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Waleed Alnumay
King Saud University, Riyadh, Saudi Arabia

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Review
Published: 20 May 2021 in Multimedia Tools and Applications
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Nature inspired algorithm plays a very vibrant role in solving the different optimization problems these days. The fundamental attitude of naturalistic approaches is to boost the competence, improvement, proficiency, success in the task except from it to help in underrating the energy use, cost, size. Several computing techniques are taking the benefits from nature inspired algorithms for solving their problems related to load balancing, scheduling and many others. These algorithms have come up with lots of improvements in the results. The aim of this analysis is to make efforts in the betterment in different areas of computing and help in solving various problems related to load balancing, scheduling and energy efficiency. The structure of the paper includes an introduction, contribution to the work, background study, which includes the role of nature inspired techniques in a different computing environment, research challenges and its applications. The sustainable goal and objective of the article is to perform the energy efficiency, load balancing and scheduling on different computing systems which include grid, cloud, distributed, fog and edge computing by using various nature inspired algorithms. This comprehensive study gives the awareness and valuable provision for the researchers in this area by providing a thorough study of different computing techniques in different research fields.

ACS Style

Surabhi Kaul; Yogesh Kumar; Uttam Ghosh; Waleed Alnumay. Nature-inspired optimization algorithms for different computing systems: novel perspective and systematic review. Multimedia Tools and Applications 2021, 1 -23.

AMA Style

Surabhi Kaul, Yogesh Kumar, Uttam Ghosh, Waleed Alnumay. Nature-inspired optimization algorithms for different computing systems: novel perspective and systematic review. Multimedia Tools and Applications. 2021; ():1-23.

Chicago/Turabian Style

Surabhi Kaul; Yogesh Kumar; Uttam Ghosh; Waleed Alnumay. 2021. "Nature-inspired optimization algorithms for different computing systems: novel perspective and systematic review." Multimedia Tools and Applications , no. : 1-23.

Journal article
Published: 22 April 2021 in Sustainable Cities and Society
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In the recent years, important key factor for urban planning is to analyze the sustainability and its functionality towards smart cities. Presently, many researchers employ the conservative machine learning based analysis but those are not appropriate for IoT based health data analysis because of their physical feature extraction and low accuracy. In this paper, we propose remote health monitoring and data analysis by integrating IoT and deep learning concepts. We proposed novel IoT based FoG assisted cloud network architecture that accumulates real-time health care data from patients via several medical IoT sensor networks, these data are analyzed using a deep learning algorithm deployed at Fog based Healthcare Platform. Furthermore, the proposed methodology is applied to the sustainable smart cities to evaluate the process for real-time. The proposed framework not only analyses the healthcare data but also provides immediate relief measures to the patient facing critical conditions and needs immediate consultancy of doctor. Performance is measure in terms of accuracy, precision and sensitivity of the proposed DHNN with task scheduling algorithm and it is obtained 97.6%, 97.9%, and 94.9%. While accuracy, precision and sensitivity for deep CNN is 96.5%, 97.5% and 94% and for Deep auto-encoder is 92%, 91%, and 82.5%.

ACS Style

Senthil Murugan Nagarajan; Ganesh Gopal Deverajan; Puspita Chatterjee; Waleed Alnumay; Uttam Ghosh. Effective task scheduling algorithm with deep learning for Internet of Health Things (IoHT) in sustainable smart cities. Sustainable Cities and Society 2021, 71, 102945 .

AMA Style

Senthil Murugan Nagarajan, Ganesh Gopal Deverajan, Puspita Chatterjee, Waleed Alnumay, Uttam Ghosh. Effective task scheduling algorithm with deep learning for Internet of Health Things (IoHT) in sustainable smart cities. Sustainable Cities and Society. 2021; 71 ():102945.

Chicago/Turabian Style

Senthil Murugan Nagarajan; Ganesh Gopal Deverajan; Puspita Chatterjee; Waleed Alnumay; Uttam Ghosh. 2021. "Effective task scheduling algorithm with deep learning for Internet of Health Things (IoHT) in sustainable smart cities." Sustainable Cities and Society 71, no. : 102945.

