An Alternative Internet-of-Things Solution Based on LoRa for PV Power Plants: Data Monitoring and Management

This paper proposes a wireless low-cost solution based on long-range (LoRa) technology able to communicate with remote PV power plants, covering long distances with minimum power consumption and maintenance. This solution includes a low-cost open-source technology at the sensor layer and a low-power wireless area network (LPWAN) at the communication layer, combining the advantages of long-range coverage and low power demand. Moreover, it offers an extensive monitoring system to exchange data in an Internet-of-Things (IoT) environment. A detailed description of the proposed system at the PV module level of integration is also included in the paper, as well as detailed information regarding LPWAN application to the PV power plant monitoring problem. In order to assess the suitability of the proposed solution, results collected in real PV installations connected to the grid are also included and discussed.This work was partially supported by the Spanish agreement (2017) between the Institute for Development of the Region of Murcia (INFO) and the Technological Center for Energy and Environment (CETENMA). The paper includes results of activities conducted under the Research Program for Groups of Scientific Excellence at Region of Murcia (Spain), the Seneca Foundation, and the Agency for Science and Technology of the Region of Murcia (Spain). This work was also supported by project AIM, Ref. TEC2016-76465-C2-1-R (AEI/FEDER, UE). The authors thank the staff of the Universidad Politécnica de Cartagena (Spain) for services and facilities provided

Model-driven engineering techniques and tools for machine learning-enabled IoT applications: A scoping review

This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.info:eu-repo/semantics/publishedVersio

Optimization of Adaptive Method for Data Reduction in Wireless Sensor Networks

The term ‘Wireless’ is a cordless technology where the nodes interact or exchange information with the sink node without wired intervention to exchange or transmit any information successfully. Characteristics of the present wireless sensor networks are applied to diverse technological furtherance in minimum power communications and very large-scale integration to sustained functionalities of sensing. Tremendous number of incentive observation and algometry of data are amassed from sensors in Wireless Sensor Networks (WSNs) for the Internet of Things (IoT) applications such as environmental monitoring. However, continuous dissemination of the sensed data postulates eminent energy imbibing. Data reduction duress the sensor nodes to surcease transmitting the data when it is diffident about freshen up. One way to reduce this kind of energy imbibing is to minimize the amount of data exchanged across the sensors, therefore the research work aims to increase the communication and spatial prediction between the sensor nodes and the sink nodes. In this research work, an Optimization of Adaptive Method for Data Reduction in Wireless Sensor Networks was proposed and implemented. The work adopted a bulging haplotype of two decoupled Least-Mean-Square (LMS) windowed filters with varying length for approximating the immediate metrics values both at the sink and source node such that sensor nodes have to send only their next sensed values that diverse substantially (when a pre-determine threshold) from the anticipated values. The experiment conducted on a real- world dataset of about 2,313,682, which were collected from 54 Mica2dot sensors thus, MATLAB was used as a tool for the implementation. The research work aims to increase the communication model and spatial prediction, which is the limitation of the base paper. The results show that our approach (OAM-DR) has achieved up to 98% communication reduction while retaining or carrying a high accuracy, (i.e. the anticipated values have a digression of ±0.5 from actual data values)

Blockchain and self sovereign identity to support quality in the food supply chain

This work presents how a digital identity management system can support food supply chains in guaranteeing the quality of the products marketed and the compliance of the several supplychain’s nodes to standards and technical regulations. Specific goal of this work is to present a system that provides full visibility of process/food certifications, which nowadays are issued by accredited and approved certification bodies (issuers) and delivered and stored in paper version by the several participants (holders) of the supply chain. The system is designed and implemented by combining the latest most innovative and disruptive technologies in the market—Self Sovereign Identity system, Blockchain, and Inter Planetary File System. The crucial aspects that it aims to hit are the storage and access of food/process certifications, and the proper eligibility verification of these certifications exploiting the concepts of the Self Sovereign Identity-based models. The proposed system, realized by using standards that are WWW Consortium-compatible and the Ethereum Blockchain, ensures eligibility, transparency, and traceability of the certifications along a food supply chain, and could be an innovation model/idea that the companies that adopt the Open Innovation paradigm might want to pursue

A Holistic Systems Security Approach Featuring Thin Secure Elements for Resilient IoT Deployments

© 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.IoT systems differ from traditional Internet systems in that they are different in scale, footprint, power requirements, cost and security concerns that are often overlooked. IoT systems inherently present different fail-safe capabilities than traditional computing environments while their threat landscapes constantly evolve. Further, IoT devices have limited collective security measures in place. Therefore, there is a need for different approaches in threat assessments to incorporate the interdependencies between different IoT devices. In this paper, we run through the design cycle to provide a security-focused approach to the design of IoT systems using a use case, namely, an intelligent solar-panel project called Daedalus. We utilise STRIDE/DREAD approaches to identify vulnerabilities using a thin secure element that is an embedded, tamper proof microprocessor chip that allows the storage and processing of sensitive data. It benefits from low power demand and small footprint as a crypto processor as well as is compatible with IoT 29 requirements. Subsequently, a key agreement based on an asymmetric cryptographic scheme, namely B-SPEKE was used to validate and authenticate the source. We find that end-to-end and independent stand-alone procedures used for validation and encryption of the source data originating from the solar panel are cost-effective in that the validation is carried out once and not several times in the chain as is often the case. The threat model proved useful not so much as a panacea for all threats but provided the framework for the consideration of known threats, and therefore appropriate mitigation plans to be deployed.Peer reviewe

