Research ProposalPDF Available

Making Electric Vehicles Energy Efficient in Smart Grids using Blockchain (PhD Synopsis)

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In this work, an encyclopedic study of the blockchain based energy trading, data trading and sharing, and incentive mechanisms, used in various fields of life like energy, finance, business, data trading, healthcare, etc., is presented. The study critically analyzes different survey papers and ranks them using a recency score. This work also presents major blockchain related future research perspectives, which provide solid working directions to the research community. The use of blockchain technology with the Electric Vehicles (EVs) is also discussed to tackle different issues related to existing systems, such as privacy, security, lack of trust, etc., and to promote transparency, data immutability and tamper proof nature. Moreover, in this study, a new and improved charging strategy, termed as Mobile vehicle-to-Vehicle (M2V) charging strategy, is used to charge the EVs. It is further compared with conventional Vehicle-to-Vehicle (V2V) and Grid-to-Vehicle (G2V) charging strategies to prove its efficacy. In the proposed work, the charging of vehicles is done in a Peer-to-Peer (P2P) manner to remove the intermediary parties and deal with the issues related to them. Moreover, to store the data related to traffic, roads and weather conditions, a Transport System Information Unit (TSIU) is used, which helps in reducing road congestion and minimizing road side accidents. In TSIU, the data is stored in InterPlanetary File System (IPFS). Furthermore, mathematical formulation of the total charging cost, the shortest distance between EVs and charging entities, time taken to traverse the shortest distance and to charge the vehicles is done using real time data of EVs. The phenomena of range anxiety and coordination at the crossroads are also dealt with in the study. Moving ahead, edge service providers (edge nodes) are introduced to ensure efficient service provisioning. A caching system is also introduced at the edge nodes to store frequently used services. The power flow and the related energy losses for G2V, V2V and M2V charging strategies are also discussed in this work. In addition, an incentive provisioning mechanism is proposed on the basis of timely delivery of credible messages, which further promotes users’ participation. To check the robustness of the proposed model, an attacker model is designed and tested against different attacks including selfish mining attack. In future, the proposed model robustness will be tested against more attacks. To prove the efficiency of the proposed work, simulations will be performed. Moreover, the security analysis of the proposed work will also be done using Oyente.
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Thesis
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Data sharing is a fascinating in-vehicle service which provide multiple benefits to the vehicle users in the Vehicular Ad-hoc Networks (VANETs). One of the interesting in-vehicle services is advertisement sharing in VANETs which enable advertisers to market their products and services in the areas of the users interest. With the help of Blockchain (BC) technology, the vehicle users can also participate in the ads dissemination process to gain monetary incentives. However, the existing BC based VANET schemes suffer from privacy, security and efficiency issues. Zero Knowledge Proof of Knowledge (ZKPoK) and certificate-less cryptography are used in the existing schemes to enable fair incentive provision and privacy preservation. These schemes incur high computational cost on the resource constrained vehicles. Moreover, the lack of conditional anonymity in the existing schemes makes the system vulnerable to internal attacker scenario. Furthermore, VANETs require secure and efficient reputation verification mechanism to prevent replay attacks and reduce the storage cost. Additionally, the reliance on a centralized entity for the certificate revocation makes the system wide open to the single point of failure vulnerability. To overcome these issues, a BC based secure, efficient and conditional anonymity enabled scheme is proposed. Elliptic Curve Digital Signature based pseudonym update mechanism is employed to enable conditional anonymity and trace malicious vehicles. InterPlanetary File System is used to efficiently store the vehicles' reputation information and reduce the storage overhead. Moreover, the Shamir Secret Sharing algorithm is used to enable distributed revocation. Security analysis is performed to show that the proposed scheme is secure against multiple known attacks. The simulation results show the effectiveness and practicality of the proposed scheme.
Article
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The bi-directional flow of energy and information in the smart grid makes it possible to record and analyze the electricity consumption profiles of consumers. Because of the increasing rate of inflation over the past few years, people started looking for means to use electricity illegally, termed as electricity theft. Many data analytics techniques are proposed in the literature for electricity theft detection (ETD). These techniques help in the detection of suspected illegal consumers. However, the existing approaches have a low ETD rate either due to improper handling of the imbalanced class problem in a dataset or the selection of inappropriate classifier. In this paper, a robust big data analytics technique is proposed to resolve the aforementioned concerns. Firstly, adaptive synthesis (ADASYN) is applied to handle the imbalanced class problem of data. Secondly, convolutional neural network (CNN) and long-short term memory (LSTM) integrated deep siamese network (DSN) is proposed to discriminate the features of both honest and fraudulent consumers. Specifically, the task of feature extraction from weekly energy consumption profiles is handed over to the CNN module while the LSTM module performs the sequence learning. Finally, the DSN contemplates on the shared features provided by the CNN-LSTM and applies final judgment. The data analytics is performed on different train-test ratios of the real-time smart meters' data. The simulation results validate the proposed model's effectiveness in terms of high area under the curve, F1-Score, precision and recall.
Article
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An essential goal of the Internet of Vehicles (IoV) is to allow vehicles’ peer connections and contact a service provider through a secure communication channel. However, privacy and authentication are considered the main objectives of secure communication. In this paper, we propose an efficient Online/Offline signcryption of heterogeneous systems based on blockchain to secure data sharing between the IoV and internet servers. The blockchain prevents tampering with compatibility information. Further, the smart contract addresses the weakness of internet servers, such as passing incorrect data to the IoV nodes. The proposed protocol satisfies the security requirements of IoV, such as integrity, authentication, unforgeability, and confidentiality in a single logical step. We introduce the construction of our Efficient Heterogeneous Signcryption Scheme for Internet of Vehicles (EHSC-IoV) and verify that our protocol is secure under the Discrete Logarithm (DL) and the Computational Diffie–Hellman (CDH) assumptions. As compared with the existing five heterogeneous signcryption schemes, the performance evaluation and simulation results show that the computational cost of the IoV nodes in our protocol reduced by about \( 75\%,84.2\%,80\%, 67.9\%\), and \( 82.4\%\), respectively, and the total energy consumption of the IoV nodes in our protocol reduced by about \( 69.4\%, 75.4\%, 75.2\%, 62.3\%,\) and \( 75.1\% \), respectively.
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The drastic increase in real-time vehicle generated data of various types has imparted a great concept of data trading in vehicular networks. Whereas immense usage of Electric Vehicles (EVs) as mobile energy carriers have supported distributed energy trading due to their bidirectional charging and discharging capabilities. The trustless environment of Internet of Electric Vehicles (IoEV), including fuel vehicles and EVs, encounters trading disputes and conflicting interests among trading parties. To address these challenges, we exploit consortium blockchain to maintain transparency and trust in trading activities. Smart contracts are used to tackle trading disputes and illegal actions. Data duplication problem occurs when a dishonest user sell previously traded data multiple times for financial gain. Therefore, data duplication validation is done through previously stored hash-list at roadside units (RSUs) employed with bloom filters for efficient data lookup. Removing data duplication at an earlier stage reduces storage cost. Moreover, an elliptic curve bilinear pairing based digital signature scheme is used to ensure the reliability and integrity of traded data. To ensure persistent availability of traded data, InterPlanetary File System (IPFS) is used, which provides fault-tolerant and a reliable data storage without any single point of failure. On the other hand, the energy trading transactions among EVs face some security and privacy protection challenges. An adversary can infer the energy trading records of EVs, and launch the data linkage attacks. To address this issue, an account generation technique is used that hides the energy trading trends. The new account generation for an EV depends upon its traded volume of energy. The experimental results verify the efficiency of the proposed data and energy trading scheme in IoEV with the reliable and secure data storage.
Article
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In Vehicular Ad-hoc Networks (VANETs), a large amount of data is shared between vehicles and Road Side Units (RSUs) in real-time. VANETs assist in sharing traffic information effectively and timely to improve traffic efficiency and reliability. However, less storage capability and selfish behavior of the vehicles are important issues that need to be tackled. The traditional storage mechanisms involve third parties for data management, which are insecure, untrustworthy, non-transparent, and unreliable. To overcome these issues, a blockchain-based data storage scheme for VANETs is proposed in this paper. It exploits the benefits of the Interplanetary File System (IPFS) and blockchain is implemented on RSUs. The RSUs receive the aggregation packets sent by vehicles. These packets contain the events' information that occur in vehicles' surroundings. After verifying an aggregation packet, the RSUs store the event's information in IPFS and the reputation values of the sender vehicle in the blockchain. The reputation value is calculated based on the witnesses' (others vehicles) opinion, whether they agree with the initiator or not about an event. The initiator is the vehicle who initializes the event. Moreover, an incentive mechanism is also proposed in this work in which monetary incentives are given to the repliers who respond to the event information. These incentives are given by the initia-tors after verifying the signatures of the repliers. All the transactions involved in the incentive process are stored in the blockchain. Finally, Oyente is used for the security analysis of the proposed smart contracts. A comparison of the proposed scheme with the logistic regression scheme is also presented.
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The rapid deployment of Electric Vehicles (EVs) and the integration of renewable energy sources have ameliorated the existing power systems and contributed to the development of greener smart communities. However, load balancing problems, security threats, privacy leakage issues, etc., remain unresolved. Many blockchain-based approaches have been used in literature to solve the aforementioned challenges. However, they are not sufficient to obtain satisfactory results because of the inefficient energy management methods and time-intensiveness of the primitive cryptographic executions on the network devices. In this paper, an efficient and secure blockchain-based Energy Trading (ET) model is proposed. It leverages the contract theory, incentive mechanism, and a reputation system for information asymmetry scenario. In order to motivate the ET entities to trade energy locally and EVs to participate in smart energy management, the proposed incentive provisioning mechanism plays a vital role. Besides, a reputation system improves the reliability and efficiency of the system and discourages the blockchain nodes from acting maliciously. A novel consensus algorithm, i.e., Proof of Work based on Reputation (PoWR), is proposed to reduce transaction confirmation latency and block creation time. Moreover, a shortest route algorithm, i.e., the Dijkstra algorithm, is implemented in order to reduce the traveling distance and energy consumption of the EVs during ET. The performance of the proposed model is evaluated using peak to average ratio, social welfare, utility of local aggregator, etc., as performance metrics. Moreover, privacy and security analyses of the system are also presented.
