2010 IEEE International Conference on Automation and Logistics, 2010
Abstract - For vehicle-to-vehicle (V2V) communication, suitable routing protocols are needed. An ... more Abstract - For vehicle-to-vehicle (V2V) communication, suitable routing protocols are needed. An adaptive cross-layer multi-path routing protocol, RS-AOMDV, is proposed. The routing metric combines hop-count, link-quality and vehicle-motion information. To make use of ...
Interoperability is one of the most challenging concerns that face healthcare information system ... more Interoperability is one of the most challenging concerns that face healthcare information system (HIS) actors. Interoperability implementation in this context may be a data exchange interfacing, a service oriented interaction or even a composition of new composite healthcare processes. In fact, optimizing efforts of interoperability achievement is a key requirement to effectively setup, develop and evolve intra- and interorganizational collaboration. To ensure interoperability project effectiveness, this paper proposes a modeling representation of health processes interoperability evolution. Interoperability degrees of involved automated processes are assessed using a ratio metric, taking into account all significant aspects, such as potentiality, compatibility and operational performance. Then, a particle swarm optimization algorithm (PSO) is used as a heuristic optimization method to find the best distribution of effort needed to establish an efficient healthcare collaborative net...
The current study uses a data-driven method for Nontechnical Loss (NTL) detection using smart met... more The current study uses a data-driven method for Nontechnical Loss (NTL) detection using smart meter data. Data augmentation is performed using six distinct theft attacks on benign users’ samples to balance the data from honest and theft samples. The theft attacks help to generate synthetic patterns that mimic real-world electricity theft patterns. Moreover, we propose a hybrid model including the Multi-Layer Perceptron and Gated Recurrent Unit (MLP-GRU) networks for detecting electricity theft. In the model, the MLP network examines the auxiliary data to analyze nonmalicious factors in daily consumption data, whereas the GRU network uses smart meter data acquired from the Pakistan Residential Electricity Consumption (PRECON) dataset as the input. Additionally, a random search algorithm is used for tuning the hyperparameters of the proposed deep learning model. In the simulations, the proposed model is compared with the MLP-Long Term Short Memory (LSTM) scheme and other traditional s...
Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of ... more Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner's premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way. INDEX TERMS Blockchain, Internet of Health Things, homomorphic encryption, federated learning, provenance.
The ambulance service is the main transport for diseased or injured people which suffers the same... more The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accelerations, caused by the movement of the vehicle, impact the performance of tasks executed by sanitary personnel, which can affect patient survival or recovery time. In this paper, we have trained, validated, and tested a system to assess driving in ambulance services. The proposed system is composed of a sensor node which measures the vehicle vibrations using an accelerometer. It also includes a GPS sensor, a battery, a display, and a speaker. When two possible routes reach the same destination point, the system compares the two routes based on previously classified data and calculates an index and a score. Thus, the index balances the possible routes in terms of time to reach the destination and the vibrations suffered in the patient cabin to recommend the route that minimises those vibrations. Three datasets are used to train, valida...
Video streaming has become extremely widespread, especially with the growing number of users and ... more Video streaming has become extremely widespread, especially with the growing number of users and the spread of mobile devices, along with the increase in the availability and diversity of multimedia applications and communication technologies. Real-time video communication requires awareness of the quality of experience (QoE) to provide customers with a satisfactory service, for example, in smart cities that use video surveillance systems. The quality of service (QoS) is dependent on network performance, which directly affects the QoE. However, reliance on traditional network infrastructure and routing protocols cannot assure QoS. The emergence of software defined networks (SDN) may eliminate current network limitations. Due to SDN’s global view and programmability characteristics, such capabilities could help in providing an automated QoS control and management. This paper introduces video streaming adaptive QoS-based routing and resource reservation (VQoSRR), which gives SDN netwo...
