Authors:Claudio Marche, Michele Nitti First page: 262 Abstract: The Internet of Things is enriching our life with an ecosystem of interconnected devices. Object cooperation allows us to develop complex applications in which each node contributes one or more services. Therefore, the information moves from a provider to a requester node in a peer-to-peer network. In that scenario, trust management systems (TMSs) have been developed to prevent the manipulation of data by unauthorized entities and guarantee the detection of malicious behaviour. The community concentrates effort on designing complex trust techniques to increase their effectiveness; however, two strong assumptions have been overlooked. First, nodes could provide the wrong services due to malicious behaviours or malfunctions and insufficient accuracy. Second, the requester nodes usually cannot evaluate the received service perfectly. For this reason, a trust system should distinguish attackers from objects with poor performance and consider service evaluation errors. Simulation results prove that advanced trust algorithms are unnecessary for such scenarios with these deficiencies. Citation: IoT PubDate: 2022-03-23 DOI: 10.3390/iot3020015 Issue No:Vol. 3, No. 2 (2022)
Authors:Mehreen Tahir, Muhammad Intizar Ali First page: 273 Abstract: Federated Learning (FL) is a state-of-the-art technique used to build machine learning (ML) models based on distributed data sets. It enables In-Edge AI, preserves data locality, protects user data, and allows ownership. These characteristics of FL make it a suitable choice for IoT networks due to its intrinsic distributed infrastructure. However, FL presents a few unique challenges; the most noteworthy is training over largely heterogeneous data samples on IoT devices. The heterogeneity of devices and models in the complex IoT networks greatly influences the FL training process and makes traditional FL unsuitable to be directly deployed, while many recent research works claim to mitigate the negative impact of heterogeneity in FL networks, unfortunately, the effectiveness of these proposed solutions has never been studied and quantified. In this study, we thoroughly analyze the impact of heterogeneity in FL and present an overview of the practical problems exerted by the system and statistical heterogeneity. We have extensively investigated state-of-the-art algorithms focusing on their practical use over IoT networks. We have also conducted a comparative analysis of the top available federated algorithms over a heterogeneous dynamic IoT network. Our analysis shows that the existing solutions fail to effectively mitigate the problem, thus highlighting the significance of incorporating both system and statistical heterogeneity in FL system design. Citation: IoT PubDate: 2022-04-22 DOI: 10.3390/iot3020016 Issue No:Vol. 3, No. 2 (2022)
Authors:Rubayyi Alghamdi, Martine Bellaiche First page: 285 Abstract: Using the Internet of Things (IoT) for various applications, such as home and wearables devices, network applications, and even self-driven vehicles, detecting abnormal traffic is one of the problematic areas for researchers to protect network infrastructure from adversary activities. Several network systems suffer from drawbacks that allow intruders to use malicious traffic to obtain unauthorized access. Attacks such as Distributed Denial of Service attacks (DDoS), Denial of Service attacks (DoS), and Service Scans demand a unique automatic system capable of identifying traffic abnormality at the earliest stage to avoid system damage. Numerous automatic approaches can detect abnormal traffic. However, accuracy is not only the issue with current Intrusion Detection Systems (IDS), but the efficiency, flexibility, and scalability need to be enhanced to detect attack traffic from various IoT networks. Thus, this study concentrates on constructing an ensemble classifier using the proposed Integrated Evaluation Metrics (IEM) to determine the best performance of IDS models. The automated Ranking and Best Selection Method (RBSM) is performed using the proposed IEM to select the best model for the ensemble classifier to detect highly accurate attacks using machine learning and deep learning techniques. Three datasets of real IoT traffic were merged to extend the proposed approach’s ability to detect attack traffic from heterogeneous IoT networks. The results show that the performance of the proposed model achieved the highest accuracy of 99.45% and 97.81% for binary and multi-classification, respectively. Citation: IoT PubDate: 2022-04-28 DOI: 10.3390/iot3020017 Issue No:Vol. 3, No. 2 (2022)
Authors:Jaime K Devine, Lindsay P. Schwartz, Jake Choynowski, Steven R Hursh First page: 315 Abstract: Global demand for sleep-tracking wearables, or consumer sleep technologies (CSTs), is steadily increasing. CST marketing campaigns often advertise the scientific merit of devices, but these claims may not align with consensus opinion from sleep research experts. Consensus opinion about CST features has not previously been established in a cohort of sleep researchers. This case study reports the results of the first survey of experts in real-world sleep research and a hypothetical purchase task (HPT) to establish economic valuation for devices with different features by price. Forty-six (N = 46) respondents with an average of 10 ± 6 years’ experience conducting research in real-world settings completed the online survey. Total sleep time was ranked as the most important measure of sleep, followed by objective sleep quality, while sleep architecture/depth and diagnostic information were ranked as least important. A total of 52% of experts preferred wrist-worn devices that could reliably determine sleep episodes as short as 20 min. The economic value was greater for hypothetical devices with a longer battery life. These data set a precedent for determining how scientific merit impacts the potential market value of a CST. This is the first known attempt to establish a consensus opinion or an economic valuation for scientifically desirable CST features and metrics using expert elicitation. Citation: IoT PubDate: 2022-06-08 DOI: 10.3390/iot3020018 Issue No:Vol. 3, No. 2 (2022)
