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The routing in underwater acoustic sensor networks (UASNs) has become a challenging issue due to several problems. First, in UASN, the distance between the nodes changes due to their mobility with the water current, thus increasing the network’s energy consumption. Second problem in UASNs is the occurrence of the void hole, which affects the network’s performance. Because nodes are unable to deliver data towards the destination due to the absence of forwarder nodes (FNs) in the network. Thus, the objective of routing in UASNs is to overcome the issues mentioned earlier to prolong the network’s lifetime. Therefore, a Q-learning based energy-efficient and balanced data gathering (QL-EEBDG) routing protocol is proposed in this paper. In QL-EEBDG, the FNs are selected according to their residual energy and grouped according to their neighboring nodes’ energies. Using energy as the main selection parameter assures efficient energy consumption in the network. Moreover, efficient selection of the FNs increases the lifetime of the network. However, the void node recovery process fails when the topology of the network is changed. Therefore, to avoid void holes in QL-EEBDG, a QL-EEBDG adjacent node (QL-EEBDG-ADN) scheme is proposed. It finds alternate neighbor routes for packet transmission and ensures continuous communication in the network. Extensive simulations are carried out for the performance evaluation of the proposed technique with existing baseline protocols, namely efficient balanced energy consumption based data gathering (EBDG), enhanced EBDG (EEBDG) and QELAR. The performance parameters used in the simulations are network lifetime, energy tax, network stability period and packet delivery ratio (PDR). The simulation results depict that the proposed QL-EEBDG-ADN outperforms the baseline protocols by approximately 11% better PDR and 25% better energy tax.
Zahoor Ali Khan; Obaida Abdul Karim; Shahid Abbas; Nadeem Javaid; Yousaf Bin Zikria; Usman Tariq. Q-learning based energy-efficient and void avoidance routing protocol for underwater acoustic sensor networks. Computer Networks 2021, 197, 108309 .
AMA StyleZahoor Ali Khan, Obaida Abdul Karim, Shahid Abbas, Nadeem Javaid, Yousaf Bin Zikria, Usman Tariq. Q-learning based energy-efficient and void avoidance routing protocol for underwater acoustic sensor networks. Computer Networks. 2021; 197 ():108309.
Chicago/Turabian StyleZahoor Ali Khan; Obaida Abdul Karim; Shahid Abbas; Nadeem Javaid; Yousaf Bin Zikria; Usman Tariq. 2021. "Q-learning based energy-efficient and void avoidance routing protocol for underwater acoustic sensor networks." Computer Networks 197, no. : 108309.
Nowadays, sensors inserted in mobile applications are used for gathering data for an explicit assignment that can effectively save cost and time in crowd sensing networks (CSNs). The true value and essence of gathered statistics depend on the participation level from all the members of a CSN, i.e., service providers, data collectors, and service consumers. In comparison with the centralized conventional mechanisms that are susceptible to privacy invasion, attacks, and manipulation, this article proposes a decentralized incentive and reputation mechanism for CSN. The monetary rewards are used to motivate the data collectors and to encourage the participants to take part in the network activities. Whereas the issue of privacy leakage is dealt with using Advanced Encryption Standard (AES128) technique. Additionally, a reputation system is implemented to tackle issues like data integrity, fake reviews, and conflicts among entities. Through registering reviews, the system encourages data utilization by providing correct, consistent, and reliable data. Furthermore, simulations are performed for analyzing the gas consumed by smart contracts. Similarly, the encryption technique is ratified by comparing its execution time with other techniques that are previously used in literature. Lastly, the reputation system is inspected through analyzing the gas consumption and mining time of input string length.
Zainib Noshad; Asad Ullah Khan; Shahid Abbas; Zain Abubaker; Nadeem Javaid; Muhammad Shafiq; Jin-Ghoo Choi. An Incentive and Reputation Mechanism Based on Blockchain for Crowd Sensing Network. Journal of Sensors 2021, 2021, 1 -14.
AMA StyleZainib Noshad, Asad Ullah Khan, Shahid Abbas, Zain Abubaker, Nadeem Javaid, Muhammad Shafiq, Jin-Ghoo Choi. An Incentive and Reputation Mechanism Based on Blockchain for Crowd Sensing Network. Journal of Sensors. 2021; 2021 ():1-14.
Chicago/Turabian StyleZainib Noshad; Asad Ullah Khan; Shahid Abbas; Zain Abubaker; Nadeem Javaid; Muhammad Shafiq; Jin-Ghoo Choi. 2021. "An Incentive and Reputation Mechanism Based on Blockchain for Crowd Sensing Network." Journal of Sensors 2021, no. : 1-14.
