Recent Submissions

  • An Efficient Encryption and Compression of Sensed IoTMedical Images Using Auto-Encoder.

    ElKafrawy, Passent; elsayed, Ayman; Torkey, Hanaa; Ahmed, Abdelmoty M.; Aboghazalah, Maie; External Collaboration; Virtual Reality Lab; 0; 0; Computer Science; et al. (EBSCO, 2023)
    Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption ofmedical images is very important to secure patient information. Encrypting these images consumes a lot of time onedge computing; therefore, theuse of anauto-encoder for compressionbefore encodingwill solve such a problem. In this paper, we use an auto-encoder to compress amedical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the vector was received and decrypted. Two convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder, which is utilized to compress and encrypt the images, and the other assesses the classification accuracy of the image after decryption and decoding. Different hyperparameters of the encoder were tested, followed by the classification of the image to verify that no critical information was lost, to test the encryption and encoding resolution. In this approach, sixteen hyperparameter permutations are utilized, but this research discusses three main cases in detail. The first case shows that the combination of Mean Square Logarithmic Error (MSLE), ADAgrad, two layers for the auto-encoder, and ReLU had the best auto-encoder results with a Mean Absolute Error (MAE) = 0.221 after 50 epochs and 75% classification with the best result for the classification algorithm. The second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error (MSE), RMSprop, three layers for the auto-encoder, and ReLU, which had the best classification accuracy of 65%, the auto-encoder gives MAE = 0.31 after 50 epochs. The third case is the worst, which is the combination of the hinge, RMSprop, three layers for the auto-encoder, and ReLU, providing accuracy of 20% and MAE = 0.485.
  • A Blockchain-Enabled IoT Logistics System for Efficient Tracking and Management of High-Price Shipments: A Resilient, Scalable and Sustainable Approach to Smart Cities

    Mohammed Balfaqih; Zain Balfagih; Miltiadis D Lytras; Khaled Mofawiz Alfawaz; Abdulrahman A Alshdadi; Eesa Alsolami; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; et al. (MDPI, 2023-09-20)
    The concept of a smart city is aimed at enhancing the quality of life for urban residents, and logistic services are a crucial component of this effort. Despite this, the logistics industry has encountered issues due to the exponential growth of logistics volumes, as well as the complexity of processes and lack of transparency. Consequently, it is necessary to develop an efficient management system that offers traceability and condition monitoring capabilities to ensure the safe and high-quality delivery of goods. Moreover, it is crucial to guarantee the accuracy and dependability of distribution data. In this context, this paper proposes a blockchain-enabled IoT logistics system for the efficient tracking and management of high-price shipments. A smart contract based on blockchain technology has been designed for automatic approval and payment, with the aim of distributing shipping information exclusively among legitimate logistics parties. To ensure authentication, a zero-knowledge proof is used to conceal the blockchain address. Moreover, an intelligent parcel (iParcel) containing piezoresistive sensors is developed to pack delivered goods during the shipping process for violation detection such as severe falls or theft. The iParcels are automatically tracked and traced, and if a violation occurs, the contract is cancelled, and payment is refunded. The transaction fee per party is reasonable, particularly for high-price products that guarantee successful shipment.
  • Risk prediction model for cannabis use with artificial intelligence approach

