Skillschain: A decentralized application that uses educational robotics and blockchain to disrupt the educational process

Our epoch is continuously disrupted by the rapid technological advances in various scientific domains that aim to drive forward the Fourth Industrial R evolution. This disruption resulted in the introduction of fields that present advanced ways to train students as well as ways to secure the exchange of data and guarantee the integrity of those data. In this paper, a decentralized application (dApp), namely skillsChain, is introduced that utilizes Blockchain in educational robotics to securely track the development of students’ skills so as to be transferable beyond the confines of the academic world. This work outlines a state-of-the-art architecture in which educational robotics can directly execute transactions on a public ledger when certain requirements are met without the need of educators. In addition, it allows students to safely exchange their skills’ records with third parties. The proposed application was designed and deployed on a public distributed ledger and the final results present its efficacy

Skillschain: A decentralized application that uses educational robotics and blockchain to disrupt the educational process

Our epoch is continuously disrupted by the rapid technological advances in various scientific domains that aim to drive forward the Fourth Industrial R evolution. This disruption resulted in the introduction of fields that present advanced ways to train students as well as ways to secure the exchange of data and guarantee the integrity of those data. In this paper, a decentralized application (dApp), namely skillsChain, is introduced that utilizes Blockchain in educational robotics to securely track the development of students’ skills so as to be transferable beyond the confines of the academic world. This work outlines a state-of-the-art architecture in which educational robotics can directly execute transactions on a public ledger when certain requirements are met without the need of educators. In addition, it allows students to safely exchange their skills’ records with third parties. The proposed application was designed and deployed on a public distributed ledger and the final results present its efficacy

Firmware Update Using Multiple Gateways in LoRaWAN Networks

The remarkable evolution of the IoT raised the need for an efficient way to update the device’s firmware. Recently, a new process was released summarizing the steps for firmware updates over the air (FUOTA) on top of the LoRaWAN protocol. The FUOTA process needs to be completed quickly to reduce the systems’ interruption and, at the same time, to update the maximum number of devices with the lowest power consumption. However, as the literature showed, a single gateway cannot optimize the FUOTA procedure and offer the above mentioned goals since various trade-offs arise. In this paper, we conducted extensive experiments via simulation to investigate the impact of multiple gateways during the firmware update process. To achieve that, we extended the FUOTAsim simulation tool to support multiple gateways. The results revealed that several gateways could eliminate the trade-offs that appeared using a single gatewayThis work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy

ParkChain: An IoT parking service based on blockchain

The IoT ecosystem is evolving quickly, developing several applications in different sectors. The majority of these applications use centralized infrastructures something that poses several challenges especially related to trust and data security. Recently, blockchain has been introduced as an effective solution to IoT applications trying to provide solutions to these challenges. In this paper, we introduce a new decentralized IoT application using blockchain technology, namely, ParkChain. The ParkChain application operates in a way that no one can delete, revert, hack or question the time a registered vehicle securely entered a parking area. In order to evaluate ParkChain, we have implemented the smart contract on top of Ethereum Blockchain along with a traditional Image Processing / Computer Vision approach for License Plate Recognition, and using a Raspberry Pi we control the access on a parking place. We report on several experiments that assess the performance of ParkChain application

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

Wireless sensor networks have recently been widely used in several applications and scenarios, especially because they have the ability and flexibility for establishing a scalable and reliable wireless network. Cooperative multi-robotic systems (CMRS) are one example of these applications where establishing a wireless network between robots is essential and paramount to their operation. Further, these robots can utilize their mobility to provide sensing functionality for areas that are not covered by the static sensor. This can be achieved by equipping the robots with specific sensors to sense the area of interest (AoI) and report the sensed data to a remote monitoring center for further processing and decision-making. However, the nodes that form the sensor network have limited energy, and, as such, efficient algorithms in clusters’ formation, packets’ routing, and energy and mobility management are paramount. In this paper, a literature survey is presented containing the most related works that have been proposed to solve these challenges utilizing fuzzy logic. Most of the literature work attempted to utilize a de-centralized approach, where certain input parameters such as the residual energy, communication link quality, network congestion status, the nodes’ distance to the sink node and its location with respect to the other nodes, and the data and their sampling rate are all used as inputs to the fuzzy logic controller. These input parameters are used to determine several performance vital factors such as the cluster formation and its cluster head, best route to the sink node, optimal power management policies in terms of sleep/awake times needed to maximize the network lifetime, nodes’ mobility management policies to maintain network connectivity, and best route in terms of packet loss and delay