Energy optimization plays a vital role in energy management, economic savings, effective planning, reliable and secure power grid operation. However, energy optimization is challenging due to the uncertain and intermittent nature of renewable energy sources (RES) and consumer’s behavior. A rigid energy optimization model with assertive intermittent, stochastic, and non-linear behavior capturing abilities is needed in this context. Thus, a novel energy optimization model is developed to optimize the smart microgrid’s performance by reducing the operating cost, pollution emission and maximizing availability using RES. To predict the behavior of RES like solar and wind probability density function (PDF) and cumulative density function (CDF) are proposed. Contrarily, to resolve uncertainty and non-linearity of RES, a hybrid scheme of demand response programs (DRPS) and incline block tariff (IBT) with the participation of industrial, commercial, and residential consumers is introduced. For the developed model, an energy optimization strategy based on multi-objective wind-driven optimization (MOWDO) algorithm and multi-objective genetic algorithm (MOGA) is utilized to optimize the operation cost, pollution emission, and availability with/without the involvement in hybrid DRPS and IBT. Simulation results are considered in two different cases: operating cost and pollution emission, and operating cost and availability with/without participating in the hybrid scheme of DRPS and IBT. Simulation results illustrate that the proposed energy optimization model optimizes the performance of smart microgrid in aspects of operation cost, pollution emission, and availability compared to the existing models with/without involvement in hybrid scheme of DRPS and IBT. Thus, results validate that the proposed energy optimization model’s performance is outstanding compared to the existing models.
Kalim Ullah; Ghulam Hafeez; Imran Khan; Sadaqat Jan; Nadeem Javaid. A multi-objective energy optimization in smart grid with high penetration of renewable energy sources. Applied Energy 2021, 299, 117104 .
AMA StyleKalim Ullah, Ghulam Hafeez, Imran Khan, Sadaqat Jan, Nadeem Javaid. A multi-objective energy optimization in smart grid with high penetration of renewable energy sources. Applied Energy. 2021; 299 ():117104.
Chicago/Turabian StyleKalim Ullah; Ghulam Hafeez; Imran Khan; Sadaqat Jan; Nadeem Javaid. 2021. "A multi-objective energy optimization in smart grid with high penetration of renewable energy sources." Applied Energy 299, no. : 117104.
In the present era, smart and efficient vehicular network architectures are necessary due to fast technological advancements in vehicles. Many problems arise in these complex networks, which can be handled using blockchain and the Internet of Things (IoT). We proposed a comprehensive blockchain based 5G vehicular network architecture, which is cost-effective, scalable, secure, and handles various vehicular network issues in a smart city. The proposed architecture consists of all essential components like reputation system, incentive mechanism, and priority based techniques to handle different limitations in the literature. Simulations results for different scenarios depict the high execution cost of a single controller node, minor node, and ordinary node as 106305, 85864, and 65491 gas values and transaction cost as 130521, 109824, and 89195 gas values. The results depict the effectiveness of the proposed architecture in terms of scalability, time and cost-effectiveness.
Usama Arshad; Munam Ali Shah; Nadeem Javaid. Futuristic blockchain based scalable and cost-effective 5G vehicular network architecture. Vehicular Communications 2021, 100386 .
AMA StyleUsama Arshad, Munam Ali Shah, Nadeem Javaid. Futuristic blockchain based scalable and cost-effective 5G vehicular network architecture. Vehicular Communications. 2021; ():100386.
Chicago/Turabian StyleUsama Arshad; Munam Ali Shah; Nadeem Javaid. 2021. "Futuristic blockchain based scalable and cost-effective 5G vehicular network architecture." Vehicular Communications , no. : 100386.
Tendency towards installation of distributed photo-voltaic (PV) systems has led to an increased emphasis on grid frequency control. Grid-interactive buildings equipped with heating ventilation and air conditioning (HVAC) system has a great potential to regulate the frequency in renewable energy resources rich grids. This paper integrates distributed PV systems in a decoupled building-to-transmission-network (B2TN) and explicitly formulates the interaction between a grid and the buildings through real time pricing (RTP). As grid frequency conveys the information about operating conditions of a power system, therefore, frequency based RTP generators namely; linear, hyperbolic tangent and inverse hyperbolic tangent are used. A price responsive model predictive controller (MPC) for optimal scheduling of HVAC load is developed where reference temperature set point is dynamically adjusted subject to RTP. Moreover, the clear and cloud covered sky impacts of PV power generation on frequency and RTP deviations are investigated. Comparatively, inverse hyperbolic tangent model maps the frequency deviations on a wider RTP range which increases the scale of reference temperature set point resulting peak frequency deviation suppression up to 40% and load regulation beyond 12 MW without inherently affecting the building electricity cost when compared to ordinary MPC (without demand response service).
Obaid Ur Rehman; Shahid A. Khan; Nadeem Javaid. Decoupled building-to-transmission-network for frequency support in PV systems dominated grid. Renewable Energy 2021, 178, 930 -945.
AMA StyleObaid Ur Rehman, Shahid A. Khan, Nadeem Javaid. Decoupled building-to-transmission-network for frequency support in PV systems dominated grid. Renewable Energy. 2021; 178 ():930-945.
Chicago/Turabian StyleObaid Ur Rehman; Shahid A. Khan; Nadeem Javaid. 2021. "Decoupled building-to-transmission-network for frequency support in PV systems dominated grid." Renewable Energy 178, no. : 930-945.
In this paper, two supervised learning models based solutions are proposed for Electricity Theft Detection (ETD). In the first solution, Adaptive Synthetic Edited Nearest Neighbor (ADASYNENN) is used to solve class imbalanced problem. For feature extraction, Locally Linear Embedding (LLE) technique is utilized. Moreover, Self-Attention Generative Adversarial Network (SAGAN) is used in combination with Convolutional Neural Network (CNN) for the classification of electricity consumers. In the second solution, Synthetic Minority Oversampling Technique Edited Nearest Neighbor (SMOTEENN) is proposed. Moreover, a novel classification model, named as ERNET, which is based on EfficientNet, Residual Network (ResNet) and Gated Recurrent Unit (GRU), is used to detect Non-Technical Losses (NTLs). We also used a Sparse Auto Encoder (SAE) for effective feature extraction that makes the classification more robust and easy. Furthermore, a robust Root Mean Square Propagation (RMSProp) optimizer is used to improve the learning rate of the model. To validate the proposed models, simulations are performed using different performance metrics, such as precision, recall, F1-score, Area Under the Curve (AUC), FPR and Root Mean Square Error (RMSE). All simulations are performed using State Grid Corporation of China (SGCC) dataset. The proposed models are compared with benchmark models, such as SAGAN, Wide and Deep Convolutional Neural Network (WDCNN), CNN and Long Short Term Memory (LSTM). The simulation results prove that the proposed models outperform the existing models in terms of the aforementioned performance metrics.
Nadeem Javaid; Hira Gul; Sobia Baig; Faisal Shehzad; Chengjun Xia; Lin Guan; Tanzeela Sultana. Using GANCNN and ERNET for Detection of Non Technical Losses to Secure Smart Grids. IEEE Access 2021, 9, 98679 -98700.
AMA StyleNadeem Javaid, Hira Gul, Sobia Baig, Faisal Shehzad, Chengjun Xia, Lin Guan, Tanzeela Sultana. Using GANCNN and ERNET for Detection of Non Technical Losses to Secure Smart Grids. IEEE Access. 2021; 9 (99):98679-98700.
Chicago/Turabian StyleNadeem Javaid; Hira Gul; Sobia Baig; Faisal Shehzad; Chengjun Xia; Lin Guan; Tanzeela Sultana. 2021. "Using GANCNN and ERNET for Detection of Non Technical Losses to Secure Smart Grids." IEEE Access 9, no. 99: 98679-98700.
Purpose This study aims to examine the role of ethical leaders on the knowledge-sharing behavior of public sector employees. Ethical leaders engender knowledge-sharing behavior of employees by influencing their psychological capital. Design/methodology/approach To explore the mechanism by which ethical leaders shape the knowledge-sharing behavior of employees, cross-sectional self-reported data (n = 339) are collected from employees working at decision-making positions in federal ministries in Pakistan. Findings Analysis results indicate that ethical leaders influence public employees to share knowledge with colleagues. Moreover, the mediary role of ethical values, organizational identification and altruism is evident, however, the mediary role of self-efficacy is not evident from results. Practical implications This implies that policymakers should be cognizant of the indirect mechanism by which ethical leaders positively influence the behavior of public employees. This knowledge helps them consider the recruitment, promotion and training of employees, especially the leaders, in line with the required ethical value consideration in public sector organizations. Originality/value This research is based on originally collected data from the field.
Quratulain Amber; Abdul Baseer Qazi; Nadeem Javaid; Iram A. Khan; Mansoor Ahmad. Knowledge sharing in public organizations in Pakistan: leaders’ ethical role and psychological capital of employees. Information Discovery and Delivery 2021, ahead-of-p, 1 .
AMA StyleQuratulain Amber, Abdul Baseer Qazi, Nadeem Javaid, Iram A. Khan, Mansoor Ahmad. Knowledge sharing in public organizations in Pakistan: leaders’ ethical role and psychological capital of employees. Information Discovery and Delivery. 2021; ahead-of-p (ahead-of-p):1.
Chicago/Turabian StyleQuratulain Amber; Abdul Baseer Qazi; Nadeem Javaid; Iram A. Khan; Mansoor Ahmad. 2021. "Knowledge sharing in public organizations in Pakistan: leaders’ ethical role and psychological capital of employees." Information Discovery and Delivery ahead-of-p, no. ahead-of-p: 1.
Electricity theft (ET) causes major revenue loss in power utilities. It reduces the quality of supply, raises production cost, causes legal consumers to pay the higher cost, and impacts the economy as a whole. In this article, we use the State Grid Corporation of China (SGCC) dataset, which contains electricity consumption data of 1035 days for two classes: normal and fraudulent. In this work, ET detection model is proposed that consists of four steps: interpolation, data balancing, feature extraction, and classification. First, missing values of the dataset are recovered using the interpolation method. Second, resampling technique is implemented. ET consumers are 9% in the SGCC dataset that make the model inefficient to correctly classify both classes (normal and theft). A hybrid resampling technique is proposed, named synthetic minority oversampling technique with near miss. Third, residual network extracts the latent features from the SGCC dataset. Fourth, three tree based classifiers, such as decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost) are applied to train the encoded feature vectors for classification. Besides, search for good hyperparameters is a challenging task, which is usually done manually and takes a considerable amount of time. To resolve this problem, Bayesian optimizer is used to simplify the tuning process of DT, RF, and AdaBoost. Finally, the results indicate that RF outperforms DT and AdaBoost.
Arooj Arif; Nadeem Javaid; Abdulaziz Aldegheishem; Nabil Alrajeh. Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities. Concurrency and Computation: Practice and Experience 2021, 33, e6316 .
AMA StyleArooj Arif, Nadeem Javaid, Abdulaziz Aldegheishem, Nabil Alrajeh. Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities. Concurrency and Computation: Practice and Experience. 2021; 33 (17):e6316.
Chicago/Turabian StyleArooj Arif; Nadeem Javaid; Abdulaziz Aldegheishem; Nabil Alrajeh. 2021. "Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities." Concurrency and Computation: Practice and Experience 33, no. 17: e6316.
Renewable and sustainable energy with advancement in information and communication technologies bear huge expectations in power sector. Whereas, switching from traditional to networked power grid requires a long process. Currently, renewable energy (RE) injection into existing power systems is in transition state, which is a sophisticated and multidisciplinary task. In this article, RE integration engineering efforts are discussed that take a step ahead towards green energy. RE integration engineering refers to the controlling and configuring tasks regarding distributed power generation sources, information and communication technologies and dispatchable loads. An extensive literature review of this domain is conducted considering main objectives with associated constraints, techniques used and miscellaneous parameters that lead towards green energy. As with the induction of renewable energy sources (RESs) and utilization of microgrids (MGs), the power generation uncertainty factor is evolved which limits networked grid to operate within its full capacity and perceived advantages. In this study, an hierarchal conceptual framework is also presented that is hybrid in nature, i.e., adds functionalities of both centralized as well as distributed control to avoid bottle necks and complexities to minimize the power generation uncertainty effects. Furthermore, a brief discussion is provided keeping a broader perspective in forth coming power networks for eco-friendly smart cities. The focus of the discussion is on RE integration engineering in perspectives of precise forecasting problem, dynamic dispatch problem, demand responsiveness and market implications that tends to lead towards eco-friendly and power aware smart cities.
Danish Mahmood; Nadeem Javaid; Ghufran Ahmed; Suleman Khan; Valdemar Monteiro. A review on optimization strategies integrating renewable energy sources focusing uncertainty factor - paving path to eco-friendly smart cities. Sustainable Computing: Informatics and Systems 2021, 30, 100559 .
AMA StyleDanish Mahmood, Nadeem Javaid, Ghufran Ahmed, Suleman Khan, Valdemar Monteiro. A review on optimization strategies integrating renewable energy sources focusing uncertainty factor - paving path to eco-friendly smart cities. Sustainable Computing: Informatics and Systems. 2021; 30 ():100559.
Chicago/Turabian StyleDanish Mahmood; Nadeem Javaid; Ghufran Ahmed; Suleman Khan; Valdemar Monteiro. 2021. "A review on optimization strategies integrating renewable energy sources focusing uncertainty factor - paving path to eco-friendly smart cities." Sustainable Computing: Informatics and Systems 30, no. : 100559.
