In this paper, we propose a 2.45 GHz RF energy harvesting system to power a battery free Internet of Things (IoT) sensor node. The system operates from an RF signal with a power as low as -20 dBm and includes an RF to DC converter, a storage super capacitor and a voltage DC/DC up-conversion. The system components are sized to provide the required electric energy to operate the connected object. The tested connected object includes an ARM microcontroller, an ultra low-power sensor (temperature, pressure and humidity) and a Bluetooth Low Energy interface for a wireless transmission toward a terminal such as a smartphone or tablet. It requires 200 mu J per transmission cycle, which must be provided by the proposed harvesting system. This system is tested in an anechoic chamber and using a constant power RF signal to characterize the energy recovery time needed for measuring and transmitting the sensor data. Promising results show that the system is capable of sending data after 5 hours of RF energy harvesting from a source distant with one meter.
Unmanned aerial vehicle (UAV) technologies have played an important role in the beyond-fifth-generation (B5G) networks. To meet the increasing demand for UAV swarm communications, in this paper we propose an intra-swarm UAV-to-UAV (IaS-U2U) communication mechanism for enabling data services in the so-called out-of-coverage (OOC) scenario, where the conventional terrestrial cellular network infrastructure is not available, for example in the cases of mountains, oceans, deserts and forests. Specifically, an optimization problem is formulated by jointly considering the single-pair transmission rate (STR) and the propulsion power (PP), where the UAVs' pairing and relative positioning are taken into account. To solve the complicated joint optimization problem, we decompose it into the UAV pairing process and the relative position optimization (RPO) process, where in the latter a successive convex approximation (SCA) based RPO algorithm is proposed. Simulation results show that the new scheme can provide notable gains in terms of energy efficiency (EE) over selected benchmark methods under different conditions.
People with dyslexia have difficulties in fluently reading and writing characters which highly affect their learning progress. It is very important to identify dyslexic students that need intervention and extra support during their childhood. However, the waiting time for dyslexia assessment services is often long. To address the above problem, we propose a cloud-based early identification system of dyslexia. We design and develop a mobile app with AWS cloud platform as server. We have identified 27 representative traditional Chinese characters for handwriting data collection. After the first round of the data collection, 66 children aged 5-7 were recruited in Hong Kong. We carry out K-means clustering algorithm to investigate the characteristics of data points on a feature map for each character. We find out that some Chinese words contain more distinguishable characteristics for identifying children with dyslexia. Since children aged 5-7 are still learning how to write traditional Chinese characters properly, children with no risk of dyslexia still have certain possibilities of writing characters with characteristics of handwriting by children with dyslexia. It increases the difficulties of the early identification on developmental dyslexia of Chinese language. Finally, we present our findings and future work.
Vehicular Ad hoc Networks (VANETs) are the basic support for Intelligent Transportation Systems (ITS), providing a framework for its multiple entities to communicate. The communications and services provided to the road entities are generally implemented by means of an On-Board Unit (OBU) and Road Side Unit (RSU), sharing a rather similar hardware and software architectures. These devices need to support multiple communications types and provide access to the needed vehicle data to the applications. Most of the existing solutions demand that the application developers have development control and knowledge about the OBU internals. Thus, most applications are developed by OBU makers and implemented directly on it. However, solutions based on middleware agnosticism separate the OBU internals and software development, facilitating the application development by software makers or researchers. This paper proposes an Application Programming Interface (API) and protocol that can easily be used to access the vehicle internals and services through an agnostic architecture without any knowledge on how they are implemented.
Due to the critical condition and unique treatments required by ICU's patients, their vital signs should be continuously and reliably monitored. In this paper, we present a system to address this problem including the design considerations. Our system has three components: i) a mobile application to present the collected data from the hospitals server, ii) a database server with patients data and iii) a transactional server that manages and keeps the connection between the other two components. The mobile application implements a vital signs monitor for each patient including an underlying emergency notification system. After several simulations, we were able to identify that the proposed solution takes around 3 seconds to generate the notification after an emergency alarm occurs. Moreover, the application uses an average of 2.11 Kb/s of traffic data and a maximum of 250Mb of RAM. This system is a feature that will help to track the ICU patients status, allowing doctors and ICU managers to work outside the unit and to determine when their presence is required. Results show that relevant and accurate notifications can effectively reduce the time response in cases of emergency and consequently increases the likelihood of the patients' recovery.