Traffic accidents have become an important problem for governments, researchers and vehicle manuf... more Traffic accidents have become an important problem for governments, researchers and vehicle manufacturers over the last few decades. However, accidents are unfortunate and frequently occur on the road and cause death, damage to infrastructure, and health injuries. Therefore, there is a need to develop a protocol to avoid or prevent traffic accidents at the extreme level in order to reduce human loss. The aim of this research is to develop a new protocol, named as the Traffic Accidents Reduction Strategy (TARS), for Vehicular Ad-hoc NETworks (VANETs) to minimize the number of road accidents, decrease the death rate caused by road accidents, and for the successful deployment of the Intelligent Transportation System (ITS). We have run multiple simulations and the results showed that our proposed scheme has outperformed DBSR and POVRP routing protocols in terms of the Message Delivery Ratio (MDR), Message Loss Ratio (MLR), Average Delay, and Basic Safety Message.
Applications of Internet of Things underwater wireless sensor networks, such as imaging underwate... more Applications of Internet of Things underwater wireless sensor networks, such as imaging underwater life, environmental monitoring, and supervising geological processes on the ocean floor, demand a prolonged network lifetime. However, these networks face many challenges, such as high path loss, limited available bandwidth, limited battery power, and high attenuation. For a longer network lifetime, both balanced and efficient energy consumption are equally important. In this paper, we propose a new routing protocol, called balanced energy adaptive routing (BEAR), to prolong the lifetime of UWSNs. The proposed BEAR protocol operates in three phases: 1) initialization phase; 2) tree construction phase; and 3) data transmission phase. In the initialization phase, all nodes share information related to their residual energy level and location. In the tree construction phase, our proposed BEAR exploits the location information for: a) selecting neighbour nodes and b) choosing the facilitating and successor nodes based on the value of cost function. In order to balance the energy consumption among the successor and the facilitator nodes, BEAR chooses nodes with relatively higher residual energy than the average residual energy of the network. The results of our extensive simulations show that BEAR outperforms its counterpart protocols in terms of network lifetime.
This paper estimates the impact of policies on the current status of Healthcare Human Resources (... more This paper estimates the impact of policies on the current status of Healthcare Human Resources (HHR) in Saudi Arabia and explores the initiatives that will be adopted to achieve Saudi Vision 2030. Retrospective time-series data from the Ministry of Health (MOH) and statistical yearbooks between 2003 and 2015 are analyzed to identify the impact of these policies on the health sector and the number of Saudi and non-Saudi physicians, nurses and allied health specialists employed by MOH, Other Government Hospitals (OGH) and Private Sector Hospitals (PSH). Moreover, multiple regressions are performed with respect to project data until 2030 and meaningful inferences are drawn. As a local supply of professional medical falls short of demand, either policy to foster an increase in supply are adopted or the Saudization policies must be relaxed. The discrepancies are identified in terms of a high rate of non-compliance of Saudization in the private sector and this is being countered with alt...
In smart grids, electricity theft is the most significant challenge. It cannot be identified easi... more In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset's dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models' performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD. INDEX TERMS Electricity theft detection, generative adversarial network, GoogLeNet, imbalanced data, Urban planning, SMOTEENN. NOMENCLATURE AdaBoost Adaptive boosting ADASYN Adaptive synthetic AMI Advanced metering infrastructure The associate editor coordinating the review of this manuscript and approving it for publication was Yang Li. AUC Area under the curve ANN Artificial neural network AI Artificial intelligence ARIMA Auto regressive integrated moving average BBHA Binary black hole algorithm BGRU Bidirectional gated recurrent unit CatBoost Categorical boosting 25036 This work is licensed under a Creative Commons Attribution 4.
2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2021
In order to flatten the curve and lower human-to-human transmission of COVID-19 pathogen, one of ... more In order to flatten the curve and lower human-to-human transmission of COVID-19 pathogen, one of the critical suggestions by health professionals is to monitor COVID-19 virus status of each human dynamically which is not a pragmatic solution unless the COVID-19 positive, negative, or symptomatic subjects are identified and have a secure health certificate generated based on daily health status. In this paper, we have developed a Blockchain and off-chain based secure health status and user biometric storage system. The health status is being visualized through a distributed QR code app. We have also incorporated deep learning-based face recognition and QR code recognition system in which the facial features are mapped to the QR code of a subject. We have developed three distributed apps (dApps): for the citizens, hospital authorities, and COVID-19 status checking entities. The system allows, for example, supermarkets, malls, and airports, to inquire about the health status of any subject through our developed application using already installed cameras. Our system will allow full life-cycle of the health certificate and biometric user management: creation through dApps, secure storage at Blockchain and off-chain, privacy-preserving sharing with the community of interest, and dynamic visualization.
