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A novel cooperative link selection mechanism for enhancing the robustness in scale-free IoT networks

- Khan, Muhammad, Javaid, Nadeem, Javaid, Sakeena, Khalid, Adia, Nasser, Nidal, Imran, Muhammad

  • Authors: Khan, Muhammad , Javaid, Nadeem , Javaid, Sakeena , 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. 2222-2227
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  • Description: In today's world, Internet of Things (IoT) helps people in many fields by enabling smart city projects in health monitoring, smart parking, industrial optimization, home energy management, etc. Daily life objects are connected with the Internet to allow access to their owners to keep an eye on their surroundings. The IoT network is comprised of nodes that are smart enough to perform any function and provide benefits to the people. However, any fault in the network opens up the risk of leaking personal information. The aim is to develop a scale-free network, which controls the effects of malicious attacks and consequently improves the network robustness. In this paper, our prime focus is to mitigate the effect of malicious nodes by providing a robust strategy to maintain the network stability. In this regard, we propose a topology named as a Cooperation based Edge Swap (CES) for improving the network robustness in the scale-free network. The CES uses the edge/link selection mechanism by involving the cooperation using a Rayleigh fading to swap the network topology for improving the network robustness. The simulations' outcome depicts the performance of the CES in terms of improving the network robustness. © 2020 IEEE.

A cloud-based IoMT data sharing scheme with conditional anonymous source authentication

- Wang, Yan-Ping, Wang, Xiao-Fen, Dai, Hong-Ning, Zhang, Xiao-Song, Su, Yu, Imran, Muhammad, Nasser, Nidal

  • Authors: Wang, Yan-Ping , Wang, Xiao-Fen , Dai, Hong-Ning , Zhang, Xiao-Song , Su, Yu , Imran, Muhammad , Nasser, Nidal
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 IEEE Global Communications Conference, GLOBECOM 2022, Virtual, online, 4-8 December 2022, 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings p. 2915-2920
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  • Description: As a rapidly growing subset of the Internet of Thing (IoT), the cloud-based Internet of Medical Thing (IoMT) has been widely applied in remote healthcare industries, which allows the physicians to monitor patients' body parameters remotely to offer continuous and timely healthcare. These healthcare parameters usually contain sensitive information, such as heart rates, glucose levels and etc., and the exposure of them may pose serious threats to the patients' health and lives. To guarantee security and privacy, many IoMT data sharing schemes have been proposed. However, most of these schemes either exhibit a one-to-one data sharing structure or fail to protect the patients' privacy. Since the data usually needs to be shared to different physicians, patients may want to be assisted without revealing their identities. To meet these requirements in healthcare systems, we propose a multi-receiver secure healthcare data sharing scheme, in which the patients are allowed to share their IoMT data to multiple physicians simultaneously for a multidisciplinary treatment, and the conditional anonymity is achieved where data source authentication is provided without revealing the patient's identity. When the patient health condition is abnormal, the hospital can correctly and quickly trace the patient's identity and inform him/her immediately. Our scheme is formally proved to achieve multiple security properties including confidentiality, unforgeability and anonymity. Simulation results demonstrate that the proposed scheme is efficient and practical. © 2022 IEEE.

Device-centric adaptive data stream management and offloading for analytics applications in future internet architectures

- Rehman, Muhammad, Liew, Chee, Wah, Teh, Imran, Muhammad, Salah, Khaled, Nasser, Nidal, Svetinovic, Davor

  • Authors: Rehman, Muhammad , Liew, Chee , Wah, Teh , Imran, Muhammad , Salah, Khaled , Nasser, Nidal , Svetinovic, Davor
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 114, no. (2021), p. 155-168
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  • Description: Information-Centric Networking (ICN) enables in-network data management and communication between multiple parties by replicating data and activating interactions between decoupled senders and receivers. Existing data management and offloading schemes in ICNs primarily use the transport layer hence it becomes inefficient to actively develop and update the ICN standards because of continuously evolving heterogeneous future internet architectures such as mobile edge cloud computing (MECC) architectures. In this paper, we present an adaptive execution model for mobile data stream mining (MDSM) applications in MECC environments to enable device-centric adaptive data management and offloading. We designed the proposed execution model considering multiple factors of complexity such as volume and velocity of continuously streaming data, the selection of data fusion and data preprocessing methods, the choice of learning models, learning rates, learning modes, mobility, limited computational and memory resources in mobile devices, the high coupling between application components, and dependency over Internet connections. We integrated the proposed execution model with multiple MDSM applications mapping to a real-word use-case for activity detection using MECC as a future network architecture. We thoroughly evaluated the proposed execution model in terms of battery power consumption, memory utilization, makespan, accuracy, and the amount of data reduced during in-network communication. The comparison showed that our proposed adaptive execution model outperformed the static and dynamic execution models which were deployed in the same ICN architecture. © 2020 Elsevier B.V.