This paper describes efforts to improve the security of the Internet of Things world. One of the many problems faced by the users, but also producers or owners of networks or people who deal with cybersecurity in a broad sense on a daily basis, such as employees of CSIRTs, is the issue of vulnerabilities in devices or software. Even though the most serious vulnerabilities are more commonly presented in the news, still the vast majority of them is only known to cybersecurity specialists and not to the users who own the vulnerable devices and therefore can be in danger. The results of our recent research show that there is still no satisfactory source of information about vulnerabilities in Internet of Things devices and software. This is why we decided to create such a repository (variotdbs.pl) where information about vulnerabilities and exploits targeting IoT devices can be easily found by everyone.
Agriculture is one of the most important economic sectors in the world. On the one hand, most of its importance resides in the food that is produced. On the other hand, this sector employs millions of people in the world both directly and indirectly. The introduction of Internet of Things (IoT) solutions for crop monitoring and management was the next step to improve the quality of the product, the quantity in the production, and to reduce the use of resources such as water. In this paper, a practical design of a WiFi-based Wireless Sensor Network (WSN) for citrus crop monitoring is presented. A mathematical model is obtained from the results of practical tests performed with low-cost devices for different height configurations of both the Access Point (AP) and the emitter according to the distance. The maximum coverage for the AP for each configuration is obtained as well. The results show the number of sensor nodes necessary to monitor the field according to its extension, and the number of APs needed to provide coverage for all the nodes deployed on the citrus field for each configuration. This way, a tool for the design of WSNs to monitor citrus plots is provided.
Mobile networks' fault management can take advantage of Machine Learning (ML) algorithms making its maintenance more proactive and preventive. Currently, Network Operations Centers (NOCs) still operate in reactive mode, where the troubleshoot is only performed after the problem identification. The network evolution to a preventive maintenance enables the problem prevention or quick resolution, leading to a greater network and services availability, a better operational efficiency and, above all, ensures customer satisfaction. In this paper, different algorithms for Sequential Pattern Mining (SPM) and Association Rule Learning (ARL) are explored, to identify alarm patterns in a live Long Term Evolution (LTE) network, using Fault Management (FM) data. A comparative performance analysis between all the algorithms was carried out, having observed, in the best case scenario, a decrease of 3.31% in the total number of alarms and 70.45% in the number of alarms of a certain type. There was also a considerable reduction in the number of alarms per network node in a considered area, having identified 39 nodes that no longer had any unresolved alarm. These results demonstrate that the recognition of sequential alarm patterns allows taking the first steps in the direction of preventive maintenance in mobile networks.
The new coronavirus pandemic has brought disruption to the world. The lack of mass testing for the population is among the significant dilemmas to be solved by countries, especially underdeveloped ones. An alternative to deal with the lack of tests is detecting the disease by analyzing radiographic images. To process different types of images automatically, we employed deep learning algorithms to achieve success in recognizing different diagnostics. This work aims to train a deep learning model capable of automatically recognizing the COVID-19 diagnosis through radiographic images. Comparing images of coronavirus, healthy lung, and bacterial and viral pneumonia, we obtained a result with 93
Digital Twins are a key concept and an enabling technology within the digital transformation. They can communicate bilaterally with their physical counterpart. Thus the twins are reflecting each other's properties and state changes. Digital Twins add value to many use case domains, e. g., smart manufacturing. Nevertheless, today's implementations are mostly data and state representations of their physical counterparts, tying the digital twin to a passive and reactive role. The authors argue that semantically enhanced digital twins that autonomously perform actions and interactions on digital market spaces can add substantial value to various use cases. An architectural approach is proposed, and an implemented proof of concept shows the feasibility.