This paper investigates the performance of a bufferaided IoT based cooperative relay network. In ... more This paper investigates the performance of a bufferaided IoT based cooperative relay network. In the proposed design, each location in a buffer is assumed to act independently called the virtual relay having a buffer of size 1. The relay selection is based on instantaneous strength of the wireless link and status of the virtual relay buffer. The proposed scheme is analyzed for symmetric and asymmetric channel conditions and also for symmetric and asymmetric buffer sizes at the relay. Markov chain is used to model the evolution of buffer status and to derive the closed-form expressions for outage probability, diversity gain, delay and throughput. The proposed design achieves the diversity gain of (L1 + L2 + • • • + LK), where Li is the buffer size of the i-th relay and K is the total number of relays.
Appropriate network design is very significant for Underwater Wireless Sensor Networks (UWSNs). A... more Appropriate network design is very significant for Underwater Wireless Sensor Networks (UWSNs). Application-oriented UWSNs are planned to achieve certain objectives. Therefore, there is always a demand for efficient data routing schemes, which can fulfill certain requirements of application-oriented UWSNs. These networks can be of any shape, i.e., rectangular, cylindrical or square. In this paper, we propose chain-based routing schemes for application-oriented cylindrical networks and also formulate mathematical models to find a global optimum path for data transmission. In the first scheme, we devise four interconnected chains of sensor nodes to perform data communication. In the second scheme, we propose routing scheme in which two chains of sensor nodes are interconnected, whereas in third scheme single-chain based routing is done in cylindrical networks. After finding local optimum paths in separate chains, we find global optimum paths through their interconnection. Moreover, we develop a computational model for the analysis of end-to-end delay. We compare the performance of the above three proposed schemes with that of Power Efficient Gathering System in Sensor Information Systems (PEGASIS) and Congestion adjusted PEGASIS (C-PEGASIS). Simulation results show that our proposed 4-chain based scheme performs better than the other selected schemes in terms of network lifetime, end-to-end delay, path loss, transmission loss, and packet sending rate.
With the emergence of smart grid (SG), the consumers have the opportunity to integrate renewable ... more With the emergence of smart grid (SG), the consumers have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management. In this paper, we introduce generic home energy management control system (HEMCS) to efficiently schedule the household load and integrate RESs. The HEMCS is based on the genetic algorithm, binary particle swarm optimization, winddriven optimization (WDO), and our proposed genetic WDO algorithm to schedule appliances of single and multiple homes. For energy cost calculation, real-time pricing (RTP) and inclined block rate schemes are combined, because in case of only RTP, there is a possibility of building peaks during off-peak hours that may damage the entire power system.Moreover, to control the demand under the grid station capacity, the feasible region is defined and a problem is formulated using multiple knapsack. Energy efficient integration of RESs in SG is a challenging task due to time varying and their intermittent ...
Abstract The fossil fuel based power generators emit CO 2 and expensive electricity. In this pape... more Abstract The fossil fuel based power generators emit CO 2 and expensive electricity. In this paper, fog as a virtual power plant (FaaVPP) is proposed to integrate power of distributed renewable power generators and the utility for a community. A prosumer–consumer and service providing company oriented linear model is proposed to minimize power consumption cost for prosumers and maximize profit for the company. The mathematical proof of linear model validates the significance for service provider and energy users. Moreover, outcome of case studies advocate the efficiency of the model. Efficient resource utilization techniques of fog resources ensure the near-real time service provision to the community. In the paper, effects of resource utilization techniques e.g., processing time (PT), response time (RT), computing cost and energy consumed by the resources are also analyzed.
