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This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design's innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis' residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy. [ABSTRACT FROM AUTHOR]

This paper presents the design, implementation, and operation of a novel distributed fault-tolerant middleware. It uses interconnected WSNs that implement the Map-Reduce paradigm, consisting of several low-cost and low-power mini-computers (Raspberry Pi). Specifically, we explain the steps for the development of a novice, fault-tolerant Map-Reduce algorithm which achieves high system availability, focusing on network connectivity. Finally, we showcase the use of the proposed system based on simulated data for crowd monitoring in a real case scenario, i.e. a historical building in Greece (M. Hatzidakis' residence). The technical novelty of this article lies in presenting a viable low-cost and low-power solution for crowd sensing without using complex and resource-intensive AI structures or image/video recognition techniques. [ABSTRACT FROM AUTHOR]

This article presents a Wireless Sensor Network (WSN) system, developed to monitor concealed box‐shaped underground passages to protect wildlife. The system's purpose is to recognize the species of passengers (ie, human, canis, flying species, and so on) without using video sensors (image recognition) through data provided by sensors that measure distance and weight. Moreover, the information provided by the WSN was analyzed via a variety of methods, including a neural pattern recognition network, as well as clustering algorithms, which were able to recognize the species of passengers with certainty scores of over 90%. The main concern for future study is the evaluation of these passages, which are frequently located along main highways, and using data fusion to evaluate low‐cost and low‐power microsensors regarding their effectiveness, that is, whether they are frequently utilized by animals. [ABSTRACT FROM AUTHOR]

Waste generated by health care activities includes a broad range of materials, from used needles and syringes to soiled dressings, body parts, diagnostic samples, medical devices and radioactive materials. As a result of poor practice and large volumes, only a very small percentage of medical waste is actually disposed of properly in final reception units while the rest is unaccounted for potentially exposing the community at large to infection, toxic effects and injuries, and risking polluting the environment. This paper discusses, Greenactions, a novel Pervasive Computing system for the remote end-to-end management of hazardous medical waste. Greenactions provides real-time trace-ability for 100% of medical waste, by continually monitoring the full life cycle of each waste container, from their delivery to the hospitals, through to their collection and disposal, and providing remedial action in real-time, whenever an incident occurs. This is achieved by employing both fixed and handheld RFID and sensor technology, supported by a state-machine model that knows at any time the current and next state of each waste container. Deployed together with a small fleet of appropriately modified vehicles for waste collection, Greenactions provides an integrated solution can be applied in any waste collection and tracking scenario, without requiring any costly, proprietary infrastructure thus alleviating the burden of medical waste management from health-care units. A prototype system was developed using open source technology that is ready to be deployed to pilot healthcare units in Athens, while a set of KPIs were implemented for evaluating the efficiency of the system. [ABSTRACT FROM AUTHOR]

In previous work, we introduced a novel concept of a generalised event, an abstract event, which we define as a change of state of abstract predicates that represent knowledge about the surrounding world. Abstract predicates are defined by formulae in temporal first-order logic (Abstract Event Specification Language (AESL)) whose leaf predicates represent low-level sensor-derived knowledge. Abstract events are detected by Rete Networks structured as a deductive knowledge-base. Current Abstract Event detectors cannot express sufficiently well certain high-level situations, such activity derived from user trajectories. In this work we introduce a novel type of abstract event detector, a hidden Markov Model detector (hMM-detector). hMM-detectors are implemented as pattern recognition engines that use several stochastic models, hidden Markov Models (hMMs), in order to classify observed activities to the most likely activity class. We link hMM-detectors with AESL by specifying a new AESL operator for defining hMM-based Abstract Events, thus increasing AESL's expressive power. We describe the experimental evaluation of the above work that was carried out at the University of Cambridge. hMM-Detectors were trained and tested with real data from the Active BAT location system. We evaluate the expressiveness of the enhanced AESL by discussing three case studies in healthcare that relate to continuous monitoring of elderly or injured patients. We demonstrate that AESL can be used in order to improve the dependability of continuous monitoring of patients and the provision of high-quality healthcare. [ABSTRACT FROM AUTHOR]

Purpose – The event-driven paradigm is appropriate for context aware, distributed applications, yet basic events may be too low level to be meaningful to users. The authors aim to argue that this bottom-up approach is insufficient to handle very low-level sensor data or to express all the queries users might wish to make. Design/methodology/approach – The authors propose an alternative model for querying and subscribing transparently to distributed state in a real-time, ubiquitous, sensor-driven environment such as is found in Sentient Computing. Findings – The framework consists of four components: a state-based, temporal first-order logic (TFOL) model that represents the concrete state of the world, as perceived by sensors; an expressive TFOL-based language, the Abstract Event Specification Language (AESL) for creating abstract event definitions, subscriptions and queries; a higherorder service (Abstract Event Detection Service) that accepts a subscription containing an abstract event definition as an argument and in return publishes an interface to a further service, an abstract event detector; and a satisfiability service that applies classical, logical satisfiability in order to check the satisfiability of the AESL definitions against the world model, in a manner similar to a constraint-satisfaction problem. Originality/value – The paper develops a model-based approach, appropriate for distributed, heterogeneous environments. [ABSTRACT FROM AUTHOR]

Presence sensing systems are gaining importance and are utilized in various contexts such as smart homes, Ambient Assisted Living (AAL) and surveillance technology. Typically, these systems utilize motion sensors or cameras that have a limited field of view, leading to potential monitoring gaps within a room. However, humans release carbon dioxide (CO 2 ) through respiration which spreads within an enclosed space. Consequently, an observable rise in CO 2 concentration is noted when one or more individuals are present in a room. This study examines an approach to detect the presence or absence of individuals indoors by analyzing the ambient air's CO 2 concentration using simple Markov Chain Models. The proposed scheme achieved an accuracy of up to 97% in both experimental and real data demonstrating its efficacy in practical scenarios., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Karasoulas, Keroglou, Katsiri and Sirakoulis.)