An adaptive and flexible brain energized full body exoskeleton with IoT edge for assisting the paralyzed patients
- Jacob, Sunil, Alagirisamy, Mukil, Menon, Varun, Kumar, B. Manoj, Balasubramanian, Venki
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Menon, Varun , Kumar, B. Manoj , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 100721-100731
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- Description: The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Menon, Varun , Kumar, B. Manoj , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 100721-100731
- Full Text:
- Reviewed:
- Description: The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
- Li, Xiaomin, Wan, Jiafu, Dai, Hong-Ning, Imran, Muhammad, Xia, Min, Celesti, Antonio
- Authors: Li, Xiaomin , Wan, Jiafu , Dai, Hong-Ning , Imran, Muhammad , Xia, Min , Celesti, Antonio
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 7 (2019), p. 4225-4234
- Full Text: false
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- Description: At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing. © 2005-2012 IEEE.
- Nasser, Nasser, Fadlullah, Zubair, Fouda, Mostafa, Ali, Asmaa, Imran, Muhammad
- Authors: Nasser, Nasser , Fadlullah, Zubair , Fouda, Mostafa , Ali, Asmaa , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Computer Networks Vol. 205, no. (2022), p.
- Full Text: false
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- Description: The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial–terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach. © 2021 Elsevier B.V.
RBFK cipher : a randomized butterfly architecture-based lightweight block cipher for IoT devices in the edge computing environment
- Rana, Sohel, Mondal, Mondal, Kamruzzaman, Joarder
- Authors: Rana, Sohel , Mondal, Mondal , Kamruzzaman, Joarder
- Date: 2023
- Type: Text , Journal article
- Relation: Cybersecurity Vol. 6, no. 1 (2023), p.
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- Description: Internet security has become a major concern with the growing use of the Internet of Things (IoT) and edge computing technologies. Even though data processing is handled by the edge server, sensitive data is generated and stored by the IoT devices, which are subject to attack. Since most IoT devices have limited resources, standard security algorithms such as AES, DES, and RSA hamper their ability to run properly. In this paper, a lightweight symmetric key cipher termed randomized butterfly architecture of fast Fourier transform for key (RBFK) cipher is proposed for resource-constrained IoT devices in the edge computing environment. The butterfly architecture is used in the key scheduling system to produce strong round keys for five rounds of the encryption method. The RBFK cipher has two key sizes: 64 and 128 bits, with a block size of 64 bits. The RBFK ciphers have a larger avalanche effect due to the butterfly architecture ensuring strong security. The proposed cipher satisfies the Shannon characteristics of confusion and diffusion. The memory usage and execution cycle of the RBFK cipher are assessed using the fair evaluation of the lightweight cryptographic systems (FELICS) tool. The proposed ciphers were also implemented using MATLAB 2021a to test key sensitivity by analyzing the histogram, correlation graph, and entropy of encrypted and decrypted images. Since the RBFK ciphers with minimal computational complexity provide better security than recently proposed competing ciphers, these are suitable for IoT devices in an edge computing environment. © 2023, The Author(s).
- Authors: Rana, Sohel , Mondal, Mondal , Kamruzzaman, Joarder
- Date: 2023
- Type: Text , Journal article
- Relation: Cybersecurity Vol. 6, no. 1 (2023), p.
- Full Text:
- Reviewed:
- Description: Internet security has become a major concern with the growing use of the Internet of Things (IoT) and edge computing technologies. Even though data processing is handled by the edge server, sensitive data is generated and stored by the IoT devices, which are subject to attack. Since most IoT devices have limited resources, standard security algorithms such as AES, DES, and RSA hamper their ability to run properly. In this paper, a lightweight symmetric key cipher termed randomized butterfly architecture of fast Fourier transform for key (RBFK) cipher is proposed for resource-constrained IoT devices in the edge computing environment. The butterfly architecture is used in the key scheduling system to produce strong round keys for five rounds of the encryption method. The RBFK cipher has two key sizes: 64 and 128 bits, with a block size of 64 bits. The RBFK ciphers have a larger avalanche effect due to the butterfly architecture ensuring strong security. The proposed cipher satisfies the Shannon characteristics of confusion and diffusion. The memory usage and execution cycle of the RBFK cipher are assessed using the fair evaluation of the lightweight cryptographic systems (FELICS) tool. The proposed ciphers were also implemented using MATLAB 2021a to test key sensitivity by analyzing the histogram, correlation graph, and entropy of encrypted and decrypted images. Since the RBFK ciphers with minimal computational complexity provide better security than recently proposed competing ciphers, these are suitable for IoT devices in an edge computing environment. © 2023, The Author(s).
