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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]

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]