    Hakkarainen, P; Karjalainen, K.; Subasi, A; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; Computer Science; 0; Unlu, A. (Taylor and Francis, 2023)
    Background Identifying the most important predictors of substance use is crucial for developing effective prevention policies. Traditional statistical methods have some limitations in this regard. To address these limitations, the researchers utilized artificial intelligence (AI) methods to identify the top 10 most important predictors of cannabis use in Finland. Objective The objective of this study was to apply AI techniques to identify the key predictors of cannabis use in Finland. Specifically, the researchers aimed to determine the top 10 most important features related to cannabis use from a dataset consisting of 3229 observations and 313 questionnaire items, with 48 selected for preprocessing. Methods The researchers employed the recursive feature elimination (RFE) method as part of their AI analysis. This technique was used on 60 processed variables, following the application of missing data imputation, resampling, and scaling techniques. The RFE method allowed the researchers to narrow down the 60 variables to the top 10 most important features associated with cannabis use. Results The AI models developed using the selected features were able to predict cannabis use with a remarkable accuracy of 96% for the previous 12 months. The results of the study revealed that the social settings of individuals played the most significant role in predicting cannabis use in the context of Finland. Conclusions In conclusion, this study demonstrated the effectiveness of AI-based approaches in identifying the most critical predictors of cannabis use in Finland. The research highlighted that social settings had the highest impact on cannabis use in this setting. Moreover, the study showcased the potential of AI methods not only for identifying key risk indicators among various factors but also for optimizing the utilization of limited public resources when devising prevention strategies. These findings can be valuable for shaping targeted and efficient prevention policies to address cannabis use in Finland.
  • Promoting accuracy in low-magnification histopathology grading: With augmentation and multi-dilation model.

    Subasi, Abdulhamit; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; Computer Science; 0; Gan, Zonghan (Elsevier, 2023)
    Advances in artificial intelligence have facilitated the automated grading of histopathology slides. Yet, the magnification of whole slide scanners (WSS) has restrained the accuracy of patch-based grading. In this work, we found that augmentation can significantly promote grading performance under this issue, even when the data volume is large (>140 K). With augmentation and a multi-dilation model, the CovXNet, we yielded a Balanced Accuracy of 92.13%, which is the current highest for the Breast Histopathology Dataset (40X magnification) also the first time both sensitivity and specificity >90%. However, in this focused grading task, augmentation only improves models with high invariance (the CovXNet and BCA-CNN). Pre-trained ResNet has lower invariance in this task, but fine-tuning can significantly improve both accuracy and invariance. For the CropNet attention model, adapting with max pooling but not augmentation offers promotions. Additionally, this work also found two types of common errors in high-starred codes, when using random.shuffle for data-label composited array, or the integrated shuffle function of ImageDataGenerator, which fake a higher accuracy by masking class 0 as class 1. Using Sklearn.shuffle instead is safer. All codes are available on our GitHub.
  • A Blockchain-Enabled IoT Logistics System for Efficient Tracking and Management of High-Price Shipments: A Resilient, Scalable and Sustainable Approach to Smart Cities

    Balfaqih, Mohammed; Balfagih, Zain; Lytras, Miltiadis; Alfawaz, Khaled; Alshdadi, Abdulrahman; Alsolami, Eesa; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; et al. (MDPI, 2023-09-20)
    The concept of a smart city is aimed at enhancing the quality of life for urban residents, and logistic services are a crucial component of this effort. Despite this, the logistics industry has encountered issues due to the exponential growth of logistics volumes, as well as the complexity of processes and lack of transparency. Consequently, it is necessary to develop an efficient management system that offers traceability and condition monitoring capabilities to ensure the safe and high-quality delivery of goods. Moreover, it is crucial to guarantee the accuracy and dependability of distribution data. In this context, this paper proposes a blockchain-enabled IoT logistics system for the efficient tracking and management of high-price shipments. A smart contract based on blockchain technology has been designed for automatic approval and payment, with the aim of distributing shipping information exclusively among legitimate logistics parties. To ensure authentication, a zero-knowledge proof is used to conceal the blockchain address. Moreover, an intelligent parcel (iParcel) containing piezoresistive sensors is developed to pack delivered goods during the shipping process for violation detection such as severe falls or theft. The iParcels are automatically tracked and traced, and if a violation occurs, the contract is cancelled, and payment is refunded. The transaction fee per party is reasonable, particularly for high-price products that guarantee successful shipment.
  • The influence of AmeriCorps members on ecosystem management, Journal of Cleaner Production

