Countering stasistical attacks in cloud-based searchable encryption
- Authors: Ahsan, M. , Ali, Ihsan , Bin Idris, Mohd , Imran, Muhammad , Shoaib, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Parallel Programming Vol. 48, no. 3 (2020), p. 470-495
- Full Text: false
- Reviewed:
- Description: Searchable encryption (SE) is appearing as a prominent solution in the intersection of privacy protection and efficient retrieval of data outsourced to cloud computing storage. While it preserves privacy by encrypting data, yet supports search operation without data leakage. Due to its applicability, many research communities have proposed different SE schemes under various security definitions with numerous customary features (i.e. multi keyword search, ranked search). However, by reason of multi-keyword ranked search, SE discloses encrypted document list corresponding to multiple (secure) query keywords (or trapdoor). Such disclosure of statistical information helps an attacker to analyze and deduce the content of the data. To counter statistical information leakage in SE, we propose a scheme referred to as Countering Statistical Attack in Cloud based Searchable Encryption (CSA-CSE) that resorts to randomness in all components of an SE. CSA-CSE adopts inverted index that is built with a hash digest of a pair of keywords. Unlike existing schemes, ranking factors (i.e. relevance scores) rank the documents and then they no longer exist in the secure index (neither in order preserving encrypted form). Query keywords are also garbled with randomness in order to hide actual query/result statistics. Our security analysis and experiment on request for comments database ensure the security and efficiency of CSA-CSE. © 2018, Springer Science+Business Media, LLC, part of Springer Nature. Correction to: Countering Statistical Attacks in Cloud-Based Searchable Encryption (International Journal of Parallel Programming, (2020), 48, 3, (470-495), 10.1007/s10766-018-0584-8)The original article has been published with an incorrect grant number in the acknowledgements which should be RG # 1439-036. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
DE-RUSBoost : an efficient electricity theft detection scheme with additive communication layer
- Authors: Mujeeb, Sana , Javaid, Nadeem , Khalid, Rabiya , Imran, Muhammad , Naseer, Nidal
- Date: 2020
- Type: Text , Conference paper
- Relation: 2020 IEEE International Conference on Communications, ICC 2020, Dublin, Ireland, 7 to 11 June, IEEE International Conference on Communications Vol. 2020-June
- Full Text: false
- Reviewed:
- Description: Modern power grids depend on the Advanced Metering Infrastructure (AMI) for consumption monitoring, energy management and billing. However, AMIs are vulnerable to electricity theft cyber attacks due to addition of communication layer. Electricity theft is one of the major Non-Technical Losses (NTLs) in the electricity distribution systems that has become a global concern, recently. Although the machine learning techniques are widely used for Electricity Theft Detection (ETD) in literature, some significant challenges need to be address. (i) The consumption data is usually unlabeled, there should be proper method to label the data. (ii) The fair consumers significantly outnumber the fraudulent consumers, which negatively impacts the performance of classification algorithm. (iii) The performance of classifier must be validated using proper performance evaluation measures. In this paper, an enhanced ETD model is proposed that is an optimized classifier Differential Evaluation Random Under Sampling Boosting (DE-RUSBoost) is used for classification. Proposed classifier DE-RUSBoost is optimized using a metaheuristic optimization algorithm named Differential Evaluation (DE). The proposed method is evaluated on a real-world dataset, i.e., State Grid Corporation of China (SGCC) datasets. DE-RUSBoost achieves the highest accuracy of 96% and low false detection rate of 0.004. The proposed method outperforms its counterparts in terms of accuracy and false detection rate. © 2020 IEEE.
Deep learning and big data technologies for IoT security
- Authors: Amanullah, Mohamed , Habeeb, Riyaz , Nasaruddin, Fariza , Gani, Abdullah , Ahmed, Ejaz , Nainar, Abdul , Akim, Nazihah , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Computer Communications Vol. 151, no. (2020), p. 495-517
- Full Text: false
- Reviewed:
- Description: Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects. © 2020 Elsevier B.V.
