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Title: Efficient Secure Data Clustering In Vehicular Ad Hoc Networks
Authors: Bali, Rasmeet Singh
Supervisor: Kumar, Neeraj
Keywords: Vehicular adhoc networks, clustering, predictive algorithm, cluster head, secure clsutering
Issue Date: 14-Oct-2016
Abstract: 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 in different domains such as safety applications, healthcare, data dissemination and online entertainment. Vehicles in VANETs act as intelligent machines to provide various resources to the end users with/without the aid of the existing infrastructure. The development of dedicated standards used for communication such as DSRC and WAVE have resulted in designing an Intelligent Transport System (ITS) which offers a number of promising solutions for efficient traffic management to increase passenger safety. However, future ITS solutions may require enhanced and robust message delivery solutions due to the high mobility and varying density of vehicles on the road. The availability of limited communication resources due to high mobility and uneven distribution of vehicles in VANETs makes it difficult for the vehicles to maintain end-to-end connection for timely delivery of messages. Clustering can be one of the viable solutions to solve the aforementioned problem to have a better network throughput. It is grouping of the vehicles based upon metrics such as density, velocity and geographical locations of vehicles. But, there are number of challenges that needs to be addressed for designing an efficient solution for clustering. Most of the existing solutions reported in the literature use a combination of parameters to depict driver behavior for an optimized message delivery. But in these solutions, due to large number of nodes and lack of routers, a flat network scheme may cause serious scalability and hidden terminal problems. Vehicles require additional infrastructure like a GPS, transceivers, Lane Detection System, Digital maps, RSU’s, odometer etc. for cluster formation and maintenance. These facts motivate us to analyze and develop new clustering techniques to improve the network stability with reliable communication among vehicles. To address the issue of efficient resource utilization, so that available resources could be utilized for other network management applications, a predictive clustering scheme has been designed. Future mobility of vehicles on the road is predicted by a novel future mobility prediction algorithm which assists in clustering and determining the member vehicles within a cluster. The proposed algorithms estimate the clustering duration to determine the number of vehicles in a cluster based on an average predictive variation algorithm. These algorithms have been extensively studied using simulation by varying the number of vehicles and cluster durations in comparison to existing schemes. The predictive clustering scheme has been further improved by incorporating learning automata into the prediction process. A Predictive Clustering Algorithm using Learning Automata (PCALA) is proposed in the designed solution. The learning automata stationed on vehicles are used to estimate future positions of the vehicles more accurately. The actions of the automata are rewarded or penalized based upon their current prediction accuracy and their previous actions. Extensive simulations are performed to evaluate the performance of the proposed scheme with respect to various metrics. Results obtained confirm the effectiveness of the PCALA in comparison to predictive clustering scheme. Due to dynamically changing topology, wireless medium, and lack of centralized monitoring points, information related to vehicular applications can be altered or misused. These security breaches can lead to disastrous results such as loss of life or financial frauds. Therefore, some security mechanisms need to be implemented in VANETs for enhancing security. With this objective, a Cloud Based Distributive Intrusion Detection System (DIDS) is proposed for detecting attacks in VANETs. To secure communication among vehicles, a standard HMAC based cryptography technique is used. The viability of the proposed scheme is measured through simulations and the test results show its adaptability in real-time environment. Internet-enabled devices have the capabilities of computing as well as communication to provide ease to a number of applications for the end users. The significance of this type of environment can be further enhanced by inclusion of vehicular clustering. However, 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 novel secure clustering algorithms for efficient data dissemination between vehicles. A new trust metric based on dynamically varying transmission 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 performing secure clustering and trust establishment are 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 depict satisfactory performance of the proposed scheme in vehicular environment.
Appears in Collections:Doctoral Theses@CSED

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