Search Results (34)

The integration of electric vehicles into modern mobility ecosystems relies heavily on reliable charging station infrastructures that support real-time communications and data-driven functionalities. Existing solutions often face security vulnerabilities in their firmware update mechanisms, compromising safety, user trust, and the broader deployment of these stations in emerging digital and connected environments. This paper aims to address these gaps by proposing a blockchain-based framework designed to provide secure, tamper-proof firmware updates for charging stations in a Web of Things environment. The approach uses decentralized ledger technologies to validate firmware integrity, authenticate update sources, and mitigate the risk of malicious or fraudulent content. In a comprehensive experimental setup, the proposed method demonstrates enhanced resilience against unauthorized firmware modifications and improved traceability of update transactions through immutable records. Results highlight a reduction in firmware compromise events, as well as improved detection and notification efficiencies in real-time networked systems. These findings suggest that integrating blockchain technology into firmware update workflows strengthens security in electric vehicle charging infrastructures. Consequently, the adoption of decentralized verification approaches can drive broader trust in connected mobility services, supporting safer and more efficient charging station networks while fostering future innovation in sustainable transport. Full article

As the transition to sustainable transportation has accelerated with the rise of electric vehicles (EVs), ensuring drivers have access to charging to maximize the electric miles driven is critical to lowering carbon emissions in the transportation sector. Limited charging station capacity and poor reliability, especially during peak travel times, long-distance travels, holidays, and events, have hindered the adoption of EVs and threaten the progress toward reducing greenhouse gas emissions. Adaptive, flexible deployment strategies combined with innovative approaches integrating mobility and renewable energy are essential to address these systemic challenges and bridge the current infrastructure gap. To address these challenges, this study proposes a self-contained, mobile charging station (MCS). Designed for rapid deployment, the proposed MCS increases charging capacity during demand surges while minimizing reliance on fossil fuels. The feasibility of integrating a solar canopy with this MCS to further reduce carbon emissions is also studied. This study weighed the pros and cons of differing cell chemistries, sized the battery using data provided by the United States’ largest public CPO, and discussed the feasibility of a solar canopy for off-grid energy. Full article

The combination of software-defined networking (SDN) and satellite–ground integrated networks (SGINs) is gaining attention as a key infrastructure for meeting the granular quality-of-service (QoS) demands of next-generation mobile communications. However, due to the unpredictable nature of end-user requests and the limited resource capacity of low Earth orbit (LEO) satellites, improper Virtual Network Function (VNF) deployment can lead to significant increases in end-to-end (E2E) delay. To address this challenge, we propose an online algorithm that jointly deploys VNFs and forms routing paths in an event-driven manner in response to end-user requests. The proposed algorithm selectively deploys only the essential VNFs required for each Service Function Chain (SFC), focusing on minimizing E2E delay—a critical QoS parameter. By defining a minimum-hop region (MHR) based on the geographic coordinates of the routing endpoints, we reduce the search space for candidate base stations, thereby designing paths that minimize propagation delays. VNFs are then deployed along these paths to further reduce E2E delay. Simulations demonstrate that the proposed algorithm closely approximates the global optimum, achieving up to 97% similarity in both E2E delay and CPU power consumption, with an average similarity of approximately 90%. Full article

There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they suffer from a practical limitation: they require successful pre-segmentation of continuous sensor data streams prior to automated recognition, i.e., they assume that an oracle is present during deployment, and that it is capable of identifying time windows of interest across discrete sensor events. To overcome this limitation, we propose a novel graph-guided neural network approach that performs activity recognition by learning explicit co-firing relationships between sensors. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home in a data-driven manner. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms and hierarchical pooling of node embeddings. We demonstrate the effectiveness of our proposed approach by conducting several experiments on CASAS datasets, showing that the resulting graph-guided neural network outperforms the state-of-the-art method for HAR in smart homes across multiple datasets and by large margins. These results are promising because they push HAR for smart homes closer to real-world applications. Full article