2010 IEEE International Conference on Automation and Logistics, 2010
Abstract - For vehicle-to-vehicle (V2V) communication, suitable routing protocols are needed. An ... more Abstract - For vehicle-to-vehicle (V2V) communication, suitable routing protocols are needed. An adaptive cross-layer multi-path routing protocol, RS-AOMDV, is proposed. The routing metric combines hop-count, link-quality and vehicle-motion information. To make use of ...
Interoperability is one of the most challenging concerns that face healthcare information system ... more Interoperability is one of the most challenging concerns that face healthcare information system (HIS) actors. Interoperability implementation in this context may be a data exchange interfacing, a service oriented interaction or even a composition of new composite healthcare processes. In fact, optimizing efforts of interoperability achievement is a key requirement to effectively setup, develop and evolve intra- and interorganizational collaboration. To ensure interoperability project effectiveness, this paper proposes a modeling representation of health processes interoperability evolution. Interoperability degrees of involved automated processes are assessed using a ratio metric, taking into account all significant aspects, such as potentiality, compatibility and operational performance. Then, a particle swarm optimization algorithm (PSO) is used as a heuristic optimization method to find the best distribution of effort needed to establish an efficient healthcare collaborative net...
The current study uses a data-driven method for Nontechnical Loss (NTL) detection using smart met... more The current study uses a data-driven method for Nontechnical Loss (NTL) detection using smart meter data. Data augmentation is performed using six distinct theft attacks on benign users’ samples to balance the data from honest and theft samples. The theft attacks help to generate synthetic patterns that mimic real-world electricity theft patterns. Moreover, we propose a hybrid model including the Multi-Layer Perceptron and Gated Recurrent Unit (MLP-GRU) networks for detecting electricity theft. In the model, the MLP network examines the auxiliary data to analyze nonmalicious factors in daily consumption data, whereas the GRU network uses smart meter data acquired from the Pakistan Residential Electricity Consumption (PRECON) dataset as the input. Additionally, a random search algorithm is used for tuning the hyperparameters of the proposed deep learning model. In the simulations, the proposed model is compared with the MLP-Long Term Short Memory (LSTM) scheme and other traditional s...
Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of ... more Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner's premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way. INDEX TERMS Blockchain, Internet of Health Things, homomorphic encryption, federated learning, provenance.
The ambulance service is the main transport for diseased or injured people which suffers the same... more The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accelerations, caused by the movement of the vehicle, impact the performance of tasks executed by sanitary personnel, which can affect patient survival or recovery time. In this paper, we have trained, validated, and tested a system to assess driving in ambulance services. The proposed system is composed of a sensor node which measures the vehicle vibrations using an accelerometer. It also includes a GPS sensor, a battery, a display, and a speaker. When two possible routes reach the same destination point, the system compares the two routes based on previously classified data and calculates an index and a score. Thus, the index balances the possible routes in terms of time to reach the destination and the vibrations suffered in the patient cabin to recommend the route that minimises those vibrations. Three datasets are used to train, valida...
Video streaming has become extremely widespread, especially with the growing number of users and ... more Video streaming has become extremely widespread, especially with the growing number of users and the spread of mobile devices, along with the increase in the availability and diversity of multimedia applications and communication technologies. Real-time video communication requires awareness of the quality of experience (QoE) to provide customers with a satisfactory service, for example, in smart cities that use video surveillance systems. The quality of service (QoS) is dependent on network performance, which directly affects the QoE. However, reliance on traditional network infrastructure and routing protocols cannot assure QoS. The emergence of software defined networks (SDN) may eliminate current network limitations. Due to SDN’s global view and programmability characteristics, such capabilities could help in providing an automated QoS control and management. This paper introduces video streaming adaptive QoS-based routing and resource reservation (VQoSRR), which gives SDN netwo...
