Low-overhead Hardware Supervision for Securing an IoT Bluetooth-enabled Device: Monitoring Radio Frequency and Supply Voltage

2022 ◽  
Vol 18 (1) ◽  
pp. 1-28
Author(s):  
Abdelrahman Elkanishy ◽  
Paul M. Furth ◽  
Derrick T. Rivera ◽  
Ahameed A. Badawy
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Supply Voltage ◽  
Classification Performance ◽  
Portable Devices ◽  
Wireless Communication Protocol ◽  
Industrial Internet ◽  
Iot Devices ◽  
On Chip ◽  
Communication Control

Over the past decade, the number of Internet of Things (IoT) devices increased tremendously. In particular, the Internet of Medical Things (IoMT) and the Industrial Internet of Things (IIoT) expanded dramatically. Resource restrictions on IoT devices and the insufficiency of software security solutions raise the need for smart Hardware-Assisted Security (HAS) solutions. These solutions target one or more of the three C’s of IoT devices: Communication, Control, and Computation. Communication is an essential technology in the development of IoT. Bluetooth is a widely-used wireless communication protocol in small portable devices due to its low energy consumption and high transfer rates. In this work, we propose a supervisory framework to monitor and verify the operation of a Bluetooth system-on-chip (SoC) in real-time. To verify the operation of the Bluetooth SoC, we classify its transmission state in real-time to ensure a secure connection. Our overall classification accuracy is measured as 98.7%. We study both power supply current (IVDD) and RF domains to maximize the classification performance and minimize the overhead of our proposed supervisory system.

scholarly journals Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3715
Author(s):  
Ioan Ungurean ◽  
Nicoleta Cristina Gaitan
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Development Process ◽  
Fog Computing ◽  
Low Latency ◽  
Practical Implementation ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Starting Point ◽  
Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

Improving the management of geographically distributed IB system using industrial technology Internet of things

2020 ◽  
pp. 143-149
Author(s):  
А.И. Сухотерин
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Industrial Internet Of Things ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Distributed Security ◽  
Industrial Internet ◽  
Is Management ◽  
Geographically Distributed ◽  
Key Indicators

В статье рассматривается проблемы управления ИБ на территориально-распределённых объектах защиты. Во избежание простоев и для сохранения безопасности на предприятии необходимо внедрение технологий, позволяющих обнаруживать и прогнозировать риски. Предлагается с помощью промышленного интернета-вещей обеспечить непрерывный интеллектуальный мониторинг ключевых показателей, что дает возможность определить проблему и принять необходимые меры для ее решения. Оперативный в режиме реального времени анализ поможет специалисту ИБ быстрее находить уязвимые места и предотвратить несанкционированные действия на предприятии. This article discusses the problems of is management on geographically distributed security objects. In order to avoid downtime and to maintain security at the enterprise, it is necessary to introduce technologies that allow detecting and predicting risks. It is proposed to use the industrial Internet of things to provide continuous intellectual monitoring of key indicators, which makes it possible to identify the problem and take the necessary measures to solve it. Real-time real-time analysis will help the IB specialist find vulnerabilities faster and prevent unauthorized actions in the enterprise .

Blockchain for Industrial Internet of Things (IIoT)

2021 ◽  
pp. 32-47
Author(s):  
Rinki Sharma
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Manufacturing Process ◽  
Industrial Internet Of Things ◽  
Research Issues ◽  
Effective Implementation ◽  
Blockchain Technology ◽  
Industrial Internet ◽  
Manufacturing Applications ◽  
Decentralized Architecture

Over the years, the industrial and manufacturing applications have become highly connected and automated. The incorporation of interconnected smart sensors, actuators, instruments, and other devices helps in establishing higher reliability and efficiency in the industrial and manufacturing process. This has given rise to the industrial internet of things (IIoT). Since IIoT components are scattered all over the network, real-time authenticity of the IIoT activities becomes essential. Blockchain technology is being considered by the researchers as the decentralized architecture to securely process the IIoT transactions. However, there are challenges involved in effective implementation of blockchain in IIoT. This chapter presents the importance of blockchain in IIoT paradigm, its role in different IIoT applications, challenges involved, possible solutions to overcome the challenges and open research issues.

Microsense

2016 ◽  
Vol 7 (3) ◽  
pp. 38-55
Author(s):  
Srinivasa K.G. ◽  
Ganesh Hegde ◽  
Kushagra Mishra ◽  
Mohammad Nabeel Siddiqui ◽  
Abhishek Kumar ◽  
...  
Keyword(s):  
Real Time ◽  
Nodal Point ◽  
System On Chip ◽  
Streaming Data ◽  
Portable Devices ◽  
Time Data ◽  
Unified Framework ◽  
Real Time Data ◽  
Varied Degree ◽  
On Chip

With the advancement of portable devices and sensors, there has been a need to build a universal framework, which can serve as a nodal point to aggregate data from different kinds of devices and sensors. We propose a unified framework that will provide a robust set of guidelines for sensors with varied degree of complexities connected to common set of System-on-Chip (SoC). These will help to monitor, control and visualize real time data coming from different type of sensors connected to these SoCs. We have defined a set of APIs, which will help the sensors to register with the server. These APIs will be the standard to which the sensors will comply while streaming data when connected to the client platforms.