    Zhuhadar, Lily Popova; McCreary, Allie; Miltiadis, Lytras; Maria, Wells; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; Computer Science; 0; et al. (Elsevier, 2023-09-25)
    Millions of acres of public lands across the United States (U.S.) face imminent threats from invasive species and wildfires, jeopardizing ecosystem health, wildlife habitats, recreational resources, and community safety. The responsible agencies, including the U.S. Forest Service, National Park Service, Bureau of Land Management, and state park agencies, are limited in their capacity to effectively manage these lands. Consequently, AmeriCorps members play a crucial role in curbing invasive species proliferation and mitigating wildfire fuel loads, safeguarding communities, and habitats. Here, the results of a national evaluation to assess the impact of AmeriCorps members on invasive species and wildfire fuel loads is presented. This study is a comprehensive analysis of AmeriCorps members' effectiveness in achieving ecosystem management objectives, revealing positive responses in native species cover and notable improvements in treated plots. AmeriCorps units that yielded significant results operated under specific temperature conditions and adhered to distinct pre- and post-intervention data collection intervals. Despite occasional challenges, the overall efficacy of AmeriCorps' efforts in eradicating targeted invasive species and promoting positive native species responses was confirmed. While linear regression models indicated successful forest fuel mitigation in AmeriCorps programs, the Difference-in-Difference models revealed less conspicuous outcomes, suggesting limited modifications in parameters such as the height of the lowest living branch, canopy cover, and litter depth. By analyzing data from multiple AmeriCorps programs in diverse geographic regions, this study contributes valuable insights into the effectiveness of AmeriCorps programs in ecological conservation. It represents one of the first multi-state and comprehensive examinations of AmeriCorps members' effectiveness in invasive species management and wildfire fuel mitigation in the USA, underscoring its significance in the field of ecosystem management.
  • The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions

    Zhuhadar, Lily Popova; Lytras, Miltiadis; External Collaboration; NA; 0; 0; Computer Science; 0; Zhuhadar, Lily Popova (MDPI, 2023-09-04)
    Artificial Intelligence (AI) has experienced rapid advancements in recent years, facilitating the creation of innovative, sustainable tools and technologies across various sectors. Among these applications, the use of AI in healthcare, particularly in the diagnosis and management of chronic diseases like diabetes, has shown significant promise. Automated Machine Learning (AutoML), with its minimally invasive and resource-efficient approach, promotes sustainability in healthcare by streamlining the process of predictive model creation. This research paper delves into advancements in AutoML for predictive modeling in diabetes diagnosis. It illuminates their effectiveness in identifying risk factors, optimizing treatment strategies, and ultimately improving patient outcomes while reducing environmental footprint and conserving resources. The primary objective of this scholarly inquiry is to meticulously identify the multitude of factors contributing to the development of diabetes and refine the prediction model to incorporate these insights. This process fosters a comprehensive understanding of the disease in a manner that supports the principles of sustainable healthcare. By analyzing the provided dataset, AutoML was able to select the most fitting model, emphasizing the paramount importance of variables such as Glucose, BMI, DiabetesPedigreeFunction, and BloodPressure in determining an individual’s diabetic status. The sustainability of this process lies in its potential to expedite treatment, reduce unnecessary testing and procedures, and ultimately foster healthier lives. Recognizing the importance of accuracy in this critical domain, we propose that supplementary factors and data be rigorously evaluated and incorporated into the assessment. This approach aims to devise a model with enhanced accuracy, further contributing to the efficiency and sustainability of healthcare practices.
  • A Blockchain Based Framework for Efficient Water Management and Leakage Detection in Urban Areas

    Mohammad, Naqash; Toqeer, Syed; Saad, Alqahtani; Muhammad, Siddiqui; Ali, Alzahrani; Mohammad, Nauman; External Collaboration; Artificial Intelligence & Cyber Security Lab; 0; 0; et al. (MDPI, 2023-09-22)
    Sustainable urban water management is essential to handle water scarcity, leakage, and inefficient distribution. This paper covers water management in urban areas, including an intro- duction, an overview of water management practices, the characteristics and functioning of water distribution systems, monitoring and control systems for efficient distribution, smart systems for optimization, strategies for water conservation and waste management, per capita water demand analysis, and desalination plant overviews. The article proposes a blockchain-based water manage- ment architecture with IoT sensors for accurate reporting. The framework uses blockchain technology to authenticate and share real-time data between sensors and the water distribution dashboard. It also has a modular API for water leakage detection and flow control to decrease water waste and enhance distribution. The suggested approach might enhance water management; however, its execution is complex. Maintaining the framework’s efficacy is advised. The research provides insights into water management and proposes a technology solution employing blockchain and IoT sensors for trustworthy data reporting and effective water distribution to promote sustainable urban water management.
  • Government and the global digital transformation: the other side of the mirror