A System Proposal for Information Management in Building Sector Based on BIM, SSI, IoT and Blockchain

This work presents a Self Sovereign Identity based system proposal to show how Blockchain, Building Information Modeling, Internet of Thing devices, and Self Sovereign Identity concepts can support the process of building digitalization, guaranteeing the compliance standards and technical regulations. The proposal ensures eligibility, transparency and traceability of all information produced by stakeholders, or generated by IoT devices appropriately placed, during the entire life cycle of a building artifact. By exploiting the concepts of the Self Sovereign Identity, our proposal allows the identification of all involved stakeholders, the storage off-chain of all information, and that on-chain of the sole data necessary for the information notarization and certification, adopting multi-signature approval mechanisms where appropriate. In addition it allows the eligibility verification of the certificated information, providing also useful information for facility management. It is proposed as an innovative system and companies that adopt the Open Innovation paradigm might want to pursue it. The model proposal is designed exploiting the Veramo platform, hence the Ethereum Blockchain, and all the recommendations about Self Sovereign Identity systems given by the European Blockchain Partnership, and by the World Wide Web Consortium

A Smart and Secure Logistics System Based on IoT and Cloud Technologies

Recently, one of the hottest topics in the logistics sector has been the traceability of goods and the monitoring of their condition during transportation. Perishable goods, such as fresh goods, have specifically attracted attention of the researchers that have already proposed different solutions to guarantee quality and freshness of food through the whole cold chain. In this regard, the use of Internet of Things (IoT)-enabling technologies and its specific branch called edge computing is bringing different enhancements thereby achieving easy remote and real-time monitoring of transported goods. Due to the fast changes of the requirements and the difficulties that researchers can encounter in proposing new solutions, the fast prototype approach could contribute to rapidly enhance both the research and the commercial sector. In order to make easy the fast prototyping of solutions, different platforms and tools have been proposed in the last years, however it is difficult to guarantee end-to-end security at all the levels through such platforms. For this reason, based on the experiments reported in literature and aiming at providing support for fast-prototyping, end-to-end security in the logistics sector, the current work presents a solution that demonstrates how the advantages offered by the Azure Sphere platform, a dedicated hardware (i.e., microcontroller unit, the MT3620) device and Azure Sphere Security Service can be used to realize a fast prototype to trace fresh food conditions through its transportation. The proposed solution guarantees end-to-end security and can be exploited by future similar works also in other sectors

Real-time localisation system for GPS denied open areas using smart street furniture

Real-time measurement of crowd dynamics has been attracting significant interest, as it has many applications including real-time monitoring of emergencies and evacuation plans. To effectively measure crowd behaviour, an accurate estimate for pedestrians’ locations is required. However, estimating pedestrians’ locations is a great challenge especially for open areas with poor Global Positioning System (GPS) signal reception and/or lack of infrastructure to install expensive solutions such as video-based systems. Street furniture assets such as rubbish bins have become smart, as they have been equipped with low-power sensors. Currently, their role is limited to certain applications such as waste management. We believe that the role of street furniture can be extended to include building real-time localisation systems as street furniture provides excellent coverage across different areas such as parks, streets, homes, universities. In this thesis, we propose a novel wireless sensor network architecture designed for smart street furniture. We extend the functionality of sensor nodes to act as soft Access Point (AP), sensing Wifi signals received from surrounding Wifi-enabled devices. Our proposed architecture includes a real-time and low-power design for sensor nodes. We attached sensor nodes to rubbish bins located in a busy GPS denied open area at Murdoch University (Perth, Western Australia), known as Bush Court. This enabled us to introduce two unique Wifi-based localisation datasets: the first is the Fingerprint dataset called MurdochBushCourtLoC-FP (MBCLFP) in which four users generated Wifi fingerprints for all available cells in the gridded Bush Court, called Reference Points (RPs), using their smartphones, and the second is the APs dataset called MurdochBushCourtLoC-AP (MBCLAP) that includes auto-generated records received from over 1000 users’ devices. Finally, we developed a real-time localisation approach based on the two datasets using a four-layer deep learning classifier. The approach includes a light-weight algorithm to label the MBCLAP dataset using the MBCLFP dataset and convert the MBCLAP dataset to be synchronous. With the use of our proposed approach, up to 19% improvement in location prediction is achieved

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection