Microservices in IoT Middleware Architectures: Architecture, Trends, and Challenges

Champaneria, Tushar; Jardosh, Sunil; Makwana, Ashwin

doi:10.1007/978-981-19-3575-6_39

Cited by 2 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, a taxonomy of programming models for WSN middleware is presented, highlighting the importance of power and resources, scalability, mobility, dynamic network topology and organization, real-world integration and implementation, and broader considerations like data aggregation, quality of service, and security. In the coming years, middleware architectures will be developed focusing on case-specific applications such as IIoT, Industry 4.0 automation processes, Virtual Reality, embedded system sensor devices, computer protocols and networks, energy optimization, device heterogeneity, the Internet of nano-Things, WSAN, edge computing, and deep learning on microcontrollers (TinyML) [ 36 , 37 , 38 ].…”

Section: Related Workmentioning

confidence: 99%

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Gazis,

Katsiri

2024

Sensors

View full text Add to dashboard Cite

This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design’s innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis’ residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy.

show abstract

Section: Related Workmentioning

confidence: 99%

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Gazis,

Katsiri

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, the microservices model is particularly convenient relative to dynamic IoT applications, which usually consider architectures that are based on the usage of microservices. The contribution that is reported in paper [ 9 ] represents an overview regarding the practice and use of IoT microservices architectures. Thus, the authors assess the requirements of a typical IoT middleware through an investigation that addresses the requirements for the design and implementation of the appropriate middleware architectures.…”

Section: Introductionmentioning

confidence: 99%

Relevant Cybersecurity Aspects of IoT Microservices Architectures Deployed over Next-Generation Mobile Networks

Aldea

Bocu

Vasilescu

2022

Sensors

View full text Add to dashboard Cite

The design and implementation of secure IoT platforms and software solutions represent both a required functional feature and a performance acceptance factor nowadays. This paper describes relevant cybersecurity problems considered during the proposed microservices architecture development. Service composition mechanisms and their security are affected by the underlying hardware components and networks. The overall speedup of the platforms, which are implemented using the new 5G networks, and the capabilities of new performant IoT devices may be wasted by an inadequate combination of authentication services and security mechanisms, by the architectural misplacing of the encryption services, or by the inappropriate subsystems scaling. Considering the emerging microservices platforms, the Spring Boot alternative is used to implement data generation services, IoT sensor reading services, IoT actuators control services, and authentication services, and ultimately assemble them into a secure microservices architecture. Furthermore, considering the designed architecture, relevant security aspects related to the medical and energy domains are analyzed and discussed. Based on the proposed architectural concept, it is shown that well-designed and orchestrated architectures that consider the proper security aspects and their functional influence can lead to stable and secure implementations of the end user’s software platforms.

show abstract

Microservices in IoT Middleware Architectures: Architecture, Trends, and Challenges

Cited by 2 publications

References 26 publications

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Relevant Cybersecurity Aspects of IoT Microservices Architectures Deployed over Next-Generation Mobile Networks

Contact Info

Product

Resources

About