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Sehrish Malik
Division of Information and Communication Engineering, Kongju National University, Cheonan 331717, Korea

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Journal article
Published: 20 April 2021 in Actuators
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In today’s world, smart buildings are considered an overarching system that automates a building’s complex operations and increases security while reducing environmental impact. One of the primary goals of building management systems is to promote sustainable and efficient use of energy, requiring coherent task management and execution of control commands for actuators. This paper proposes a predictive-learning framework based on contextual feature selection and optimal actuator control mechanism for minimizing energy consumption in smart buildings. We aim to assess multiple parameters and select the most relevant contextual features that would optimize energy consumption. We have implemented an artificial neural network-based particle swarm optimization (ANN-PSO) algorithm for predictive learning to train the framework on feature importance. Based on the relevance of attributes, our model was also capable of re-adding features. The extracted features are then applied as input parameters for the training of long short-term memory (LSTM) and optimal control module. We have proposed an objective function using a velocity boost-particle swarm optimization (VB-PSO) algorithm that reduces energy cost for optimal control. We then generated and defined the control tasks based on the fuzzy rule set and optimal values obtained from VB-PSO. We compared our model’s performance with and without feature selection using the root mean square error (RMSE) metric in the evaluation section. This paper also presents how optimal control can reduce energy cost and improve performance resulting from lesser learning cycles and decreased error rates.

ACS Style

Sehrish Malik; Wafa Shafqat; Kyu-Tae Lee; Do-Hyeun Kim. A Feature Selection-Based Predictive-Learning Framework for Optimal Actuator Control in Smart Homes. Actuators 2021, 10, 84 .

AMA Style

Sehrish Malik, Wafa Shafqat, Kyu-Tae Lee, Do-Hyeun Kim. A Feature Selection-Based Predictive-Learning Framework for Optimal Actuator Control in Smart Homes. Actuators. 2021; 10 (4):84.

Chicago/Turabian Style

Sehrish Malik; Wafa Shafqat; Kyu-Tae Lee; Do-Hyeun Kim. 2021. "A Feature Selection-Based Predictive-Learning Framework for Optimal Actuator Control in Smart Homes." Actuators 10, no. 4: 84.

Journal article
Published: 31 January 2021 in Actuators
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The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.

ACS Style

Sehrish Malik; DoHyeun Kim. Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory. Actuators 2021, 10, 27 .

AMA Style

Sehrish Malik, DoHyeun Kim. Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory. Actuators. 2021; 10 (2):27.

Chicago/Turabian Style

Sehrish Malik; DoHyeun Kim. 2021. "Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory." Actuators 10, no. 2: 27.

Journal article
Published: 21 December 2020 in IEEE Access
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Smart factory also known as smart manufacturing is an emerging field with the revolution of industry 4.0. With the help of all these concepts, the smart factory integrates the manufacturing assets and represents industrial networks. In this paper, we focus on integrated solutions for smart factory concerns; by proposing an efficient task management mechanism based on an efficient and resource-aware scheduling scheme named as ACM-FEF. The scheduling algorithm used for the efficient task management is hybrid of the two scheduling approaches as agent cooperation mechanism (ACM) and fair emergency first (FEF) scheduling scheme. ACM is a decentralized scheduling approach which focuses on the production maximization goals per machine, and also pays attention to the production goals of all the machine networks involved in the smart factory. FEF scheduling scheme focuses on minimizing the tasks starvation rate and maximizing the machine utilization by efficiently using the machine slots. The proposed hybrid mechanism aims to efficiently plan tasks execution, maximize machines’ resource utilization, maximize productivity, minimize production delays, efficiently handle exceptions and efficiently control smart factory actuators.

ACS Style

Sehrish Malik; DoHyeun Kim. A Hybrid Scheduling Mechanism Based on Agent Cooperation Mechanism and Fair Emergency First in Smart Factory. IEEE Access 2020, 8, 227064 -227075.

AMA Style

Sehrish Malik, DoHyeun Kim. A Hybrid Scheduling Mechanism Based on Agent Cooperation Mechanism and Fair Emergency First in Smart Factory. IEEE Access. 2020; 8 ():227064-227075.

Chicago/Turabian Style

Sehrish Malik; DoHyeun Kim. 2020. "A Hybrid Scheduling Mechanism Based on Agent Cooperation Mechanism and Fair Emergency First in Smart Factory." IEEE Access 8, no. : 227064-227075.

