End-user Devices Research Articles

Enabling Processing Power Scalability with Internet of Things (IoT) Clusters

Internet of things (IoT) devices play a crucial role in the design of state-of-the-art infrastructures, with an increasing demand to support more complex services and applications. However, IoT devices are known for having limited computational capacities. Traditional approaches used to offload applications to the cloud to ease the burden on end-user devices, at the expense of a greater latency and increased network traffic. Our goal is to optimize the use of IoT devices, particularly those being underutilized. In this paper, we propose a pragmatic solution, built upon the Erlang programming language, that allows a group of IoT devices to collectively execute services, using their spare resources with minimal interference, and achieving a level of performance that otherwise would not be met by individual execution.

The electricity- and CO2-saving potentials offered by regulation of European video-streaming services

Massive increases in Internet data traffic over the last years have led to rapidly rising electricity demand and CO2 emissions, giving rise to environmental externalities and network congestion costs. One particular concern is the rise in data traffic generated by video-streaming services. We analyze the electricity-saving potential related to video streaming in Europe from 2020 to 2030. To this end, three trend scenarios (Business-as-usual, Gray, and Green) are considered and modeled bottom-up, taking specific electricity consumption (and trends) of data transmission networks, end-use devices, and data centers into account. Using these scenarios, we examine in more detail the approximate electricity-saving impact that regulatory interventions and technical standards can have on the electricity consumption of end-users, network operators, and data centers. The model results reveal that regulatory intervention can have a significant impact on electricity consumption and CO2 emissions of the residential houshold sector.

Towards a Distributed Caching Service at the WiFi Edge Using Wi-Cache

Caching content close to the end users, e.g., at cellular base stations (BSs), WiFi access points (APs), and end user devices is known to improve efficiency and effectiveness of content delivery. This motivates the development of caching-as-a-service where edge networks and devices provide storage capacity to content providers, and enable them to strategically populate these caches to improve user experience in the targeted network. In this paper, we describe Wi-Cache, a prototype for providing caching-as-a-service at the WiFi edge. Wi-Cache is an SDN (Software Defined Networking) based distributed content caching system at the WiFi edge that uses storage at the APs for caching content. Wi-Cache caches content on wireless APs and delivers them to mobile clients when they are requested. It allows content providers to have fine-grained control over the AP-caches and also execute efficient content placement and delivery algorithms at the WiFi edge using a set of APIs that are provided by Wi-Cache. We also show the effectiveness of the Wi-Cache system using an extensive set of experiments.

Extension of the content, functionality and usability of the online FSK‐Web model repository

In December 2016 EFSA and BfR stepped into a Framework Partnership Agreement (FPA) to work jointly on the development, improvement and dissemination of global food safety tools. As outlined in the Specific Agreement #7 of this FPA one specific objective was to work towards the development of a fully operational online model repository that includes features to create, upload, download and execute models with user-defined inputs for the predefined model parameters. The user interface of the so-called FSK-Web model repository had to be designed such, that it is user-friendly and usable on various end-user devices and web browser software. The developed “FSK-Web 1.0” platform now even contains additional services, e.g. to link models with EFSA’s Knowledge Junction (KJ) or to join up to four models in a graphical user-interface (GUI) online. Detailed manuals, video tutorials and an online e-learning course were developed to support the use of the FSK-Web repository. A repository disclaimer and a model curation policy were drafted and will be released after being agreed with EFSA. The project created three key outcomes: (1) an operational curated web-based food safety model repository, and a significant number of executable QMRA models in FSKX format relevant for risk assessment; (2) improved usability and annotation of the various services offered by the FSK-Web model repository including manuals and online resources explaining the functionality and use of all aspects of the model repository; (3) provision of the possibility to link different FSKX models to create new combined models, including the option to combine models programmed in different scripting languages (also execution of combined R / Python models supported).