    Visvizi, Anna; No Collaboration; NA; 0; 0; Entrepreneurship; 0; Visvizi, Anna (2023-10-30)
    Amid the global digital transformation, the seemingly simple question of the government delivering on peace, justice and strong institutions turns into a conundrum. Several factors weigh in on what the government can do, envisages to do and dares to do today. State power, raison d’étre, the nature of the global order, including global governance, are just a few of the issues that need to be factored in the analysis. Clearly, questions of ideology, including the degree of government intervention in the economy, the very definition of market economy and even of the scope of (economic) freedom are equally important in the conversation about the government today. People, and so personalities, do matter too, in that they influence the way things are done, expressed and communicated, regardless of the institutional, i.e. frequently rigid, structural confines.
  • A Blockchain-Enabled IoT Logistics System for Efficient Tracking and Management of High-Price Shipments: A Resilient, Scalable and Sustainable Approach to Smart Cities

    Mohammed Balfaqih; balfagih, zain; Lytras, Miltiadis; Khaled Mofawiz Alfawaz; Abdulrahman A Alshdadi; Eesa Alsolami; External Collaboration; NA; NA; NA; et al. (MDPI, 2023-09-20)
    The concept of a smart city is aimed at enhancing the quality of life for urban residents, and logistic services are a crucial component of this effort. Despite this, the logistics industry has encountered issues due to the exponential growth of logistics volumes, as well as the complexity of processes and lack of transparency. Consequently, it is necessary to develop an efficient management system that offers traceability and condition monitoring capabilities to ensure the safe and high-quality delivery of goods. Moreover, it is crucial to guarantee the accuracy and dependability of distribution data. In this context, this paper proposes a blockchain-enabled IoT logistics system for the efficient tracking and management of high-price shipments. A smart contract based on blockchain technology has been designed for automatic approval and payment, with the aim of distributing shipping information exclusively among legitimate logistics parties. To ensure authentication, a zero-knowledge proof is used to conceal the blockchain address. Moreover, an intelligent parcel (iParcel) containing piezoresistive sensors is developed to pack delivered goods during the shipping process for violation detection such as severe falls or theft. The iParcels are automatically tracked and traced, and if a violation occurs, the contract is cancelled, and payment is refunded. The transaction fee per party is reasonable, particularly for high-price products that guarantee successful shipment.
  • The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions

    Lily Popova Zhuhadar; Lytras, Miltiadis; External Collaboration; NA; NA; NA; Computer Science; NA; Lily Popova Zhuhadar (MDPI, 2023-09-08)
    Artificial Intelligence (AI) has experienced rapid advancements in recent years, facilitating the creation of innovative, sustainable tools and technologies across various sectors. Among these applications, the use of AI in healthcare, particularly in the diagnosis and management of chronic diseases like diabetes, has shown significant promise. Automated Machine Learning (AutoML), with its minimally invasive and resource-efficient approach, promotes sustainability in healthcare by streamlining the process of predictive model creation. This research paper delves into advancements in AutoML for predictive modeling in diabetes diagnosis. It illuminates their effectiveness in identifying risk factors, optimizing treatment strategies, and ultimately improving patient outcomes while reducing environmental footprint and conserving resources. The primary objective of this scholarly inquiry is to meticulously identify the multitude of factors contributing to the development of diabetes and refine the prediction model to incorporate these insights. This process fosters a comprehensive understanding of the disease in a manner that supports the principles of sustainable healthcare. By analyzing the provided dataset, AutoML was able to select the most fitting model, emphasizing the paramount importance of variables such as Glucose, BMI, DiabetesPedigreeFunction, and BloodPressure in determining an individual’s diabetic status. The sustainability of this process lies in its potential to expedite treatment, reduce unnecessary testing and procedures, and ultimately foster healthier lives. Recognizing the importance of accuracy in this critical domain, we propose that supplementary factors and data be rigorously evaluated and incorporated into the assessment. This approach aims to devise a model with enhanced accuracy, further contributing to the efficiency and sustainability of healthcare practices.
  • Study of image sensors for enhanced face recognition at a distance in the Smart City context