Grape Leaf Diseases Identification System Using Convolutional Neural Networks and LoRa Technology

Image transmission over Low-Power Wide Area Networks (LP-WAN) protocols has always been a difficult task since it necessitates high data rates and high energy consumption. Long Range (LoRa) is one such protocol, which is excellent for transferring data over long distances but has generated severe doubts regarding the viability of image transmission due to its low data rate. This paper demonstrates the application results of an integrated LoRa and Deep Learning-based computer vision system that can efficiently identify grape leaf diseases using low-resolution images. In particular, the focus in this paper is to combine the two technologies, LoRa and Deep Learning, to make the transmission of the images and the identification of the diseases possible. To achieve this objective, the framework utilizes a combination of on-site and simulation experiments along with different LoRa parameters and Convolutional Neural Model (CNN) model fine-tuning. Based on the evaluation, the proposed framework proved that the transmission of images using LoRa is possible within the protocol limitations (such as limited bandwidth and low duty cycle). Our fine-tuned model can efficiently identify grape leaves diseases. The technique is both efficient and adaptive to the specifics of each leaf disease, while it does not need any training data to adjust parameters. It is worth noting that today, end-user trust in Machine and Deep Learning models has increased significantly because of novel solutions in the field of Explainable Artificial Intelligence (XAI). In this study, we use the Grad-CAM method to visualize the output layer judgments of the CNN. The disease's spot region is highly activated, according to the visualization findings. This is how the network distinguishes between different grape leaf diseases

WSN Evaluation in Industrial Environments First results and lessons learned

The GINSENG project develops performance-controlled wireless sensor networks that can be used for time-critical applications in hostile environments such as industrial plant automation and control. GINSENG aims at integrating wireless sensor networks with existing enterprise resource management solutions using a middleware. A cornerstone is the evaluation in a challenging industrial environment — an oil refinery in Portugal. In this paper we first present our testbed. Then we introduce our solution to access, debug and flash the sensor nodes remotely from an operations room in the plant or from any location with Internet access. We further present our experimental methodology and show some exemplary results from the refinery testbed

Educational robotics: platforms, competitions and expected learning outcomes

Summarization: Motivated by the recent explosion of interest around Educational Robotics (ER), this paper attempts to re-approach this area by suggesting new ways of thinking and exploring the related concepts. The contribution of the paper is fourfold. First, future readers can use this paper as a reference point for exploring the expected learning outcomes of educational robotics. From an exhaustive list of potential learning gains, we propose a set of six learning outcomes that can offer a starting point for a viable model for the design of robotic activities. Second, the paper aims to serve as a survey for the most recent ER platforms. Driven by the growing number of available robotics platforms, we have gathered the most recent ER kits. We also propose a new way to categorize the platforms, free from their manufacturers' vague age boundaries. The proposed categories, including No Code, Basic Code, and Advanced Code, are derived from the prior knowledge and the programming skills that a student needs to use them efficiently. Third, as the number of ER competitions, and tournaments increases in parallel with ER platforms' increase, the paper presents and analyses the most popular robotic events. Robotics competitions encourage participants to develop and showcase their skills while promoting specific learning outcomes. The paper aims to provide an overview of those structures and discuss their efficacy. Finally, the paper explores the educational aspects of the presented ER competitions and their correlation with the six proposed learning outcomes. This raises the question of which primary features compose a competition and achieve its' pedagogical goals. This paper is the first study that correlates potential learning gains with ER competitions to the best of our knowledge.Presented on: IEEE Acces