- Jha, Devki Nandan, Alwasel, Khaled, Alshoshan, Areeb, Huang, Xianghua, Naha, Ranesh, Battula, Sudheer, Garg, Saurabh, Puthal, Deepak, James, Philip, Zomaya, Albert, Dustdar, Schahram, Ranjan, Rajiv
- Authors: Jha, Devki Nandan , Alwasel, Khaled , Alshoshan, Areeb , Huang, Xianghua , Naha, Ranesh , Battula, Sudheer , Garg, Saurabh , Puthal, Deepak , James, Philip , Zomaya, Albert , Dustdar, Schahram , Ranjan, Rajiv
- Date: 2020
- Type: Text , Journal article
- Relation: Software, practice & experience Vol. 50, no. 6 (2020), p. 844-867
- Full Text: false
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- Description: Summary With the proliferation of Internet of Things (IoT) and edge computing paradigms, billions of IoT devices are being networked to support data‐driven and real‐time decision making across numerous application domains, including smart homes, smart transport, and smart buildings. These ubiquitously distributed IoT devices send the raw data to their respective edge device (eg, IoT gateways) or the cloud directly. The wide spectrum of possible application use cases make the design and networking of IoT and edge computing layers a very tedious process due to the: (i) complexity and heterogeneity of end‐point networks (eg, Wi‐Fi, 4G, and Bluetooth) (ii) heterogeneity of edge and IoT hardware resources and software stack (iv) mobility of IoT devices and (iii) the complex interplay between the IoT and edge layers. Unlike cloud computing, where researchers and developers seeking to test capacity planning, resource selection, network configuration, computation placement, and security management strategies had access to public cloud infrastructure (eg, Amazon and Azure), establishing an IoT and edge computing testbed that offers a high degree of verisimilitude is not only complex, costly, and resource‐intensive but also time‐intensive. Moreover, testing in real IoT and edge computing environments is not feasible due to the high cost and diverse domain knowledge required in order to reason about their diversity, scalability, and usability. To support performance testing and validation of IoT and edge computing configurations and algorithms at scale, simulation frameworks should be developed. Hence, this article proposes a novel simulator IoTSim‐Edge, which captures the behavior of heterogeneous IoT and edge computing infrastructure and allows users to test their infrastructure and framework in an easy and configurable manner. IoTSim‐Edge extends the capability of CloudSim to incorporate the different features of edge and IoT devices. The effectiveness of IoTSim‐Edge is described using three test cases. Results show the varying capability of IoTSim‐Edge in terms of application composition, battery‐oriented modeling, heterogeneous protocols modeling, and mobility modeling along with the resources provisioning for IoT applications.
- Usman, Muhammad, Jan, Mian, Jolfaei, Alireza, Xu, Min, He, Xiangjian, Chen, Jinjun
- Authors: Usman, Muhammad , Jan, Mian , Jolfaei, Alireza , Xu, Min , He, Xiangjian , Chen, Jinjun
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 9 (2020), p. 6114-6123
- Full Text: false
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- Description: Industrial Internet of Things applications demand trustworthiness in terms of quality of service (QoS), security, and privacy, to support the smooth transmission of data. To address these challenges, in this article, we propose a distributed and anonymous data collection (DaaC) framework based on a multilevel edge computing architecture. This framework distributes captured data among multiple level-one edge devices (LOEDs) to improve the QoS and minimize packet drop and end-to-end delay. Mobile sinks are used to collect data from LOEDs and upload to cloud servers. Before data collection, the mobile sinks are registered with a level-two edge-device to protect the underlying network. The privacy of mobile sinks is preserved through group-based signed data collection requests. Experimental results show that our proposed framework improves QoS through distributed data transmission. It also helps in protecting the underlying network through a registration scheme and preserves the privacy of mobile sinks through group-based data collection requests. © 2005-2012 IEEE.