Authors:Vasileios Nikolopoulos, Mara Nikolaidou, Maria Voreakou, Dimosthenis Anagnostopoulos First page: 91 Abstract: In Fog Computing, fog colonies are formed by nodes cooperating to provide services to end-users. To enable efficient operation and seamless scalability of fog colonies, decentralized control over participating nodes should be promoted. In such cases, autonomous Fog Nodes operate independently, sharing the context in which all colony members provide their services. In the paper, we explore different techniques of context diffusion and knowledge sharing between autonomous Fog Nodes within a fog colony, using ECTORAS, a publish/subscribe protocol. With ECTORAS, nodes become actively aware of their operating context, share contextual information and exchange operational policies to achieve self-configuration, self-adaptation and context awareness in an intelligent manner. Two different ECTORAS implementations are studied, one offering centralized control with the existence of a message broker, to manage colony participants and available topics, and one fully decentralized, catering to the erratic topology that Fog Computing may produce. The two schemes are tested as the Fog Colony size is expanding in terms of performance and energy consumption, in a prototype implementation based on Raspberry Pi nodes for smart building management. Citation: IoT PubDate: 2022-01-18 DOI: 10.3390/iot3010005 Issue No:Vol. 3, No. 1 (2022)
Authors:Suzanne K. Thomas, Adam Pockett, Krishna Seunarine, Michael Spence, Dimitrios Raptis, Simone Meroni, Trystan Watson, Matt Jones, Matthew J. Carnie First page: 109 Abstract: The number of interconnected devices, often referred to as the Internet of Things (IoT), is increasing at a considerable rate. It is inevitable therefore that so too will the energy demand. IoT describes a range of technologies such as sensors, software, smart meters, wearable devices, and communication beacons for the purpose of connecting and exchanging data with other devices and systems over the internet. Often not located near a mains supply power source, these devices may be reliant on primary battery cells. To avoid the need to periodically replace these batteries, it makes sense to integrate the technologies with a photovoltaic (PV) cell to harvest ambient light, so that the technologies can be said to be self-powered. Perovskite solar cells have proven extremely efficient in low-light conditions but in the absence of ambient and low-light testing standards, or even a consensus on what is defined by “ambient light”, it is difficult to estimate the energy yield of a given PV technology in a given scenario. Ambient light harvesting is complex, subject to spectral considerations, and whether the light source is directly incident on the PV cell. Here, we present a realistic scenario-driven method for measuring the energy yield for a given PV technology in various situations in which an IoT device may be found. Furthermore, we show that laboratory-built p-i-n perovskite devices, for many scenarios, produce energy yields close to that of commercial GaAs solar cells. Finally, we demonstrate an IoT device, powered by a mesoporous carbon perovskite solar module and supercapacitor, and operating through several day–night cycles. Citation: IoT PubDate: 2022-01-18 DOI: 10.3390/iot3010006 Issue No:Vol. 3, No. 1 (2022)
Authors:Rahul Agrahari, Matthew Nicholson, Clare Conran, Haytham Assem, John D. Kelleher First page: 123 Abstract: In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best feature set is statistically significant. Furthermore, our analysis of the features used by the most successful model indicates that features related to mean and variance are the most informative for anomaly detection. We also find that features based on model forecast errors are useful for anomaly detection for some but not all datasets. Citation: IoT PubDate: 2022-01-29 DOI: 10.3390/iot3010008 Issue No:Vol. 3, No. 1 (2022)
Authors:Bastien Confais, Benoît Parrein First page: 145 Abstract: Current network architectures such as Cloud computing are not adapted to provide an acceptable Quality of Service (QoS) to the large number of tiny devices that compose the Internet of Things (IoT) [...] Citation: IoT PubDate: 2022-02-16 DOI: 10.3390/iot3010009 Issue No:Vol. 3, No. 1 (2022)
Authors:Emmanuel Tuyishimire, Antoine Bagula, Slim Rekhis, Noureddine Boudriga First page: 147 Abstract: The use of Unmanned Aerial Vehicles (UAVs) in data transport has attracted a lot of attention and applications, as a modern traffic engineering technique used in data sensing, transport, and delivery to where infrastructure is available for its interpretation. Due to UAVs’ constraints such as limited power lifetime, it has been necessary to assist them with ground sensors to gather local data, which has to be transferred to UAVs upon visiting the sensors. The management of such ground sensor communication together with a team of flying UAVs constitutes an interesting data muling problem, which still deserves to be addressed and investigated. This paper revisits the issue of traffic engineering in Internet-of-Things (IoT) settings, to assess the relevance of using UAVs for the persistent collection of sensor readings from the sensor nodes located in an environment and their delivery to base stations where further processing is performed. We propose a persistent path planning and UAV allocation model, where a team of heterogeneous UAVs coming from various base stations are used to collect data from ground sensors and deliver the collected information to their closest base stations. This problem is mathematically formalised as a real-time constrained optimisation model, and proven to be NP-hard. The paper proposes a heuristic solution to the problem and evaluates its relative efficiency through performing experiments on both artificial and real sensors networks, using various scenarios of UAVs settings. Citation: IoT PubDate: 2022-02-24 DOI: 10.3390/iot3010010 Issue No:Vol. 3, No. 1 (2022)