Journal article
Published: 04 December 2020 in IEEE Access
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Smart home environments account to a major portion of the total energy consumption in today’s world. The residents of smart home environments wish to find solutions that reduce the energy costs along with providing an optimal indoor environment for the residents. Another significant aspect in smart home systems is efficiency of tasks management and control commands’ execution for smart home actuators. In this paper, we propose an optimal control solution for smart home environment based on smart home energy optimization and control tasks’ load dispatching and scheduling. Optimal control is achieved by first defining an objective function for minimizing energy cost which is implemented using VB-PSO (velocity boost particle swarm optimization) algorithm. Next, the control tasks are generated using rule set implemented in fuzzy logic; defined based on optimal values achieved from VB-PSO. A Markov model based mechanism dispatches control tasks at scheduler, for efficient scheduling and optimal control. The results show that the proposed optimization scheme saves up to 29.73% energy costs on average, in comparison to baseline scheme. The proposed tasks’ load dispatching scheme of admission control, makes the job of load balancing among the processors efficient while giving priority to the urgent tasks. The results for scheduler evidently show the low dropping probabilities for urgent tasks along with showing 34.9% reduction in tasks’ starvation rate and 36.82% reduction in average tasks’ instances missing rates.

ACS Style

Sehrish Malik; Kyutae Lee; DoHyeun Kim. Optimal Control Based on Scheduling for Comfortable Smart Home Environment. IEEE Access 2020, 8, 218245 -218256.

AMA Style

Sehrish Malik, Kyutae Lee, DoHyeun Kim. Optimal Control Based on Scheduling for Comfortable Smart Home Environment. IEEE Access. 2020; 8 (99):218245-218256.

Chicago/Turabian Style

Sehrish Malik; Kyutae Lee; DoHyeun Kim. 2020. "Optimal Control Based on Scheduling for Comfortable Smart Home Environment." IEEE Access 8, no. 99: 218245-218256.

Erratum
Published: 06 December 2019 in Sensors
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The authors wish to make the following erratum to this paper

ACS Style

Shabir Ahmad; Sehrish Malik; Dong-Hwan Park; DoHyeun Kim. Erratum: Ahmad, S.; Malik, S.; Park, D.-H.; Kim, D. Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles. Sensors 2019, 19, 5380 .

AMA Style

Shabir Ahmad, Sehrish Malik, Dong-Hwan Park, DoHyeun Kim. Erratum: Ahmad, S.; Malik, S.; Park, D.-H.; Kim, D. Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles. Sensors. 2019; 19 (24):5380.

Chicago/Turabian Style

Shabir Ahmad; Sehrish Malik; Dong-Hwan Park; DoHyeun Kim. 2019. "Erratum: Ahmad, S.; Malik, S.; Park, D.-H.; Kim, D. Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles." Sensors 19, no. 24: 5380.

Journal article
Published: 20 November 2019 in Applied Sciences
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Geo-sensor is the term used for the deployment of a wireless sensor network (WSN) in a real environment, which can be a hideous task due to many influential variables in a given environment. The spatial context of a sensor in a smart environment can be of huge significance and can also play an important role in improving the smart services provision. In this work, we propose a DIY geo-sensor framework and data composition toolbox for the deployment of sensors data in smart IoT environments along with geographical context. A geo-sensor framework is deployed, which enables the registration of multiple geo-sensor networks by providing multiple geo-sensor platforms. The framework’s logic is based on the combination of a geo-sensor service registry, geo-sensor composition toolbox, and geo-sensor platforms. A geo-sensor platform provides the content to the toolbox, enabling relaxed real-time geo-sensor data management. Our proposed work is two-fold. Firstly, we propose the design details for the geo-sensor framework and composition toolbox. The proposed design for the geo-sensor framework aims to provide a DIY platform for multiple geo-sensor networks and services deployment, giving access to multiple users resulting in reuse of resources and reduction in deployment costs by avoiding duplicate deployments. Secondly, we implement the proposed design based on RESTful web services and SOAP web services. Performance comparison analysis is then performed among the two web services to find the best suited implementation for given scenarios. The results of the performance analysis prove that RESTful web services are the better choice for ease of implementation, access, and light-weightiness.

ACS Style

Sehrish Malik; DoHyeun Kim; Kim. Geo-Sensor Framework and Composition Toolbox for Efficient Deployment of Multiple Spatial Context Service Platforms in Sensor Networks. Applied Sciences 2019, 9, 4993 .

AMA Style

Sehrish Malik, DoHyeun Kim, Kim. Geo-Sensor Framework and Composition Toolbox for Efficient Deployment of Multiple Spatial Context Service Platforms in Sensor Networks. Applied Sciences. 2019; 9 (23):4993.