Journal article
Published: 01 March 2021 in IEEE Consumer Electronics Magazine
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The promise of automated-driving cars cause the automotive and consumer electronics (CE) sector to rethink what it means to drive, but the relationship between the car and the consumer. Recent trend in Internet of Vehicle Things (IoVT) promotes robust interactions in between humans and vehicles which altimetry points to enhance human abilities such as hearing or emotion awareness as a part of safety concern. The voice-based interactions (speech recognition, stress monitoring) will improve in-time awareness of the vehicle status. Unfortunately, the existing modulation domain speech enhancement techniques achieve low satisfactory performance in detecting humans stress emotions where the environmental noise is inevitable and varies with every passing location of vehicle. In this direction, we propose frontend processing framework, in particular to stress emotion detection cases in different non-stationary noisy environments. This study encompasses three Inter-related issues: (i) analysis, modification, and synthesis of noisy speech emotion in modulation domain in realtime background noise, (ii) extracting set of Mel-frequency cepstral coefficients (MFCC) features from noisy speech stimuli for speech emotion recognition, and (iii) evaluation of overall system performance by means of objective parameters, and confusion matrix in adverse environments using speech emotion database Interactive Emotional Dyadic Motion Capture (IEMOCAP)

ACS Style

Pavan Paikrao; Amrit Mukherjee; Deepak Kumar Jain; Pushpita Chatterjee; Waleed Alnumay. Smart emotion recognition framework: A secured IOVT perspective. IEEE Consumer Electronics Magazine 2021, PP, 1 -1.

AMA Style

Pavan Paikrao, Amrit Mukherjee, Deepak Kumar Jain, Pushpita Chatterjee, Waleed Alnumay. Smart emotion recognition framework: A secured IOVT perspective. IEEE Consumer Electronics Magazine. 2021; PP (99):1-1.

Chicago/Turabian Style

Pavan Paikrao; Amrit Mukherjee; Deepak Kumar Jain; Pushpita Chatterjee; Waleed Alnumay. 2021. "Smart emotion recognition framework: A secured IOVT perspective." IEEE Consumer Electronics Magazine PP, no. 99: 1-1.

Journal article
Published: 18 January 2021 in Sustainable Computing: Informatics and Systems
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Cloudlet is an important part of providing cloud services in Mobile Edge Computing (MEC) with sustainability. As the number of mobile users grows rapidly in the current era, the load in the cloudlet becomes very high. The cloudlet is considered in the middle layer for providing cloud services with low latency and energy efficiency. Hence the task allocation and scheduling inside of the cloudlet is a challenging job. In the recent past, many research works conducted without considering real-time network parameters. In this work, a heuristic load balancing strategy is designed and analyzed to minimize the task completion time and makespan, which enhances the efficiency of cloud services. The proposed method is considered a dynamic task allocation strategy to the cloudlet concerning network bandwidth, network delay, energy efficiency, and this approach is compared with the queue-based task allocation strategy. The experiment is conducted to compare the proposed method with the recently developed standard algorithms over the synthetic dataset. Experimental results of the proposed work show a significant improvement in task scheduling in terms of energy and execution cost, as well as time, compared to the existing methods. The proposed method outperforms the standard algorithm in most of the observed cases with sustainability.

ACS Style

Dhritiman Mukherjee; Sudarshan Nandy; Senthilkumar Mohan; Yasser D. Al-Otaibi; Waleed S. Alnumay. Sustainable task scheduling strategy in cloudlets. Sustainable Computing: Informatics and Systems 2021, 30, 100513 .

AMA Style

Dhritiman Mukherjee, Sudarshan Nandy, Senthilkumar Mohan, Yasser D. Al-Otaibi, Waleed S. Alnumay. Sustainable task scheduling strategy in cloudlets. Sustainable Computing: Informatics and Systems. 2021; 30 ():100513.

Chicago/Turabian Style

Dhritiman Mukherjee; Sudarshan Nandy; Senthilkumar Mohan; Yasser D. Al-Otaibi; Waleed S. Alnumay. 2021. "Sustainable task scheduling strategy in cloudlets." Sustainable Computing: Informatics and Systems 30, no. : 100513.

Review
Published: 11 January 2021 in Sensors
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Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.

ACS Style

Sparsh Sharma; Suhaib Ahmed; Mohd Naseem; Waleed S. Alnumay; Saurabh Singh; Gi Hwan Cho. A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering. Sensors 2021, 21, 463 .

AMA Style

Sparsh Sharma, Suhaib Ahmed, Mohd Naseem, Waleed S. Alnumay, Saurabh Singh, Gi Hwan Cho. A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering. Sensors. 2021; 21 (2):463.

