Rasmeet Singh Bali

Clustering in vehicular ad hoc networks

Over the last few years, Vehicular Ad Hoc Networks (VANETs) have emerged as a new class of efficient information dissemination technology among communities of users mainly because of their wide range of applications such as Intelligent Transport Systems (ITS), Safety applications, and entertainment during the mobility of the vehicles. Vehicles in VANETs act as an intelligent machine, which provides various resources to the end users with/without the aid of the existing infrastructure. But due to the high mobility and sparse distribution of the vehicles on the road, it is a challenging task to route the messages to their final destination in VANETs. To address this issue, clustering has been widely used in various existing proposals in literature. Clustering is a mechanism of grouping of vehicles based upon some predefined metrics such as density, velocity, and geographical locations of the vehicles. Motivated from these factors, in this paper, we have analyzed various challenges and existing solutions used for clustering in VANETs. Our contributions in this paper are summarized as follows: Firstly, a complete taxonomy on clustering in VANETs has been provided based upon various parameters. Based upon this categorization, a detailed discussion is provided for each category of clustering which includes challenges, existing solutions and future directions. Finally, a comprehensive analysis of all the existing proposals in literature is provided with respect to number of parameters such as topology selected, additional infrastructure requirements, road scenario, node mobility, data handled, and relative direction, density of the nodes, relative speed, communication mode, and communication overhead. The analysis provided for various existing proposals allows different users working in this domain to select one of the proposals with respect to its merits over the others. Clustering in VANETs with respect to various challenges and issues is explored.Detailed taxonomy for clustering in VANETs is provided in the text.Comparison of various clustering approaches with respect to various metrics.

Secure clustering for efficient data dissemination in vehicular cyber-physical systems

From the past few years, there has been tremendous growth with respect to the usage of Internet-enabled devices for computing and storage across the globe. These devices have the capabilities of computing as well as communication facilities for smooth execution of various applications. The significance of this type of environment where variety of Internet-enabled devices are interconnected is known as Internet of Things (IoT). Objects/things in IoT may be considered as vehicles and this type of environment is called as Vehicular Cyber-Physical Systems (VCPS). In VCPS environment, there are two planes-physical and cyber. On physical plane, all types of computing and communication devices reside while at the cyber plane service providers such as cloud may exist. However, in VCPS environment, security is one of the major concerns as devices communicate with one another using different protocols which are susceptible to various types of attacks. To address these issues, we propose a novel secure clustering for efficient data dissemination between different devices in VCPS environment. A new trust metric based on varying transmission characteristics of vehicles is defined for trust computation among the different devices which is evaluated both at local, and global levels. This trust metric is used to establish the current security level of vehicles and is the key parameter for creating secure clusters. Algorithms for secure clustering and trust establishment are also designed in the proposed scheme. The performance of the proposed scheme is evaluated with respect to different evaluation metrics in various network scenarios. The results obtained clearly depicts the satisfactory performance of the proposed scheme in VCPS environment. A new secure clustering for efficient data dissemination in vehicular environment is proposed.A new secure trust metric is evaluated for cooperation among the nodes.Performance of the proposed scheme is found satisfactory with respect to various metrics.

Learning Automata-assisted Predictive Clustering approach for Vehicular Cyber-Physical System

LA-based predictive clustering is proposed by predicting the future mobility of the nodes in VANETs.Connecting dominating set is constructed to maintain connectivity of the vehicles.Predictive Clustering Algorithm using LA is proposed.The performance of the proposed scheme is evaluated in different network scenarios with respect to various metrics. Display Omitted Vehicular Cyber-Physical Systems (VCPS) are the most popular systems of the modern era due to their abilities to disseminate the safety related information to the moving vehicles on time. For efficient data dissemination, vehicles form a cluster with other vehicles in VCPS environment. But, due to high velocity and constant topological changes, cluster maintenance is one of the most difficult tasks to be performed in this environment. To address this issue, in this paper, we propose a novel Learning Automata (LA)-based hybrid clustering scheme for vehicles in VCPS environment. We have improved our existing solution Energy Efficient Predictive Clustering (EEPC) approach, by incorporating the future mobility prediction computed by LA stationed on the vehicles. For this purpose, a Predictive Clustering Algorithm using Learning Automata (PCALA) is proposed. Extensive simulations are performed to evaluate the performance of the proposed scheme with respect to various metrics. Results obtained confirm the effectiveness of the proposed scheme in comparison to the existing EEPC scheme.