This paper proposes a blockchain based trust management method for agents in a multi-agent system. In this work, three objectives are achieved: trust, cooperation and privacy. The trust of agents depends on the credibility of trust evaluators, which is verified using the proposed methods of trust distortion, consistency and reliability. To enhance the cooperation between agents, a tit-3-for-tat (T3FT) repeated game strategy is developed. This strategy is more forgiving than the existing tit-for-tat (TFT) strategy. It encourages cheating agents to re-establish their trust by cooperating for three consecutive rounds of play. Also, a proof-of-cooperation consensus protocol is proposed to improve agents’ cooperation while creating and validating blocks. The privacy of agents is preserved in this work using the publicly verifiable secret sharing mechanism. Simulation results validate the effectiveness of the proposed work. From the simulation results, the proposed trust method outperforms an existing fuzzy logic trust method in terms of detecting cheating behavior of agents in the system. Besides, the proposed T3FT is effective as compared to the existing tit-for- 2-tat and TFT strategies in the literature. Moreover, security analysis of the proposed method is performed. The analysis shows that the proposed work is safe from bad-mouthing and on-off trust related attacks.
Rabiya Khalid; Omaji Samuel; Nadeem Javaid; Abdulaziz Aldegheishem; Muhammad Shafiq; Nabil Alrajeh. A Secure Trust Method for Multi-Agent System in Smart Grids Using Blockchain. IEEE Access 2021, 9, 59848 -59859.
AMA StyleRabiya Khalid, Omaji Samuel, Nadeem Javaid, Abdulaziz Aldegheishem, Muhammad Shafiq, Nabil Alrajeh. A Secure Trust Method for Multi-Agent System in Smart Grids Using Blockchain. IEEE Access. 2021; 9 (99):59848-59859.
Chicago/Turabian StyleRabiya Khalid; Omaji Samuel; Nadeem Javaid; Abdulaziz Aldegheishem; Muhammad Shafiq; Nabil Alrajeh. 2021. "A Secure Trust Method for Multi-Agent System in Smart Grids Using Blockchain." IEEE Access 9, no. 99: 59848-59859.
Microgrids have recently emerged as a building block for smart grids combining distributed renewable energy sources (RESs), energy storage devices, and load management methodologies. The intermittent nature of RESs brings several challenges to the smart microgrids, such as reliability, power quality, and balance between supply and demand. Thus, forecasting power generation from RESs, such as wind turbines and solar panels, is becoming essential for the efficient and perpetual operations of the power grid and it also helps in attaining optimal utilization of RESs. Energy demand forecasting is also an integral part of smart microgrids that helps in planning the power generation and energy trading with commercial grid. Machine learning (ML) and deep learning (DL) based models are promising solutions for predicting consumers’ demands and energy generations from RESs. In this context, this manuscript provides a comprehensive survey of the existing DL-based approaches, which are developed for power forecasting of wind turbines and solar panels as well as electric power load forecasting. It also discusses the datasets used to train and test the different DL-based prediction models, enabling future researchers to identify appropriate datasets to use in their work. Even though there are a few related surveys regarding energy management in smart grid applications, they are focused on a specific production application such as either solar or wind. Moreover, none of the surveys review the forecasting schemes for production and load side simultaneously. Finally, previous surveys do not consider the datasets used for forecasting despite their significance in DL-based forecasting approaches. Hence, our survey work is intrinsically different due to its data-centered view, along with presenting DL-based applications for load and energy generation forecasting in both residential and commercial sectors. The comparison of different DL approaches discussed in this manuscript reveals that the efficiency of such forecasting methods is highly dependent on the amount of the historical data and thus a large number of data storage devices and high processing power devices are required to deal with big data. Finally, this study raises several open research problems and opportunities in the area of renewable energy forecasting for smart microgrids.
Sheraz Aslam; Herodotos Herodotou; Syed Muhammad Mohsin; Nadeem Javaid; Nouman Ashraf; Shahzad Aslam. A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renewable and Sustainable Energy Reviews 2021, 144, 110992 .
AMA StyleSheraz Aslam, Herodotos Herodotou, Syed Muhammad Mohsin, Nadeem Javaid, Nouman Ashraf, Shahzad Aslam. A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renewable and Sustainable Energy Reviews. 2021; 144 ():110992.
Chicago/Turabian StyleSheraz Aslam; Herodotos Herodotou; Syed Muhammad Mohsin; Nadeem Javaid; Nouman Ashraf; Shahzad Aslam. 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids." Renewable and Sustainable Energy Reviews 144, no. : 110992.
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) are 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.