Efficient data trading and storage in internet of vehicles using consortium blockchain
- Authors: Sadiq, Ayesha , Javaid, Nadeem , Samuel, Omaji , Khalid, Adia , Haider, Noman , Imran, Muhammad
- Date: 2020
- Type: Text , Conference paper
- Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2143-2148
- Full Text: false
- Reviewed:
- Description: The radically increasing amount and enormous types of data generated by vehicles have brought in the innovated application of data trading in the Internet of Vehicles (IoV). However, the trustless environment in IoV enabled data trading faces conflicting interests and disputes of trading parties. To build trust, we exploit consortium blockchain for secure data trading with information transparency. In addition, a hash list of traded data is maintained by roadside units accompanied by bloom filters for fast lookup, to avoid data duplication. The reliability and integrity of trading data are ensured by using the digital signature scheme based on elliptic curve bilinear pairing. For long term availability of traded data, an external distributed storage, i.e., InterPlanetary File System (IPFS) can provide reliable, high capacity storage resources. The experimental results verified that our proposed solution is efficient for data trading in IoV and reliable for long term availability of data storage. © 2020 IEEE.
Electric Load Forecasting using EEMD and machine learning techniques
- Authors: Naz, Aqdas , Javaid, Nadeem , Khalid, Adia , Shoaib, Muhammad , Imran, Muhammad
- Date: 2020
- Type: Text , Conference paper
- Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyprosu, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2124-2127
- Full Text: false
- Reviewed:
- Description: The significance of electricity cannot be overlooked in terms of advancements in economic and technological fields. In this study, Ensemble Empirical Mode Decomposition (EEMD) method in combination with the Ensemble Bi-Long Short Term Memory (EBiLSTM) and Support Vector Machine (SVM) is used. Non linear and non stationary IMFs are forecast using EBiLSTM forecasting algorithm as it performs efficiently in complex and non linear scenario. Whereas, linear IMFs are forecast using SVM as EBiLSTM take high computational time unlike SVM. The proposed technique EEMD-EBiLSTM-SVM gives good results. © 2020 IEEE.
Electricity theft detection using pipeline in machine learning
- Authors: Anwar, Mubbashra , Javaid, Nadeem , Khalid, Adia , Imran, Muhammad , Shoaib, Muhammad
- Date: 2020
- Type: Text , Conference paper
- Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2138-2142
- Full Text: false
- Reviewed:
- Description: Electricity theft is the primary cause of electrical power loss that significantly affects the revenue loss and the quality of electrical power. Nevertheless, the existing methods for the detection of this criminal behavior of theft are diversified and complicated since the imbalanced nature of the dataset, and high dimensionality of time-series data make it challenging to extract meaningful information. This paper addresses these problems by developing a novel electricity theft detection model, integrating three algorithms in a pipeline. The proposed method first applies the synthetic minority oversampling technique (SMOTE) for balancing the dataset, secondly integration of kernel function and principal component analysis (KPCA) for the feature extraction from high dimensional time-series data, and support vector machine (SVM) for the classification. Besides, the performance of the proposed pipeline is measured using a comprehensive list of performance metrics. Extensive experiments are performed by using real electricity consumption data, and results show that the proposed method outperforms other methods in terms of theft detection. © 2020 IEEE.
Impact of feature selection on non-technical loss detection
- Authors: Ghori, Khawaja , Rabeeh Ayaz, Abbasi , Awais, Muhammad , Imran, Muhammad , Ullah, Atta , Szathmary, Laszlo
- Date: 2020
- Type: Text , Conference paper
- Relation: 6th Conference on Data Science and Machine Learning Applications, CDMA 2020, Riyadh, Saudi Arabia, 4 to 5 March 2020, Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020 p. 19-24
- Full Text: false
- Reviewed:
- Description: Over the years, many countries have faced huge financial deficits due to Non-Technical Loss (NTL) in power sector. There are many ways of attempting to illegal use of electricity like by-passing and reversing meters. There have been many attempts to bring down NTL using manual and automated techniques. Manual NTL detection is not proving fruitful as it incurs heavy costs and has a low hit ratio. Due to the shortcoming of manual NTL detection, automated detection of NTL using machine learning classifiers is gaining attention in the research community. The datasets containing NTL belong to the class imbalance domain where regular consumers (negative class) out weight the representation of irregular consumers (positive class). To identify the right number of representative records, many techniques are proposed but selecting the right features in deciding NTL is equally an important task where not much has been contributed to the literature. In this paper, we propose the Incremental Feature Selection (IFS) algorithm which first uses feature importance to identify the most relevant features for NTL detection and then these features are used to test three classifiers namely CatBoost, Decision Tree (DT) Classifier and K-Nearest Neighbors (KNN) for NTL detection. This way, we have not only identified the most relevant features for NTL detection in a real dataset but also have brought down the overall computation time of the classifiers. Moreover, our proposed framework is tested on three performance evaluation metrics used in imbalance domain. The results show that using the most relevant features identified by the IFS algorithm, the three classifiers have the same or slightly better efficiency as compared to using all features. © 2020 IEEE.