Chicago/Turabian Style

Sehrish Malik; DoHyeun Kim; Kim. 2019. "Geo-Sensor Framework and Composition Toolbox for Efficient Deployment of Multiple Spatial Context Service Platforms in Sensor Networks." Applied Sciences 9, no. 23: 4993.

Journal article
Published: 02 November 2019 in Sensors
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Electric-vehicle technology is an emerging area offering several benefits such as economy due to low running costs. Electric vehicles can also help to significantly reduce CO 2 emission, which is a vital factor for environmental pollution. Modern vehicles are equipped with driver-assistance systems that facilitate drivers by offloading some of the tasks a driver does while driving. Human beings are prone to errors. Therefore, accidents and fatalities can happen if the driver fails to perform a particular task within the deadline. In electric vehicles, the focus has always been to optimize the power and battery life, and thus, any additional hardware can affect their battery life significantly. In this paper, the design of driver-assistance systems has been introduced to automate and assist in some of the vital tasks, such as a braking system, in an optimized manner. We revamp the idea of the traditional driver-assistance system and propose a generic lightweight system based on the leading factors and their impact on accidents. We model tasks for these factors and simulate a low-cost driver-assistance system in a real-time context, where these scenarios are investigated and tasks schedulability is formally proved before deploying them in electric vehicles. The proposed driver-assistance system offers many advantages. It decreases the risk of accidents and monitors the safety of driving. If, at some point, the risk index is above a certain threshold, an automated control algorithm is triggered to reduce it by activating different actuators. At the same time, it is lightweight and does not require any dedicated hardware, which in turn has a significant advantage in terms of battery life. Results show that the proposed system not only is accurate but also has a very negligible effect on energy consumption and battery life.

ACS Style

Shabir Ahmad; Sehrish Malik; Dong-Hwan Park; DoHyeun Kim. Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles. Sensors 2019, 19, 4761 .

AMA Style

Shabir Ahmad, Sehrish Malik, Dong-Hwan Park, DoHyeun Kim. Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles. Sensors. 2019; 19 (21):4761.

Chicago/Turabian Style

Shabir Ahmad; Sehrish Malik; Dong-Hwan Park; DoHyeun Kim. 2019. "Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles." Sensors 19, no. 21: 4761.

Journal article
Published: 22 October 2019 in Sustainability
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The information and communication technology (ICT) is witnessing a revolutionary era with the advancements in the Internet of Things (IoT). An IoT network is a combination of sensor and actuator networks, connected and communicating in certain ways to design and provide IoT services to the end users. These IoT services are created by mapping physical-world objects into virtual-world objects. In this work, we propose a novel approach of IoT services orchestration based on multiple sensor and actuator platforms using virtual objects in online IoT application-store (app-store). In this work, we focused on combining the concepts of do-it-yourself (DIY) IoT marketplace, virtual objects (VOs), and virtual services. We built a fusion IoT services platform on a previously proposed IoT application store. The IoT application store enables the sharing and discovery of IoT VOs, along with micro-services associated with each VO uploaded into the application store. The fusion IoT services platform enables the user to fetch the desired or all VOs from the IoT app store and map the available VOs to form the fusion IoT services. The user can either select all the available VOs and see all the possible services’ combinations or select the desired (DIY) services and customize the virtual services scope. The performance of the proposed fusion IoT services platform was evaluated on the basis of the service connection times, service response times with varying load of VOs, virtual users, and active platforms. The proposed idea also offers a sustainable solution by proposing the reuse of existing resources and reducing duplicate deployments, which can lessen the total cost of the physical networks’ deployment and maintenance. To the best of our knowledge, the proposed work is the first of its kind.

ACS Style

Sehrish Malik; Shabir Ahmad; DoHyeun Kim. A Novel Approach of IoT Services Orchestration Based on Multiple Sensor and Actuator Platforms Using Virtual Objects in Online IoT App-Store. Sustainability 2019, 11, 5859 .

AMA Style

Sehrish Malik, Shabir Ahmad, DoHyeun Kim. A Novel Approach of IoT Services Orchestration Based on Multiple Sensor and Actuator Platforms Using Virtual Objects in Online IoT App-Store. Sustainability. 2019; 11 (20):5859.

Chicago/Turabian Style

Sehrish Malik; Shabir Ahmad; DoHyeun Kim. 2019. "A Novel Approach of IoT Services Orchestration Based on Multiple Sensor and Actuator Platforms Using Virtual Objects in Online IoT App-Store." Sustainability 11, no. 20: 5859.

