Ganesh Khekare

Bio: Ganesh Khekare is an academic researcher from College of Engineering, Pune. The author has contributed to research in topics: Smart city & Traffic engineering. The author has an hindex of 4, co-authored 13 publications receiving 97 citations. Previous affiliations of Ganesh Khekare include Bhagwant University.

A smart city framework for intelligent traffic system using VANET

Abstract: The total number of vehicles in the world has experienced a remarkable growth, increasing traffic density which results in more and more accidents. Therefore the manufactures, researchers and government is shifting focus towards improving the on road safety rather than improving the quality of the roads. The good development in the wireless technologies emerged various new type of networks, such as Vehicular Ad Hoc Network (VANET), which provides communication between vehicles themselves and between vehicles and road side units. Various new concepts such as smart cities and living labs [1] are introduced in the recent years where VANETs plays an important role. A survey of various Intelligent Traffic Systems (ITS) and various routing protocols with respect to our proposed scheme is described in this paper. It also introduces a new scheme consist of a smart city framework that transmit information about traffic conditions that will help the driver to take appropriate decisions. It consists of a warning message module composed of Intelligent Traffic Lights (ITLs) which provides information to the driver about current traffic conditions.

The Optimal Path Finding Algorithm Based on Reinforcement Learning

Abstract: Urbanization has been extensively increased in the last decade. In proportion, the number of vehicles throughout the world is increasing broadly. The detailed survey of available optimal path algorithms is done in this article, and to ease the overall traveling process, a dynamic algorithm is proposed. The proposed algorithm takes into consideration multiple objectives like dynamic traffic density, distance, history data, etc. and provides an optimal route solution. It is hinged on reinforcement learning and capable of deciding the optimal route on its own. A comparative analysis of the proposed algorithm is done with a genetic algorithm, particle swarm optimization algorithm, and the artificial neural networks algorithm. Through simulation results, it is proved that the proposed algorithm has better efficiency, decision making, and stability. It will ease the driver's headache and make the journey more comfortable with traffic less short distance routes that will minimize overall travel time making a positive impact on traffic jams, accidents, fuel consumption, and pollution.

Design of emergency system for intelligent traffic system using VANET

Abstract: Smart cities would like to plan how to minimize their transportation problems due to the increasing population that produces congested roads. VANETs (Vehicular Ad Hoc Networks) aim at helping to alleviate this issue improving vehicles' mobility, increasing road safety and also seeking to have more sustainable cities. At the beginning of the development of vehicular technologies, the main goal was to have more efficient and safer roads. Nowadays, thanks to the huge development in wireless technologies and their application in vehicles, it's possible to use Intelligent Transportation System (ITS) that will change our way to drive, improve road safety, and help emergency services. A survey of assorted Intelligent Traffic Systems (ITS) with respect to our proposed scheme is described in this paper. It introduces a new system consist of a smart city framework which has Intelligent Traffic Lights (ITLs) situated at every intersection that transmit information about traffic conditions and provide the driver congestion free shortest path in emergency that helps to take proper trip decisions.

Multilingual Text & Handwritten Digit Recognition and Conversion of Regional languages into Universal Language Using Neural Networks

Abstract: The Character recognition techniques equate an illustrative identity with the image of character. Handwritten human character recognition is a machine's ability to obtain and recognize handwritten information from various sources such as papers, photos, tactile touch devices etc. Recognition of handwriting and computer characters is an evolving field of study and has broad uses in banks, offices and industries. The key objective of this research work is to develop a knowledgeable framework for “Handwritten Character Recognition (HCR) victimization Neural Network” which might effectively acknowledge selected type-format character victimization as the substitute Neural Network approach. Neural method is the best method for controlling images, thus style parts square measure less all around plot as compared to various designs. Neural computers do parallel results. Neural computers square measure run during a manner that's utterly different from traditional operation. Neural computers square measure conditioned (not programmed) in such a way, that how it's given in an explicit beginning state (data input); they either assign the information (input file or computer file) into one amongst the quantity of categories or permit the initial data to evolve to maximize an explicit fascinating property. In this research work, a purely handwritten digit recognition using machine learning model as well as character recognition matlab model is used. A translator using MATLAB to beat the barrier of various languages is designed. The projected style is also used for English, Marathi and Guajarati text to speech conversion into English language. Input is taken in English, Marathi and Gujrati text manually to the interface or image of written text or handwritten text and output can be translated in English Language by facilitating use of Optical Character Recognition (OCR) technique. The projected methodology is also used to produce help to folks that lack the ability of speech or non-native speakers. On the other hand, purely handwritten digit recognition using machine learning algorithms is used to interpret the human handwriting to the second person easily and effectively.