Model compression for IoT applications in industry 4.0 via multiscale knowledge transfer
- Authors: Fu, Shipeng , Li, Zhen , Liu, Kai , Din, Sadia , Imran, Muhammad , Yang, Xiaomin
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 9 (2020), p. 6013-6022
- Full Text: false
- Reviewed:
- Description: Recently, Industry 4.0 has attracted much attention. It has close relations with the Internet of Things (IoT). On the other hand, convolutional neural networks (CNNs) have shown promising performance in many foundational services of the IoT applications. For the IoT applications with high-speed data streams and the requirement of time-sensitive actions, fast processing is demanded on small-scale platforms or even on IoT devices themselves. Therefore, it is inappropriate to employ cumbersome CNNs in IoT applications, making the study of model compression necessary. In knowledge transfer, it is common to employ a deep, well-trained network, called teacher, to guide a shallow, untrained network, called student, to have better performance. Previous works have made many attempts to transfer single-scale knowledge from teacher to student, leading to degradation of generalization ability. In this article, we introduce multiscale representations to knowledge transfer, which facilitates the generalization ability of student. We divide student and teacher into several stages. Student learns from multiscale knowledge provided by teacher at the end of each stage. Extensive experiments demonstrate the effectiveness of our proposed method both on image classification and on single image super-resolution. The huge performance gap between student and teacher is significantly narrowed down by our proposed method, making student suitable for IoT applications. © 2005-2012 IEEE.
Process migration-based computational offloading framework for IoT-supported mobile edge/cloud computing
- Authors: Yousafzai, Abdullah , Yaqoob, Ibrar , Imran, Muhammad , Gani, Abdullah , Md Noor, Rafidah
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 7, no. 5 (2020), p. 4171-4182
- Full Text: false
- Reviewed:
- Description: Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computation-intensive applications that often require broad bandwidth, stringent response time, long-battery life, and heavy-computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this article, we analyze the effect of platform-dependent native applications on computational offloading in edge networks and propose a lightweight process migration-based computational offloading framework. The proposed framework does not require application binaries at edge servers and thus seamlessly migrates native applications. The proposed framework is evaluated using an experimental testbed. Numerical results reveal that the proposed framework saves almost 44% of the execution time and 84% of the energy consumption. Hence, the proposed framework shows profound potential for resource-intensive IoT application processing in MEC. © 2014 IEEE.
Randomized nonlinear one-class support vector machines with bounded loss function to detect of outliers for large scale IoT data
- Authors: Razzak, Imran , Zafar, Khurram , Imran, Muhammad , Xu, Guandong
- Date: 2020
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 112, no. (2020), p. 715-723
- Full Text: false
- Reviewed:
- Description: Exponential growth of large scale data industrial internet of things is evident due to the enormous deployment of IoT data acquisition devices. Detection of unusual patterns from large scale IoT data is important though challenging task. Recently, one-class support vector machines is extensively being used for anomaly detection. It tries to find an optimal hyperplane in high dimensional data that best separates the data from anomalies with maximum margin. However, the hinge loss of traditional one-class support vector machines is unbounded, which results in larger loss caused by outliers affecting its performance for anomaly detection. Furthermore, existing methods are computationally complex for larger data. In this paper, we present novel anomaly detection for large scale data by using randomized nonlinear features in support vector machines with bounded loss function rather than finding optimized support vectors with unbounded loss function. Extensive experimental evaluation on ten benchmark datasets shows the robustness of the proposed approach against outliers such as 0.8239, 0.7921, 0.7501, 0.6711, 0.6692, 0.4789, 0.6462, 0.6812, 0.7271 and 0.7873 accuracy for Gas Sensor Array, Human Activity Recognition, Parkinson's, Hepatitis, Breast Cancer, Blood Transfusion, Heart, ILPD and Wholesale Customers datasets respectively. In addition to this, introduction of randomized nonlinear feature helps to considerably decrease the computational complexity and space complexity from O(N3) to O(Bkn) and O(N2) to O(Bkn). Thus, very attractive for larger datasets. © 2020 Elsevier B.V.