    Llaurado, Jose M.; Pujol, Francisco A.; Tomas, David; Visvizi, Anna; Pujol, Mar; External Collaboration; NA; 0; 0; Computer Science; et al. (Springer, 2023-09-07)
    Smart monitoring and surveillance systems have become one of the fundamental areas in the context of security applications in Smart Cities. In particular, video surveillance for Human Activity Recognition (HAR) applied to the recognition of potential offenders and to the detection and prevention of violent acts is a challenging task that is still undergoing. This paper presents a method based on deep learning for face recognition at a distance for security applications. Due to the absence of available datasets on face recognition at a distance, a methodology to generate a reliable dataset that relates the distance of the individuals from the camera, the focal length of the image sensors and the size in pixels of the target face is introduced. To generate the extended dataset, the Georgia Tech Face and Quality Dataset for Distance Faces databases were chosen. Our method is then tested and applied to a set of commercial image sensors for surveillance cameras using this dataset. The system achieves an average accuracy above 99% for several sensors and allows to calculate the maximum distance for a sensor to get the required accuracy in the recognition, which could be crucial in security applications in smart cities.
  • Think human, act digital: activating data-driven orientation in innovative start-ups

    Visvizi, Anna; Troisi, Orlando; Grimaldi, Mara; Loia, Francesca; External Collaboration; NA; 0; 0; Entrepreneurship; 0; et al. (Emerald Publishing, 2022-12-19)
    The study queries the drivers of innovation management in contemporary data-driven organizations/companies. It is argued that data-driven organizations that integrate a strategic orientation grounded in data, human abilities and proactive management are more effective in triggering innovation. Research reported in this paper employs constructivist grounded theory, Gioia methodology, and the abductive approach. The data collected through semi-structured interviews administered to 20 Italian start-up founders are then examined. The paper identifies the key enablers of innovation development in data-driven companies and reveals that data-driven companies may generate different innovation patterns depending on the kind of capabilities activated.
  • Government regulation and organizational effectiveness in the health-care supply chain

    Hussain, M.; Ahmad, S.Z.; Visvizi, Anna; External Collaboration; NA; 0; 0; Entrepreneurship; 0; Hussain, M. (Emerald Publishing, 2022-12-10)
    In the context of the debate on ensuring health-care efficiency, this study aims to identify and prioritize factors and subfactors that influence organizational effectiveness (OE) in the health-care supply chain. For the purpose of this qualitative study, triangulation was applied to identify, explore and systematically analyze the OE-related practices used by diverse stakeholders along the health-care supply chain. Sixty-two OE practices were thus identified. Subsequently, these were grouped in six different nodes before the analytical hierarchical process (AHP) was used to identify the weightings of specific practices (and related factors) and their impact on OE. The findings suggest that external factors associated with government regulation, including government directives and branding, are the most critical factors that influence OE-related practices, while cost-related factors are the least important.
  • The case of rWallet: A blockchain-based tool to navigate some challenges related to irregular migration