Self-aware autonomous city : from sensing to planning

- Chen, Bo-Wei, Imran, Muhammad, Nasser, Nidal, Shoaib, Muhammad

  • Authors: Chen, Bo-Wei , Imran, Muhammad , Nasser, Nidal , Shoaib, Muhammad
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE Communications Magazine Vol. 57, no. 4 (2019), p. 33-39
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  • Description: This article presents a knowledge mining model, where a city can plan its development based on existing knowledge during city expansion, for example, telecommunication resource allocation and crowd forecasts in a new region. Unlike most works that focused on Internet-of-Things (IoT) sensing, this study is aimed at urban planning by using harvested data, from the perspective of city architects. For large-scale metropolitan areas, a massive amount of data is generated every day, either from static surveys or dynamic IoT sensing. For urban planners, data collection is not their primary concern. How to transfer harvested knowledge from exiting parts of the city to suburban/rural/untapped areas is a new challenge. This is because those areas still lack sufficient statistics, and the density of IoT deployment is low. Therefore, development is risky and uncertain. To exploit new regions requires knowledge inference. Such a transition needs data interpretation from historical city dynamics, involving sensor deployment, human activities, and resource allocation in the vicinity. With the proposed model, a city can estimate the requirement for resources when the peripheral areas on the outskirts of a city develop. The same model can be applied to enterprises for resource deployment, and applications are not merely limited to governments. © 1979-2012 IEEE.

Secure energy trading for electric vehicles using consortium blockchain and k-nearest neighbor

- Ashfaq, Tehreem, Javaid, Nadeem, Javed, Muhammad, Imran, Muhammad, Haider, Noman, Nasser, Nidal

TFPMS : transactions filtering pattern matching scheme for vehicular networks based on blockchain

- Iftikhar, Muhammad, Javaid, Nadeem, Javaid, Sakeena, Imran, Muhammad, Nasser, Nidal

  • 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
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  • 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.

Collisionless fast pattern formation mechanism for dynamic number of UAVs

- Raja, Gunasekaran, Saran, V., Anbalagan, Sudha, Bashir, Ali, Imran, Muhammad, Nasser, Nidal

  • Authors: Raja, Gunasekaran , Saran, V. , Anbalagan, Sudha , Bashir, Ali , Imran, Muhammad , Nasser, Nidal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Global Communications Conference, GLOBECOM 2020, Virtual, Taipei, China, 7 to 11 December 2020, 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
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  • Description: Unmanned Aerial Vehicle (UAV) is an emerging technology that assists in various automated activities where human involvement is minimal. Though individual UAVs are extremely useful entities, their productivity can further be increased by deploying multi-UAVs. Pattern formation among multi-UAVs is one of the key functionalities in a swarm environment that is essential for several UAV missions namely military expedition, search and rescue operations, drone based delivery mechanisms etc. In this paper, to facilitate pattern formation among UAVs in an effective manner, a Time-Interleaved Pattern Formation (TIPF) Mechanism is proposed. The existing systems work for a fixed number of drones whose pattern switching mechanisms are preprogrammed. However, the TIPF mechanism enables switching patterns among dynamic number of drones (UAVs) on the fly by inducing a small delay between each UAV movement. The TIPF mechanism avoids collision, which occurs due to the simultaneous movement of UAVs. The proposed TIPF mechanism encompasses a Centralised Coordinate Calculation (CCC) algorithm to easily calculate the coordinates of UAVs in a given pattern. Further, this mechanism has also been simulated and tested in our proposed virtual IP based Software In The Loop (V-SITL) environment. This proposed V-SITL environment offers increased scalability on account of the entire UAV system being simulated in a single computer. The TIPF mechanism has been simulated for 8 drones in a dynamic manner for square and triangle patterns. The simulation results show that the pattern formation time avoids collision in a time interleaving rate of 52.63%. © 2020 IEEE.