Traffic accidents have become an important problem for governments, researchers and vehicle manuf... more Traffic accidents have become an important problem for governments, researchers and vehicle manufacturers over the last few decades. However, accidents are unfortunate and frequently occur on the road and cause death, damage to infrastructure, and health injuries. Therefore, there is a need to develop a protocol to avoid or prevent traffic accidents at the extreme level in order to reduce human loss. The aim of this research is to develop a new protocol, named as the Traffic Accidents Reduction Strategy (TARS), for Vehicular Ad-hoc NETworks (VANETs) to minimize the number of road accidents, decrease the death rate caused by road accidents, and for the successful deployment of the Intelligent Transportation System (ITS). We have run multiple simulations and the results showed that our proposed scheme has outperformed DBSR and POVRP routing protocols in terms of the Message Delivery Ratio (MDR), Message Loss Ratio (MLR), Average Delay, and Basic Safety Message.
Applications of Internet of Things underwater wireless sensor networks, such as imaging underwate... more Applications of Internet of Things underwater wireless sensor networks, such as imaging underwater life, environmental monitoring, and supervising geological processes on the ocean floor, demand a prolonged network lifetime. However, these networks face many challenges, such as high path loss, limited available bandwidth, limited battery power, and high attenuation. For a longer network lifetime, both balanced and efficient energy consumption are equally important. In this paper, we propose a new routing protocol, called balanced energy adaptive routing (BEAR), to prolong the lifetime of UWSNs. The proposed BEAR protocol operates in three phases: 1) initialization phase; 2) tree construction phase; and 3) data transmission phase. In the initialization phase, all nodes share information related to their residual energy level and location. In the tree construction phase, our proposed BEAR exploits the location information for: a) selecting neighbour nodes and b) choosing the facilitating and successor nodes based on the value of cost function. In order to balance the energy consumption among the successor and the facilitator nodes, BEAR chooses nodes with relatively higher residual energy than the average residual energy of the network. The results of our extensive simulations show that BEAR outperforms its counterpart protocols in terms of network lifetime.
This paper estimates the impact of policies on the current status of Healthcare Human Resources (... more This paper estimates the impact of policies on the current status of Healthcare Human Resources (HHR) in Saudi Arabia and explores the initiatives that will be adopted to achieve Saudi Vision 2030. Retrospective time-series data from the Ministry of Health (MOH) and statistical yearbooks between 2003 and 2015 are analyzed to identify the impact of these policies on the health sector and the number of Saudi and non-Saudi physicians, nurses and allied health specialists employed by MOH, Other Government Hospitals (OGH) and Private Sector Hospitals (PSH). Moreover, multiple regressions are performed with respect to project data until 2030 and meaningful inferences are drawn. As a local supply of professional medical falls short of demand, either policy to foster an increase in supply are adopted or the Saudization policies must be relaxed. The discrepancies are identified in terms of a high rate of non-compliance of Saudization in the private sector and this is being countered with alt...
In smart grids, electricity theft is the most significant challenge. It cannot be identified easi... more In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset's dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models' performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD. INDEX TERMS Electricity theft detection, generative adversarial network, GoogLeNet, imbalanced data, Urban planning, SMOTEENN. NOMENCLATURE AdaBoost Adaptive boosting ADASYN Adaptive synthetic AMI Advanced metering infrastructure The associate editor coordinating the review of this manuscript and approving it for publication was Yang Li. AUC Area under the curve ANN Artificial neural network AI Artificial intelligence ARIMA Auto regressive integrated moving average BBHA Binary black hole algorithm BGRU Bidirectional gated recurrent unit CatBoost Categorical boosting 25036 This work is licensed under a Creative Commons Attribution 4.
2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2021
In order to flatten the curve and lower human-to-human transmission of COVID-19 pathogen, one of ... more In order to flatten the curve and lower human-to-human transmission of COVID-19 pathogen, one of the critical suggestions by health professionals is to monitor COVID-19 virus status of each human dynamically which is not a pragmatic solution unless the COVID-19 positive, negative, or symptomatic subjects are identified and have a secure health certificate generated based on daily health status. In this paper, we have developed a Blockchain and off-chain based secure health status and user biometric storage system. The health status is being visualized through a distributed QR code app. We have also incorporated deep learning-based face recognition and QR code recognition system in which the facial features are mapped to the QR code of a subject. We have developed three distributed apps (dApps): for the citizens, hospital authorities, and COVID-19 status checking entities. The system allows, for example, supermarkets, malls, and airports, to inquire about the health status of any subject through our developed application using already installed cameras. Our system will allow full life-cycle of the health certificate and biometric user management: creation through dApps, secure storage at Blockchain and off-chain, privacy-preserving sharing with the community of interest, and dynamic visualization.