Robustness optimization of scale-free IoT Networks
- Authors: Usman, Muhammad , Javaid, Nadeem , Khalid, Adia , Nasser, Nidal , Imran, Muhammad
- Date: 2020
- Type: Text , Conference paper
- Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyrprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2240-2244
- Full Text: false
- Reviewed:
- Description: In today's modern world, people are cultivating towards the Internet of Things (IoT) networks due to their various demands in health monitoring, smart homes, traffic management, and industrial optimization, etc., IoT networks comprise of sensor nodes that have multiple functionalities to fulfill the demands of individuals. With the advancement in technology, the need for IoT networks is increasing as the devices are getting smarter day by day. The scale-free topology is considered to be the best topology for IoT networks because it is more robust against the attacks. For a scale-free network, robustness optimization is essential. Therefore, in this paper, to enhance the robustness, we have optimized a scale-free network through proposed the Improved Scale-Free Network (ISFN) technique. In ISFN, the edges are swapped based on their degree and nodes distance operation. This technique does not change the degree of the nodes of original topology which makes the optimized topology remains scale-free. Through experiments, we have compared the ISFN with two existing techniques, i.e., ROSE and SA. The results prove that by increasing the number of nodes, ISFN outperforms these existing techniques. © 2020 IEEE.
Secure energy trading for electric vehicles using consortium blockchain and k-nearest neighbor
- Authors: Ashfaq, Tehreem , Javaid, Nadeem , Javed, Muhammad , Imran, Muhammad , Haider, Noman , Nasser, Nidal
- Date: 2020
- Type: Text , Conference paper
- Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2235-2239
- Full Text: false
- Reviewed:
- Description: In this paper, we deal with some major energy issues related to the charging of vehicles in vehicular network. The exponential increase of Electric Vehicles (EVs) has led to the more complex problems. In general, there are two major issues related to th EVs. First, its difficult to find a nearest charging station with required energy. Second, how much energy is needed to reach charging station from current location. In traditional systems, the energy trading between charging station and EVs is not secured due to centralized girds. To deal with this problem, a consortium blockchain based secure energy trading system is proposed. Blockchain is used for secure energy trading with moderate cost. The main purpose of the proposed system is resource reduction and find out the present state of charging stations. Simulations and results show that the proposed schemes outperform the conventional schemes in terms of minimizing the charging cost of battery and expenses of EVs. © 2020 IEEE.
Securing internet of medical things with friendly-jamming schemes
- Authors: Li, Xuran , Dai, Hong-Ning , Wang, Qubeijian , Imran, Muhammad , Li, Dengwang , Imran, Muhammad Ali
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Computer Communications Vol. 160, no. (2020), p. 431-442
- Full Text: false
- Reviewed:
- Description: The Internet of Medical Things (IoMT)-enabled e-healthcare can complement traditional medical treatments in a flexible and convenient manner. However, security and privacy become the main concerns of IoMT due to the limited computational capability, memory space and energy constraint of medical sensors, leading to the in-feasibility for conventional cryptographic approaches, which are often computationally-complicated. In contrast to cryptographic approaches, friendly jamming (Fri-jam) schemes will not cause extra computing cost to medical sensors, thereby becoming potential countermeasures to ensure security of IoMT. In this paper, we present a study on using Fri-jam schemes in IoMT. We first analyze the data security in IoMT and discuss the challenges. We then propose using Fri-jam schemes to protect the confidential medical data of patients collected by medical sensors from being eavesdropped. We also discuss the integration of Fri-jam schemes with various communication technologies, including beamforming, Simultaneous Wireless Information and Power Transfer (SWIPT) and full duplexity. Moreover, we present two case studies of Fri-jam schemes in IoMT. The results of these two case studies indicate that the Fri-jam method will significantly decrease the eavesdropping risk while leading to no significant influence on legitimate transmission. © 2020
Securing smart cities through blockchain technology : architecture, requirements, and challenges
- Authors: Hakak, Saqib , Khan, Wazir , Gilkar, Gulshan , Imran, Muhammad , Guizani, Nadra
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Network Vol. 34, no. 1 (2020), p. 8-14
- Full Text: false
- Reviewed:
- Description: In recent years, unprecedented work has been done in the area of smart cities. The purpose of developing smart cities is to enhance quality of life factors for people dwelling within them. To achieve that purpose, technologies such as IoT and cloud computing have been utilized. Blockchain technology is also among the promising technologies that can offer countless valuable services to its end users. It is a immutable programmable digital register for the purpose of recording virtual assets having some value and was primarily developed for digital currencies like Bitcoin. To fully utilize the services of blockchain technology within smart cities, characteristics of blockchain technology, and its key requirements and research challenges need to be identified. Hence, in this article, an attempt has been made to identify the characteristics of blockchain technology. Furthermore, indispensable requirements for incorporating blockchain technology within smart cities are enumerated. A conceptual architecture for securing smart city using blockchain technology is proposed and explained using a possible use case study. An overview of a real-world three-blockchain- based smart city case study is also presented. Finally, several imperative research challenges are identified and discussed. © 2019 IEEE.