Continuous discovery and update of applications or their boundaries running in cloud environments in an automatic way is a highly required function of modern data center operation solutions. Prior attempts to address this problem within various products or projects were/are applying rule-driven approaches or machine learning techniques on specific types of data–network traffic as well as property/configuration data of infrastructure objects, which all have their drawbacks in effectively identifying roles of those resources. The current proposal (ADLog) leverages log data of sources, which contain incomparably richer contextual information, and demonstrates a reliable way of discriminating application objects. Specifically, using native constructs of VMware Aria Operations for Logs in terms of event types and their distributions, we group those entities, which then can be potentially enriched with indicative tags automatically and recommended for further management tasks and policies. Our methods differentiate not only diverse kinds of applications, but also their specific deployments, thus providing hierarchical representation of the applications in time and topology. For several applications under Aria Ops management in our experimental test bed, we discover those in terms of similarity behavior of their components with a high accuracy. The validation of the proposal paves the path for an AI-driven solution in cloud management scenarios. Full article

In response to the challenges introduced by global warming and increased eutrophication, this paper presents an innovative modeling and simulation (M&S)-driven model for developing an automated inland water monitoring system. This system is grounded in a layered Internet of Things (IoT) architecture and seamlessly integrates cloud, fog, and edge computing to enable sophisticated, real-time environmental surveillance and prediction of harmful algal and cyanobacterial blooms (HACBs). Utilizing autonomous boats as mobile data collection units within the edge layer, the system efficiently tracks algae and cyanobacteria proliferation and relays critical data upward through the architecture. These data feed into advanced inference models within the cloud layer, which inform predictive algorithms in the fog layer, orchestrating subsequent data-gathering missions. This paper also details a complete development environment that facilitates the system lifecycle from concept to deployment. The modular design is powered by Discrete Event System Specification (DEVS) and offers unparalleled adaptability, allowing developers to simulate, validate, and deploy modules incrementally and cutting across traditional developmental phases. Full article

Aircraft are composed of many electronic systems: sensors, displays, navigation equipment, and communication elements. These elements require a reliable interconnection, which is a major challenge for communication networks since high reliability and predictability requirements must be verified for safe operation. In addition, their verification via hardware deployments is limited because these are costly and it is difficult to try different architectures and configurations, thus delaying design and development in this area. Therefore, verification at early stages in the design process is of great importance and must be supported with simulation. In this context, this work presents an event-driven link-level framework and simulator for the validation of avionics networks. The tool presented supports communication protocols commonly used in avionics, such as Avionics Full-Duplex Switched Ethernet (AFDX), as well as Ethernet, which is used with static routing. Also, the simulator provides accurate results by employing realistic models for various devices. The proposed platform was evaluated in the Clean Sky’s Disruptive Cockpit for Large Passenger Aircraft architecture scenario, showing the capabilities of the simulator. Verification speed is a key factor in its application, so the computational cost was analyzed, proving that the execution time is linearly dependent on the number of messages sent and that the increase in the number of nodes has few quadratic components. Full article

Accelerating the deployment of Photovoltaic (PV) systems is a key contributing factor in achieving climate neutrality. Even though solar power is one of the cheapest energy sources and its deployment is growing rapidly around the world, an even faster growth is required to achieve existing climate goals. Besides the role that finance and permitting can play as enablers or barriers to this, the key elements to enable fast PV deployment are the use of education, and science and data-driven tools to empower citizens, installers, and investors to make their decisions based on robust scientific evidence. This perspective article aims to summarize the key concepts presented and discussed during the side event at COP27 on PV resources towards climate neutrality. The article will accomplish this by highlighting two key aspects: (1) the advantages of using solar-related education and data-driven tools, and (2) showcasing the significance of education, improved data and tools, community involvement, and PV mapping in expediting the deployment of PV systems. Full article