    Visvizi, Anna; Mora, Higinio; Varela-Guzman, Erick; External Collaboration; NA; 0; 0; Computer Science; 0; Visvizi, Anna (Elsevier, 2023-02-01)
    Migration (irregular and forced) represents one of the major challenges the international community faces today. Inasmuch as the phenomenon of irregular and forced migration is the marker of the state of socio-economic systems around the world, the response to and the ways of navigating the resulting multi-scalar challenges mirror not only the efficiency of the global regulatory frameworks, but also our civility. Recognizing the potential inherent in sophisticated information and communication technology (ICT), specifically the blockchain technology and smart contracts, this paper focuses on the special case of “welcome centers” that irregular migrants enter in the hope of acquiring international legal protection and thus refugee status. Since the process may be time-consuming and the living conditions undignified, this paper proposes a tool, named here “responsible wallet”, aka rWallet, that bears the promise of navigating some of these challenges. rWallet derives from the recognition that in modern societies ICT should serve the purpose of improving the quality of life of all people.
  • How Effective is Research Funding? Exploring Research Performance Indicators?

    Soroya, S.H.; Umar, M.; Aljohani, R.N.; Visvizi, Anna; Nawaz, Raheel; External Collaboration; NA; 0; 0; Computer Science; et al. (https://jscires.org/, 2023-01-03)
    This paper deploys bibliometric indices and semantic techniques for understanding to what extent research grants are likely to impact publications, research direction, and co-authorship rate of principal investigators. It includes semantic analysis in the research funding evaluation process to effectively study short-term and long-term funding impact on publication outputs. Our dataset consists of researchers who received research grants from the National ICT Research and Development funding program of Pakistan. Whereas Pakistani researchers’ publications dataset was extracted from Scopus. We show several interesting case studies to conclude that bibliometric-based quantitative assessment combined with semantics can build better sustainable pathways to deploy evaluation frameworks for research funding effectively. The funding data of closed projects from 2007 to 2013 was obtained from ICT R&D public records. The publications dataset was extracted from Scopus data and the details of the statistics were, publications=61,421; researchers=42,376, organizations=213; funded projects=17, funded researchers=23 and funded organizations=10. A significant positive impact (more research output after allocation of funds) has been found for almost all studied organizations. Similarly, a positive funding impact on research output and average co-authorship for the studied cases (investigators under consideration) was found. However, no funding impact was found on the research focus of investigators, i.e., research focus remained almost unchanged after grant allocation. Also, the study suggested the best possible match candidates for collaboration or potential reviewers against the selected project by semantically analyzing the executive summary. Most funded researchers and research organizations have found a positive funding impact on research output (i.e., number of publications). Using semantics along with bibliometric indicators (relating to funding and impacts) can be constructive in making funding programs more effective and for better impacts evaluation; it is recommended for funding agencies to use it in formal framework formation and proposal evaluation process.
  • Social mining for terroristic behavior detection through Arabic tweets characterization

    Alhalabi, Wadee; Jari Jussila; Kamal Jambi; Anna Visvizi; Hafsa Qureshi; Lytras, Miltiadis; Areej Malibari; Raniah Samir Adham; University Collaboration; External Collaboration; et al. (North-Holland, 2021-03-01)
    In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis.
  • Handover performance evaluation of centralized and distributed network-based mobility management in vehicular urban environment

    Balfaqih, Mohammed; Ismail, M; Nordin, R; balfagih, zain; External Collaboration; Artificial Intelligence & Cyber Security Lab; Computer Science; Balfaqih, Mohammed (IEEE, 2017-05-08)
    The demand for real-time applications such as short video clips of CCTVs on a roadway, raises the need of for seamless and ubiquitous Internet connectivity in vehicular networks. Wireless Access in Vehicular Environment (WAVE) architecture defines the IPv6 mobility protocols to be deployed in network layer. However, the Current IP mobility management protocols, including Proxy Mobile IPv6 (PMIPv6), employ a centralized and single anchor to register MU's location information and establish communication which causes excessive burden on that central anchor. Recently, Distributed Mobility Management (DMM) based on PMIPv6 has been introduced to overcome these problems. In this paper, we investigate the vehicle velocity effect in urban environment where a map within the city of Bangi is taken. We compare the handover performance of network-based DMM with PMIPv6 in aspects of handover latency, session recovery, packet loss and throughput. The results show that network-based DMM outperforms PMIPv6 slightly.
  • Artificial Intelligence: Towards Digital Transformation of Life, Work, and Education