Nadeem Javaid; Naeem Jan; Muhammad Umar Javed. An adaptive synthesis to handle imbalanced big data with deep siamese network for electricity theft detection in smart grids. Journal of Parallel and Distributed Computing 2021, 153, 44 -52.
AMA StyleNadeem Javaid, Naeem Jan, Muhammad Umar Javed. An adaptive synthesis to handle imbalanced big data with deep siamese network for electricity theft detection in smart grids. Journal of Parallel and Distributed Computing. 2021; 153 ():44-52.
Chicago/Turabian StyleNadeem Javaid; Naeem Jan; Muhammad Umar Javed. 2021. "An adaptive synthesis to handle imbalanced big data with deep siamese network for electricity theft detection in smart grids." Journal of Parallel and Distributed Computing 153, no. : 44-52.
In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset’s dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models’ performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD.
Abdulaziz Aldegheishem; Mubbashra Anwar; Nadeem Javaid; Nabil Alrajeh; Muhammad Shafiq; Hasan Ahmed. Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks. IEEE Access 2021, 9, 25036 -25061.
AMA StyleAbdulaziz Aldegheishem, Mubbashra Anwar, Nadeem Javaid, Nabil Alrajeh, Muhammad Shafiq, Hasan Ahmed. Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks. IEEE Access. 2021; 9 ():25036-25061.
Chicago/Turabian StyleAbdulaziz Aldegheishem; Mubbashra Anwar; Nadeem Javaid; Nabil Alrajeh; Muhammad Shafiq; Hasan Ahmed. 2021. "Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks." IEEE Access 9, no. : 25036-25061.
Developments in sensors and communication technology lead to the emergence of smart communities where diverse collaborative applications can be enabled. One such application is the Smart Market Place (SMP), where participants of the smart community can trade resources, such as energy, internet bandwidth, water, etc., using a virtual currency (such as ether). However, most of the existing SMP trading models are proposed to trade a single resource and also restrict a participant to perform only a single transaction at a time. Restriction on multiple parallel transactions is imposed to protect the participants against the double-spending attack in the SMP. This work proposes a secure multi-resource trading (SMRT) model that is based on public Ethereum blockchain. SMRT allows participant of a SMP to trade multiple resources and initiate parallel transactions. Moreover, detailed security analysis and adversary model are presented to test the effectiveness and to assess the resilience of the proposed model against the double-spending attack. The adversary model is based on partial progress towards block production which is influenced by time advantage and average computing power. Furthermore, simulation based analysis and comparison of SMRT is also presented in terms of security, performance, cost and latency of transactions. It is observed that SMRT not only provides protection against the double spending attack, but it also reduces the computational overhead of the proposed model up to 50% as compared to existing trading models.
Bello Musa Yakubu; Majid I. Khan; Nadeem Javaid; Abid Khan. Blockchain-based secure multi-resource trading model for smart marketplace. Computing 2021, 103, 379 -400.
AMA StyleBello Musa Yakubu, Majid I. Khan, Nadeem Javaid, Abid Khan. Blockchain-based secure multi-resource trading model for smart marketplace. Computing. 2021; 103 (3):379-400.
Chicago/Turabian StyleBello Musa Yakubu; Majid I. Khan; Nadeem Javaid; Abid Khan. 2021. "Blockchain-based secure multi-resource trading model for smart marketplace." Computing 103, no. 3: 379-400.
In Vehicular Ad hoc Networks (VANETs), a large amount of data is shared between vehicles and Road Side Units (RSUs) in real-time. VANETs improve traffic efficiency and reliability by timely sharing road events and traffic information. However, there is a need to tackle the issues of both less data storage capability and selfish behavior of the vehicles. The conventional data storage mechanisms involve a third party for data management and are non-transparent, unreliable, untrustworthy, and insecure. Therefore, a blockchain based data storage system is presented in this paper to overcome the aforementioned issues. The proposed system exploits benefits of an Interplanetary File System (IPFS). Due to the resource constraints of vehicles, the blockchain is implemented on the RSUs. The RSUs get the aggregated packets sent by the vehicles. The packets contain the events’ information that occur in the vehicles’ surroundings. After verifying a packet, RSUs store the information related to the event in IPFS and reputation value of the sender vehicle in the blockchain. The reputation value of a vehicle is calculated based upon the correctness of an event it signs or initiates. Moreover, an incentive mechanism is also proposed to provide monetary incentives to the replier vehicles who respond to the events’ information. The incentives are provided by the initiators after verification of the repliers’ signatures. The initiator is a vehicle who initializes the event. The transactions performed during the incentive process are stored in the blockchain. Finally, Oyente tool is used to analyze the security of the proposed smart contract. A comparison of the proposed scheme with the logistic regression scheme is also presented.
