Amit Dua

A systematic review on routing protocols for Vehicular Ad Hoc Networks

Vehicular Ad Hoc Networks (VANETs) have emerged as a new powerful technology with an aim of providing safety for the persons sitting in the vehicles. Vehicles may be connected to the Internet with/without the existing infrastructure using various IEEE standards such as IEEE 802.11p. But as nodes in VANETs have very high mobility, so there are lots of challenges to route the packets to their final destination which need to be addressed by existing/proposing new solutions for the same. Keeping in view of the above, this paper provides a detailed description of various existing routing techniques in literature with an aim of selecting a particular strategy depending upon its applicability in a particular application. A detailed categorization of various routing techniques is provided in the paper with critical discussion on each categorization with respect to its advantages, disadvantages, various constraints and applications. Finally, numbers of parameters are selected for comparison and analysis of all the existing routing schemes in the literature.

Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid

Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand-supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.

Efficient TDMA based virtual back off algorithm for adaptive data dissemination in VANETs

With an exponential growth of Internet related technologies, there is an emergence of new class of efficient data dissemination using Vehicular Ad Hoc networks for the safety of peoples. As there are limited number of channels in IEEE 802.11 a/b/g/p standards, so efficient mechanisms are required to allocate the same for new incoming requests from the users. Keeping in view of the same, we propose a modified TDMA based virtual back off algorithm for VANETs. Vehicles are assumed to be arrived using Poisson distribution and served as exponential. By varying the distance from RSUs, we have provided the analysis of end-to end delay for varying speed of vehicles.

Secure message communication among vehicles using elliptic curve cryptography in smart cities

Message exchange among vehicles is an integral part of communication in smart cities. Messages are exchanged to inform the other vehicles about emergency situations such as-safety alerts, and location privacy. Due to the usage of an insecure wireless medium, malicious activities in vehicles, i.e., illegal use of the false messages, can astray other vehicles. Security in communication among the vehicles can be provided by encrypting the messages using various security keys. However, it has been found from the literature that existing schemes for secure communication require large key size, and therefore may these schemes may not be applicable to smart cities. To address these issues, a secure message communication scheme among vehicles based on elliptic curve cryptography (ECC) is proposed. The proposed scheme needs smaller key size leading to mathematically simple and cost effective solution. Furthermore, the scheme provides mutual authentication, confidentiality, and forward secrecy. Security analysis prove that the proposed scheme is suitable to be adapted in smart city environment.

EnergyChain: Enabling Energy Trading for Smart Homes using Blockchains in Smart Grid Ecosystem

The amalgamation of information and communication technologies in power industry has led to a revolution known as smart grid (SG). The energy consumers interact with the power utility using a bidirectional communication channel for energy trading in SG ecosystem. However, the traditional energy trading mechanisms strongly rely on trusted third parties which act as a single point of failure. Therefore, it is important to equip SG with a decentralized and secure energy trading system which can execute contracts and handle negotiations among various trading parties. Hence, in this paper, EnergyChain, a blockchain model for storing and accessing the data generated by smart homes in a secure manner is proposed. EnergyChain works in following phases: 1) a miner node is selected on the basis of power capacity of various smart homes, 2) a block creation and validation scheme is presented, and 3) a transaction handling mechanism is designed for secure energy trading. After evaluation, the superiority of EnergyChain is validated. The results obtained show that EnergyChain outperforms the traditional scheme in terms of communication costs and computation time.

An efficient fuzzy rule-based big data analytics scheme for providing healthcare-as-a-service

With advancements in information and communication technology (ICT), there is an increase in the number of users availing remote healthcare applications. The data collected about the patients in these applications varies with respect to volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges that needs a specialized approach. To address this issue, a new fuzzy rule-based classifier for big data handling using cloud-based infrastructure is presented in this paper, with an aim to provide Healthcare-as-a-Service (HaaS) to the users located at remote locations. The proposed scheme is based upon the cluster formation using the modified Expectation-Maximization (EM) algorithm and processing of the big data on the cloud environment. Then, a fuzzy rule-based classifier is designed for an efficient decision making about the data classification in the proposed scheme. The proposed scheme is evaluated with respect to different evaluation metrics such as classification time, response time, accuracy and false positive rate. The results obtained are compared with the standard techniques to confirm the effectiveness of the proposed scheme.