Authors:Martina Pappalardo, Antonio Virdis, Enzo Mingozzi First page: 169 Abstract: The Internet of Things (IoT) brings Internet connectivity to devices and everyday objects. This huge volume of connected devices has to be managed taking into account the severe energy, memory, processing, and communication constraints of IoT devices and networks. In this context, the OMA LightweightM2M (LWM2M) protocol is designed for remote management of constrained devices, and related service enablement, through a management server usually deployed in a distant cloud data center. Following the Edge Computing paradigm, we propose in this work the introduction of a LWM2M Proxy that is deployed at the network edge, in between IoT devices and management servers. On one hand, the LWM2M Proxy improves various LWM2M management procedures whereas, on the other hand, it enables the support of QoS-aware services provided by IoT devices by allowing the implementation of advanced policies to efficiently use network, computing, and storage (i.e., cache) resources at the edge, thus providing benefits in terms of reduced and more predictable end-to-end latency. We evaluate the proposed solution both by simulation and experimentally, showing that it can strongly improve the LWM2M performance and the QoS of the system. Citation: IoT PubDate: 2022-02-26 DOI: 10.3390/iot3010011 Issue No:Vol. 3, No. 1 (2022)
Authors:Pavana Pradeep, Krishna Kant First page: 191 Abstract: Internet of Things (IoT) systems are becoming ubiquitous in various cyber–physical infrastructures, including buildings, vehicular traffic, goods transport and delivery, manufacturing, health care, urban farming, etc. Often multiple such IoT subsystems are deployed in the same physical area and designed, deployed, maintained, and perhaps even operated by different vendors or organizations (or “parties”). The collective operational behavior of multiple IoT subsystems can be characterized via (1) a set of operational rules and required safety properties and (2) a collection of IoT-based services or applications that interact with one another and share concurrent access to the devices. In both cases, this collective behavior often leads to situations where their operation may conflict, and the conflict resolution becomes complex due to lack of visibility into or understanding of the cross-subsystem interactions and inability to do cross-subsystem actuations. This article addresses the fundamental problem of detecting and resolving safety property violations. We detail the inherent complexities of the problem, survey the work already performed, and layout the future challenges. We also highlight the significance of detecting/resolving conflicts proactively, i.e., dynamically but with a look-ahead into the future based on the context. Citation: IoT PubDate: 2022-02-28 DOI: 10.3390/iot3010012 Issue No:Vol. 3, No. 1 (2022)
Authors:Maximilien Charlier, Remous-Aris Koutsiamanis, Bruno Quoitin First page: 219 Abstract: In this paper, we present and evaluate an ultra-wideband (UWB) indoor processing architecture that allows the performing of simultaneous localizations of mobile tags. This architecture relies on a network of low-power fixed anchors that provide forward-ranging measurements to a localization engine responsible for performing trilateration. The communications within this network are orchestrated by UWB-TSCH, an adaptation to the ultra-wideband (UWB) wireless technology of the time-slotted channel-hopping (TSCH) mode of IEEE 802.15.4. As a result of global synchronization, the architecture allows deterministic channel access and low power consumption. Moreover, it makes it possible to communicate concurrently over multiple frequency channels or using orthogonal preamble codes. To schedule communications in such a network, we designed a dedicated centralized scheduler inspired from the traffic aware scheduling algorithm (TASA). By organizing the anchors in multiple cells, the scheduler is able to perform simultaneous localizations and transmissions as long as the corresponding anchors are sufficiently far away to not interfere with each other. In our indoor positioning system (IPS), this is combined with dynamic registration of mobile tags to anchors, easing mobility, as no rescheduling is required. This approach makes our ultra-wideband (UWB) indoor positioning system (IPS) more scalable and reduces deployment costs since it does not require separate networks to perform ranging measurements and to forward them to the localization engine. We further improved our scheduling algorithm with support for multiple sinks and in-network data aggregation. We show, through simulations over large networks containing hundreds of cells, that high positioning rates can be achieved. Notably, we were able to fully schedule a 400-cell/400-tag network in less than 11 s in the worst case, and to create compact schedules which were up to 11 times shorter than otherwise with the use of aggregation, while also bounding queue sizes on anchors to support realistic use situations. Citation: IoT PubDate: 2022-03-02 DOI: 10.3390/iot3010013 Issue No:Vol. 3, No. 1 (2022)
Authors:Carlo Giannelli, Marco Picone First page: 259 Abstract: During the last decade, the advent of the Internet of Things (IoT) and its quick and pervasive evolution have significantly revolutionized the Information Technology ecosystem [...] Citation: IoT PubDate: 2022-03-15 DOI: 10.3390/iot3010014 Issue No:Vol. 3, No. 1 (2022)
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