Chicago/Turabian Style

Sparsh Sharma; Suhaib Ahmed; Mohd Naseem; Waleed S. Alnumay; Saurabh Singh; Gi Hwan Cho. 2021. "A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering." Sensors 21, no. 2: 463.

Journal article
Published: 10 January 2021 in Sustainable Computing: Informatics and Systems
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Wearable wireless sensors, or Wireless Body Sensor Network (WBSN), is one of the most recent fields of research in healthcare applications. The sensors are placed on the human body to detect the critical medical parameters and transmit the collected data to a centralized node. Though the devices work at low energy and power constraints, maintenance of such devices is a crucial aspect. In light of this requirement, an efficient medium access control (MAC) protocol should be utilized to extend the life time of the network. In this paper, we propose a new “Priority based Energy Efficient (PrEE) MAC protocol” incorporated with various priorities in predefined devices based on data sensing and normal devices changing to priority when the value is more than the threshold, by modifying IEEE 802.15.4 header bits. The PrEE MAC protocol is designed based on the IEEE 802.15.4 and its Superframe structure. The simulation results show that the energy efficiency and lifetime of the proposed MAC protocol is more as compared to the existing MAC protocol.

ACS Style

Ananda Kumar Subramanian; Uttam Ghosh; Sangeetha Ramaswamy; Waleed S. Alnumay; Pradip Kumar Sharma. PrEEMAC: Priority based energy efficient MAC protocol for Wireless Body Sensor Networks. Sustainable Computing: Informatics and Systems 2021, 30, 100510 .

AMA Style

Ananda Kumar Subramanian, Uttam Ghosh, Sangeetha Ramaswamy, Waleed S. Alnumay, Pradip Kumar Sharma. PrEEMAC: Priority based energy efficient MAC protocol for Wireless Body Sensor Networks. Sustainable Computing: Informatics and Systems. 2021; 30 ():100510.

Chicago/Turabian Style

Ananda Kumar Subramanian; Uttam Ghosh; Sangeetha Ramaswamy; Waleed S. Alnumay; Pradip Kumar Sharma. 2021. "PrEEMAC: Priority based energy efficient MAC protocol for Wireless Body Sensor Networks." Sustainable Computing: Informatics and Systems 30, no. : 100510.

Article
Published: 05 January 2021 in Neural Processing Letters
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Mobile ubiquitous computing has not only enriched human comfort but also has a deep impact on changing standards of human daily life. Modern inventions can be even more automated by using the Internet of Things (IoT) and Artificial Intelligence (AI). Mobile devices, body area networks, and embedded computing systems allow healthcare providers to continuously assess and monitor their patients but also bring privacy concerns. This paper proposes a smartphone-based end-to-end novel framework named PP-SPA for privacy-preserved Human Activity Recognition (HAR) and real-time activity functioning support using the smartphone-based virtual personal assistant. PP-SPA helps to improve the routine life functioning of the Cognitive Impaired individuals. PP-SPA uses a highly accurate machine learning model that takes input from smartphone sensors (i.e., accelerometer, gyroscope, magnetometer, and GPS) for accurate HAR and uses a digital diary to recommend real-time support for improvement in individual’s health. PP-SPA yields a proficient accuracy of 90% with the Hoeffding Tree and Logistic Regression algorithm which endeavors reasonable models in terms of uncertainty.

ACS Style

Abdul Rehman Javed; Muhammad Usman Sarwar; Saif Ur Rehman; Habib Ullah Khan; Yasser D. Al-Otaibi; Waleed S. Alnumay. PP-SPA: Privacy Preserved Smartphone-Based Personal Assistant to Improve Routine Life Functioning of Cognitive Impaired Individuals. Neural Processing Letters 2021, 1 -18.

AMA Style

Abdul Rehman Javed, Muhammad Usman Sarwar, Saif Ur Rehman, Habib Ullah Khan, Yasser D. Al-Otaibi, Waleed S. Alnumay. PP-SPA: Privacy Preserved Smartphone-Based Personal Assistant to Improve Routine Life Functioning of Cognitive Impaired Individuals. Neural Processing Letters. 2021; ():1-18.

Chicago/Turabian Style

Abdul Rehman Javed; Muhammad Usman Sarwar; Saif Ur Rehman; Habib Ullah Khan; Yasser D. Al-Otaibi; Waleed S. Alnumay. 2021. "PP-SPA: Privacy Preserved Smartphone-Based Personal Assistant to Improve Routine Life Functioning of Cognitive Impaired Individuals." Neural Processing Letters , no. : 1-18.