Security risk assessment for 5G networks : national perspective
- Authors: Batalla, Jordi , Andrukiewicz, Elzbieta , Gomez, German , Sapiecha, Piotr , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Wireless Communications Vol. 27, no. 4 (2020), p. 16-22
- Full Text: false
- Reviewed:
- Description: The development of 5G presents regulators with several security-related challenges. Implementation of a security framework that would be capable of addressing all concerns of users and, by extension, of governments, is one of them. In March 2019, the European Commission presented a plan to achieve, throughout the European Union, a concerted response to security concerns related to 5G networks. This article presents one of the national responses that consists of assessing the risk pursuant to the EN-ISO/IEC 27005 standard and introducing relevant risk mitigation measures. The article focuses on the introduction of a methodical approach to developing 5G security regulations based on an analysis of different risk scenarios. It also strives to propose the applicable government-level mitigation measures aiming to counteract any 5G security threats should these be encountered. When implemented efficiently, the said mitigation measures guarantee that the new networks will be designed in a proper manner. They will also impact cybersecurity of all networks over the coming years. © 2002-2012 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**
TFPMS : transactions filtering pattern matching scheme for vehicular networks based on blockchain
- Authors: Iftikhar, Muhammad , Javaid, Nadeem , Javaid, Sakeena , Imran, Muhammad , Nasser, Nidal
- Date: 2020
- Type: Text , Conference paper
- Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2128-2132
- Full Text: false
- Reviewed:
- Description: An Intelligent Transportation System (ITS) aims to achieve efficiency of traffic by minimizing its problems, such as traffic congestion, road accidents, etc. It is not only limited to control traffic congestion but also enhances the safety and comfort of the commuters. For road traffic safety and efficient infrastructure usage, vehicles need to communicate with each other to disseminate information related to traffic. However, vehicles cannot directly communicate with each other and other infrastructure because of privacy and security concerns. In the proposed work, blockchain is implemented on Road Side Units (RSUs) that are used to provide reliable communication between vehicles. Furthermore, cloud and edge servers are used to tackle the storage issue. We proposed a Transactions Filtering Pattern Matching Scheme (TFPMS) to filter the transactions before sending them to the blockchain network. In this way, it saves storage space and reduces computational overhead of blockchain. Moreover, we are exploiting consortium blockchain to implement our proposed scheme. Simulations are performed based on the number of transactions and cost to achieve high-quality data sharing between vehicles, which result in a reduction in storage overhead as compared to the existing schemes. © 2020 IEEE.
Transformer based deep intelligent contextual embedding for twitter sentiment analysis
- Authors: Naseem, Usman , Razzak, Imran , Musial, Katarzyna , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 113, no. (2020), p. 58-69
- Full Text: false
- Reviewed:
- Description: Along with the emergence of the Internet, the rapid development of handheld devices has democratized content creation due to the extensive use of social media and has resulted in an explosion of short informal texts. Although a sentiment analysis of these texts is valuable for many reasons, this task is often perceived as a challenge given that these texts are often short, informal, noisy, and rich in language ambiguities, such as polysemy. Moreover, most of the existing sentiment analysis methods are based on clean data. In this paper, we present DICET, a transformer-based method for sentiment analysis that encodes representation from a transformer and applies deep intelligent contextual embedding to enhance the quality of tweets by removing noise while taking word sentiments, polysemy, syntax, and semantic knowledge into account. We also use the bidirectional long- and short-term memory network to determine the sentiment of a tweet. To validate the performance of the proposed framework, we perform extensive experiments on three benchmark datasets, and results show that DICET considerably outperforms the state of the art in sentiment classification. © 2020 Elsevier B.V.
