. This paper proposed an ad hoc TROPHY (TAD-HOC) routing protocol for the VANET network for increasing efficiency and effective resource utilization of the network. To improve the overall performance, ad hoc network is combined with Trustworthy VANET ROuting with grouP autHentication keYs (TROPHY) protocol. The proposed TAD-HOC protocol transmits data based on time demand in the VANET network with the desired authentication. Results of the proposed approach show the increased performance of the VANET network with packet delay, transmission range, and end-to-end delay. The comparative analysis of the proposed approach with I-AODV, AODV-R, and AODV-L shows that the proposed TAD-HOC exhibited effective performance.

Performance Analysis of Traffic Types in AODV Routing Protocol for VANETs

Abstract: Vehicular Ad-hoc Networks (VANETs) are the intelligent system that allows vehicles to act like nodes, where there is a starting node, end node and any vehicle between these nodes can act as an intermediate node. Every node has high mobility and fast-changing topology. Each node can sense its coverage area to make available services related to traffic control. To make a connection between nodes routing is done with the help of routing protocol. The reactive routing protocol called Ad-Hoc On-demand Distance Vector (AODV) is the most advanced and commonly used in topology-based routing. Nodes (i.e. Vehicles) are fast moving thus the connection between nodes breaks frequently. This paper, we are going to analyze the Quality of Service (QoS) parameters for UDP and TCP traffic types. This paper is presenting a normal AODV routing protocol to analyze QoS parameters that are Delay and Throughput. We are using Linux based platform software's and doing result analysis using Network Simulator-2 (NS-2).

Optimization of Memory Oriented Network-on-Chip for FPGA

Abstract: In this paper we identify that by the bottleneck link interconnect throughput is limited, tons of files gets created automatically in cache and speed of the network becomes slow down also sometimes system gets hang. Hence we are describing Optimization of cache memory by analyzing the Network on chip (NoC) and field programmable gate array. In this paper with the help of ring topology we are introducing the DWT. Discrete wavelet Transform (DWT) is used to compress the cache memory. By implementing this proposed method System will get faster. Using the terms of FPGA, we believe that our implementation system will provide high gain and less intricacy.

Monocular Camera based Computer Vision System for Cost Effective Autonomous Vehicle

Abstract: According to the world health organization, there were 1.25 million road accident deaths globally in 2013. To decrease this count, it has been said that autonomous vehicles can play a major role. The autonomous vehicle uses different sensors such as LIDAR, RADAR, Ultrasonic sensors, and cameras for detection of lane lines, objects, traffic sign, traffic lights, and driving path. There are several successful projects on autonomous vehicles, but the main limitations are high costs and regulations. The camera can be a cost-effective solution as compare to LIDAR in all the basic detection needed for autonomous driving. The proper placement of sensors on the vehicle plays an important role in efficient design, working and reducing the cost of the overall autonomous vehicle system. This paper discusses the parameters considered while deciding the positioning of the camera, and the geometric calculation for the best positioning of a monocular camera on an autonomous vehicle.

Performance of LTE downlink scheduling algorithm with load

Abstract: Long Term Evolution (LTE) represents an emerging and promising technology for providing a high throughput. In this paper we are analyzing the two proposed algorithms namely Max Rate (MR) and Round Robin (RR) at different value of SNR. Result shows that maximum throughput is achieved by MR and fairness by RR.

Hybrid Genetic Firefly Algorithm-Based Routing Protocol for VANETs

Abstract: Vehicular Adhoc Networks (VANETs) are used for efficient communication among the vehicles to vehicle (V2V) infrastructure. Currently, VANETs are facing node management, security, and routing problems in V2V communication. Intelligent transportation systems have raised the research opportunity in routing, security, and mobility management in VANETs. One of the major challenges in VANETs is the optimization of routing for desired traffic scenarios. Traditional protocols such as Adhoc On-demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination Sequence Distance Vector (DSDV) are perfect for generic mobile nodes but do not fit for VANET due to the high and dynamic nature of vehicle movement. Similarly, swarm intelligence routing algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) routing techniques are partially successful for addressing optimized routing for sparse, dense, and realistic traffic network scenarios in VANET. Also, the majority of metaheuristics techniques suffer from premature convergence, being stuck in local optima, and poor convergence speed problems. Therefore, a Hybrid Genetic Firefly Algorithm-based Routing Protocol (HGFA) is proposed for faster communication in VANET. Features of the Genetic Algorithm (GA) are integrated with the Firefly algorithm and applied in VANET routing for both sparse and dense network scenarios. Extensive comparative analysis reveals that the proposed HGFA algorithm outperforms Firefly and PSO techniques with 0.77% and 0.55% of significance in dense network scenarios and 0.74% and 0.42% in sparse network scenarios, respectively.