Microservices are becoming an integral component in the architecture design and development of Internet-based distributed systems, such as the Internet of Things. A critical ongoing challenge in microservice architecture design is to ensure the integrity and availability of data. Blockchain technology offers the solution to these challenges in the distributed microservice architecture. Blockchains use consensus algorithms for validating their transactions and also providing extra security. Taming the advantages of consensus algorithms in blockchain-based architecture models, this paper proposes a trustworthy consensus algorithm to tackle data integrity challenges in microservice architectures. The results of the evaluation highlight the efficacy of the proposed algorithm in real-world scenarios of microservice architecture endeavour. Lessons learned in applying the algorithms, and future research directions are also discussed.
Co-channel interference and noise power could affect the performance of the MIMO system and can be evaluated with respect to the user's signal to noise and interference ratio. As a result, the desired transmission rate of the users could be satisfied by consuming more transmit power. Owing to this, a quantum optimization strategy can be utilized in order to minimize the transmit power, as well as to achieve an optimum trade-off within the throughput and the resulting interference and noise. In this study, a constrained quantum optimization algorithm (CQOA) has been implemented in the MIMO-downlink system to reduce the transmit power and computational complexity. An analytical study is conducted along with a comparison between the water filling algorithm-based binary searching algorithm (WFA-BSA), exhaustive algorithm-based water filling algorithm (EWFA), and the CQOA. Finally, simulation results show that the aforementioned methods consume similar total transmit power, however, the computational complexity of the quantum strategy is dramatically low compared to the other methods.
Utilizing mmW carrier frequency bands (aka above-6 GHz) at the network edge, is a key enabling factor to achieve near Gbps throughput in 5G-NR technology. The propagation characteristics of mmW signals in outdoor environment complicates 5G mmW network design. Previously used in backhaul networks, the service availability of mmW radio technologies significantly relies on the Line-of-Sight (LoS) signal path between the communicating radios. LiDAR is considered a popular source of high-resolution aerial survey data suitable for accurate LoS assessment. Maintaining low radio mounting height is another cost-related factor in practical 5G mmW network design. In this work we present a comprehensive LoS assessment problem incorporating radio mounting height. Then we propose a new LoS assessment algorithm using LiDAR data that is computationally optimized for the practical aspects of 5G mmW network design. Empowered by a novel method to topologically sort terrain data, it achieves constant-time, O(1), complexity to execute LoS assessment per user location, whereas the complexity of retrofitted LoS algorithms for the same task grows linearly with respect to the data dimensions. The improvements in the run-time efficiency are verified in numerical results for a real deployment scenario.
Multi-microgrids (MMGs) provide economic and environmental benefits to society by improving operational flexibility, stability and reliability of a smart grid. MMGs have greater complexity than conventional power networks due to the use of multiple infrastructures, communication protocols, controllers, and intelligent electronic devices. The distributed and heterogeneous connectivity technologies of the MMGs and their need to exchange information with external sources as well as the vulnerabilities in the communication networks and software-based components, make MMGs susceptible to cyberattacks. In this work, we present a conceptual framework for collaborative adaptive cybersecurity that is able to proactively detect security incidents. The framework utilizes federated learning for collaborative training of shared prediction models in a decentralized manner. The methodology used in this research is mainly analytical. This involves analysis of how the principles of a collaborative adaptive cybersecurity can be applied to the MMG environments resulting in the development of theoretical models which can then be validated in practice by prototyping and using real time simulation.
The development of smart vehicles and rich cloud services have led to the emergence of vehicular edge computing. To perform the distributed computation tasks efficiently, Coded Distributed Computing (CDC) was proposed to reduce communication costs and mitigate the straggler effects through the use of coding techniques. In this paper, we propose a double auction mechanism to allocate the resources of the edge servers to the vehicles in order to complete the CDC tasks. Specifically, the vehicles use the PolyDot codes to manage the tradeoff between communication costs and recovery threshold. Given the requirements of various vehicles, the double auction mechanism matches the edge servers with the required resources to the vehicles. Besides, the double auction mechanism also determines the prices that the vehicles need to pay for the resources of the edge servers. The double auction mechanism satisfies the properties of individual rationality, incentive compatibility and budget-balance.
