This project focuses on solving various research issues pertaining to VANETs e.g. Vehicular communication, V2G operations etc.

Team Members

  • Vinay Chamola (Senior Member, IEEE)
  • Vikas Hassija
  • Tejasvi Alladi


  • Richard Yu, Carleton University, Canada (Fellow, IEEE)
  • Joel Rodrigues, INATEL, Brazil (Fellow, IEEE)
  • Mohsen Guizani, Qatar University (Fellow, IEEE)

Our publications in VANETs

Electric vehicles (EVs) have been slowly replacing conventional fuel based vehicles since the last decade. EVs are not only environment-friendly but when used in conjunction with a smart grid, also open up new possibilities and a Vehicle-Smart Grid ecosystem, commonly called V2G can be achieved. This would not only encourage people to switch to environment-friendly EVs or Plug-in Hybrid Electric Vehicles (PHEVs), but also positively aid in load management on the power grid, and present new economic benefits to all the entities involved in such an ecosystem. Nonetheless, privacy and security remains a serious concern of smart grids. The devices used in V2G are tiny, inexpensive, and resource constrained, which renders them susceptible to multiple attacks. Any protocol designed for V2G systems must be secure, lightweight, and must protect the privacy of the vehicle owner. Since EVs and charging stations are generally not guarded by people, physical security is also a must. To tackle these issues, we propose Physical Unclonable Functions (PUF) based Secure User Key-Exchange Authentication (SUKA) protocol for V2G systems. The proposed protocol uses PUFs to achieve a two-step mutual authentication between an EV and the Grid Server. It is lightweight, secure, and privacy preserving. Simulations show that the proposed protocol performs better and provides more security features than state-of-the-art V2G authentication protocols. The security of the proposed protocol is shown using a formal security model and analysis.

The Vehicle-to-Grid (V2G) network is, where the battery-powered vehicles provide energy to the power grid, is highly emerging. A robust, scalable, and cost-optimal mechanism that can support the increasing number of transactions in a V2G network is required. Existing studies use traditional blockchain as to achieve this requirement. Blockchain-enabled V2G networks require a high computation power and are not suitable for microtransactions due to the mining reward being higher than the transaction value itself. Moreover, the transaction throughput in the generic blockchain is too low to support the increasing number of frequent transactions in V2G networks. To address these challenges, in this paper, a lightweight blockchain-based protocol called Directed Acyclic Graph-based V2G network (DV2G) is proposed. Here blockchain refers to any Distributed Ledger Technology (DLT) and not just the bitcoin chain of blocks. A tangle data structure is used to record the transactions in the network in a secure and scalable manner. A game theory model is used to perform negotiation between the grid and vehicles at an optimized cost. The proposed model does not require the heavy computation associated to the addition of the transactions to the data structure and does not require any fees to post the transaction. The proposed model is shown to be highly scalable and supports the micro-transactions required in V2G networks.

Data sharing and content offloading among vehicles is an imperative part of the Internet of Vehicles (IoV). A peer-to-peer connection among vehicles in a distributed manner is a highly promising solution for fast communication among vehicles. To ensure security and data tracking, existing studies use blockchain as a solution. The blockchain-enabled Internet of Vehicles (BIoV) requires high computation power for the miners to mine the blocks and let the chain grow. Over and above, the blockchain consensus is probabilistic and the block generated today can be eventually declared as a fork and can be pruned from the chain. This reduces the overall efficiency of the protocol because the correct work done initially is eventually not used if it becomes a fork. To address these challenges, in this paper, we propose a Directed Acyclic Graph enabled IoV (DAGIoV) framework. We make use of a tangle data structure where each node acts as a miner and eventually the network achieves consensus among the nodes. A game-theoretic approach is used to model the interactions between the vehicles providing and consuming offloading services. The proposed model is proven to be highly scalable and well suited for micro transactions or frequent data transfer among the nodes in the vehicular network.