Applying Security to a Big Stream Cloud Architecture for the Internet of Things

2020 ◽  
pp. 1260-1284
Author(s):  
Laura Belli ◽  
Simone Cirani ◽  
Luca Davoli ◽  
Gianluigi Ferrari ◽  
Lorenzo Melegari ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Large Data ◽  
The Internet ◽  
Low Latency ◽  
Stream Data ◽  
Memory Processing ◽  
Cloud Architecture ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) is expected to interconnect billions (around 50 by 2020) of heterogeneous sensor/actuator-equipped devices denoted as “Smart Objects” (SOs), characterized by constrained resources in terms of memory, processing, and communication reliability. Several IoT applications have real-time and low-latency requirements and must rely on architectures specifically designed to manage gigantic streams of information (in terms of number of data sources and transmission data rate). We refer to “Big Stream” as the paradigm which best fits the selected IoT scenario, in contrast to the traditional “Big Data” concept, which does not consider real-time constraints. Moreover, there are many security concerns related to IoT devices and to the Cloud. In this paper, we analyze security aspects in a novel Cloud architecture for Big Stream applications, which efficiently handles Big Stream data through a Graph-based platform and delivers processed data to consumers, with low latency. The authors detail each module defined in the system architecture, describing all refinements required to make the platform able to secure large data streams. An experimentation is also conducted in order to evaluate the performance of the proposed architecture when integrating security mechanisms.

scholarly journals Setting Up Telemedicine: Internet of Things to Support Rural Oncology Clinics in a Low- and Middle-Income Country—A Pilot Project in Kenya

2020 ◽  
Vol 6 (Supplement_1) ◽  
pp. 58-58
Author(s):  
Lamech Sigu ◽  
Fredrick Chite ◽  
Emma Achieng ◽  
Andrew Koech
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Length Of Hospital Stay ◽  
Human Interaction ◽  
Sub Saharan Africa ◽  
Patient Treatment ◽  
The Internet ◽  
Tumor Boards ◽  
Iot Devices ◽  
Set Up

PURPOSE The Internet of Things (IoT) is a technology that involves all things connected to the Internet that share data over a network without requiring human-to-human interaction or human-to-computer interaction. Information collected from IoT devices can help physicians identify the best treatment process for patients and reach accurate and expected outcomes. METHODS The International Cancer Institute is partnering to set up remote oncology clinics in sub-Saharan Africa. Medical oncologists and expert teams from across the world connect with oncology clinics in other Kenyan counties—Kisumu, Meru, Makueni, Garissa, Kakamega, Bungoma, Siaya, and Vihiga counties. The furthest county is Garissa, approximately 651.1 km from Eldoret, and the nearest is Vihiga at 100.4 km from Eldoret. This study began July 2019, and as of November 30th, the team has hosted 21 sessions with an average of 11 participants attending a session led by a medical oncologist. RESULTS IoT devices have become a way by which a patient gets all the information he or she needs from a physician without going to the clinic. Patient monitoring can be done in real time, allowing access to real-time information with improved patient treatment outcomes and a decrease in cost. Through IoT-enabled devices, the International Cancer Institute has set up weekly virtual tumor boards during which cancer cases are presented and discussed by all participating counties. An online training module on cancer is also offered. Furthermore, remote monitoring of a patient’s health helps to reduce the length of hospital stay and prevents readmissions. CONCLUSION In our setting, which has a few oncologists, use of IoT and tumor boards has helped to improve patient decision support as well as training for general physicians.

scholarly journals Internet of Things: A Comprehensive Study on Machine Learning-based Intrusion Detection approaches

2021 ◽  
Author(s):  
Priyanka Gupta ◽  
Lokesh Yadav ◽  
Deepak Singh Tomar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Research Work ◽  
Security And Privacy ◽  
Iot Security ◽  
Advantages And Disadvantages ◽  
Iot Devices

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.

scholarly journals An Efficient Data Stream Analytics Model for Real Time Internet of Things (Iot) Applications

2020 ◽  
Vol 8 (6) ◽  
pp. 495-500
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Private Information ◽  
Large Scale ◽  
Layer System ◽  
Collection Data ◽  
Efficient Data ◽  
Multiple Environments ◽  
Iot Devices

Internet of Things (IoT), data analytics is supporting multiple applications. These numerous applications try to gather data from different environments, here the gathered data may be homogeneous or heterogeneous, but most of the data collected from multiple environments were heterogeneous, the task of gathering, processing, storing and the analysis that is being performed on data are still challenging. Providing security to all these things is also a challenging task due to untrusted networks and big data. Big data management in the ever-expanding network may rise several non-trivial concerns on data collection, data-efficient processing, analytics, and security. However, the above said scenarios depends on large scale sensor deployed. Sensors continuously transmit data to clouds for real time use, which can raise the issue of privacy disclosure because IoT devices may gather data including a kind of sensitive private information. In this context, we propose a two-layer system or model for analyzing IoT data, collected from multiple applications. The first layer is mainly used for gathering data from multiple environments and acts as a service-oriented interface to ingest data. The second layer is responsible for storing and analyses data securely. The Proposed solutions are implemented by the use of open source components.

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