Journal article
Published: 02 January 2021 in Sustainable Computing: Informatics and Systems
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The rapid advancements in wireless technology and enhanced computing power of handheld devices, enable the users to perform transactions anywhere, anytime during roaming. Carrying out ongoing transactions during roaming is a crucial field for research in the field of mobile communication. In order to ensure a high quality of service (QoS), an energy-efficient handover process is essential for accomplishing the ongoing transaction. The performance of mobile communication is mainly deteriorated by the roaming and low battery power requirement of mobile host. Due to limited channel availability, most of the handover requests are failed. Energy-efficient enhanced mobility management queuing model is proposed by combining two existing schemes GE/GE/C/N/FCFS and scheme GE/GE/C/N/PR to strengthen the performance. In this research, EMMM scales down the dropping rate of handover transaction request (HTR) and new transaction request (NTR).The proposed model has achieved the enhancement of channel utilization along with the reduction in handover failure and low drop and blocking rate of HTR and NTR, respectively.

ACS Style

Ashok Kumar Yadav; Karan Singh; Ali Ahmadian; Senthilkumar Mohan; Syed Bilal Hussain Shah; Waleed S. Alnumay. EMMM: Energy-efficient mobility management model for context-aware transactions over mobile communication. Sustainable Computing: Informatics and Systems 2021, 30, 100499 .

AMA Style

Ashok Kumar Yadav, Karan Singh, Ali Ahmadian, Senthilkumar Mohan, Syed Bilal Hussain Shah, Waleed S. Alnumay. EMMM: Energy-efficient mobility management model for context-aware transactions over mobile communication. Sustainable Computing: Informatics and Systems. 2021; 30 ():100499.

Chicago/Turabian Style

Ashok Kumar Yadav; Karan Singh; Ali Ahmadian; Senthilkumar Mohan; Syed Bilal Hussain Shah; Waleed S. Alnumay. 2021. "EMMM: Energy-efficient mobility management model for context-aware transactions over mobile communication." Sustainable Computing: Informatics and Systems 30, no. : 100499.

Journal article
Published: 23 November 2020 in Computer Communications
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Industrial Internet of Things (IIoT) is a convincing stage by interfacing different sensors around us to the Internet, giving incredible chances for the acknowledgment of brilliant living. It is a fast growing technology in the present scenario. IIoT has its effect on almost every advanced field in the society. It has impact not only on work, but also on the living style of individual and organization. Due to high availability of internet, the connecting cost is decreasing and more advanced systems has been developed with Wi-Fi capabilities. The concept of connecting any device with internet is “IIoT”, which is becoming new rule for the future. This manuscript discusses about the applications of Internet of Things in different areas like- automotive industries, embedded devices, environment monitoring, agriculture, construction, smart grid, health care, etc. A regressive review of the existing systems of the automotive industry, emergency response, and chain management on IIoT has been carried out, and it is observed that IIoT found its place almost in every field of technology.

ACS Style

Praveen Kumar Malik; Rohit Sharma; Rajesh Singh; Anita Gehlot; Suresh Chandra Satapathy; Waleed S. Alnumay; Danilo Pelusi; Uttam Ghosh; Janmenjoy Nayak. Industrial Internet of Things and its Applications in Industry 4.0: State of The Art. Computer Communications 2020, 166, 125 -139.

AMA Style

Praveen Kumar Malik, Rohit Sharma, Rajesh Singh, Anita Gehlot, Suresh Chandra Satapathy, Waleed S. Alnumay, Danilo Pelusi, Uttam Ghosh, Janmenjoy Nayak. Industrial Internet of Things and its Applications in Industry 4.0: State of The Art. Computer Communications. 2020; 166 ():125-139.

Chicago/Turabian Style

Praveen Kumar Malik; Rohit Sharma; Rajesh Singh; Anita Gehlot; Suresh Chandra Satapathy; Waleed S. Alnumay; Danilo Pelusi; Uttam Ghosh; Janmenjoy Nayak. 2020. "Industrial Internet of Things and its Applications in Industry 4.0: State of The Art." Computer Communications 166, no. : 125-139.