A secured framework for SDN-based edge computing in IoT-enabled healthcare system
- Li, Junxia, Cai, Jinjin, Khan, Fazlullah, Rehman, Ateeq, Balasubramanian, Venki
- Authors: Li, Junxia , Cai, Jinjin , Khan, Fazlullah , Rehman, Ateeq , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 135479-135490
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- Description: The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**
- Authors: Li, Junxia , Cai, Jinjin , Khan, Fazlullah , Rehman, Ateeq , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 135479-135490
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**
- Tang, Hao, Li, Di, Wan, Jiafu, Imran, Muhammad, Shoaib, Muhammad
- Authors: Tang, Hao , Li, Di , Wan, Jiafu , Imran, Muhammad , Shoaib, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 7, no. 5 (2020), p. 4248-4259
- Full Text: false
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- Description: The development of Industry 4.0 has provided the possibility to meet frequent changes in product type and batches, a sharp decline in the delivery cycle, constraints of quality cost, and other relevant parameters of customized production mode. Intelligent manufacturing, as a core of Industry 4.0, represents a deep integration of new IT technologies, such as the industrial Internet of Things and service-oriented architecture, and manufacturing process. To realize intelligent manufacturing, this article introduces a cloud-assisted and edge-decision-making manufacturing architecture that contains a cloud and production edges. An intelligent production edge is designed to provide the traditional devices the abilities of data access and self-decision making. Besides, the proposed architecture is modeled as a multiagent system with the edge intelligence support, describing the agent-based reconfiguration mechanism from the three aspects, namely, agent interaction, agent behavior, and negotiation mechanism. The experimental results show that the reconfigurable method based on the proposed architecture can be used in the mixed-flow production scenario based on random orders, to improve the adaptability and robustness. © 2014 IEEE.
RaSEC : an intelligent framework for reliable and secure multilevel edge computing in industrial environments
- Usman, Muhammad, Jolfaei, Alireza, Jan, Mian
- Authors: Usman, Muhammad , Jolfaei, Alireza , Jan, Mian
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol. 56, no. 4 (2020), p. 4543-4551
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- Description: Industrial applications generate big data with redundant information that is transmitted over heterogeneous networks. The transmission of big data with redundant information not only increases the overall end-to-end delay but also increases the computational load on servers which affects the performance of industrial applications. To address these challenges, we propose an intelligent framework named Reliable and Secure multi-level Edge Computing (RaSEC), which operates in three phases. In the first phase, level-one edge devices apply a lightweight aggregation technique on the generated data. This technique not only reduces the size of the generated data but also helps in preserving the privacy of data sources. In the second phase, a multistep process is used to register level-two edge devices (LTEDs) with high-level edge devices (HLEDs). Due to the registration process, only legitimate LTEDs can forward data to the HLEDs, and as a result, the computational load on HLEDs decreases. In the third phase, the HLEDs use a convolutional neural network to detect the presence of moving objects in the data forwarded by LTEDs. If a movement is detected, the data is uploaded to the cloud servers for further analysis; otherwise, the data is discarded to minimize the use of computational resources on cloud computing platforms. The proposed framework reduces the response time by forwarding useful information to the cloud servers and can be utilized by various industrial applications. Our theoretical and experimental results confirm the resiliency of our framework with respect to security and privacy threats. © 1972-2012 IEEE.
- Authors: Usman, Muhammad , Jolfaei, Alireza , Jan, Mian
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol. 56, no. 4 (2020), p. 4543-4551
- Full Text:
- Reviewed:
- Description: Industrial applications generate big data with redundant information that is transmitted over heterogeneous networks. The transmission of big data with redundant information not only increases the overall end-to-end delay but also increases the computational load on servers which affects the performance of industrial applications. To address these challenges, we propose an intelligent framework named Reliable and Secure multi-level Edge Computing (RaSEC), which operates in three phases. In the first phase, level-one edge devices apply a lightweight aggregation technique on the generated data. This technique not only reduces the size of the generated data but also helps in preserving the privacy of data sources. In the second phase, a multistep process is used to register level-two edge devices (LTEDs) with high-level edge devices (HLEDs). Due to the registration process, only legitimate LTEDs can forward data to the HLEDs, and as a result, the computational load on HLEDs decreases. In the third phase, the HLEDs use a convolutional neural network to detect the presence of moving objects in the data forwarded by LTEDs. If a movement is detected, the data is uploaded to the cloud servers for further analysis; otherwise, the data is discarded to minimize the use of computational resources on cloud computing platforms. The proposed framework reduces the response time by forwarding useful information to the cloud servers and can be utilized by various industrial applications. Our theoretical and experimental results confirm the resiliency of our framework with respect to security and privacy threats. © 1972-2012 IEEE.