    Sarirete, Akila; balfagih, zain; Brahimi, Tayeb; El-Amin, Mohamed; Lytras, Miltiadis; Visvizi, Anna; College collaboration; Artificial Intelligence & Cyber Security Lab; Computer Science; Sarirete, Akila (Elsevier B.V., 2021)
    A new time of intelligence is rising, with Artificial Intelligence (AI) at the forefront of the changes we are living in a rapidly evolving world. AI is restructuring our lives, inspiring change, and hence shaping our future world. In the time of the Covid-19 pandemic, AI offers several opportunities thus giving the science of medicine the power to turning mountains of data into lifesaving breakthroughs, identifying diseases from a simple drop of blood, and attempting to stop the spread of the disease while developing effective vaccines in record time. Usually, the process can take 10 to 15 years to develop. AI, robots, and drones are being deployed to stop the spread of the disease, help track the pandemic, enforce restrictive measures, and provide critical support to healthcare delivery. Using AI, it is possible to transport medical supplies by drone, disinfect patient rooms, and search approved prescription databases for drugs that may also be effective against Covid-19. AI is also a tool to finding quick ways to bring cures to market, assisting customers in stores, adjusting to new inputs, performing human-like tasks, and enabling the achievement of 134 targets across all 17 sustainable development goals (SDGs) of the United Nations (UN). According to the consulting firm PwC (***), by 2030, AI could boost global GDP by 14%, with the highest gains in China and North America. The fastest-growing industries will be healthcare, financial services, and retails. In the Middle East, AI is expected to grow at a rate of 20% to 34% per year, with the U.A.E. and Saudi Arabia leading the way. The potential impact of AI in the Middle East is estimated to be US$320 billion by 2030. This impact could be even larger if governments continue to push the boundaries of innovation and implementation of AI across businesses sectors. The PwC consulting firm, the second-largest professional services network globally, estimated that by 2030 AI could contribute $135 billion or 12.4 percent to Saudi Arabia's GDP. AI opened a pool of opportunities for digital transformation and innovative services, one of the key concepts of the Saudi Vision 2030. There is no doubt that the development of non-oil sectors through investment in AI technologies could strategically position Saudi Arabia to serve as a springboard for the future.
  • 5G and Beyond; Paving the way for 6G

    Sarirete, Akila; El-Amin, Mohamed; balfagih, zain; Brahimi, Tayeb; Hussein, Aziza; Lytras, Miltiadis; Visvizi, Anna; College collaboration; Artificial Intelligence & Cyber Security Lab; Computer Science; et al. (Elsevier B.V., 2021)
    In recent times, 5G has gained prevalence in learning and technology that it will be the game-changer of the coming decade. In the wake of the fourth industrial revolution, governments and businesses worldwide are also beginning to realize the seismic shift towards 5G. The speed of development and growth of 5G has not only gained momentum in the network and application domains but has also increased investment by leading technology companies such as Ericson, Huawei, Google, and AT&T. At a recent summit "AI for the Good of Humanity" Global Artificial Intelligence (AI), held in Riyadh on 21-22 October, and organized by the Saudi Data and AI Authority (SDAIA), Charles Yang, President of Huawei Middle East, said ""Huawei has adopted an ambitious long-term research and development strategy regarding AI, creating unprecedented opportunities through the synergy of AI with 5G connectivity, cloud, computing, and industrial applications". Introducing 5G technology marks the start of a new age of communication, impacting almost every aspect of everyday life. According to Deloitte, nearly 2,5 billion people are using smartphones, and 1,2 billion are using tablets. During 2017 alone, more than 175 billion applications were downloaded, some with over one billion users, which led to more data communication and interaction of billions of devices. The average download speed went from 8 Mbps on 3G to 32.5 Mbps on 4G and up to 240 Mbps on 5G. By 2021, the video will account for 78% of mobile data traffic, up from 60% in 2016. CISCO predicted that in 2019, and for the first time in history, the mobile network traffic would surpass the fixed network traffic.

View more