Achieving sub-10 m indoor ranging with LoRaWAN is difficult because multipath, human blockage, and micro-climate dynamics induce non-stationary attenuation in received signal strength indicator (RSSI) measurements. We present a lightweight, interpretable pipeline that couples an environment-aware multi-wall path loss model with a forward-only, innovation-driven Kalman prefilter for RSSI. The model augments distance and wall terms with frequency, signal-to-noise ratio (SNR), and co-located environmental covariates (temperature, relative humidity, carbon dioxide, particulate matter, and barometric pressure), and is inverted deterministically for distance estimation. On a one-year single-gateway office dataset comprising over 2 million uplinks, the approach attains a mean absolute error (MAE) of 4.74 m and a root mean square error (RMSE) of 6.76 m in distance estimation, improving over a COST-231 multi-wall baseline (12.07 m MAE) and its environment-augmented variant (7.76 m MAE. Filtering reduces RSSI volatility from 10.33 to 5.43 dB and halves path loss error to 5.35 dB while raising R-squared from 0.82 to 0.89. The result is a single-anchor LoRaWAN ranging method with constant per-packet cost that is accurate, robust, and interpretable, providing a strong building block for multi-gateway localization.

Accurate localization is essential for a wide range of applications, including asset tracking, smart agriculture, and animal monitoring. While traditional localization methods, such as Global Navigation Satellite System (GNSS), Wi-Fi, and Bluetooth Low Energy (BLE), offer varying levels of accuracy and coverage, they have drawbacks regarding power consumption, infrastructure requirements, and deployment flexibility. Ultra-Wideband (UWB) is emerging as an alternative, offering centimeter-level accuracy and energy efficiency, especially suitable for medium to large field monitoring with capabilities to work indoors and outdoors. However, existing UWB localization systems require infrastructure with mains power to supply the anchors, which impedes their scalability and ease of deployment. This underscores the need for a fully battery-powered and energy-efficient localization system. This paper presents an energy-optimized, battery-operated UWB localization system that leverages Long Range Wide Area Network (LoRaWAN) for data transmission to a server backend. By employing single-sided two-way ranging (SS-TWR) in a time-slotted localization approach, the power consumption both on the anchor and the tag is reduced, while maintaining high accuracy. With a low average power consumption of 20.44 mW per anchor and 7.19 mW per tag, the system allows fully battery-powered operation for up to 25 days, achieving average accuracy of 13.96 cm with self-localizing anchors on a 600 m2 testing ground. To validate its effectiveness and ease of installation in a challenging application scenario, ten anchors and two tags were successfully deployed in a tropical zoological biome where they could be used to track Aldabra Giant Tortoises (Aldabrachelys gigantea).

--Water distribution systems in rural areas face serious challenges such as a lack of real-time monitoring, vulnerability to cyberattacks, and unreliable data handling. This paper presents an integrated framework that combines LoRaW ANbased data acquisition, a machine learning-driven Intrusion Detection System (IDS), and a blockchain-enabled Digital Twin (BC-DT) platform for secure and transparent water management. The IDS filters anomalous or spoofed data using a Long Short-T erm Memory (LSTM) Autoencoder and Isolation Forest before validated data is logged via smart contracts on a private Ethereum blockchain using Proof of Authority (PoA) consensus. The verified data feeds into a real-time DT model supporting leak detection, consumption forecasting, and predictive maintenance. Experimental results demonstrate that the system achieves over 80 transactions per second (TPS) with under 2 seconds of latency while remaining cost-effective and scalable for up to 1,000 smart meters. This work demonstrates a practical and secure architecture for decentralized water infrastructure in under-connected rural environments. While remote-sensing techniques have proven valuable for water quality monitoring [1], [2], regarding water distribution, efficient distribution is a significant issue in rural regions, especially where infrastructure is poor and digital monitoring is scarce.

Although the LoRaW AN network can support a larger node scale than the LoRa private network, as the number of devices increases, the performance of the LoRaW AN network in terms of network congestion and energy consumption faces significant challenges. The limited spectrum resources and channel congestion will lead to a decrease in the communication efficiency of the netwo rk, which in turn affects the reliability of data transmission. How to achieve efficient and energy - saving resource allocation while ensuring network performance remains a key issue. In order to improve the overall performance of the LoRaW AN network, optim izing the transmission strategy parameters such as the spreading factor, transmit power, and receive window of the uplink and downlink is considered to be an effective means. By reasonably configuring these parameters, network conflicts can be effectively reduced, signal attenuation can be reduced, and signal coverage can be increased, thereby improving network reliability and communication quality. However, most of the existing optimization methods focus on the adjustment of the spreading factor and transm it power of the uplink, and rarely consider the impact of the downlink on network performance. To address this problem, this chapter proposes a History - E nhanced t wo - phase Actor - Critic a lgorithm with a s hared Transformer (HEA T), which aims to improve the resource allocation strategy of the LoRaW AN network and improve the overall performance of the network. This chapter conducts multiple sets of comparative experiments between HEA T and various popular methods under different device densities and traffic int ensities to verify the effectiveness of HEA T. 2 System Model and Problem Representation In order to efficiently verify the effectiveness of various LoRaW AN resource allocation strategies, this section describes and models the LoRa link behavior and the LoRaW AN standard in detail. Subsequently, this section proposes the target problem of LoRaW AN resource allocation and expresses the target problem as a Markov decision process.

For large-scale multi-gateway LoRaWAN networks, this study proposes a cloud-edge collaborative resource allocation and decision-making method based on edge intelligence, HEAT-LDL (HEAT-Local Distill Lyapunov), which realizes collaborative decision-making between gateways and terminal nodes. HEAT-LDL combines the Actor-Critic architecture and the Lyapunov optimization method to achieve intelligent downlink control and gateway load balancing. When the signal quality is good, the network server uses the HEAT algorithm to schedule the terminal nodes. To improve the efficiency of autonomous decision-making of terminal nodes, HEAT-LDL performs cloud-edge knowledge distillation on the HEAT teacher model on the terminal node side. When the downlink decision instruction is lost, the terminal node uses the student model and the edge decider based on prior knowledge and local history to make collaborative autonomous decisions. Simulation experiments show that compared with the optimal results of all compared algorithms, HEAT-LDL improves the packet success rate and energy efficiency by 20.5% and 88.1%, respectively.

--The use of fingerprinting localization techniques in outdoor IoT settings has started to gain popularity over the recent years. Communication signals of Low Power Wide Area Networks (LPW AN), such as LoRaW AN, are used to estimate the location of low power mobile devices. In this study, a publicly available dataset of LoRaW AN RSSI measurements is utilized to compare different machine learning methods and their accuracy in producing location estimates. The tested methods are: the k Nearest Neighbours method, the Extra Trees method and a neural network approach using a Multilayer Perceptron. T o facilitate the reproducibility of tests and the comparability of results, the code and the train/validation/test split of the dataset used in this study have become available. The neural network approach was the method with the highest accuracy, achieving a mean error of 358 meters and a median error of 204 meters. The proliferation of the usage of Internet-of-Things (IoT) technologies and Low Power Wide Area Networks (LPW AN), such as LoRaW AN or Sigfox, over the last decade has created a new landscape in the field of outdoor localization. Low power devices of LPW ANs cannot afford the battery consumption of a chip-set of a Global Navigation Satellite System (GNSS), such as the GPS. Therefore, an alternative approach is needed in order to localize these low power devices. The devices communicate with fixed basestations deployed in urban and rural areas through RF messages.