Adversarial learning-based bias mitigation for fatigue driving detection in fair-intelligent IoV

- Han, Mingzhe, Wu, Jun, Bashir, Ali, Yang, Wu, Imran, Muhammad, Nasser, Nidal

  • Authors: Han, Mingzhe , Wu, Jun , Bashir, Ali , Yang, Wu , Imran, Muhammad , Nasser, Nidal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Global Communications Conference, GLOBECOM 2020, Virtual, Taipei, China, 7 to 11 December 2020, 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
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  • Description: Fatigue driving is one of main causes of traffic accidents. To avoid such traffic accidents, divers' fatigue detection has been used in Intelligent Internet of Vehicles (IIoV). IIoV usually dynamically allocate computing resources according to drivers' fatigue degree to improve the real-time of fatigue detection model. However, the traditional fatigue detection model may have bias on certain groups, which would further cause unfair resource allocation. To solve the problem, this paper proposes an improved IIoV framework, named Fair-Intelligent Internet of Vehicles (FIIoV). Compared with IIoV, we improve two layers in FIIoV, i.e., the detection layer and the normalization layer. The detection layer uses Convolutional Neural Network (CNN) to detect drivers' fatigue degree, and then uses adversarial network to achieve fairness of detection models. The normalization layer achieves the distribution of different sensitive feature values from historical detection results generated in the detection layer, and then uses the distribution to normalize the output of the detection layer to improve the fairness and accuracy of fatigue detection models. Simulation results show that both accuracy and fairness of FIIoV is improved compared with the original IIoV. © 2020 IEEE.

Modeling and analysis of finite-scale clustered backscatter communication networks

- Wang, Qiu, Zhou, Yong, Dai, Hong-Ning, Zhang, Guopeng, Imran, Muhammad, Nasser, Nidal

  • Authors: Wang, Qiu , Zhou, Yong , Dai, Hong-Ning , Zhang, Guopeng , Imran, Muhammad , Nasser, Nidal
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Communications, ICC 2023, Rome, 28 May-1 June 2023, ICC 2023 - IEEE International Conference on Communications Vol. 2023-May, p. 1456-1461
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  • Description: Backscatter communication (BackCom) is an intriguing technology that enables devices to transmit information by reflecting environmental radio frequency signals while consuming ultra-low energy. Applying BackCom in the Internet of things (IoT) networks can effectively address the power-unsustainability issue of energy-constraint devices. Considering many practical IoT applications, networks are finite-scale and devices are needed to be deployed at hotspot regions organized in clusters to cooperate for specific tasks. This paper considers finite-scale clustered backscatter communication networks (F-CBackCom Nets). To ensure communications, this paper establishes a theoretic model to analyze the communication connectivity of F-CBackCom Nets. Different from prior studies analyzing the connectivity with a focus on the transmission pair located at the center of the network, this paper analyzes the connectivity of a transmission pair located in an arbitrary location, because the performance of transmission pairs potentially varies with their network location. Extensive simulations validate the accuracy of our analytical model. Our results show that the connectivity of a transmission pair can be affected by its network location. Our analytical model and results can offer beneficial implications for constructing F-CBackCom Nets. © 2023 IEEE.

A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing

- Nasser, Nidal, Emad-ul-Haq, Qazi, Imran, Muhammad, Ali, Asmaa, Razzak, Imran, Al-Helali, Abdulaziz

  • Authors: Nasser, Nidal , Emad-ul-Haq, Qazi , Imran, Muhammad , Ali, Asmaa , Razzak, Imran , Al-Helali, Abdulaziz
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 35, no. 19 (2023), p. 13775-13789
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  • Description: Coronavirus (COVID-19) is a very contagious infection that has drawn the world’s attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data’s intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system’s robustness and effectiveness using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a tenfold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Federated learning based trajectory optimization for UAV enabled MEC

- Nehra, Anushka, Consul, Prakhar, Budhiraja, Ishan, Kaur, Gagandeep, Nasser, Nidal, Imran, Muhammad