Blockchain leveraged decentralized IoT eHealth framework
- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Internet of Things Vol. 9, no. March 2020 p. 100159
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- Description: Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data.
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Internet of Things Vol. 9, no. March 2020 p. 100159
- Full Text:
- Reviewed:
- Description: Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data.
Performance and cryptographic evaluation of security protocols in distributed networks using applied pi calculus and Markov Chain
- Edris, Ed, Aiash, Mahdi, Khoshkholghi, Mohammad, Naha, Ranesh, Chowdhury, Abdullahi, Loo, Jonathan
- Authors: Edris, Ed , Aiash, Mahdi , Khoshkholghi, Mohammad , Naha, Ranesh , Chowdhury, Abdullahi , Loo, Jonathan
- Date: 2023
- Type: Text , Journal article
- Relation: Internet of Things (Netherlands) Vol. 24, no. (2023), p.
- Full Text:
- Reviewed:
- Description: The development of cryptographic protocols goes through two stages, namely, security verification and performance analysis. The verification of the protocol's security properties could be analytically achieved using threat modelling, or formally using formal methods and model checkers. The performance analysis could be mathematical or simulation-based. However, mathematical modelling is complicated and does not reflect the actual deployment environment of the protocol in the current state of the art. Simulation software provides scalability and can simulate complicated scenarios, however, there are times when it is not possible to use simulations due to a lack of support for new technologies or simulation scenarios. Therefore, this paper proposes a formal method and analytical model for evaluating the performance of security protocols using applied pi-calculus and Markov Chain processes. It interprets algebraic processes and associates cryptographic operatives with quantitative measures to estimate and evaluate cryptographic costs. With this approach, the protocols are presented as processes using applied pi-calculus, and their security properties are an approximate abstraction of protocol equivalence based on the verification from ProVerif and evaluated using analytical and simulation models for quantitative measures. The interpretation of the quantities is associated with process transitions, rates, and measures as a cost of using cryptographic primitives. This method supports users’ input in analysing the protocol's activities and performance. As a proof of concept, we deploy this approach to assess the performance of security protocols designed to protect large-scale, 5G-based Device-to-Device communications. We also conducted a performance evaluation of the protocols based on analytical and network simulator results to compare the effectiveness of the proposed approach. © 2023 The Author(s)
- Authors: Edris, Ed , Aiash, Mahdi , Khoshkholghi, Mohammad , Naha, Ranesh , Chowdhury, Abdullahi , Loo, Jonathan
- Date: 2023
- Type: Text , Journal article
- Relation: Internet of Things (Netherlands) Vol. 24, no. (2023), p.
- Full Text:
- Reviewed:
- Description: The development of cryptographic protocols goes through two stages, namely, security verification and performance analysis. The verification of the protocol's security properties could be analytically achieved using threat modelling, or formally using formal methods and model checkers. The performance analysis could be mathematical or simulation-based. However, mathematical modelling is complicated and does not reflect the actual deployment environment of the protocol in the current state of the art. Simulation software provides scalability and can simulate complicated scenarios, however, there are times when it is not possible to use simulations due to a lack of support for new technologies or simulation scenarios. Therefore, this paper proposes a formal method and analytical model for evaluating the performance of security protocols using applied pi-calculus and Markov Chain processes. It interprets algebraic processes and associates cryptographic operatives with quantitative measures to estimate and evaluate cryptographic costs. With this approach, the protocols are presented as processes using applied pi-calculus, and their security properties are an approximate abstraction of protocol equivalence based on the verification from ProVerif and evaluated using analytical and simulation models for quantitative measures. The interpretation of the quantities is associated with process transitions, rates, and measures as a cost of using cryptographic primitives. This method supports users’ input in analysing the protocol's activities and performance. As a proof of concept, we deploy this approach to assess the performance of security protocols designed to protect large-scale, 5G-based Device-to-Device communications. We also conducted a performance evaluation of the protocols based on analytical and network simulator results to compare the effectiveness of the proposed approach. © 2023 The Author(s)
- Rafique, Wajid, Qi, Lianyong, Yaqoob, Ibrar, Imran, Muhammad, Rasool, Raojan, Dou, Wanchun