Efficient and Secure Routing Protocol Based on Artificial Intelligence Algorithms With UAV-Assisted for Vehicular Ad Hoc Networks in Intelligent Transportation Systems

Abstract: Vehicular Ad hoc Networks (VANETs) that are considered as a subset of Mobile Ad hoc Networks (MANETs) can be applied in the field of transportation especially in Intelligent Transportation Systems (ITS). The routing process in these networks is a challenging task due to rapid topology changes, high vehicle mobility and frequent disconnection of links. Therefore, developing an efficient routing protocol that satisfies restriction of delay and minimum overhead is faced with many difficulties and limitations. Also, the detection of malicious vehicles is a significant task in VANETs. To address these issues, using Unmanned Aerial Vehicles (UAVs) can be helpful to cope with these limitations. In this paper, operation of UAVs in ad hoc mode and their cooperation with vehicles in VANETs are studied to help in the process of routing and detection of malicious vehicles. A routing protocol named VRU is proposed that includes two distinct ways of routing of data: (1) delivering packets of data between vehicles with the help of UAVs using a protocol named VRU_vu, and (2) routing packet of data between UAVs using a protocol named VRU_u. The NS-2.35 simulator under Linux Ubuntu 12.04 is utilized in order to appraise the performance of VRU routing components in an urban scenario. Also, VanetMobiSim generator of mobility and MobiSim are used to produce the motions of vehicles and to produce the motions of UAVs, respectively. The performance analysis displays that VRU protocol can improve the packet delivery ratio by 16% and detection ratio by 7% compared to other reviewed routing protocol. Also, VRU protocol decreases end-to-end delay by an average of 13% and overhead by 40%.

Secure Message Communication Protocol Among Vehicles in Smart City

Abstract: Secure messages exchange among different vehicles is one of the most challenging tasks in future smart cities. Any malicious activity has the potential to compromise the confidentiality, integrity, and authenticity of messages exchanged between different vehicles. To ensure secure message communication among the vehicles in a smart city environment, a novel scheme using elliptic curve cryptographic (ECC) technique has been presented in this paper. For this purpose, a two-level authentication key exchange scheme has been designed. In the first level authentication, $\text{CH}$ s are verified by series of messages exchanged between $\text{CH}$ s and the $\text{CA}$ . The verified $\text{CH}$ s are responsible for authentication of vehicles in the second level authentication, followed by exchange of messages between $\text{CH}$ and vehicle. The security analysis using widely accepted Burrows–Abadi–Needham logic, formal security analysis using random oracle model and verification using the widely known automated validation of Internet security protocols and applications (AVISPA) tool, and also the informal security analysis have been done with respect to various types of attacks. Moreover, a comparative analysis of the proposed scheme with existing related schemes reveals that it generates low overhead and latency, and high reliability during messages exchange between vehicles and the $\text{CA}$ .

Smart traffic lights switching and traffic density calculation using video processing

Abstract: Congestion in traffic is a serious problem nowadays Although it seems to pervade everywhere, mega cities are the ones most affected by it And it's ever increasing nature makes it imperative to know the road traffic density in real time for better signal control and effective traffic management There can be different causes of congestion in traffic like insufficient capacity, unrestrained demand, large Red Light delays etc While insufficient capacity and unrestrained demand are somewhere interrelated, the delay of respective light is hard coded and not dependent on traffic Therefore the need for simulating and optimizing traffic control to better accommodate this increasing demand arises In recent years, video monitoring and surveillance systems have been widely used in traffic management for traveler's information, ramp metering and updates in real time The traffic density estimation and vehicle classification can also be achieved using video monitoring systems This paper presents the method to use live video feed from the cameras at traffic junctions for real time traffic density calculation using video and image processing It also focuses on the algorithm for switching the traffic lights according to vehicle density on road, thereby aiming at reducing the traffic congestion on roads which will help lower the number of accidents In turn it will provide safe transit to people and reduce fuel consumption and waiting time It will also provide significant data which will help in future road planning and analysis In further stages multiple traffic lights can be synchronized with each other with an aim of even less traffic congestion and free flow of traffic