Internet of things (IoT) systems are composed of variety of units from different domains. While developing a complete IoT system, different professionals from different domains may have to work in collaboration. In this paper we provide a framework which allows using discrete and continuous time modeling and simulation approaches in combination for IoT systems. The proposed framework demonstrates on how to model Ad-hoc and general IoT systems for software engineering purpose. We demonstrate that model-based software engineering on one hand can provide a common platform to overcome communication gaps among collaborating stakeholders whereas, on the other hand can model and integrate heterogeneous components of IoT systems. While modeling heterogeneous IoT systems, one of the major challenges is to apply continuous and discrete time modeling on intrinsically varying components of the system. Another difficulty may be how to compose these heterogeneous components into one whole system. The proposed framework provides a road-map to model discrete, continuous, Ad-hoc, general systems along with composition mechanism of heterogeneous subsystems. The framework uses a combination of Agent-based modeling, Aspect-oriented modeling, contract-based modeling and services-oriented modeling concepts. We used this framework to model a scenario example of a service-oriented IoT system as proof of concept. We analyzed our framework with existing systems and discussed it in details. Our framework provides a mechanism to model different viewpoints. The framework also enhances the completeness and consistency of the IoT software models.
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http://dx.doi.org/10.3934/mbe.2021458 | DOI Listing |
Heliyon
January 2025
Department of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud University, Riyadh, 11633, Saudi Arabia.
The rapid growth of Internet of Things (IoT) devices presents significant cybersecurity challenges due to their diverse and resource-constrained nature. Existing security solutions often fall short in addressing the dynamic and distributed environments of IoT systems. This study aims to propose a novel deep learning framework, SecEdge, designed to enhance real-time cybersecurity in mobile IoT environments.
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January 2025
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia.
The Internet of Medical Things (IoMT) has revolutionized healthcare by bringing real-time monitoring and data-driven treatments. Nevertheless, the security of communication between IoMT devices and servers remains a huge problem because of the inherent sensitivity of the health data and susceptibility to cyber threats. Current security solutions, including simple password-based authentication and standard Public Key Infrastructure (PKI) approaches, typically do not achieve an appropriate balance between security and low computational overhead, resulting in the possibility of performance bottlenecks and increased vulnerability to attacks.
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January 2025
Torrens University Australia, Fortitude Valley, QLD 4006, Leaders Institute, 76 Park Road, Woolloongabba, QLD 4102, Brisbane, Queensland, Australia.
Sensors (Basel)
January 2025
College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
As the Internet of Things (IoT) expands globally, the challenge of signal transmission in remote regions without traditional communication infrastructure becomes prominent. An effective solution involves integrating aerial, terrestrial, and space components to form a Space-Air-Ground Integrated Network (SAGIN). This paper discusses an uplink signal scenario in which various types of data collection sensors as IoT devices use Unmanned Aerial Vehicles (UAVs) as relays to forward signals to low-Earth-orbit satellites.
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January 2025
Department of Instruction and Leadership, Duquesne University, Pittsburgh, PA 15282, USA.
This article examines how sensor technologies (such as environmental sensors, biometric sensors, and IoT devices) intersect with conversational AI models like ChatGPT 4.0. In particular, this article explores how data from different sensors in real time can improve AI models' comprehension of surroundings, user contexts, and physical conditions.
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