Hybrid Genetic Firefly Algorithm-Based Routing Protocol for VANETs

Abstract: Vehicular Adhoc Networks (VANETs) are used for efficient communication among the vehicles to vehicle (V2V) infrastructure. Currently, VANETs are facing node management, security, and routing problems in V2V communication. Intelligent transportation systems have raised the research opportunity in routing, security, and mobility management in VANETs. One of the major challenges in VANETs is the optimization of routing for desired traffic scenarios. Traditional protocols such as Adhoc On-demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination Sequence Distance Vector (DSDV) are perfect for generic mobile nodes but do not fit for VANET due to the high and dynamic nature of vehicle movement. Similarly, swarm intelligence routing algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) routing techniques are partially successful for addressing optimized routing for sparse, dense, and realistic traffic network scenarios in VANET. Also, the majority of metaheuristics techniques suffer from premature convergence, being stuck in local optima, and poor convergence speed problems. Therefore, a Hybrid Genetic Firefly Algorithm-based Routing Protocol (HGFA) is proposed for faster communication in VANET. Features of the Genetic Algorithm (GA) are integrated with the Firefly algorithm and applied in VANET routing for both sparse and dense network scenarios. Extensive comparative analysis reveals that the proposed HGFA algorithm outperforms Firefly and PSO techniques with 0.77% and 0.55% of significance in dense network scenarios and 0.74% and 0.42% in sparse network scenarios, respectively.

Reliable Group of Vehicles (RGoV) in VANET

Abstract: Nowadays vehicles on the roads can communicate using a special type of wireless network called Vehicular Ad-hoc Networks (VANETs). It has been demonstrated by the researchers that because of the unique features such as high density of vehicles and frequent change of network topology, VANETs are not supported by the traditional routing protocols. The routing consistency of such highly dynamic networks must be taken into account in VANETs as communication links are disintegrated in VANETs more often than Mobile ad-hoc Networks (MANETs). The nature of VANET communication can bring extreme routing overhead to the network, therefore to increase network performance, the overhead issue must be tackled. The proposed protocol is focused on reducing the overhead to get the improved PDR performance of the network. The improvement is achieved by permitting communication amongst only those nodes which are considered reliable in terms of availability and geographical position. The reliability factor simply reduces unnecessary nodes from the communication process and selects a set of reliable nodes that are discovered with the help of clustering technique throughout the routing process. Simulation experiments using the network simulator are presented to demonstrate the efficacy of the proposed protocol. The results show that the proposed protocol has enhanced network performance effectively compared to prior approaches.

YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors

Abstract: In this work, we present an uncertainty-based method for sensor fusion with camera and radar data. The outputs of two neural networks, one processing camera and the other one radar data, are combined in an uncertainty aware manner. To this end, we gather the outputs and corresponding meta information for both networks. For each predicted object, the gathered information is post-processed by a gradient boosting method to produce a joint prediction of both networks. In our experiments we combine the YOLOv3 object detection network with a customized $1D$ radar segmentation network and evaluate our method on the nuScenes dataset. In particular we focus on night scenes, where the capability of object detection networks based on camera data is potentially handicapped. Our experiments show, that this approach of uncertainty aware fusion, which is also of very modular nature, significantly gains performance compared to single sensor baselines and is in range of specifically tailored deep learning based fusion approaches.

Improving AODV Routing Protocol for Image Transmission Over Mobile Video Sensor Networks

Abstract: Wireless Sensor Networks (WSNs) have become extremely popular for sensing, collecting, and transmitting data across different environments. In particular, the AODV protocol is widely used to improve the behavior of WSNs in various applications. A bottleneck in the protocol's performance is the amount of data that need to be moved between different nodes. This bottleneck becomes evident in applications based on multimedia contents, such as images or videos, in which huge chunks of data need to be delivered over long distances. In this article, we propose a new method to enhance the performance of the AODV protocol. Simulation results show that the proposed method improves the performance of the AODV protocol for image-based applications. The technique increases the quality of the delivered images, extends the network's lifetime, and reduces the delay and the network overhead associated with providing such images.