This paper investigates the performance of a bufferaided IoT based cooperative relay network. In ... more This paper investigates the performance of a bufferaided IoT based cooperative relay network. In the proposed design, each location in a buffer is assumed to act independently called the virtual relay having a buffer of size 1. The relay selection is based on instantaneous strength of the wireless link and status of the virtual relay buffer. The proposed scheme is analyzed for symmetric and asymmetric channel conditions and also for symmetric and asymmetric buffer sizes at the relay. Markov chain is used to model the evolution of buffer status and to derive the closed-form expressions for outage probability, diversity gain, delay and throughput. The proposed design achieves the diversity gain of (L1 + L2 + • • • + LK), where Li is the buffer size of the i-th relay and K is the total number of relays.
Appropriate network design is very significant for Underwater Wireless Sensor Networks (UWSNs). A... more Appropriate network design is very significant for Underwater Wireless Sensor Networks (UWSNs). Application-oriented UWSNs are planned to achieve certain objectives. Therefore, there is always a demand for efficient data routing schemes, which can fulfill certain requirements of application-oriented UWSNs. These networks can be of any shape, i.e., rectangular, cylindrical or square. In this paper, we propose chain-based routing schemes for application-oriented cylindrical networks and also formulate mathematical models to find a global optimum path for data transmission. In the first scheme, we devise four interconnected chains of sensor nodes to perform data communication. In the second scheme, we propose routing scheme in which two chains of sensor nodes are interconnected, whereas in third scheme single-chain based routing is done in cylindrical networks. After finding local optimum paths in separate chains, we find global optimum paths through their interconnection. Moreover, we develop a computational model for the analysis of end-to-end delay. We compare the performance of the above three proposed schemes with that of Power Efficient Gathering System in Sensor Information Systems (PEGASIS) and Congestion adjusted PEGASIS (C-PEGASIS). Simulation results show that our proposed 4-chain based scheme performs better than the other selected schemes in terms of network lifetime, end-to-end delay, path loss, transmission loss, and packet sending rate.
With the emergence of smart grid (SG), the consumers have the opportunity to integrate renewable ... more With the emergence of smart grid (SG), the consumers have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management. In this paper, we introduce generic home energy management control system (HEMCS) to efficiently schedule the household load and integrate RESs. The HEMCS is based on the genetic algorithm, binary particle swarm optimization, winddriven optimization (WDO), and our proposed genetic WDO algorithm to schedule appliances of single and multiple homes. For energy cost calculation, real-time pricing (RTP) and inclined block rate schemes are combined, because in case of only RTP, there is a possibility of building peaks during off-peak hours that may damage the entire power system.Moreover, to control the demand under the grid station capacity, the feasible region is defined and a problem is formulated using multiple knapsack. Energy efficient integration of RESs in SG is a challenging task due to time varying and their intermittent ...
Abstract The fossil fuel based power generators emit CO 2 and expensive electricity. In this pape... more Abstract The fossil fuel based power generators emit CO 2 and expensive electricity. In this paper, fog as a virtual power plant (FaaVPP) is proposed to integrate power of distributed renewable power generators and the utility for a community. A prosumer–consumer and service providing company oriented linear model is proposed to minimize power consumption cost for prosumers and maximize profit for the company. The mathematical proof of linear model validates the significance for service provider and energy users. Moreover, outcome of case studies advocate the efficiency of the model. Efficient resource utilization techniques of fog resources ensure the near-real time service provision to the community. In the paper, effects of resource utilization techniques e.g., processing time (PT), response time (RT), computing cost and energy consumed by the resources are also analyzed.
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