Domestic Gas Meter Durability in Hydrogen and Natural Gas Mixtures

Blending hydrogen into the natural gas infrastructure is becoming a very promising practice to increase the exploitation of renewable energy sources which can be used to produce “green” hydrogen. Several research projects and field experiments are currently aimed at evaluating the risks associated with utilization of the gas blend in end-use devices such as the gas meters. In this paper, the authors present the results of experiments aimed at assessing the effect of hydrogen injection in terms of the durability of domestic gas meters. To this end, 105 gas meters of different measurement capabilities and manufacturers, both brand-new and withdrawn from service, were investigated in terms of accuracy drift after durability cycles of 5000 and 10,000 h with H2NG mixtures and H2 concentrations of 10% and 15%. The obtained results show that there is no metrologically significant or statistically significant influence of hydrogen content on changes in gas meter indication errors after subjecting the meters to durability testing with a maximum of 15% H2 content over 10,000 h. A metrologically significant influence of the long-term operation of the gas meters was confirmed, but it should not be made dependent on the hydrogen content in the gas. No safety problems related to the loss of external tightness were observed for either the new or 10-year-old gas meters.

QoS Improvement Using In-Network Caching Based on Clustering and Popularity Heuristics in CCN.

Content-Centric Networking (CCN) has emerged as a potential Internet architecture that supports name-based content retrieval mechanism in contrast to the current host location-oriented IP architecture. The in-network caching capability of CCN ensures higher content availability, lesser network delay, and leads to server load reduction. It was observed that caching the contents on each intermediate node does not use the network resources efficiently. Hence, efficient content caching decisions are crucial to improve the Quality-of-Service (QoS) for the end-user devices and improved network performance. Towards this, a novel content caching scheme is proposed in this paper. The proposed scheme first clusters the network nodes based on the hop count and bandwidth parameters to reduce content redundancy and caching operations. Then, the scheme takes content placement decisions using the cluster information, content popularity, and the hop count parameters, where the caching probability improves as the content traversed toward the requester. Hence, using the proposed heuristics, the popular contents are placed near the edges of the network to achieve a high cache hit ratio. Once the cache becomes full, the scheme implements Least-Frequently-Used (LFU) replacement scheme to substitute the least accessed content in the network routers. Extensive simulations are conducted and the performance of the proposed scheme is investigated under different network parameters that demonstrate the superiority of the proposed strategy w.r.t the peer competing strategies.

Interactive CFD simulations with virtual reality to support learning in mixing

This study presents a user-friendly digital learning environment with interactive computational fluid dynamics simulations for higher education in chemical engineering. A client-server network is introduced to maintain an automated two-way connection between multiple CFD solvers and a game engine as a cross-platform development tool. A modular software development approach is pursued to obtain long-lasting connected digital applications. To prevail over heavy calculations and standalone features, a content delivery network is structured, through which any end-user devices are made employable regardless of hardware performance. From desktop to virtual reality applications, multiple digital platforms are demonstrated within a case study of mixing. The system performance of technical integrity is assessed to ensure a feasible and effective connection through the client-server network. A task-centered educational model is implemented to design digital learning environments concerning complex skills to be developed by learners. Discussions are drawn upon the applicability of the system and also integrity to traditional teaching practices.

IoT device fabrication using roll-to-roll printing process

With the development of technology, wireless and IoT devices are increasingly used from daily life to industry, placing demands on rapid and efficient manufacturing processes. This study demonstrates the fabrication of an IoT device using a roll-to-roll printing process, which could shorten the device fabrication time and reduce the cost of mass production. Here, the fabricated IoT device is designed to acquire data through the sensor, process the data, and communicate with end-user devices via Bluetooth communication. For fabrication, a four-layer circuit platform consisting of two conductive layers, an insulating layer including through holes, and a solder resist layer is directly printed using a roll-to-roll screen printing method. After the printing of the circuit platform, an additional layer of solder paste is printed to assemble the electrical components into the device, inspiring the fully roll-to-roll process for device fabrication. Successful IoT device deployment opens the chance to broaden the roll-to-roll fabrication process to other flexible and multilayer electronic applications.

Place Branding for Smart Cities and Smart Tourism Destinations: Do They Communicate Their Smartness?

Smart cities and smart tourism destinations integrate technological infrastructures and end-user devices with the aim of providing more satisfying experiences. They generate expectations of superior innovation, greater interactivity, participation and a better image. Therefore, they need to communicate their smartness and include it in their branding. The study analyses the smart content present in the tourist and non-tourist official Twitter accounts of a selection of smart cities and smart destinations, through semantic analysis, in order to find out which smart concepts are strategically branded to create a smart tourism destination image. The results show that the best cities in the ranking for each smart topic are not the ones with highest percentages of tweets on that topic. The study also shows that the number and percentage of tweets that communicate smart aspects on the platforms analyzed are low, showing the smartness is not included in their branding. It has also been observed that, almost all cities communicate or do not communicate the same concepts, evincing an undifferentiated branding strategy. The results of this study are interesting for urban policy-makers and tourism destination marketers to improve their branding.