Parking lot allocation problem has received much attention in recent years. There have been various works in the literature that target the parking slot allocation problem. However, most of these works use algorithms that run on centralized servers and are based on some predictions on historical data. Due to the dynamic nature of vehicular networks, the accuracy of such prediction models is not high which ends up in a chaotic situation for the parking lot owners as well as the vehicle owners. Therefore, a distributed Parking slot Allocation Framework based on Adaptive Pricing Algorithm and Virtual Voting is proposed in this paper. The proposed model is based on virtual voting and hashgraph consensus algorithm. Using the model, all users and parking lot owners can easily come to consensus finality about the allocation of a parking slot with the use of minimal bandwidth. The proposed model provides a fair, fast and cost-optimal parking slot allocation method. The perfect ordering of allocation requests is also maintained based on consensus timestamp. Further, an adaptive pricing model is proposed to enhance the overall revenue of the parking lot owners and comfort of the users. The proposed model is deterministic and can reduce the average parking cost and time. Performance evaluations reveal that the proposed model outperforms its counterparts in terms of accurate parking slot allocation, reduced cost and parking lot resource utilization.

The exponential surge in the number of vehicles on the road has aggravated the traffic congestion problem across the globe. Several attempts have been made over the years to predict the traffic scenario accurately and consequently avoiding further congestion. Crowdsourcing has come forward as one of the most adopted methods for predicting traffic intensity using live data. However, the privacy concerns and the lack of motivation for the live users to help in the traffic prediction process have rendered existing crowdsourcing models inefficient. Towards this end, we present an advanced blockchain-based secure crowdsourcing model. Not only does our model ensure privacy preservation of the users, but by incorporating a revenue model, it also provides them with an incentive to participate in the traffic prediction process willingly. For accurate and efficient traffic jam probability estimation, our work proposes a neural network-based smart contract to be deployed onto the blockchain network. The results reveal that the proposed model is highly efficient in terms of attaining high participation and consequently obtaining highly accurate predictions.
Adverse weather conditions such as fog, haze, snow, mist and glare create visibility problems for applications of autonomous vehicles. To ensure safe and smooth operations in frequent bad weather scenarios, image dehazing is crucial to any vehicular motion and navigation task on road or air. Moreover, the commonly deployed mobile systems are resource constrained in nature. Therefore, it is important to ensure memory, compute and run-time efficiency of dehazing algorithms. In this manuscript we propose ReViewNet, a fast, lightweight and robust dehazing system suitable for autonomous vehicles. The network uses components like spatial feature pooling, quadruple color-cue, multi-look architecture and multi-weighted loss to effectively dehaze images captured by cameras of autonomous vehicles. The effectiveness of the proposed model is analyzed by exhaustive quantitative evaluation on five benchmark datasets demonstrating its supremacy over other existing state-of-the-art methods. Further, a component-wise ablation and loss weight ratio analysis demonstrates the contribution of each and every component of the network. We also show the qualitative analysis with special use cases and visual responses on distinctive vehicular vision instances, establishing the effectiveness of the proposed method in numerous hazy weather conditions for autonomous vehicular applications.
The daily fluctuations in the power requirements and the regulation of voltage and frequency cause substantial energy dissipation. These lead to a reduction in the operational efficiency of the power grid. V2G (Vehicle 2 Grid) enabled electricvehicles (EVs) can act as a reactive power resource and can provide active power regulation, load matching, and currentharmonic filtering. We propose a smart framework based on Internet of Things (IoT) and Edge computing to manage theV2G operations efficiently. The proposed framework can handle distributed energy sources, and can help in grid stabilization,increasing its reliability, and improving the power efficiency. V2G energy transfers can affect the EV’s battery lifetime, however if carefully managed, they can be economical both for the grid operators, as well as the EV owners. The proposed framework creates an optimum charging schedule for each EV to maximize the profit of the EV owners, keeping the preferences set by the vehicle owner and the grid requirement in consideration.