  • Authors: Nehra, Anushka , Consul, Prakhar , Budhiraja, Ishan , Kaur, Gagandeep , Nasser, Nidal , Imran, Muhammad
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Communications, ICC 2023 Vol. 2023-May, p. 1640-1645
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  • Description: We present a moving mobile edge computing architecture in which unmanned aerial vehicles (UAV) serve as an equipment, providing computational power and allowing task offloading from mobile devices (MD). By improving user association, resource allocation, and UAV trajectory, we optimizing the energy consumption of all MDs. Towards that purpose, we provide a Trajectory optimization technique for making real-time choices while considering all the situation of the environment, followed by a DRL-based Trajectory control approach (RLCT). The RLCT approach may be adapted to any UAV takeoff point and can find the solution faster. The FL is introduced to address the Optimization problem in a Semi-distributed DRL technique to deal with UAV trajectory constraints. The proposed FRL approach enables devices to rapidly train the models locally while communicating with a local server to construct a network globally. The simulation results in the result section shows that the proposed technique RLCT and FRL in the paper outperforms the existing methods' while the FRL performs best among all. © 2023 IEEE.

Robustness optimization of scale-free IoT Networks

- Usman, Muhammad, Javaid, Nadeem, Khalid, Adia, Nasser, Nidal, Imran, Muhammad

  • 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
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  • 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.

Ear in the sky : terrestrial mobile jamming to prevent aerial eavesdropping

- Wang, Qubeijian, Liu, Yalin, Dai, Hong-Ning, Imran, Muhammad, Nasser, Nidal

  • Authors: Wang, Qubeijian , Liu, Yalin , Dai, Hong-Ning , Imran, Muhammad , Nasser, Nidal
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE Global Communications Conference, GLOBECOM 2021, Madrid, 7-11 December 2021, 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
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  • Description: The emerging unmanned aerial vehicles (UAVs) pose a potential security threat for terrestrial communications when UAVs can be maliciously employed as UAV-eavesdroppers to wiretap confidential communications. To address such an aerial security threat, we present a friendly jamming scheme named terrestrial mobile jamming (TMJ) to protect terrestrial confidential communications from UAV eavesdropping. In our TMJ scheme, a jammer moving along the protection area can emit jamming signals toward the UAV-eavesdropper so as to reduce the eavesdropping risk. We evaluate the performance of our scheme by analyzing a secrecy-capacity maximization problem subject to the legitimate connectivity and eavesdropping probability. In addition, we investigate the optimized position for the jammer as well as its jamming power. Simulation results verify the effectiveness of the proposed scheme. © 2021 IEEE.

A trust management system for multi-agent system in smart grids using blockchain technology

- Samuel, Omaji, Javaid, Nadeem, Khalid, Adia, Imran, Muhammad, Nasser, Nidal

  • Authors: Samuel, Omaji , Javaid, Nadeem , Khalid, Adia , Imran, Muhammad , Nasser, Nidal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Global Communications Conference, GLOBECOM 2020, Virtual, Taipei China, 7 to 11 December 2020, 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings Vol. 2020-January
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  • Description: 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. © 2020 IEEE.

Integration of blockchain and network softwarization for space-air-ground-sea integrated networks

- Dai, Hong-Ning, Wu, Yulei, Imran, Muhammad, Nasser, Nidal

  • Authors: Dai, Hong-Ning , Wu, Yulei , Imran, Muhammad , Nasser, Nidal
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE internet of things magazine Vol. 5, no. 1 (2022), p. 166-172
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  • Description: Space-air-ground-sea integrated networks (SAGSINs) are promising to offer ubiquitous Internet services across the globe while confronting research challenges such as security vulnerabilities, privacy leakage concerns, and difficulty in resource sharing. On one hand, emerging network slicing and network softwarization technologies can fulfill diverse requirements with the provision of various services on top of heterogeneous SAGSIN hardware and software resources. On the other hand, blockchain and smart contracts can compensate for network slicing and softwarization to offer secure and automatic network services. This article presents an investigation on the convergence of blockchains with network slicing and network softwarization technologies for SAGSINs from the perspectives of network management and brokerage services of SAGSINs. In contrast to existing studies, this article is the first to incorporate blockchains into network slicing and network softwarization dedicated for SAGSINs. This article starts with a summary of key characteristics and challenges of SAGSINs. Then a review of network slicing and network softwarization is given in the context of SAGSINs. This article next presents an integrated framework of network slicing, network softwarization, and blockchain for SAGSINs. Moreover, this article outlines a set of open issues and research challenges that would be useful to guide future research in this area.
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