Life cycle inventory and carbon footprint assessment of wireless ICT networks for six demographic areas

The aim of this work is to quantify, assess, and identify hotspots in the environmental sustainability of newly constructed ICT networks designed to provide internet access (4 G LTE mobile technology) to regions still lacking this service. The analysis has been carried out on six demographic areas, from high-density urban and peri‑urban to remote rural, using ISO 14,040. A Dynamic Inventory Model (DIM) relating demographic/connectivity features with foreground material and energy inventories was validated using real data from Peru. The results showed carbon footprints between 81 and 103 kg CO2 eq./subscription/year, equivalent to 1.35 – 1.73 kg CO2 eq./Gb. Most of this (between 68 and 86%) correspond to end user devices, primarily in the form of embodied emissions. Operational emissions account for about one-third of the total and derive primarily from the electricity consumed by end user devices, and to a lower extent by access networks and data centers. Linear correlations were observed between operational - embodied carbon emissions and the number of subscribers. This trend was overturned in very small ICT networks designed to serve sparsely populated rural areas, due to higher energy consumption and carbon emissions per functional unit generated by access and IP network components. The robustness of these results was studied through sensitivity and uncertainty analyses.

Intelligent Resource Management at the Edge for Ubiquitous IoT: An SDN-Based Federated Learning Approach

The ubiquitous nature of Internet of Things (IoT) devices has posited many challenges that need innovative solutions in the 5G era. Software defined networks (SDNs) are becoming indispensable in managing several aspects of next-generation IoT networking that arise from the need to control highly heterogeneous, geographically dispersed, mobile IoT devices. One such aspect is cache management at the edge. Recently, multiple forms of edge resources, including mobile device clouds and micro-edge data centers have emerged to provide scalable cache placement locations that reduce the costs for the mobile network operator (MNO). As all of these service locations are registered with the MNO (or links established after registration with the 5G base station, BS), content should be placed according to the user's demand and the cost the user is willing to pay to receive the desired level of QoS. To this end, it is important to understand the future popularity of the content for its optimal placement considering the highly dynamic user mobility. In this article, we address two key aspects of a mobile IoT network: security and seamless connectivity for data delivery. We rely on the federated learning (FL) architecture, which enables harnessing data and computational capabilities at end-user devices to train machine learning models. We study FL concepts in the domain of edge computing for IoT use cases, such as caching. We draw conclusions from various state-of-the-art models and posit several challenges that can be overcome via a novel proposed control algorithm.

Design of upper limb prosthesis using real-time motion detection method based on EMG signal processing

Many people suffer from disability, caused by arm amputees and congenital disorder of the musculoskeletal system. An upper limb prosthesis can help disabled people to do their usual life activity. The aim of the paper is to develop upper limb prosthetic device controlled by myoelectric signals. At present the most complicated problem in upper limb prosthetics is connected with processing of neuromuscular signals for their use in control of the prosthesis. In modern prosthesis the intensity of muscle activity is extracted from the electromyographic signals. Prosthetic devices should ideally compensate for the loss of fine, coordinated movements of the hand, provide proprioceptive feedback, and have an esthetic exterior. From this point of view, identification of single finger movements seems very attractive. On the one hand it allows to restore functionality of amputated limb close to the healthy one. On the other hand existing results in the field of multifinger control require complicated computations. This leads to cost increase of the end-user device. To overcome this problem, we propose amplitude-based window recognition method with low computational complexity. The proposed solution can be implemented in real-time control using low cost Arduino nano microcontroller. A prototype of prosthetic hand with novel engineering solutions and proposed software is suggested. Experimental and statistical validation of identification scheme on a group of healthy people is considered. The proposed solution is tested in real time in autonomous mode. Experimental results for disabled man is provided. A discussion of the obtained results is given.

Cutting Through the Complexity of Reverse Engineering Embedded Devices

Performing security analysis of embedded devices is a challenging task. They present many difficulties not usually found when analyzing commodity systems: undocumented peripherals, esoteric instruction sets, and limited tool support. Thus, a significant amount of reverse engineering is almost always required to analyze such devices. In this paper, we present Incision, an architecture and operating-system agnostic reverse engineering framework. Incision tackles the problem of reducing the upfront effort to analyze complex end-user devices. It combines static and dynamic analyses in a feedback loop, enabling information from each to be used in tandem to improve our overall understanding of the firmware analyzed. We use Incision to analyze a variety of devices and firmware. Our evaluation spans firmware based on three RTOSes, an automotive ECU, and a 4G/LTE baseband. We demonstrate that Incision does not introduce significant complexity to the standard reverse engineering process and requires little manual effort to use. Moreover, its analyses produce correct results with high confidence and are robust across different OSes and ISAs.