Journal article
Published: 17 June 2019 in Sustainability
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With the swift growth in tourism all around the world, it has become vital to introduce advancements and improvements to the services provided to the tourists, in order to ensure their ease of travel and satisfaction. Optimal travel route identification and recommendation is one of these amenities, which requires our attention as a basic and much-needed facility to improve the experience of travelers. In this work, we propose an optimal route recommendation mechanism for the prediction of the next tourist attraction and optimal route recommendation to the predicted tourist attraction. The algorithms used in the proposed methodology are neural networks for prediction and particle swarm optimization for finding the optimal route. We design an objective function for the route optimization based on the five route parameters of distance, road congestion, weather conditions, route popularity, and user preference. The data used is the tourism data of Jeju Island from December 2016 to December 2017. The performance analysis in the prediction mechanism is performed based on the accuracy of test data results with varying route sizes, while for route optimization, the obtained results are compared with the non-optimized technique. Also, comparisons analysis is performed by comparing the performance of the applied particle swarm optimization algorithm with an identical system-level implementation of the genetic algorithm, which is one of most widely used optimization algorithms. An extended comparative analysis with some related recommendation system studies is also performed based on key optimization factors in route optimization.

ACS Style

Sehrish Malik; DoHyeun Kim. Optimal Travel Route Recommendation Mechanism Based on Neural Networks and Particle Swarm Optimization for Efficient Tourism Using Tourist Vehicular Data. Sustainability 2019, 11, 3357 .

AMA Style

Sehrish Malik, DoHyeun Kim. Optimal Travel Route Recommendation Mechanism Based on Neural Networks and Particle Swarm Optimization for Efficient Tourism Using Tourist Vehicular Data. Sustainability. 2019; 11 (12):3357.

Chicago/Turabian Style

Sehrish Malik; DoHyeun Kim. 2019. "Optimal Travel Route Recommendation Mechanism Based on Neural Networks and Particle Swarm Optimization for Efficient Tourism Using Tourist Vehicular Data." Sustainability 11, no. 12: 3357.

Journal article
Published: 12 April 2019 in Sustainability
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Industrial revolution is advancing, and the augmented role of autonomous technology and embedded Internet of Things (IoT) systems is at its vanguard. In autonomous technology, real-time systems and real-time computing are of core importance. It is crucial for embedded IoT devices to respond in real-time; along with fulfilling all the constraints. Many combinations for existing approaches have been proposed with different trade-offs between the resources constraints and tasks dropping rate. Hence, it highlights the significance of a task scheduler which not only takes care of complex nature task input; but also maximizes the CPU throughput. A complex nature task input is when combinations of hard real-time tasks and soft real-time tasks, with different priorities and urgency measures, arrive at the scheduler. In this work, we propose a custom tailored adaptive and intelligent scheduling algorithm for the efficient execution and management of hard and soft real time tasks in embedded IoT systems. The proposed scheduling algorithm aims to distribute the CPU resources fairly to the possibly starving, in overloaded cases, soft real-time tasks while focusing on the execution of high priority hard real-time tasks as its primary objective. The proposal is achieved with the help of two intelligent measures; Urgency Measure (UM) and Failure Measure (FM). The proposed mechanism reduces the rate of tasks missed and the rate of tasks starved, by utilizing the free CPU units for maximum CPU utilization and quick response times. We have performed comparisons of our proposed scheme based on performance metrics as percentage of task instances missed, number of tasks with missed instances, and tasks starvation rate to evaluate the CPU utilization. We first compare our proposed approach with multiple traditional and combined scheduling approaches, and then we evaluate the effect of intelligent modules by comparing the intelligent FEF with non-intelligent FEF. We also evaluate the proposed algorithm in contrast to the most commonly-used hybrid scheduling scheme in embedded systems. The results show that the proposed algorithm out performs the other algorithms, by significantly reducing the task starvation rate and increasing the CPU utilization.

ACS Style

Sehrish Malik; Shabir Ahmad; Israr Ullah; Dong Hwan Park; DoHyeun Kim. An Adaptive Emergency First Intelligent Scheduling Algorithm for Efficient Task Management and Scheduling in Hybrid of Hard Real-Time and Soft Real-Time Embedded IoT Systems. Sustainability 2019, 11, 2192 .