Adia Khalid; Muhammad Sohaib Iftikhar; Ahmad Almogren; Rabiya Khalid; Muhammad Khalil Afzal; Nadeem Javaid. A blockchain based incentive provisioning scheme for traffic event validation and information storage in VANETs. Information Processing & Management 2020, 58, 102464 .
AMA StyleAdia Khalid, Muhammad Sohaib Iftikhar, Ahmad Almogren, Rabiya Khalid, Muhammad Khalil Afzal, Nadeem Javaid. A blockchain based incentive provisioning scheme for traffic event validation and information storage in VANETs. Information Processing & Management. 2020; 58 (2):102464.
Chicago/Turabian StyleAdia Khalid; Muhammad Sohaib Iftikhar; Ahmad Almogren; Rabiya Khalid; Muhammad Khalil Afzal; Nadeem Javaid. 2020. "A blockchain based incentive provisioning scheme for traffic event validation and information storage in VANETs." Information Processing & Management 58, no. 2: 102464.
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.
Ayesha Sadiq; Muhammad Umar Javed; Rabiya Khalid; Ahmad Almogren; Muhammad Shafiq; Nadeem Javaid. Blockchain Based Data and Energy Trading in Internet of Electric Vehicles. IEEE Access 2020, 9, 7000 -7020.
AMA StyleAyesha Sadiq, Muhammad Umar Javed, Rabiya Khalid, Ahmad Almogren, Muhammad Shafiq, Nadeem Javaid. Blockchain Based Data and Energy Trading in Internet of Electric Vehicles. IEEE Access. 2020; 9 ():7000-7020.
Chicago/Turabian StyleAyesha Sadiq; Muhammad Umar Javed; Rabiya Khalid; Ahmad Almogren; Muhammad Shafiq; Nadeem Javaid. 2020. "Blockchain Based Data and Energy Trading in Internet of Electric Vehicles." IEEE Access 9, no. : 7000-7020.
Electricity theft is one of the main causes of non-technical losses and its detection is important for power distribution companies to avoid revenue loss. The advancement of traditional grids to smart grids allows a two-way flow of information and energy that enables real-time energy management, billing and load surveillance. This infrastructure enables power distribution companies to automate electricity theft detection (ETD) by constructing new innovative data-driven solutions. Whereas, the traditional ETD approaches do not provide acceptable theft detection performance due to high-dimensional imbalanced data, loss of data relationships during feature extraction and the requirement of experts’ involvement. Hence, this paper presents a new semi-supervised solution for ETD, which consists of relational denoising autoencoder (RDAE) and attention guided (AG) TripleGAN, named as RDAE-AG-TripleGAN. In this system, RDAE is implemented to derive features and their associations while AG performs feature weighting and dynamically supervises the AG-TripleGAN. As a result, this procedure significantly boosts the ETD. Furthermore, to demonstrate the acceptability of the proposed methodology over conventional approaches, we conducted extensive simulations using the real power consumption data of smart meters. The proposed solution is validated over the most useful and suitable performance indicators: area under the curve, precision, recall, Matthews correlation coefficient, F1-score and precision-recall area under the curve. The simulation results prove that the proposed method efficiently improves the detection of electricity frauds against conventional ETD schemes such as extreme gradient boosting machine and transductive support vector machine. The proposed solution achieves the detection rate of 0.956, which makes it more acceptable for electric utilities than the existing approaches.
Zeeshan Aslam; Fahad Ahmed; Ahmad Almogren; Muhammad Shafiq; Mansour Zuair; Nadeem Javaid. An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems. IEEE Access 2020, 8, 221767 -221782.
AMA StyleZeeshan Aslam, Fahad Ahmed, Ahmad Almogren, Muhammad Shafiq, Mansour Zuair, Nadeem Javaid. An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems. IEEE Access. 2020; 8 (99):221767-221782.
Chicago/Turabian StyleZeeshan Aslam; Fahad Ahmed; Ahmad Almogren; Muhammad Shafiq; Mansour Zuair; Nadeem Javaid. 2020. "An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems." IEEE Access 8, no. 99: 221767-221782.
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.
Adamu Sani Yahaya; Nadeem Javaid; Muhammad Umar Javed; Muhammad Shafiq; Wazir Zada Khan; Mohammed Y. Aalsalem. Blockchain-Based Energy Trading and Load Balancing Using Contract Theory and Reputation in a Smart Community. IEEE Access 2020, 8, 222168 -222186.