A Semantic and Knowledge-Based Approach for Handover Management

Handover Management (HM) is pivotal for providing service continuity, enormous reliability and extreme-low latency, and meeting sky-high data rates, in wireless communications. Current HM approaches based on a single criterion may lead to unnecessary and frequent handovers due to a partial network view that is constrained to information about link quality. In turn, HM approaches based on multicriteria may present a failure of handovers and wrong network selection, decreasing the throughput and increasing the packet loss in the network. This paper proposes SIM-Know, an approach for improving HM. SIM-Know improves HM by including a Semantic Information Model (SIM) that enables context-aware and multicriteria handover decisions. SIM-Know also introduces a SIM-based distributed Knowledge Base Profile (KBP) that provides local and global intelligence to make contextual and proactive handover decisions. We evaluated SIM-Know in an emulated wireless network. When the end-user device moves at low and moderate speeds, the results show that our approach outperforms the Signal Strong First (SSF, single criterion approach) and behaves similarly to the Analytic Hierarchy Process combined with the Technique for Order Preferences by Similarity to the Ideal Solution (AHP-TOPSIS, multicriteria approach) regarding the number of handovers and the number of throughput drops. SSF outperforms SIM-Know and AHP-TOPSIS regarding the handover latency metric because SSF runs a straightforward process for making handover decisions. At high speeds, SIM-Know outperforms SSF and AHP-TOPSIS regarding the number of handovers and the number of throughput drops and, further, improves the throughput, delay, jitter, and packet loss in the network. Considering the obtained results, we conclude that SIM-Know is a practical and attractive solution for cognitive HM.

Pathways for decarbonizing China’s building sector under global warming thresholds

To achieve the long-term climate targets of limiting global warming to well below 2 ℃ and 1.5 ℃ relative to pre-industry levels, China’s building sector must be deeply decarbonized considering its potential of substantial emissions increase. However, how to deploy the energy technologies and how much we need to pay for China’s building sector for achieving the global warming targets remain unknown. Here, we develop a bottom-up national energy technology model for building sector (NET-Building) to explore the optimal energy technology pathways, energy demands as well as costs and benefits for decarbonizing China’s building sector under 2 ℃ and 1.5 ℃ targets. The results show that to meet the 2 ℃ target, most of fossil fuel-fired end-use devices and all traditional biomass-fired end-use devices need to be significantly reduced for the whole building sector. To pursue a more aggressive 1.5 ℃ climate goal, both proportions of the ground source heat pump and solar thermal water heater need to be drastically boosted and reach 100% in commercial sector by 2050. The decarbonization of China’s building sector under 2 ℃ and 1.5 ℃ targets can obtain 44 and 137 trillion CNY net incomes until 2050, respectively.

Activity Based Control for Operating Home Appliances using IoT

While almost everything can be done technically, what else is there but to rely on it so often, without the catastrophic consequences such as laziness which is one of the worst technological consequences for humanity. Indeed, in days like pandemics, sticking to a house makes things worse. There is no time left for us to work out and to focus on our fitness goals at the end of the day. We’ve come up with a very good solution to save you. We're not talking about those big, automatic machines all over your home, but there's something really helpful. This is the first time such an exercise machine.one does not have to press any buttons to start, as the machine sensors automatically detect the person and begin to count on their own and is not less than your strong fitness trainer. The purpose of our project is therefore to interact with household items, where we can have physical health and increase life expectancy. In order to implement our proposal we will connect key sensors to the wireless communication gear where we can access the data obtained for the operation of end user devices by using it to operate in our daily life.