AMA Style

Sehrish Malik, Shabir Ahmad, Israr Ullah, Dong Hwan Park, DoHyeun Kim. An Adaptive Emergency First Intelligent Scheduling Algorithm for Efficient Task Management and Scheduling in Hybrid of Hard Real-Time and Soft Real-Time Embedded IoT Systems. Sustainability. 2019; 11 (8):2192.

Chicago/Turabian Style

Sehrish Malik; Shabir Ahmad; Israr Ullah; Dong Hwan Park; DoHyeun Kim. 2019. "An Adaptive Emergency First Intelligent Scheduling Algorithm for Efficient Task Management and Scheduling in Hybrid of Hard Real-Time and Soft Real-Time Embedded IoT Systems." Sustainability 11, no. 8: 2192.

Journal article
Published: 21 March 2019 in Electronics
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In recent years, the focus of the smart transportation industry has been shifting towards the research and development of smart cars with autonomous control. Smart cars are considered to be a smart investment, as they promote safe driving while focusing on an alternate transportation fuel resource, making them eco-friendly too. Safe driving is one of the crucial concerns in autonomous smart cars. The major issue for the better provision of safe driving is real time tasks management and an efficient inference system for autonomous control. Real time task management is of huge significance in smart cars control systems. An optimal control system consists of a knowledge base and a control unit; where the knowledge base contains the data and thresholds for rules and the control unit contains the functionality for smart vehicle autonomous control. In this work, we propose a hybrid of an inference engine and a real time task scheduler for an efficient task management and resource consumption. Our proposed hybrid inference engine and task scheduler mechanism provides an efficient way of controlling smart cars in different scenarios such as heavy rainfall, obstacle detection, driver’s focus diversion etc., while ensuring the practices of safe driving. For the performance analysis of our proposed hybrid inference based scheduling mechanism, we have simulated a non-hybrid version with the same system constraints and a basic implementation of inference engine. For performance evaluation, CPU time utilization, tasks’ missing rate, average response time are used as performance metrics.

ACS Style

Sehrish Malik; Shabir Ahmad; Bong Wan Kim; Dong Hwan Park; DoHyeun Kim. Hybrid Inference Based Scheduling Mechanism for Efficient Real Time Task and Resource Management in Smart Cars for Safe Driving. Electronics 2019, 8, 344 .

AMA Style

Sehrish Malik, Shabir Ahmad, Bong Wan Kim, Dong Hwan Park, DoHyeun Kim. Hybrid Inference Based Scheduling Mechanism for Efficient Real Time Task and Resource Management in Smart Cars for Safe Driving. Electronics. 2019; 8 (3):344.

Chicago/Turabian Style

Sehrish Malik; Shabir Ahmad; Bong Wan Kim; Dong Hwan Park; DoHyeun Kim. 2019. "Hybrid Inference Based Scheduling Mechanism for Efficient Real Time Task and Resource Management in Smart Cars for Safe Driving." Electronics 8, no. 3: 344.

Journal article
Published: 03 January 2019 in Sustainability
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Real-Time Internet of Things (RT-IoT) is a newer technology paradigm envisioned as a global inter-networking of devices and physical things enabling real-time communication over the Internet. The research in Edge Computing and 5G technology is making way for the realisation of future IoT applications. In RT-IoT tasks will be performed in real-time for the remotely controlling and automating of various jobs and therefore, missing their deadline may lead to hazardous situations in many cases. For instance, in the case of safety-critical and mission-critical IoT systems, a missed task could lead to a human loss. Consequently, these systems must be simulated, as a result, and tasks should only be deployed in a real scenario if the deadline is guaranteed to be met. Numerous simulation tools are proposed for traditional real-time systems using desktop technologies, but these relatively older tools do not adapt to the new constraints imposed by the IoT paradigm. In this paper, we design and implement a cloud-based novel architecture for the formal verification of IoT jobs and provide a simulation environment for a typical RT-IoT application where the feasibility of real-time remote tasks is perceived. The proposed tool, to the best of our knowledge, is the first of its kind effort to support not only the feasibility analysis of real-time tasks but also to provide a real environment in which it formally monitors and evaluates different IoT tasks from anywhere. Furthermore, it will also act as a centralised server for evaluating and tracking the real-time scheduled jobs in a smart space. The novelty of the platform is purported by a comparative analysis with the state-of-art solutions against attributes which is vital for any open-source tools in general and IoT in specifics.

ACS Style

Shabir Ahmad; Sehrish Malik; Israr Ullah; Dong-Hwan Park; Kwangsoo Kim; DoHyeun Kim. Towards the Design of a Formal Verification and Evaluation Tool of Real-Time Tasks Scheduling of IoT Applications. Sustainability 2019, 11, 204 .

