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IoT
Volume 3
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IoT, Volume 3, Issue 3 (September 2022) – 3 articles

Cover Story (view full-size image): Edge computing (EC) is a promising strategy to overcome the challenges of transferring huge, generated data from IoT devices to the cloud, by bringing data processing and storage close to IoT devices. We provide a comprehensive definition of edge computing and similar computing paradigms, including their similarities/differences. Then, we extensively discuss the major security/privacy threats in the context of EC-based IoT and their countermeasures. Next, we propose a secure EC-based architecture for IoT applications. Moreover, a scenario of smart public transportation is introduced, and the advantages/disadvantages of the scenario based on EC versus cloud computing are discussed. Finally, we discuss the most prominent security/privacy issues that can occur in EC-based IoT scenarios. View this paper
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17 pages, 782 KiB  
Article
Performance Evaluation of Federated Learning for Residential Energy Forecasting
by Eugenia Petrangeli, Nicola Tonellotto and Carlo Vallati
IoT 2022, 3(3), 381-397; https://doi.org/10.3390/iot3030021 - 19 Sep 2022
Cited by 3 | Viewed by 2694
Abstract
Short-term energy-consumption forecasting plays an important role in the planning of energy production, transportation and distribution. With the widespread adoption of decentralised self-generating energy systems in residential communities, short-term load forecasting is expected to be performed in a distributed manner to preserve privacy [...] Read more.
Short-term energy-consumption forecasting plays an important role in the planning of energy production, transportation and distribution. With the widespread adoption of decentralised self-generating energy systems in residential communities, short-term load forecasting is expected to be performed in a distributed manner to preserve privacy and ensure timely feedback to perform reconfiguration of the distribution network. In this context, edge computing is expected to be an enabling technology to ensure decentralized data collection, management, processing and delivery. At the same time, federated learning is an emerging paradigm that fits naturally in such an edge-computing environment, providing an AI-powered and privacy-preserving solution for time-series forecasting. In this paper, we present a performance evaluation of different federated-learning configurations resulting in different privacy levels to the forecast residential energy consumption with data collected by real smart meters. To this aim, different experiments are run using Flower (a popular federated learning framework) and real energy consumption data. Our results allow us to demonstrate the feasibility of such an approach and to study the trade-off between data privacy and the accuracy of the prediction, which characterizes the quality of service of the system for the final users. Full article
(This article belongs to the Special Issue Advanced Quality of Service Approaches in Edge Computing)
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15 pages, 8650 KiB  
Article
An Application of IoT in a Drone Inspection Service for Environmental Control
by Muriel Cabianca, Maria Laura Clemente, Gianluca Gatto, Carlo Impagliazzo, Lidia Leoni, Martino Masia and Riccardo Piras
IoT 2022, 3(3), 366-380; https://doi.org/10.3390/iot3030020 - 30 Aug 2022
Cited by 1 | Viewed by 4433
Abstract
This paper presents an exploratory activity with a drone inspection service for environmental control. The aim of the service is to provide technical support to decision-makers in environmental risk management. The proposed service uses IoT for the interaction between a mobile application, a [...] Read more.
This paper presents an exploratory activity with a drone inspection service for environmental control. The aim of the service is to provide technical support to decision-makers in environmental risk management. The proposed service uses IoT for the interaction between a mobile application, a Smart City platform, and an Unmanned Aircraft System (UAS). The mobile application allows the users to report risky situations, such as fire ignition, spills of pollutants in water, or illegal dumping; the user has only to specify the class of the event, while the geographical coordinates are automatically taken from device-integrated GPS. The message sent from the mobile application arrives to a Smart City platform, which shows all the received alerts on a 3D satellite map, to support decision-makers in choosing where a drone inspection is required. From the Smart City platform, the message is sent to the drone service operator; a CSV file defining the itinerary of the drone is automatically built and shown through the platform; the drone starts the mission providing a video, which is used by the decision-makers to understand whether the situation calls for immediate action. An experimental activity in an open field was carried out to validate the whole chain, from the alert to the drone mission, enriched by a Smart City platform to enable a decision-maker to better manage the situation. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAV) and IoT)
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34 pages, 1785 KiB  
Review
A Survey of Security Architectures for Edge Computing-Based IoT
by Elahe Fazeldehkordi and Tor-Morten Grønli
IoT 2022, 3(3), 332-365; https://doi.org/10.3390/iot3030019 - 30 Jun 2022
Cited by 20 | Viewed by 7747
Abstract
The Internet of Things (IoT) is an innovative scheme providing massive applications that have become part of our daily lives. The number of IoT and connected devices are growing rapidly. However, transferring the corresponding huge, generated data from these IoT devices to the [...] Read more.
The Internet of Things (IoT) is an innovative scheme providing massive applications that have become part of our daily lives. The number of IoT and connected devices are growing rapidly. However, transferring the corresponding huge, generated data from these IoT devices to the cloud produces challenges in terms of latency, bandwidth and network resources, data transmission costs, long transmission times leading to higher power consumption of IoT devices, service availability, as well as security and privacy issues. Edge computing (EC) is a promising strategy to overcome these challenges by bringing data processing and storage close to end users and IoT devices. In this paper, we first provide a comprehensive definition of edge computing and similar computing paradigms, including their similarities and differences. Then, we extensively discuss the major security and privacy attacks and threats in the context of EC-based IoT and provide possible countermeasures and solutions. Next, we propose a secure EC-based architecture for IoT applications. Furthermore, an application scenario of edge computing in IoT is introduced, and the advantages/disadvantages of the scenario based on edge computing and cloud computing are discussed. Finally, we discuss the most prominent security and privacy issues that can occur in EC-based IoT scenarios. Full article
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