Internet of Things as System of Systems: A Review of Methodologies, Frameworks, Platforms, and Tools

Fortino, Giancarlo; Savaglio, Claudio; Spezzano, Giandomenico; Zhou, MengChu

doi:10.1109/tsmc.2020.3042898

Cited by 173 publications

(63 citation statements)

References 86 publications

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“…For data processing and information inference, it is also possible to embed intelligence at edge devices, which can be enabled by machine learning (ML) algorithms [6,7]. Deep learning [8], a subset of Machine Learning, can be implemented on edge devices, such as mobile phones, wearables and the Internet of Things (IoT) nodes [9,10]. Deep learning is more resilient to noise and able to deal with non-linearity.…”

Section: Introductionmentioning

confidence: 99%

Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Wardana

Gardner

Fahmy

2021

Sensors

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Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively.

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Section: Introductionmentioning

confidence: 99%

Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Wardana

Gardner

Fahmy

2021

Sensors

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“…Wireless communication plays a significant role in various Internet of Things (IoT) applications [1,2]. Selecting an appropriate wireless communication technology that fits the requirements of the IoT application is important.…”

Section: Introductionmentioning

confidence: 99%

QoS Enabled Heterogeneous BLE Mesh Networks

Basu

Baert

Hoebeke

2021

JSAN

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Bluetooth Low Energy (BLE) is a widely known short-range wireless technology used for various Internet of Things (IoT) applications. Recently, with the introduction of BLE mesh networks, this short-range barrier of BLE has been overcome. However, the added advantage of an extended range can come at the cost of a lower performance of these networks in terms of latency, throughput and reliability, as the core operation of BLE mesh is based on advertising and packet flooding. Hence, efficient management of the system is required to achieve a good performance of these networks and a smoother functioning in dense scenarios. As the number of configuration points in a standard mesh network is limited, this paper describes a novel set of standard compliant Quality of Service (QoS) extensions for BLE mesh networks. The resulting QoS features enable better traffic management in the mesh network, providing sufficient redundancy to achieve reliability whilst avoiding unnecessary packet flooding to reduce collisions, as well as the prioritization of certain traffic flows and the ability to control end-to-end latencies. The QoS-based system has been implemented and validated in a small-scale BLE mesh network and compared against a setup without any QoS support. The assessment in a small-scale test setup confirms that applying our QoS features can enhance these types of non-scheduled and random access networks in a significant way.

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“…An IoT system, one of the core pillars for realizing IoS, comprises of loosely coupled interrelated decentralized computing devices. These smart objects, when supported by an “anywhere, anytime, and anything connection” become the basic building blocks of IoT [ 1 , 2 ]. As stated in [ 3 , 4 , 5 , 6 ], there are 30 billion interconnected nodes and 75 billion nodes are expected by 2025.…”

Section: Introductionmentioning

confidence: 99%

Towards Scalable and Efficient Architecture for Modeling Trust in IoT Environments

Ghaleb

Azzedin

2021

Sensors

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The Internet of Services (IoS) is gaining ground where cloud environments are utilized to create, subscribe, publish, and share services. The fast and significant evolution of IoS is affecting various aspects in people’s life and is enabling a wide spectrum of services and applications ranging from smart e-health, smart homes, to smart surveillance. Building trusted IoT environments is of great importance to achieve the full benefits of IoS. In addition, building trusted IoT environments mitigates unrecoverable and unexpected damages in order to create reliable, efficient, stable, and flexible smart IoS-driven systems. Therefore, ensuring trust will provide the confidence and belief that IoT devices and consequently IoS behave as expected. Before hosting trust models, suitable architecture for Fog computing is needed to provide scalability, fast data access, simple and efficient intra-communication, load balancing, decentralization, and availability. In this article, we propose scalable and efficient Chord-based horizontal architecture. We also show how trust modeling can be mapped to our proposed architecture. Extensive performance evaluation experiments have been conducted to evaluate the performance and the feasibility and also to verify the behavior of our proposed architecture.

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Internet of Things as System of Systems: A Review of Methodologies, Frameworks, Platforms, and Tools

Cited by 173 publications

References 86 publications

Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

QoS Enabled Heterogeneous BLE Mesh Networks

Towards Scalable and Efficient Architecture for Modeling Trust in IoT Environments

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