AMA Style

Shabir Ahmad, Sehrish Malik, Israr Ullah, Dong-Hwan Park, Kwangsoo Kim, DoHyeun Kim. Towards the Design of a Formal Verification and Evaluation Tool of Real-Time Tasks Scheduling of IoT Applications. Sustainability. 2019; 11 (1):204.

Chicago/Turabian Style

Shabir Ahmad; Sehrish Malik; Israr Ullah; Dong-Hwan Park; Kwangsoo Kim; DoHyeun Kim. 2019. "Towards the Design of a Formal Verification and Evaluation Tool of Real-Time Tasks Scheduling of IoT Applications." Sustainability 11, no. 1: 204.

Journal article
Published: 17 July 2018 in Processes
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Embedded devices are gaining popularity day by day due to the expanded use of Internet of Things applications. However, these embedded devices have limited capabilities concerning power and memory. Thus, the applications need to be tailored in such a way to perform the specified tasks within the constrained resources with the same accuracy. In Real-Time task scheduling, one of the challenging factors is the intelligent modelling of input tasks in such a way that it produces not only logically correct output within the deadline but also consumes minimum CPU power. Algorithms like Rate Monotonic and Earliest Deadline First compute hyper-period of input tasks for periodic repetition of the same set of tasks on CPU. However, at times when the tasks are not adequately modelled, they lead to an enormously high value of hyper-period which result in more CPU cycles and power consumption. Many state-of-the-art solutions are presented in this regard, but the main problem is that they limit tasks from having all possible period values; however, with the vision of Industry 4.0, where most of the tasks will be doing some critical manufacturing activities, it is highly discouraged to prevent them of a certain period. In this paper, we present a resource-aware approach to minimise the hyper-period of input tasks based on device profiles and allows tasks of every possible period value to admit. The proposed work is compared with similar existing techniques, and results indicate significant improvements regarding power consumptions.

ACS Style

Shabir Ahmad; Sehrish Malik; Israr Ullah; Muhammad Fayaz; Dong-Hwan Park; Kwangsoo Kim; DoHyeun Kim. An Adaptive Approach Based on Resource-Awareness Towards Power-Efficient Real-Time Periodic Task Modeling on Embedded IoT Devices. Processes 2018, 6, 90 .

AMA Style

Shabir Ahmad, Sehrish Malik, Israr Ullah, Muhammad Fayaz, Dong-Hwan Park, Kwangsoo Kim, DoHyeun Kim. An Adaptive Approach Based on Resource-Awareness Towards Power-Efficient Real-Time Periodic Task Modeling on Embedded IoT Devices. Processes. 2018; 6 (7):90.

Chicago/Turabian Style

Shabir Ahmad; Sehrish Malik; Israr Ullah; Muhammad Fayaz; Dong-Hwan Park; Kwangsoo Kim; DoHyeun Kim. 2018. "An Adaptive Approach Based on Resource-Awareness Towards Power-Efficient Real-Time Periodic Task Modeling on Embedded IoT Devices." Processes 6, no. 7: 90.

Journal article
Published: 17 May 2018 in Energies
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Electricity, the most important form of energy and an indispensable resource, primarily for commercial and residential smart buildings, faces challenges requiring its hyper efficient consumption and production. Therefore, accurate energy consumption predictions are required in order to manage and optimize the energy consumption of smart buildings. Many studies have taken advantage of the power and robustness of neural networks (NN) when it comes to accurate predictions. A few studies have also used the particle swarm optimization (PSO) algorithm along with NNs to enhance and optimize the predictions. In this work, we study prediction learning using PSO-based neural networks (PSO-NN) and propose modifications in order to increase prediction accuracy. Our proposed modifications are re-generation based PSO-NN (R-PSO-NN) and velocity boost-based PSO-NN (VB-PSO-NN). The performance metrics used are: prediction accuracy, number of particles used, and number of epochs required. We compare the results of NN, PSO-NN, R-PSO-NN and VB-PSO-NN based on the performance metrics.

ACS Style

Sehrish Malik; DoHyeun Kim. Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks. Energies 2018, 11, 1289 .

AMA Style

Sehrish Malik, DoHyeun Kim. Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks. Energies. 2018; 11 (5):1289.

Chicago/Turabian Style

Sehrish Malik; DoHyeun Kim. 2018. "Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks." Energies 11, no. 5: 1289.