Journal article
Published: 19 October 2020 in Future Generation Computer Systems
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Android smartphones are being utilized by a vast majority of users for everyday planning, data exchanges, correspondences, social interaction, business execution, bank transactions, and almost in each walk of everyday lives. With the expansion of human reliance on smartphone technology, cyberattacks against these devices have surged exponentially. Smartphone applications use permissions to utilize various functionalities of the smartphone that can be maneuvered to launch an attack or inject malware by hackers. Existing studies present various approaches to detect Android malware but lack early detection and identification. Accordingly, there is a dire need to craft an efficient mechanism for malicious applications’ detection before they exploit the data. In this paper, a novel approach DeepAMD to defend against real-world Android malware using deep Artificial Neural Network (ANN) has been adopted including an efficiency comparison of DeepAMD with conventional machine learning classifiers and state-of-the-art studies based on performance measures such as accuracy, recall, f-score, and precision. As per the experimental analysis, DeepAMD outperforms other approaches in detecting and identifying malware attacks on both Static as well as Dynamic layers. On the Static layer, DeepAMD achieves the highest accuracy of 93.4% for malware classification, 92.5% for malware category classification, and 90% for malware family classification. On the Dynamic layer, DeepAMD achieves the highest accuracy of 80.3% for malware category classification and 59% for malware family classification in comparison with the state-of-the-art techniques.

ACS Style

Syed Ibrahim Imtiaz; Saif Ur Rehman; Abdul Rehman Javed; Zunera Jalil; Xuan Liu; Waleed S. Alnumay. DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network. Future Generation Computer Systems 2020, 115, 844 -856.

AMA Style

Syed Ibrahim Imtiaz, Saif Ur Rehman, Abdul Rehman Javed, Zunera Jalil, Xuan Liu, Waleed S. Alnumay. DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network. Future Generation Computer Systems. 2020; 115 ():844-856.

Chicago/Turabian Style

Syed Ibrahim Imtiaz; Saif Ur Rehman; Abdul Rehman Javed; Zunera Jalil; Xuan Liu; Waleed S. Alnumay. 2020. "DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network." Future Generation Computer Systems 115, no. : 844-856.

Research article
Published: 15 September 2020 in Wireless Communications and Mobile Computing
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With the exponential increase in a number of web pages daily, it makes it very difficult for a search engine to list relevant web pages. In this paper, we propose a machine learning-based classification model that can learn the best features in each web page and helps in search engine listing. The existing methods for listing have lots of drawbacks like interfacing the normal operations of the website and crawling lots of useless information. Our proposed algorithm provides an optimal classification for websites which has a large number of web pages such as Wikipedia by just considering core information like link text, side information, and header text. We implemented our algorithm with standard benchmark datasets, and the results show that our algorithm outperforms the existing algorithms.

ACS Style

G. Siva Shankar; P. AshokKumar; R. Vinayakumar; Uttam Ghosh; Wathiq Mansoor; Waleed S. Alnumay. An Embedded-Based Weighted Feature Selection Algorithm for Classifying Web Document. Wireless Communications and Mobile Computing 2020, 2020, 1 -10.

AMA Style

G. Siva Shankar, P. AshokKumar, R. Vinayakumar, Uttam Ghosh, Wathiq Mansoor, Waleed S. Alnumay. An Embedded-Based Weighted Feature Selection Algorithm for Classifying Web Document. Wireless Communications and Mobile Computing. 2020; 2020 ():1-10.

Chicago/Turabian Style

G. Siva Shankar; P. AshokKumar; R. Vinayakumar; Uttam Ghosh; Wathiq Mansoor; Waleed S. Alnumay. 2020. "An Embedded-Based Weighted Feature Selection Algorithm for Classifying Web Document." Wireless Communications and Mobile Computing 2020, no. : 1-10.

Journal article
Published: 14 September 2020 in Energies
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In developed nations, the advent of distributed ledger technology is emerging as a new instrument for improving the traditional system in developing nations. Indeed, adopting blockchain technology is a necessary condition for the coming future of organizations. The distributed ledger technology provides better transparency and visibility. This study investigated the features that may influence the behavioral intention of energy experts to implement the distributed ledger technology for the energy management of developing countries. The proposed model is based on the Technology Acceptance Model construct and the diffusion of the innovation construct. Based on a survey of 178 experts working in the energy sector, the proposed model was tested using structural equation modeling. The findings showed that perceived ease of use, perceived usefulness, attitude, and cost saving had a positive and significant impact during the blockchain technology adoption. However, innovativeness showed a positive effect on the perceived ease of use whereas an insignificant impact on the perceived usefulness. The present study offers a holistic model for the implementation of innovative technologies. For the developers, it suggest rising disruptive technology solutions.