AMA StyleAdamu Sani Yahaya, Nadeem Javaid, Muhammad Umar Javed, Muhammad Shafiq, Wazir Zada Khan, Mohammed Y. Aalsalem. Blockchain-Based Energy Trading and Load Balancing Using Contract Theory and Reputation in a Smart Community. IEEE Access. 2020; 8 (99):222168-222186.
Chicago/Turabian StyleAdamu Sani Yahaya; Nadeem Javaid; Muhammad Umar Javed; Muhammad Shafiq; Wazir Zada Khan; Mohammed Y. Aalsalem. 2020. "Blockchain-Based Energy Trading and Load Balancing Using Contract Theory and Reputation in a Smart Community." IEEE Access 8, no. 99: 222168-222186.
In a multi-agent system (MAS), the trust of each agent has become hot research issues in the smart grids. The traditional trust systems that use access control and cryptography are not sufficient to handle the dynamic behavior of agents. Also, they are inefficient to solve the computational overhead of the cryptographic primitives. Based on these limitations, this paper proposes a blockchain-based trust management system for MAS. The proposed system consists of two layers: a lower layer that enables an agent to perform direct and indirect trust evaluations of other agents during interactions. Multi-source feedback from the interactions among different aggregators is feed to the blockchain. The upper layer is used to perform trust credibility of agents based on trust distortion, consistency and reliability. The credibility evaluation is used to determine the dynamic behavior of agents and also detect dishonest agents in the system. Trust model and security analysis of the proposed system are provided. Moreover, simulation results evaluate the effectiveness of the proposed trust system while the system is secure against bad-mouthing and on-off attacks.
Omaji Samuel; Nadeem Javaid; Adia Khalid; Muhammad Imrarn; Nidal Nasser. A Trust Management System for Multi-agent System in Smart Grids using Blockchain Technology. GLOBECOM 2020 - 2020 IEEE Global Communications Conference 2020, 1 -6.
AMA StyleOmaji Samuel, Nadeem Javaid, Adia Khalid, Muhammad Imrarn, Nidal Nasser. A Trust Management System for Multi-agent System in Smart Grids using Blockchain Technology. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. 2020; ():1-6.
Chicago/Turabian StyleOmaji Samuel; Nadeem Javaid; Adia Khalid; Muhammad Imrarn; Nidal Nasser. 2020. "A Trust Management System for Multi-agent System in Smart Grids using Blockchain Technology." GLOBECOM 2020 - 2020 IEEE Global Communications Conference , no. : 1-6.
The significance of electricity cannot be overlooked as all fields of life like material production, health care, educational sector, etc., depend upon it to render consistent and high-quality services, increase productivity and business continuity. To this end, energy operators have experienced a continuous increasing trend in the electricity demand for the past few decades. This may cause many issues like load shedding, increased electricity bills, imbalance between supply and demand, etc. Therefore, forecasting of electricity demand using efficient techniques is crucial for the energy operators to decide about optimal unit commitment and to make electricity dispatch plans. It also helps to avoid wastage as well as the shortage of energy. In this study, a novel forecasting model, known as ELS-net is proposed, which is a combination of an Ensemble Empirical Mode Decomposition (EEMD) method, multi-model Ensemble Bi Long Short-Term Memory (EBiLSTM) forecasting technique and Support Vector Machine (SVM). In the proposed model, EEMD is used to distinguish between linear and non-linear intrinsic mode functions (IMFs), EBiLSTM is used to forecast the non-linear IMFs and SVM is employed to forecast the linear IMFs. Using separate forecasting techniques for linear and non-linear IMFs decreases the computational complexity of the model. Moreover, SVM requires low computational time as compared to EBiLSTM for linear IMFs. Simulations are performed to examine the effectiveness of the proposed model using two different datasets: New South Wales (NSW) and Victoria (VIC). For performance evaluation, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used as performance metrics. From the simulation results, it is obvious that the proposed ELS-net model outperforms the start-of-the-art techniques, such as EMD-BILSTM-SVM, EMD-PSO-GA-SVR, BiLSTM, MLP and SVM in terms of forecasting accuracy and minimum execution time.
Nadeem Javaid; Aqdas Naz; Rabiya Khalid; Ahmad Almogren; Muhammad Shafiq; Adia Khalid. ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load. IEEE Access 2020, 8, 198935 -198949.
AMA StyleNadeem Javaid, Aqdas Naz, Rabiya Khalid, Ahmad Almogren, Muhammad Shafiq, Adia Khalid. ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load. IEEE Access. 2020; 8 ():198935-198949.
Chicago/Turabian StyleNadeem Javaid; Aqdas Naz; Rabiya Khalid; Ahmad Almogren; Muhammad Shafiq; Adia Khalid. 2020. "ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load." IEEE Access 8, no. : 198935-198949.