Seismic networks in urban areas today represent key infrastructure to better address the tasks of earthquake preparation and mitigation in the pre-event phase, and are an important knowledge tool supporting disaster risk management during seismic crises and post-disaster recovery. In the last fifteen years, a decrease in instrumentation costs and the development of new low-cost devices have enhanced the deployment of several monitoring and experimental networks worldwide. This paper conducts a review of scientific work that refer to the deployment of Urban Seismic Networks (USN) in order to define the current state of the art. We collected a list of more than one hundred USNs worldwide that were operative within the period from 1994–2023. For each USN, we report the locations and objectives along with information about the timing, coverage, geometry, and technical characteristics (sensors and transmission). By reviewing all these aspects, this paper offers important insights to provide guidelines for new implementations, bearing in mind that the interest in monitoring urban areas is expected to continue to increase in the near future driven by population growth in urbanized areas. Full article

Immune therapy for cancer patients is a new and promising area that in the future may complement traditional chemotherapy. The cell expansion phase is a critical part of the process chain to produce a large number of high-quality, genetically modified immune cells from an initial sample from the patient. Smart sensors augment the ability of the control and monitoring system of the process to react in real-time to key control parameter variations, adapt to different patient profiles, and optimize the process. The aim of the current work is to develop and calibrate smart sensors for their deployment in a real bioreactor platform, with adaptive control and monitoring for diverse patient/donor cell profiles. A set of contrasting smart sensors has been implemented and tested on automated cell expansion batch runs, which incorporate advanced data-driven machine learning and statistical techniques to detect variations and disturbances of the key system features. Furthermore, a ‘consensus’ approach is applied to the six smart sensor alerts as a confidence factor which helps the human operator identify significant events that require attention. Initial results show that the smart sensors can effectively model and track the data generated by the Aglaris FACER bioreactor, anticipate events within a 30 min time window, and mitigate perturbations in order to optimize the key performance indicators of cell quantity and quality. In quantitative terms for event detection, the consensus for sensors across batch runs demonstrated good stability: the AI-based smart sensors (Fuzzy and Weighted Aggregation) gave 88% and 86% consensus, respectively, whereas the statistically based (Stability Detector and Bollinger) gave 25% and 42% consensus, respectively, the average consensus for all six being 65%. The different results reflect the different theoretical approaches. Finally, the consensus of batch runs across sensors gave even higher stability, ranging from 57% to 98% with an average consensus of 80%. Full article

This paper aims to investigate the following research questions: (1) what are the hourly patterns of heat index and heat-related emergency medical service (EMS) incidents during summertime?; and (2) how do the lagged effects of heat intensity and hourly excess heat (HEH) vary by heat-related symptoms? Using the hourly weather and heat-related EMS call data in Austin-Travis County, Texas, this paper reveals the relationship between heat index patterns on an hourly basis and heat-related health issues and evaluates the immediate health effects of extreme heat events by utilizing a distributed lag non-linear model (DLNM). Delving into the heat index intensity and HEH, our findings suggest that higher heat intensity has immediate, short-term lagged effects on all causes of heat-related EMS incidents, including in cardiovascular, respiratory, neurological, and non-severe cases, while its relative risk (RR) varies by time. HEH also shows a short-term cumulative lagged effect within 5 h in all-cause, cardiovascular, and non-severe symptoms, while there are no statistically significant RRs found for respiratory and neurological cases in the short term. Our findings could be a reference for policymakers when devoting resources, developing extreme heat warning standards, and optimizing local EMS services, providing data-driven evidence for the effective deployment of ambulances. Full article

The exponential growth of the Internet of Things (IoT) is being witnessed nowadays in different sectors. This makes IoT data communications more complex and harder to manage. Addressing such a challenge using a centralized model is an ineffective approach and would result in security and privacy difficulties. Technologies such as blockchain provide a potential solution to enable secure and effective management of IoT data communication in a distributed and trustless manner. In this paper, a novel lightweight blockchain-centric IoT architecture is proposed to address effective IoT data communication management. It is based on an event-driven smart contract that enables manageable and trustless IoT data exchange using a simple publish/subscribe model. To maintain system complexity and overhead at a minimum, the design of the proposed system relies on a single smart contract. All the system operations that enable effective IoT data communication among the different parties of the system are defined in the smart contract. There is no direct blockchain–IoT-device interaction, making the system more useable in wide IoT deployments incorporating IoT devices with limited computing and energy resources. A practical Ethereum-based implementation of the system was developed with the ability to simulate different IoT setups. The evaluation results demonstrated the feasibility and effectiveness of the proposed architecture. Considering varying-scale and varying-density experimental setups, reliable and secure data communications were achieved with little latency and resource consumption. Full article

Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning health conditions over the last two decades, after cancer around the world. The early diagnosis of COPD, particularly of lung function degradation, together with monitoring the condition by physicians, and predicting the likelihood of exacerbation events in individual patients, remains an important challenge to overcome. The requirements for achieving scalable deployments of data-driven methods using artificial intelligence for meeting such a challenge in modern COPD healthcare have become of paramount and critical importance. In this study, we have established the experimental foundations for acquiring and indeed generating biomedical observation data, for good performance signal analysis and machine learning that will lead us to the intelligent diagnosis and monitoring of COPD conditions for individual patients. Further, we investigated on the multi-resolution analysis and compression of lung audio signals, while we performed their machine classification under two distinct experiments. These respectively refer to conditions involving (1) “Healthy” or “COPD” and (2) “Healthy”, “COPD”, or “Pneumonia” classes. Signal reconstruction with the extracted features for machine learning and testing was also performed for securing the integrity of the original audio recordings. These showed high levels of accuracy together with the performances of the selected machine learning-based classifiers using diverse metrics. Our study shows promising levels of accuracy in classifying Healthy and COPD and also Healthy, COPD, and Pneumonia conditions. Further work in this study will be imminently extended to new experiments using multi-modal sensing hardware and data fusion techniques for the development of the next generation diagnosis systems for COPD healthcare of the future. Full article

With the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms hosting IoT applications. While there are well established methods for performance analysis and testing of databases, and some for the Big data domain, such methods are still lacking support for IoT due to the complexity, heterogeneity of IoT application and their data. To overcome these limitations, in this paper, we present a novel situation-aware IoT data generation framework, namely, SA-IoTDG. Given a majority of IoT applications are event or situation driven, we leverage a situation-based approach in SA-IoTDG for generating situation-specific data relevant to the requirements of the IoT applications. SA-IoTDG includes a situation description system, a SySML model to capture IoT application requirements and a novel Markov chain-based approach that supports transition of IoT data generation based on the corresponding situations. The proposed framework will be beneficial for both researchers and IoT application developers to generate IoT data for their application and enable them to perform initial testing before the actual deployment. We demonstrate the proposed framework using a real-world example from IoT traffic monitoring. We conduct experimental evaluations to validate the ability of SA-IoTDG to generate IoT data similar to real-world data as well as enable conducting performance evaluations of IoT applications deployed on different IoT middleware platforms using the generated data. Experimental results present some promising outcomes that validate the efficacy of SA-IoTDG. Learning and lessons learnt from the results of experiments conclude the paper. Full article

The severity and incidence of extreme weather events are increasing with climate change. In particular, wildfires are becoming more frequent, more intense, and longer lasting than before. Fuelled by long periods of dryness and high temperatures, the Australian wildfires of 2019/2020 were record breaking in terms of destruction and chaos. Rural communities were severely affected by power cuts disabling access to essential services. Following the wildfires, a concept for energy resilient public buildings (“Emergency Distributed Energy System”) emerged as a grassroots community idea from the wildfire-affected area of Gippsland, southeast Australia. A combination of desktop and empirical research explored international examples of energy resilience and climate mitigation, the local services and technologies that are needed in Gippsland, and the legal and regulatory challenges and enablers in Australia. The findings were informed by case studies of responses to natural disasters that included California and Greece (wildfires), New Zealand (earthquake), and India (cyclone). The results determined that community resilience can be increased by offering a more reliable electricity supply that would support greater social, political, and economic structures. The deployment of resilient energy systems should be driven by political will, economic incentives and working with communities to support a concerted shift towards low-emissions and distributed energy technologies. Full article