ACS Style

Nazir Ullah; Waleed S. Alnumay; Waleed Mugahed Al-Rahmi; Ahmed Ibrahim Alzahrani; Hosam Al-Samarraie. Modeling Cost Saving and Innovativeness for Blockchain Technology Adoption by Energy Management. Energies 2020, 13, 4783 .

AMA Style

Nazir Ullah, Waleed S. Alnumay, Waleed Mugahed Al-Rahmi, Ahmed Ibrahim Alzahrani, Hosam Al-Samarraie. Modeling Cost Saving and Innovativeness for Blockchain Technology Adoption by Energy Management. Energies. 2020; 13 (18):4783.

Chicago/Turabian Style

Nazir Ullah; Waleed S. Alnumay; Waleed Mugahed Al-Rahmi; Ahmed Ibrahim Alzahrani; Hosam Al-Samarraie. 2020. "Modeling Cost Saving and Innovativeness for Blockchain Technology Adoption by Energy Management." Energies 13, no. 18: 4783.

Journal article
Published: 25 March 2020 in IEEE Access
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This paper addresses traffic engineering (TE) issues in software-defined vehicular networking (SDVN). A brief analysis of the features of SDVN, which improves the efficiency of TE in SDVN, is presented. The feasibility of using multi-path routing with TE is substantiated. A procedure and an example of the formation of multiple routes based on a modified wave routing algorithm are given. Considering the features of the SDVN technology, a modified TE method is proposed, which reduces both the time com-plexity of forming multiple paths and the path reconfiguration time. The dynamic path reconfiguration algorithm is presented.

ACS Style

Ahed Abugabah; Ahmad Ali Alzubi; Osama Alfarraj; Mohammed Al-Maitah; Waleed S. Alnumay. Intelligent Traffic Engineering in Software-Defined Vehicular Networking Based on Multi-Path Routing. IEEE Access 2020, 8, 62334 -62342.

AMA Style

Ahed Abugabah, Ahmad Ali Alzubi, Osama Alfarraj, Mohammed Al-Maitah, Waleed S. Alnumay. Intelligent Traffic Engineering in Software-Defined Vehicular Networking Based on Multi-Path Routing. IEEE Access. 2020; 8 (99):62334-62342.

Chicago/Turabian Style

Ahed Abugabah; Ahmad Ali Alzubi; Osama Alfarraj; Mohammed Al-Maitah; Waleed S. Alnumay. 2020. "Intelligent Traffic Engineering in Software-Defined Vehicular Networking Based on Multi-Path Routing." IEEE Access 8, no. 99: 62334-62342.

Journal article
Published: 26 March 2019 in Sensors
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The Internet of things (IoT) is a heterogeneous network of different types of wireless networks such as wireless sensor networks (WSNs), ZigBee, Wi-Fi, mobile ad hoc networks (MANETs), and RFID. To make IoT a reality for smart environment, more attractive to end users, and economically successful, it must be compatible with WSNs and MANETs. In light of this, the present paper discusses a novel quantitative trust model for an IoT-MANET. The proposed trust model combines both direct and indirect trust opinion in order to calculate the final trust value for a node. A Beta probabilistic distribution is used to combine different trust evidences and direct trust has been calculated. The theory of ARMA/GARCH has been used to combine the recommendation trust evidences and predict the resultant trust value of each node in multi-step ahead. Further, a routing protocol has been designed to ensure the secure and reliable end-to-end delivery of packets by only considering trustworthy nodes in the path. Simulation results show that our proposed trust model outperforms similar existing trust models.

ACS Style

Waleed Alnumay; Uttam Ghosh; Pushpita Chatterjee. A Trust-Based Predictive Model for Mobile Ad Hoc Network in Internet of Things. Sensors 2019, 19, 1467 .

AMA Style

Waleed Alnumay, Uttam Ghosh, Pushpita Chatterjee. A Trust-Based Predictive Model for Mobile Ad Hoc Network in Internet of Things. Sensors. 2019; 19 (6):1467.

Chicago/Turabian Style

Waleed Alnumay; Uttam Ghosh; Pushpita Chatterjee. 2019. "A Trust-Based Predictive Model for Mobile Ad Hoc Network in Internet of Things." Sensors 19, no. 6: 1467.