UAV-enabled data acquisition scheme with directional wireless energy transfer for Internet of Things
- Authors: Liu, Yalin , Dai, Hong-Ning , Wang, Hao , Imran, Muhammad , Wang, Xiaofen , Shoaib, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Computer Communications Vol. 155, no. (2020), p. 184-196
- Full Text: false
- Reviewed:
- Description: Low power Internet of Things (IoT) is suffering from two limitations: battery-power limitation of IoT nodes and inflexibility of infrastructure-node deployment. In this paper, we propose an Unmanned Aerial Vehicle (UAV)-enabled data acquisition scheme with directional wireless energy transfer (WET) to overcome the limitations of low power IoT. The main idea of the proposed scheme is to employ a UAV to serve as both a data collector and an energy supplier. The UAV first transfers directional wireless energy to an IoT node which then sends back the data packets to the UAV by using the harvested energy. Meanwhile, we minimize the overall energy consumption under conditions of balanced energy supply and limited overall time. Moreover, we derive the optimal values of WET time and data transmission power. After analysing the feasibility of the optimal WET time and data transmission, we design an allocation scheme based on the feasible ranges of data size level and channel-fading degree. The numerical results show the feasibility and adaptability of our allocation scheme against the varied values of multiple system parameters. We further extend our scheme to the multi-node scenario by re-designing energy beamforming and adopting multi-access mechanisms. Moreover, we also analyse the mobility of UAVs in the proposed scheme. © 2020 Elsevier B.V.
A blockchain model for fair data sharing in deregulated smart grids
- Authors: Samuel, Omaji , Javaid, Nadeem , Awais, Muhammad , Ahmed, Zeeshan , Imran, Muhammad , Guizani, Mohsen
- Date: 2019
- Type: Text , Conference paper
- Relation: 2019 IEEE Global Communications Conference, GLOBECOM 2019, Waikoloa 9-13 December 2019
- Full Text: false
- Reviewed:
- Description: The emergence of smart home appliances has generated a high volume of data on smart meters belonging to different customers. However, customers can not share their data in deregulated smart grids due to privacy concern. Although, these data are important for the service provider in order to provide an efficient service. To encourage the customers' participation, this paper proposes an access control mechanism by fairly compensating customers for their participation in data sharing via blockchain using the concept of differential privacy. We addressed the computational issues of existing ethereum blockchain by proposing a proof of authority consensus protocol through the Pagerank mechanism in order to derive the reputation scores. Experimental results show the efficiency of the proposed model to minimize privacy risk, and maximize aggregator's profit. In addition, gas consumption, as well as the cost of the computational resources, is reduced. © 2019 IEEE.
A blockchain-based solution for enhancing security and privacy in smart factory
- Authors: Wan, Jafu , Li, Jiapeng , Imran, Muhammad , Li, Di
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 6 (2019), p. 3652-3660
- Full Text: false
- Reviewed:
- Description: Through the Industrial Internet of Things (IIoT), a smart factory has entered the booming period. However, as the number of nodes and network size become larger, the traditional IIoT architecture can no longer provide effective support for such enormous system. Therefore, we introduce the Blockchain architecture, which is an emerging scheme for constructing the distributed networks, to reshape the traditional IIoT architecture. First, the major problems of the traditional IIoT architecture are analyzed, and the existing improvements are summarized. Second, we introduce a security and privacy model to help design the Blockchain-based architecture. On this basis, we decompose and reorganize the original IIoT architecture to form a new multicenter partially decentralized architecture. Then, we introduce some relative security technologies to improve and optimize the new architecture. After that we design the data interaction process and the algorithms of the architecture. Finally, we use an automatic production platform to discuss the specific implementation. The experimental results show that the proposed architecture provides better security and privacy protection than the traditional architecture. Thus, the proposed architecture represents a significant improvement of the original architecture, which provides a new direction for the IIoT development. © 2005-2012 IEEE.