Article
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Due to the increase in the number of electricity thieves, the electric utilities are facing problems in providing electricity to their consumers in an efficient way. An accurate Electricity Theft Detection (ETD) is quite challenging due to the inaccurate classification on the imbalance electricity consumption data, the overfitting issues and the High False Positive Rate (FPR) of the existing techniques. Therefore, intensified research is needed to accurately detect the electricity thieves and to recover a huge revenue loss for utility companies. To address the above limitations, this paper presents a new model, which is based on the supervised machine learning techniques and real electricity consumption data. Initially, the electricity data are pre-processed using interpolation, three sigma rule and normalization methods. Since the distribution of labels in the electricity consumption data is imbalanced, an Adasyn algorithm is utilized to address this class imbalance problem. It is used to achieve two objectives. Firstly, it intelligently increases the minority class samples in the data. Secondly, it prevents the model from being biased towards the majority class samples. Afterwards, the balanced data are fed into a Visual Geometry Group (VGG-16) module to detect abnormal patterns in electricity consumption. Finally, a Firefly Algorithm based Extreme Gradient Boosting (FA-XGBoost) technique is exploited for classification. The simulations are conducted to show the performance of our proposed model. Moreover, the state-of-the-art methods are also implemented for comparative analysis, i.e., Support Vector Machine (SVM), Convolution Neural Network (CNN), and Logistic Regression (LR). For validation, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Receiving Operating Characteristics Area Under Curve (ROC-AUC), and Precision Recall Area Under Curve (PR-AUC) metrics are used. Firstly, the simulation results show that the proposed Adasyn method has improved the performance of FA-XGboost classifier, which has achieved F1-score, precision, and recall of 93.7%, 92.6%, and 97%, respectively. Secondly, the VGG-16 module achieved a higher generalized performance by securing accuracy of 87.2% and 83.5% on training and testing data, respectively. Thirdly, the proposed FA-XGBoost has correctly identified actual electricity thieves, i.e., recall of 97%. Moreover, our model is superior to the other state-of-the-art models in terms of handling the large time series data and accurate classification. These models can be efficiently applied by the utility companies using the real electricity consumption data to identify the electricity thieves and overcome the major revenue losses in power sector.
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While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.
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Multi-microgrid (MMG) system is a new method that concurrently incorporates different types of distributed energy resources, energy storage systems and demand responses to provide reliable and independent electricity for the community. However, MMG system faces the problems of management, real-time economic operations and controls. Therefore, this study proposes an energy management system (EMS) that turns an infinite number of MMGs into a coherence and efficient system, where each MMG can achieve its goals and perspectives. The proposed EMS employs a cooperative game to achieve efficient coordination and operations of the MMG system and also ensures a fair energy cost allocation among members in the coalition. This study considers the energy cost allocation problem when the number of members in the coalition grows exponentially. The energy cost allocation problem is solved using a column generation algorithm. The proposed model includes energy storage systems, demand loads, real-time electricity prices and renewable energy. The estimate of the daily operating cost of the MMG using a proposed deep convolutional neural network (CNN) is analyzed in this study. An optimal scheduling policy to optimize the total daily operating cost of MMG is also proposed. Besides, other existing optimal scheduling policies, such as approximate dynamic programming (ADP), model prediction control (MPC), and greedy policy are considered for the comparison. To evaluate the effectiveness of the proposed model, the real-time electricity prices of the electric reliability council of Texas are used. Simulation results show that each MMG can achieve energy cost savings through a coalition of MMG. Moreover, the proposed optimal policy method achieves MG's daily operating cost reduction up to 87.86% as compared to 79.52% for the MPC method, 73.94% for the greedy policy method and 79.42% for ADP method.
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This paper introduces the concept of hierarchical and zonal scheduling and proposes an iterative two-layer model to optimize the charging and discharging trading of electric vehicles (EVs), so as to minimize the overall load variance of the distribution network under the constraints of power flow and vehicle travel demand. In order to solve the mixed-integer programming (MIP) problem that exists in this model, an improved heuristic algorithm, the adaptive inertia weight krill herd (KH) algorithm is proposed. In addition, we design a decentralized trading architecture and related electricity trading process based on the consortium blockchain to ensure the security and privacy of two-way electricity trading between EVs and the smart grid. The IEEE nodes based simulation experiment shows that our scheme can effectively smooth power load fluctuations, and the improved KH algorithm can effectively improve the efficiency of model solving. Security analysis qualitatively proves that our scheme can ensure the security and privacy-preserving of electricity trading. Finally, our scheme is implemented in the Hyperledger Fabric to evaluate the feasibility and effectiveness.