ABSTRACT Internet of things (IoT)‐based monitoring in aquaculture presents significant challenges in remote, off‐grid environments where conventional power infrastructure is unavailable. Reliable, continuous and environmentally sustainable energy sources are essential for ensuring autonomous sensor operation and data collection integrity. This study proposes a hybrid energy harvesting system that integrates photovoltaic (PV) panels, lithium‐ion batteries and Microbial Fuel Cells (MFCs) as a novel bioelectrochemical energy source for distributed aquaculture monitoring networks. The MFC subsystem (0.01 L flat‐plate configuration, operating at 4.7 k optimal resistance with optimal generated power up to 6.657 nW) generates bioelectrochemical power for parallel battery supplementation. Experimental evaluation under realistic IoT load conditions demonstrates that the bioelectrochemical‐augmented hybrid system successfully maintains stable autonomous operation while reducing lithium battery discharge burden by 6.1% throughout complete discharge cycles directly extending battery operational lifespan. This reduction decreases electronic waste, minimises carbon footprint through extended battery lifecycle and reduces pollution and environmental burden. This work demonstrates that bioelectrochemical energy integration is technically viable for aquaculture monitoring and similar off‐grid IoT applications, establishing MFCs as a sustainable alternative energy source for greener and more resilient remote sensing infrastructure.
The proliferation of constrained Internet of Things (IoT) devices requires scalable over-the-air (OTA) firmware update mechanisms. Conventional approaches, predominantly based on one-to-one communication, such as Bluetooth Low Energy (BLE) Device Firmware Update (DFU), suffer from a scalability bottleneck that makes fleet-wide updates inefficient and energy-intensive. To address this limitation, this paper proposes FragCast, a broadcast-based OTA dissemination protocol that leverages the one-to-many communication paradigm of BLE advertising channels. The proposed solution implements an application-layer protocol that transports firmware fragments within custom-formatted BLE advertising packets. A detailed simulation framework was developed to analyse the protocol performance under different scanning profiles and fleet sizes of up to 100 devices. The proposed mechanism was also experimentally validated on Arduino UNO R4 Wi-Fi boards equipped with ESP32-S3 modules. Results show that the broadcast architecture enables efficient fleet-wide dissemination, with discovery latency remaining nearly constant as the number of receivers increases. In the evaluated configuration, a fleet of 100 devices can be updated in approximately 40 s using the Aggressive scanning profile and about 90 s using an energy-efficient iOS-like profile. These findings demonstrate that BLE advertising provides a practical and scalable foundation for OTA firmware dissemination in large fleets of constrained IoT devices.
ABSTRACT The Internet of Medical Things (IoMT) is an emerging field that has attracted significant attention in recent years. In this context, communication technology for reduced‐capacity devices has assumed a pivotal role. This research contributes to the IoMT literature by presenting a novel monitoring framework that integrates short‐ and long‐range communication systems to monitor neurodegenerative disease‐affected individuals. A further contribution is the validation of the proposed framework via proof of concept (PoC) implementations based on low‐cost commercial devices. Specifically, the system incorporates a small‐size wearable device equipped with Global Navigation Satellite System (GNSS) technology for precise geolocation, and a Bluetooth Low Energy (BLE) sensor for collecting vital health parameters. Utilising long range (LoRa) communication technology, the device relays both location and sensor data to the cloud, where they undergo processing. The cloud‐based application manages two critical functionalities. Firstly, it incorporates a geofencing capability that triggers alerts via an instant messaging service when monitored individuals cross predefined safe boundaries. Secondly, it has a visualisation web interface that displays both historical and real‐time location and health data. Field tests have been conducted in order to demonstrate the system's effectiveness in terms of GNSS accuracy, long‐range communication signal strength and alerting latencies.
ABSTRACT Wireless Sensor Networks (WSNs) enable real‐time monitoring in precision agriculture but are constrained by limited node energy. The LEACH protocol mitigates this through clustering but suffers from uneven energy depletion due to static sink placement. This paper proposes an Enhanced LEACH protocol incorporating a UAV‐based mobile sink following a circular trajectory to reduce communication distance. MATLAB simulations with 100 nodes over 5000 rounds show that the proposed method improves early network stability by 65%, increases packet delivery by 22%, and enhances energy efficiency by 38% compared with standard LEACH. Although a slight reduction in packet delivery ratio is observed, overall throughput and network lifetime are significantly improved. The proposed approach provides a computationally efficient and scalable solution for UAV‐assisted wireless sensor networks in precision agriculture.
ABSTRACT Wireless sensor networks (WSNs) are essential for modern data‐acquisition applications, but their long‐term operation is severely limited by the energy capacity of individual sensor nodes. Clustering algorithms are a key strategy for increasing energy efficiency. However, traditional algorithms, such as k‐means and low‐energy adaptive clustering hierarchy (LEACH), frequently ignore data redundancy, resulting in unnecessary energy consumption. We propose info‐kmeans, an information‐aware protocol that saves energy by dynamically scheduling sensor activity. Info‐kmeans quantifies the informational value of a node's data by combining historical readings and conditional entropy, relying on key parameters such as a historical sliding window, the number of spatial neighbours, data discretisation bins and a tunable information threshold. If the data are deemed predictable and redundant based on these parameters, the node is temporarily deactivated, saving significant energy. We evaluated info‐kmeans in a custom simulation platform against k‐means and LEACH. We demonstrate that info‐kmeans increases network lifetime by approximately twofold. Critically, it reduces cumulative reconstruction error by 15%–25% when compared to traditional clustering, demonstrating its ability to save energy whilst maintaining data integrity.