- Authors: Rafique, Wajid , Qi, Lianyong , Yaqoob, Ibrar , Imran, Muhammad , Rasool, Raojan , Dou, Wanchun
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Communications Surveys and Tutorials Vol. 22, no. 3 (2020), p. 1761-1804
- Full Text: false
- Reviewed:
- Description: Millions of sensors continuously produce and transmit data to control real-world infrastructures using complex networks in the Internet of Things (IoT). However, IoT devices are limited in computational power, including storage, processing, and communication resources, to effectively perform compute-intensive tasks locally. Edge computing resolves the resource limitation problems by bringing computation closer to the edge of IoT devices. Providing distributed edge nodes across the network reduces the stress of centralized computation and overcomes latency challenges in the IoT. Therefore, edge computing presents low-cost solutions for compute-intensive tasks. Software-Defined Networking (SDN) enables effective network management by presenting a global perspective of the network. While SDN was not explicitly developed for IoT challenges, it can, however, provide impetus to solve the complexity issues and help in efficient IoT service orchestration. The current IoT paradigm of massive data generation, complex infrastructures, security vulnerabilities, and requirements from the newly developed technologies make IoT realization a challenging issue. In this research, we provide an extensive survey on SDN and the edge computing ecosystem to solve the challenge of complex IoT management. We present the latest research on Software-Defined Internet of Things orchestration using Edge (SDIoT-Edge) and highlight key requirements and standardization efforts in integrating these diverse architectures. An extensive discussion on different case studies using SDIoT-Edge computing is presented to envision the underlying concept. Furthermore, we classify state-of-the-art research in the SDIoT-Edge ecosystem based on multiple performance parameters. We comprehensively present security and privacy vulnerabilities in the SDIoT-Edge computing and provide detailed taxonomies of multiple attack possibilities in this paradigm. We highlight the lessons learned based on our findings at the end of each section. Finally, we discuss critical insights toward current research issues, challenges, and further research directions to efficiently provide IoT services in the SDIoT-Edge paradigm. © 1998-2012 IEEE.
Cloudlet computing : recent advances, taxonomy, and challenges
- Babar, Mohammad, Khan, Muhammad, Ali, Farman, Imran, Muhammad, Shoaib, Muhammad
- Authors: Babar, Mohammad , Khan, Muhammad , Ali, Farman , Imran, Muhammad , Shoaib, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 29609-29622
- Full Text:
- Reviewed:
- Description: A cloudlet is an emerging computing paradigm that is designed to meet the requirements and expectations of the Internet of things (IoT) and tackle the conventional limitations of a cloud (e.g., high latency). The idea is to bring computing resources (i.e., storage and processing) to the edge of a network. This article presents a taxonomy of cloudlet applications, outlines cloudlet utilities, and describes recent advances, challenges, and future research directions. Based on the literature, a unique taxonomy of cloudlet applications is designed. Moreover, a cloudlet computation offloading application for augmenting resource-constrained IoT devices, handling compute-intensive tasks, and minimizing the energy consumption of related devices is explored. This study also highlights the viability of cloudlets to support smart systems and applications, such as augmented reality, virtual reality, and applications that require high-quality service. Finally, the role of cloudlets in emergency situations, hostile conditions, and in the technological integration of future applications and services is elaborated in detail. © 2013 IEEE.
- Authors: Babar, Mohammad , Khan, Muhammad , Ali, Farman , Imran, Muhammad , Shoaib, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 29609-29622
- Full Text:
- Reviewed:
- Description: A cloudlet is an emerging computing paradigm that is designed to meet the requirements and expectations of the Internet of things (IoT) and tackle the conventional limitations of a cloud (e.g., high latency). The idea is to bring computing resources (i.e., storage and processing) to the edge of a network. This article presents a taxonomy of cloudlet applications, outlines cloudlet utilities, and describes recent advances, challenges, and future research directions. Based on the literature, a unique taxonomy of cloudlet applications is designed. Moreover, a cloudlet computation offloading application for augmenting resource-constrained IoT devices, handling compute-intensive tasks, and minimizing the energy consumption of related devices is explored. This study also highlights the viability of cloudlets to support smart systems and applications, such as augmented reality, virtual reality, and applications that require high-quality service. Finally, the role of cloudlets in emergency situations, hostile conditions, and in the technological integration of future applications and services is elaborated in detail. © 2013 IEEE.