A Machine Learning-Based Intrusion Detection System for Securing Remote Desktop Connections to Electronic Flight Bag Servers

Remote desktop protocols (RDP) are commonly used for connecting and interacting with computers remotely. In this case, a server component runs on the remote computer and shares its desktop (i.e., screen) with the client component which runs on an end user device. In recent years, a number of vulnerabilities have been identified in two widely used remote desktop implementations, Microsoft Remote Desktop and RealVNC. These vulnerabilities may expose the remote server to a new attack vector. This concern is increased when it comes to a cyber-physical system (CPS) in which a client device with a low trust level connects to the critical system via the remote desktop server. In order to mitigate this risk, in this paper we propose a network based intrusion detection system (NIDS) specifically designed for securing the remote desktop connections. The propose method utilizes an innovative anomaly detection technique based on machine learning for detecting malicious TCP packets, which can carry exploits aimed at the RDP server. An empirical evaluation conducted on an avionic system setup consisting of a commercial tablet (Samsung Galaxy Tab) connected through a Virtual Network Computing (VNC) remote desktop implementation to a real electronic flight bag (EFB) server shows that the proposed method can detect malicious packets carrying real exploits (reported in recent years) with a true positive rate of 0.863 and a false positive rate of 0.0001.

Design and Experimental Characterization of a Discovery and Tracking System for Optical Camera Communications.

Visible light communications (VLC) technology is emerging as a candidate to meet the demand for interconnected devices’ communications. However, the costs of incorporating specific hardware into end-user devices slow down its market entry. Optical camera communication (OCC) technology paves the way by reusing cameras as receivers. These systems have generally been evaluated under static conditions, in which transmitting sources are recognized using computationally expensive discovery algorithms. In vehicle-to-vehicle networks and wearable devices, tracking algorithms, as proposed in this work, allow one to reduce the time required to locate a moving source and hence the latency of these systems, increasing the data rate by up to 2100%. The proposed receiver architecture combines discovery and tracking algorithms that analyze spatial features of a custom RGB LED transmitter matrix, highlighted in the scene by varying the cameras’ exposure time. By using an anchor LED and changing the intensity of the green LED, the receiver can track the light source with a slow temporal deterioration. Moreover, data bits sent over the red and blue channels do not significantly affect detection, hence transmission occurs uninterrupted. Finally, a novel experimental methodology to evaluate the evolution of the detection’s performance is proposed. With the analysis of the mean and standard deviation of novel K parameters, it is possible to evaluate the detected region-of-interest scale and centrality against the transmitter source’s ideal location.

MagAuth: Secure and Usable Two-Factor Authentication With Magnetic Wrist Wearables

Secure and usable user authentication is the first line of defense against cyber attacks on smart end-user devices. Advanced hacking techniques pose severe threats to the traditional authentication systems based on the password/PIN/fingerprint. We propose MagAuth, a secure and usable two-factor authentication scheme with commercial off-the-shelf (COTS) wrist wearables with magnetic strap bands to enhance the security and usability of password-based authentication for mobile touchscreen devices. In MagAuth, a user enrolls a self-chosen unlock pattern or touch gesture into his touchscreen device by performing it with the same hand the magnetic wrist wearable is on. The chosen unlock pattern or touch gesture serves as the first authentication factor, and the user’s behavioral features manifested in the magnetic field changes during his finger movement correspond to the second factor. The user can unlock his touchscreen device only when both authentication factors can be validated. Comprehensive user experiments confirm the high security and usability of MagAuth. In particular, MagAuth achieves an average true-positive rate up to 96.3 percent and a false-positive rate no larger than 8.4 percent. Moreover, we show that MagAuth is highly resilient to various attacks.

Inter-layer correlation-based adaptive bit allocation for enhancement layer in scalable high efficiency video coding

To facilitate the diversity of network and end-user devices, bit allocation technology must be combined with scalable high efficiency video coding (SHVC) to achieve continuous bitrate variation. However, to date, the multilayer coding feature in SHVC has not been fully utilized. In this paper, a bit allocation algorithm is proposed for the enhancement layer in SHVC. The algorithm includes two parts: a bit allocation method for the initial frame and another for subsequent frames. The bit allocation method for the initial frame consists of a bit allocation factor model constructed based on investigation of the influence of the initial target bitrate on the overall coding performance. For the bit allocation of the subsequent frames, alternate GOP coding is designed and implemented to fully exploit the inter layer correlation. In addition, an adaptive frame layer bit allocation ratio model is deduced according to the rate distortion optimization theory. Experimental results show that the proposed algorithm can improve PSNR by 0.44 dB and 0.41 dB under low delay and random-access configurations, respectively, and achieve high bitrate control accuracy. The algorithm also outperforms the state-of-the-art algorithms in term of PSNR improvement.