Nasir Abbas

Bio: Nasir Abbas is an academic researcher from Oslo University Hospital. The author has contributed to research in topics: Radioimmunotherapy & Ovarian cancer. The author has an hindex of 8, co-authored 9 publications receiving 1235 citations. Previous affiliations of Nasir Abbas include University of Oslo.

Mobile Edge Computing: A Survey

Abstract: Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

Experimental α-particle radioimmunotherapy of breast cancer using 227Th-labeled p-benzyl-DOTA-trastuzumab.

Abstract: Background The aim of the present study was to explore the biodistribution, normal tissue toxicity, and therapeutic efficacy of the internalizing low-dose rate alpha-particle-emitting radioimmunoconjugate 227Th-trastuzumab in mice with HER2-expressing breast cancer xenografts.

Fractionated therapy of HER2-expressing breast and ovarian cancer xenografts in mice with targeted alpha emitting 227Th-DOTA-p-benzyl-trastuzumab.

Abstract: Background The aim of this study was to investigate therapeutic efficacy and normal tissue toxicity of single dosage and fractionated targeted alpha therapy (TAT) in mice with HER2-expressing breast and ovarian cancer xenografts using the low dose rate radioimmunoconjugate 227Th-DOTA-p-benzyl-trastuzumab.

Targeted Alpha Therapy with 227Th-trastuzumab of Intraperitoneal Ovarian Cancer in Nude Mice

Abstract: The aim of the current study was to investigate the therapeutic effect of 227Th-radioimmunotherapy on intraperitoneally growing human bioluminescent HER2 positive ovarian cancer cells. Methods: In vitro toxicity of 227Th-trastuzumab in bioluminescent SKOV3-luc-D3 ovarian cancer cells was assessed in a growth assay. The biodistribution of intraperitoneally administrated 227Th-trastuzumab in athymic nude mice without tumor cells was determined. For in vivo therapy, seventy female athymic nude mice were intraperitoneally inoculated with tumor cells 17 days prior to injection of single 227Th-trastuzumab doses of 1000 kBq/kg, 600 kBq/kg or 400 kBq/kg, or three injections with 400 kBq/kg 227Th-trastuzumab separated by 4 weeks. Two control groups were given either 20 µg unlabeled trastuzumab or 0.9 % NaCl. In vivo bioluminescence imaging was performed weekly before and after onset of therapy. Tumor growth, survival and toxicity were compared. Results: There was a statistically significant therapeutic effect of the 227Th-trastuzumab treatment both with respect to survival and tumor growth. The maximum tolerated dosage was 600 kBq/kg 227Th-trastuzumab. In the in vitro study, two hours incubation with 20 kBq/ml of 227Th-trastuzumab, followed by washing, and subsequent culture of the cells resulted in an average absorbed radiation dose of 6 Gy after 11 days and complete growth inhibition. Conclusion: Targeted alpha therapy with 227Th-trastuzumab of human SKOV3-luc-D3 cells growing intraperitoneally in nude mice was clearly superior to unlabeled trastuzumab therapy. The results warrant further studies of 227Thradioimmunotherapy used as adjuvant treatment and for metastatic cancer.

Preclinical evaluation of 227Th-labeled and 177Lu- labeled trastuzumab in mice with HER-2-positive ovarian cancer xenografts

Abstract: ObjectiveThe aim of the present study was to compare the biodistribution, normal tissue toxicity, and therapeutic effect of two low-dose rate radioimmunoconjugates (RICs) in mice with HER2-expressing ovarian cancer xenografts: the α-particle-emitting 227Th-trastuzumab and the β-particle-emitting 177

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Abstract: In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Abstract: The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Abstract: In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL

Blockchain for Internet of Things: A Survey

Abstract: Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges, such as decentralization, poor interoperability, privacy, and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as blockchain of things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then, we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for fifth generation beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area.