Edge computing for Internet of Everything : a survey
- Kong, Xiangjie, Wu, Yuhan, Wang, Hui, Xia, Feng
- Authors: Kong, Xiangjie , Wu, Yuhan , Wang, Hui , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 9, no. 23 (2022), p. 23472-23485
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- Reviewed:
- Description: In this era of the Internet of Everything (IoE), edge computing has emerged as the critical enabling technology to solve a series of issues caused by an increasing amount of interconnected devices and large-scale data transmission. However, the deficiencies of edge computing paradigm are gradually being magnified in the context of IoE, especially in terms of service migration, security and privacy preservation, and deployment issues of edge node. These issues can not be well addressed by conventional approaches. Thanks to the rapid development of upcoming technologies, such as artificial intelligence (AI), blockchain, and microservices, novel and more effective solutions have emerged and been applied to solve existing challenges. In addition, edge computing can be deeply integrated with technologies in other domains (e.g., AI, blockchain, 6G, and digital twin) through interdisciplinary intersection and practice, releasing the potential for mutual benefit. These promising integrations need to be further explored and researched. In addition, edge computing provides strong support in applications scenarios, such as remote working, new physical retail industries, and digital advertising, which has greatly changed the way we live, work, and study. In this article, we present an up-to-date survey of the edge computing research. In addition to introducing the definition, model, and characteristics of edge computing, we discuss a set of key issues in edge computing and novel solutions supported by emerging technologies in IoE era. Furthermore, we explore the potential and promising trends from the perspective of technology integration. Finally, new application scenarios and the final form of edge computing are discussed. © 2014 IEEE.
- Authors: Kong, Xiangjie , Wu, Yuhan , Wang, Hui , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 9, no. 23 (2022), p. 23472-23485
- Full Text:
- Reviewed:
- Description: In this era of the Internet of Everything (IoE), edge computing has emerged as the critical enabling technology to solve a series of issues caused by an increasing amount of interconnected devices and large-scale data transmission. However, the deficiencies of edge computing paradigm are gradually being magnified in the context of IoE, especially in terms of service migration, security and privacy preservation, and deployment issues of edge node. These issues can not be well addressed by conventional approaches. Thanks to the rapid development of upcoming technologies, such as artificial intelligence (AI), blockchain, and microservices, novel and more effective solutions have emerged and been applied to solve existing challenges. In addition, edge computing can be deeply integrated with technologies in other domains (e.g., AI, blockchain, 6G, and digital twin) through interdisciplinary intersection and practice, releasing the potential for mutual benefit. These promising integrations need to be further explored and researched. In addition, edge computing provides strong support in applications scenarios, such as remote working, new physical retail industries, and digital advertising, which has greatly changed the way we live, work, and study. In this article, we present an up-to-date survey of the edge computing research. In addition to introducing the definition, model, and characteristics of edge computing, we discuss a set of key issues in edge computing and novel solutions supported by emerging technologies in IoE era. Furthermore, we explore the potential and promising trends from the perspective of technology integration. Finally, new application scenarios and the final form of edge computing are discussed. © 2014 IEEE.
A blockchain-based distributed peer-to-peer ecosystem for energy trading
- Authors: Islam, Mohammad
- Date: 2024
- Type: Text , Thesis , PhD
- Full Text:
- Description: Blockchain technologies are revolutionising peer-to-peer (P2P) distributed energy trading. These technologies can leverage microgrid decentralisation and immutable data storage to provide efficient and secure trading to benefit prosumers. A double auction mechanism is best suited for energy trading in a P2P microgrid. This mechanism requires a solvent cryptocurrency reserve for payment settlement. Double auctions give rise to unspent auction reservations (UARs). Existing mechanisms can settle further auctions with UARs but need improvements to do this without affecting trading efficiency. Keeping a cryptocurrency reserve solvent also requires adaptations to existing mechanisms. Auction settlements within a microgrid leave UARs, meaning that other microgrids must join for further auction settlements, and this leads to security vulnerabilities. It is important to develop an ecosystem that can enhance trading efficiency, ensure the solvency of the cryptocurrency reserve and provide security for multi-microgrid energy trading. In distributed energy trading, an auctioneer passes UARs to the next auctioneer as specified by the passing mechanism. Traditional energy trading systems use simple passing mechanisms and basic pricing mechanisms, but this adversely affects trading efficiency and buyers’ economic surplus. Traditional P2P energy trading systems use passing mechanisms that only partially consider the auction capacity of the next auctioneer. We propose a blockchain-based energy trading mechanism using a smart passing mechanism (SPM) that