The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Survey on blockchain for Internet of Things

Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.

Internet of Things (IoT) and the Energy Sector

Heterogeneous Internet of Things (HetIoT) is an emerging research field that has strong potential to transform both our understanding of fundamental computer science principles and our future living. HetIoT is being employed in increasing number of areas, such as smart home, smart city, intelligent transportation, environmental monitoring, security systems, and advanced manufacturing. Therefore, relaying on strong application fields, HetIoT will be filled in our life and provide a variety of convenient services for our future. The network architectures of IoT are intrinsically heterogeneous, including wireless sensor network, wireless fidelity network, wireless mesh network, mobile communication network, and vehicular network. In each network unit, smart devices utilize appropriate communication methods to integrate digital information and physical objects, which provide users with new exciting applications and services. However, the complexity of application requirements, the heterogeneity of network architectures and communication technologies impose many challenges in developing robust HetIoT applications. This paper proposes a four-layer HetIoT architecture consisting of sensing, networking, cloud computing, and applications. Then, the state of the art in HetIoT research and applications have been discussed. This paper also suggests several potential solutions to address the challenges facing future HetIoT, including self-organizing, big data transmission, privacy protection, data integration and processing in large-scale HetIoT.

How Can Heterogeneous Internet of Things Build Our Future: A Survey

The use of the Internet is growing in this day and age, so another area has developed to use the Internet, called Internet of Things (IoT). It facilitates the machines and objects to communicate, compute and coordinate with each other. It is an enabler for the intelligence affixed to several essential features of the modern world, such as homes, hospitals, buildings, transports and cities. The security and privacy are some of the critical issues related to the wide application of IoT. Therefore, these issues prevent the wide adoption of the IoT. In this paper, we are presenting an overview about different layered architectures of IoT and attacks regarding security from the perspective of layers. In addition, a review of mechanisms that provide solutions to these issues is presented with their limitations. Furthermore, we have suggested a new secure layered architecture of IoT to overcome these issues.

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IoT Elements, Layered Architectures and Security Issues: A Comprehensive Survey.

The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications.

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Internet of Things: Architectures, Protocols, and Applications

Objective: The elevated choline transporters (ChT), choline kinase (ChK), choline uptake, and phosphorylation in certain tumor cells have influenced the development of radiolabeled choline derivatives as diagnostic probes for imaging cell membrane proliferation. We, therefore, aimed to develop a choline-based moiety for imaging choline kinase-overexpressed tumors by single-photon emission tomography (SPECT). A novel choline-based diagnostic probe was synthesized and evaluated preclinically in various ChT- and ChK-overexpressed tumor models for SPECT imaging applications. Methods: The synthesis of diethylenetriaminepentaacetic acid-bis-choline ethylamine [DTPA-bis(ChoEA)] featured the conjugation of dimethylaminoethanol to a bifunctional chelator DTPA anhydride. [99mTc]Tc-DTPA-bis(ChoEA) was prepared, and its in vivo characteristics were evaluated in BALB/c mice and tumor-xenografted PC3, A549, and HCT116 athymic mouse models. The in vitro parameters, including cell binding and cytotoxicity, were assessed in PC3, A549, and HCT116 cell lines. To evaluate the specificity of the radioprobe, competitive binding studies were performed. Small-animal SPECT/CT diagnostic imaging was performed for in vivo evaluation. The mouse biodistribution data was further investigated to estimate the radiation dose in humans. Results: In silico studies suggested high binding with enhanced specificity. A standard radiolabeling procedure using stannous chloride as a reducing agent showed a labeling yield of 99.5 ± 0.5%. The in silico studies suggested high binding with enhanced specificity. [99mTc]Tc-DTPA-bis(ChoEA) showed high in vitro stability and specificity. The pharmacokinetic studies of [99mTc]Tc-DTPA-bis(ChoEA) in mice showed an increased tumor-to-background ratio after few minutes of intravenous administration. The first-in-human trial was also conducted. The effective dose was estimated to be 0.00467 mSv/MBq (4.67 mSv/GBq), resulting in a radiation dose of up to 1.73 mSv for the 370 MBq injection of [99mTc]Tc-DTPA-bis(ChoEA). Conclusions: The synthesized radioprobe [99mTc]Tc-DTPA-bis(ChoEA) accumulates specifically in choline kinase-overexpressed tumors with a high signal-to-noise ratio. The preclinical and first-in-man data suggested that [99mTc]Tc-DTPA-bis(ChoEA) could potentially be used as a diagnostic SPECT tracer in the monitoring and staging of cancer.

https://pubs.acs.org/doi/pdf/10.1021/acsomega.1c04256

[99mTc]Tc-DTPA-Bis(cholineethylamine) as an Oncologic Tracer for the Detection of Choline Transporter (ChT) and Choline Kinase (ChK) Expression in Cancer

Pallavi Sethi

Papers

Internet of Things: Architectures, Protocols, and Applications

[99mTc]Tc-DTPA-Bis(cholineethylamine) as an Oncologic Tracer for the Detection of Choline Transporter (ChT) and Choline Kinase (ChK) Expression in Cancer