ReIDD: reliability-aware intelligent data dissemination protocol for broadcast storm problem in vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) have emerged as fast growing networks with aims to provide safety, and comfort to the onboard passengers. But, in this environment reliable data dissemination to the destination nodes is one of the biggest challenges as there may be a congestion in the network due to blind flooding of messages to their final destination, called as broadcast storm which may lead to the performance degradation with respect to the metric such as-message delivery, reliability and response time. To address this issue, in this paper, we propose a reliability-aware intelligent data dissemination protocol for broadcast storm problem in VANETs. We have solved the above specified problem using game theory concepts in which players, strategy space and decisions with respect to the current state of the system. To reduce the message overhead with respect to the communication cost among the players of the game, in the proposed scheme, messages are routed to the next destination by selecting the most reliable path in an intelligent manner. A coalition game is formulated among the vehicles by considering vehicles as the players in the game. Each player in the game has an initial payoff value based upon the parameters such as-communication range, storage requirements, and computation power. Based upon the payoff value of the players in the game, different coalitions/clusters are formulated among the players in the proposed scheme. Then, the players send the messages to the other vehicles/players within the same coalition which increases the reliability of transmission. In the proposed scheme, messages are unicasted to their final destination in an intelligent manner. Different algorithms are designed for coalition formation, maintenance, and reliability-aware data dissemination. The performance of the proposed scheme is evaluated using various evaluation metrics such as-service time, packet delivery ration, and throughput. The results obtained prove the effectiveness of the proposed scheme as compared to the other state-of-the-art existing schemes.

An Energy Efficient Data Dissemination and Information Retrieval Scheme for VANET

With the increasing scarcity of resources, the need for energy efficient solutions has reached its pinnacle. Vehicular ad hoc networks (VANETs) is no exception and there is a need for exclusive solutions for energy efficient data dissemination and information retrieval. The proposed scheme classifies the vehicles into different types based on their storage and computation capacity. Cluster heads (CHs) are elected for each energy zone according to type of vehicle, and act as information center. Apart from CHs, the dissemination of data and access of information in efficient and correct manner is made possible with the help of vehicles having high processing capability. The broadcast storm problem that consumes high energy in terms of bandwidth, is solved using proposed game theoretic decision model. The proposed model gives suggestion to vehicles whether to disseminate the information or not based on the current energy situation of the network. Simulation results prove that the proposed model saves energy and time by minimizing the overhead and delay. The network energy is efficiently utilized by achieving higher packet delivery ratio and lower delay than other state-of-art protocols.

Collaborative P2P context-aware information propagation in vehicular ad hoc networks

With exponential growth of the Internet users in past few years, there is a need of context-aware information sharing among different inter-connected entities over the Internet. The prime objective of the inter-connected objects is that within a minimum use of available resources, maximum output with respect to parameters such as throughput and delay can be achieved. But, due to high velocity and irrelevant information propagation, there may be a performance degradation in some part of the network with respect to these parameters. To address these issues, we have designed novel algorithms for context-aware information propagation among the vehicles. The proposed scheme consists of algorithms for data access, data dissemination, and data suggestion. These algorithms are based on reliability of vehicle which is calculated as soon as vehicles enter the network and is updated after each successful execution of various operations of information propagation. The scheme works on by increasing the reliability which in turn solve the broadcast storm problem in which sometime irrelevant information may also be sent to the vehicles. Simulation results prove the merit of the proposed scheme over the other existing schemes with respect to parameters such as message overhead, connectivity ratio, and resources utilization.

Learning Automata Based Contention Aware Data Forwarding Scheme for Safety Applications in Vehicular Ad Hoc Networks

With an exponential growth of demands of the users to access various resources during mobility lead to the popularity of Vehicular Ad Hoc Networks (VANETs). Users may access various resources from cloud which consists of many resources for the ease of users. VANETs have been used in wide range of applications such as Intelligent Transport Systems (ITS), Safety alarms on roads/in community, online resource access using Internet connectivity etc. Among these applications, safety applications are most important and various proposals exist in literature for the same. But most of the existing proposals have used unicast sender based data forwarding which results an overall performance degradation with respect to the metrics such as packet delivery ratio, end-to-end delay and reliable data transmission. Keeping in view of the above, in this paper, we propose new Learning Automata based Contention Aware Data forwarding scheme for VANETs using cloud infrastructure. Learning Automata (LAs) are assumed to be located in the vehicles which share the information (such as vehicles density, directions of the vehicles or vehicles velocity etc) with the other LAs for taking the adaptive decisions about data forwarding. Based upon these values, automaton performs its action. Corresponding to each action performed by the automaton, its action may be rewarded or penalized by some constant values from the environment where it is working. Based upon the inputs from the environment, each automaton updates its action probability values for the next rounds. An adaptive Learning Automata based Contention Aware Data Forwarding (LACADF) algorithm is also proposed. The proposed scheme is evaluated with respect to different network parameters such as message overhead, throughput, delay etc. with varying density and mobility of the vehicles. The results obtained show that the proposed scheme is better than the other conventional schemes with respect to the above metrics.