uses an unspent reservation profile (URP) to represent the auctioneers’ capability to pass UARs within a P2P microgrid. We further propose an intelligent passing mechanism (iPass) that incorporates price information into URPs to enhance trading efficiency. We applied three metrics to measure trading efficiency: convergence time, auction settlements and the economic surplus of buyers and sellers. We simulated our mechanisms in Hyperledger Fabric, a permissioned blockchain framework that managed the data storage and smart contracts. Experiments showed that our SPM reduces the convergence time, increases auction settlements and increases the economic surplus of buyers compared with existing mechanisms. Experiments showed that iPass is even more efficient than other passing mechanisms, including SPM, further reducing the convergence time, increasing auction settlements and increasing the economic surplus of buyers and sellers. Settling payments in blockchain-based P2P energy trading requires maintaining the solvency of the cryptocurrency reserve to ensure a stable medium of exchange and reduce price volatility. Stablecoins, as a form of cryptocurrency—the most suitable medium of exchange—are gaining attention from central banks. A consortium of central banks has recommended compliance with capital and liquidity standards for high-quality liquid assets (HQLA). Stablecoins, as a form of HQLA, require the adaptation of these standards for P2P energy trading. We propose a mechanism (NF90) to control the inflow of stablecoins in response to the liquidity coverage ratio (LCR) for reserve resilience and to maintain solvency. The Basel III Accord recommends 100% LCR. We measured the effectiveness of NF90 using LCR as a metric simulating the mechanism in Hyperledger Fabric to manage deceni tralisation, data storage and smart contracts. NF90 was the most effective inflow control mechanism. The use of iPass for a P2P microgrid leaves UARs. Traditional trading mechanisms settle further auctions with UARs within a microgrid, which affects the economic surplus of prosumers. Auction settlements with neighbouring microgrids increase prosumers’ economic surplus, but the usual pricing of double auction mechanisms reduces their economic surplus. Other pricing mechanisms are needed in a multi-microgrid paradigm. Settling auctions for microgrids requires common computational resources that are close to microgrids. Edge computing technologies suit this need, and blockchain technology leverages immutable data storage in cloud servers. However, communication with a cloud server through proprietary edge computing devices exposes the ecosystem to security vulnerabilities. It is important to control access by prosumers and forensic users. Immutable data storage and the retrieval of data are essential. Two challenges in information security are incorporating reliable access control for users and devices while granting access to confidential data for relevant users and maintaining data persistence. This research used a blockchain structure for data persistence. We propose a framework of novel protocols to authenticate users (prosumers and auctioneers) by the edge server and of the edge server by the cloud server. Our framework also provides access to forensic users using immutable blockchain-based data storage with endpoint authentication and a role-based user access control system. We simulated the framework using the Automated Validation of Internet Security Protocols and Applications and showed that it can deal effectively with several security issues.
- Description: Doctor of Philosophy
- Authors: Islam, Mohammad
- Date: 2024
- Type: Text , Thesis , PhD
- Full Text:
- Description: Blockchain technologies are revolutionising peer-to-peer (P2P) distributed energy trading. These technologies can leverage microgrid decentralisation and immutable data storage to provide efficient and secure trading to benefit prosumers. A double auction mechanism is best suited for energy trading in a P2P microgrid. This mechanism requires a solvent cryptocurrency reserve for payment settlement. Double auctions give rise to unspent auction reservations (UARs). Existing mechanisms can settle further auctions with UARs but need improvements to do this without affecting trading efficiency. Keeping a cryptocurrency reserve solvent also requires adaptations to existing mechanisms. Auction settlements within a microgrid leave UARs, meaning that other microgrids must join for further auction settlements, and this leads to security vulnerabilities. It is important to develop an ecosystem that can enhance trading efficiency, ensure the solvency of the cryptocurrency reserve and provide security for multi-microgrid energy trading. In distributed energy trading, an auctioneer passes UARs to the next auctioneer as specified by the passing mechanism. Traditional energy trading systems use simple passing mechanisms and basic pricing mechanisms, but this adversely affects trading efficiency and buyers’ economic surplus. Traditional P2P energy trading systems use passing mechanisms that only partially consider the auction capacity of the next auctioneer. We propose a blockchain-based energy trading mechanism using a smart passing mechanism (SPM) that uses an unspent reservation profile (URP) to represent the auctioneers’ capability to pass UARs within a P2P microgrid. We further propose an intelligent passing mechanism (iPass) that incorporates price information into URPs to enhance trading efficiency. We applied three metrics to measure trading efficiency: convergence time, auction settlements and the economic surplus of buyers and sellers. We simulated our mechanisms in Hyperledger Fabric, a