Efficient clustering based OLSR routing protocol for VANET

This paper proposed a Clustering approach based routing protocol for VANETs. The proposed algorithm is a distributed clustering algorithm together with OLSR Routing protocol, which possesses excellent Data dissemination rate, where Data dissemination is defined by throughput of protocol. In addition, the algorithm is also perform excellent in terms of End to End delay and exhibits a reasonable overhead. The algorithm is achieved by utilizing a new clustering technique based protocol called OLSR-C (OC). The clustering scheme uses a vehicles position (provided by GPS) and velocity information to form clusters with low relative mobility between the cluster heads and their cluster members. The performance of the proposed algorithm is validated against the AODV, DYMO and OLSR routing protocol and outperform the routing protocol in term of End to End delay, throughput and PDR, thus provides a good benchmark for the algorithms success.

Raga identification of Hindustani music using soft computing techniques

Raga is one of the fundamental musical concept in Indian music. It is a group of swaras (musical notes) comprising of many features and qualifiers and is described as melodic concept which is led to blossom by the musician. This paper proposes a novel method for identification of raga in Hindustani music. We have identified different key parameters for raga identification and fuzzy sets have been used for its representation. Soft computing techniques are being used for recognition of raga. This work demonstrates a modified approach by combining parameters of music with soft computing.Raga recognition has been analysed through soft computing and evaluation of the proposed technique shows that reasonable accuracy has been achieved.

A vehicular dynamics based technique for efficient traffic management

The passive control of traffic lights especially in urban scenario can adversely affect the movement of vehicles. The uneven distribution of vehicles on different lanes due to statically varying traffic lights also severely affects factors such as vehicle waiting time, fuel consumption as well as environment. This paper proposes a Vehicle to Roadside based data dissemination scheme that periodically considers vehicular density, average velocity of vehicles and current traffic scenario (current time and day) within the range of a particular Road Side Unit to dynamically manage traffic lights duration and mitigate congestion problem. The above three values will also be used to categorize the lanes into three classes i.e. densely populated; medium populated or sparsely populated lanes which is used as the basis for vehicles commuting through that lane in future. The Road Side Units then broadcast this information to all the traffic lights in the region and accordingly the proposed system computes the optimum values of time duration for all the traffic lights. By combining traffic lights with the lane classes, the proposed scheme improves the overall vehicular traffic throughput. Simulation results exhibit the superior performance of scheme in comparison to statically controlled traffic light based conventional timing system.

Sage on stage to guide on side: A modern perspective

Education technology will be the key medium of future education paradigm. Although significant progress has been made during the past decade, but it has also become a challenge for teachers as well as for students. Both students as well as teachers need to be ready for accepting new advancements and challenges in teaching learning process. In this paper we discuss some new approaches for imparting education and propose a model called ε7 for changing traditional role of teachers as facilitator of education. Education system is thus becoming a decentralized system and in future will not only revolve around a teachers knowledge, but become holistic process that utilizes all aspects related to Education Technology in an effective manner.

Vehicular clustering based on future position estimation for highway scenario

Wireless communication through vehicles has been a fascinating research area for providing a variety of applications. Vehicular Ad hoc Networks has attracted attention from both industrial and research communities in recent years. It evolved with an aim for addressing comfort and safety issues of the vehicles and has received increasing interest as it integrates the capabilities of new generation wireless networks with vehicle characteristics. However the communication in VANETs faces a lot of overheads due to packet collisions. There is a need of wireless backbone architecture which gathers the nodes into the clusters from which some nodes are elected as cluster heads for handling network responsibilities in order to reduce the overheads and improve communication. Although several clustering algorithms have been proposed for VANETs, but clustering based upon prediction is still a challenge. In this paper prediction based clustering algorithm for VANETs is proposed that specifically considers vehicles moving in highways. The proposed scheme has been tested and performance evaluation results demonstrate that this scheme provides a better and stable clustering structure thereby reducing the overheads.

An intelligent clustering algorithm for VANETs

Vehicular Ad-Hoc Networks (VANETs) is an emerging technology which is being used in wide areas of applications especially to provide safety and comfort to the users sitting in the vehicles. Vehicles in VANETs act as intelligent machines for taking adaptive decisions for information dissemination from source to destinations. Vehicles can send warning messages to drivers sitting in other vehicles about varying traffic conditions to increase safety and user convenience on roads. But due to constrained mobility patterns as well as peculiar traffic conditions of vehicles, the timely delivery of messages to their final destination is a challenging task in VANETs. Clustering is one of the most effective ways of managing and stabilizing such networks. In this paper, a learning automata based clustering scheme is proposed for vehicles in VANETs. The concept of number of links and vehicular mobility is used for cluster formation and cluster-head selection. During the cluster formation process, nodes with relatively higher degree of connectivity initially form a backbone which is designated as leadership. The leadership nodes then participate in cluster-head election and efficient cluster reorganization using aggregate degree of connectivity of vehicles. Simulation results depict the effectiveness of proposed algorithm on account of its comparable cluster stability with existing protocols especially in urban scenarios.