permissioned blockchain framework that managed the data storage and smart contracts. Experiments showed that our SPM reduces the convergence time, increases auction settlements and increases the economic surplus of buyers compared with existing mechanisms. Experiments showed that iPass is even more efficient than other passing mechanisms, including SPM, further reducing the convergence time, increasing auction settlements and increasing the economic surplus of buyers and sellers. Settling payments in blockchain-based P2P energy trading requires maintaining the solvency of the cryptocurrency reserve to ensure a stable medium of exchange and reduce price volatility. Stablecoins, as a form of cryptocurrency—the most suitable medium of exchange—are gaining attention from central banks. A consortium of central banks has recommended compliance with capital and liquidity standards for high-quality liquid assets (HQLA). Stablecoins, as a form of HQLA, require the adaptation of these standards for P2P energy trading. We propose a mechanism (NF90) to control the inflow of stablecoins in response to the liquidity coverage ratio (LCR) for reserve resilience and to maintain solvency. The Basel III Accord recommends 100% LCR. We measured the effectiveness of NF90 using LCR as a metric simulating the mechanism in Hyperledger Fabric to manage deceni tralisation, data storage and smart contracts. NF90 was the most effective inflow control mechanism. The use of iPass for a P2P microgrid leaves UARs. Traditional trading mechanisms settle further auctions with UARs within a microgrid, which affects the economic surplus of prosumers. Auction settlements with neighbouring microgrids increase prosumers’ economic surplus, but the usual pricing of double auction mechanisms reduces their economic surplus. Other pricing mechanisms are needed in a multi-microgrid paradigm. Settling auctions for microgrids requires common computational resources that are close to microgrids. Edge computing technologies suit this need, and blockchain technology leverages immutable data storage in cloud servers. However, communication with a cloud server through proprietary edge computing devices exposes the ecosystem to security vulnerabilities. It is important to control access by prosumers and forensic users. Immutable data storage and the retrieval of data are essential. Two challenges in information security are incorporating reliable access control for users and devices while granting access to confidential data for relevant users and maintaining data persistence. This research used a blockchain structure for data persistence. We propose a framework of novel protocols to authenticate users (prosumers and auctioneers) by the edge server and of the edge server by the cloud server. Our framework also provides access to forensic users using immutable blockchain-based data storage with endpoint authentication and a role-based user access control system. We simulated the framework using the Automated Validation of Internet Security Protocols and Applications and showed that it can deal effectively with several security issues.
- Description: Doctor of Philosophy
Fog computing: Survey of trends, architectures, requirements, and research directions
- Naha, Ranesh, Garg, Saurabh, Georgakopoulos, Dimitrios, Jayaraman, Prem, Gao, Longxiang, Xiang, Yong, Ranjan, Rajiv
- Authors: Naha, Ranesh , Garg, Saurabh , Georgakopoulos, Dimitrios , Jayaraman, Prem , Gao, Longxiang , Xiang, Yong , Ranjan, Rajiv
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE access Vol. 6, no. (2018), p. 47980-48009
- Full Text: false
- Reviewed:
- Description: Emerging technologies such as the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big data. Data generated from IoT devices are generally processed in a cloud infrastructure because of the on-demand services and scalability features of the cloud computing paradigm. However, processing IoT application requests on the cloud exclusively is not an efficient solution for some IoT applications, especially time-sensitive ones. To address this issue, Fog computing, which resides in between cloud and IoT devices, was proposed. In general, in the Fog computing environment, IoT devices are connected to Fog devices. These Fog devices are located in close proximity to users and are responsible for intermediate computation and storage. One of the key challenges in running IoT applications in a Fog computing environment are resource allocation and task scheduling. Fog computing research is still in its infancy, and taxonomy-based investigation into the requirements of Fog infrastructure, platform, and applications mapped to current research is still required. This survey will help the industry and research community synthesize and identify the requirements for Fog computing. This paper starts with an overview of Fog computing in which the definition of Fog computing, research trends, and the technical differences between Fog and cloud are reviewed. Then, we investigate numerous proposed Fog computing architectures and describe the components of these architectures in detail. From this, the role of each component will be defined, which will help in the deployment of Fog computing. Next, a taxonomy of Fog computing is proposed by considering the requirements of the Fog computing paradigm. We also discuss existing research works and gaps in resource allocation and scheduling, fault tolerance, simulation tools, and Fog-based microservices. Finally, by addressing the limitations of current research works, we present some open issues, which will determine the future research direction for the Fog computing paradigm.
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