The tropical area has a large area of peatland, which is an important ecosystem that is regarded as home by millions of people, plants and animals. However, the dried-up and degraded peatland becomes extremely easy to burn, and in case of fire, it will further release transboundary haze. In order to protect the peatland, an improved tropical peatland fire weather index (FWI) system is proposed by combining the ground water level (GWL) with the drought code (DC). In this paper, LoRa based IoT system for peatland management and detection was deployed in Raja Musa Forest Reserve (RMFR) in Kuala Selangor, Malaysia. Then, feasibility of data collection by the IoT system was verified by comparing the correlation between the data obtained by the IoT system and the data from Malaysian Meteorological Department (METMalaysia). An improved model was proposed to apply the ground water level (GWL) for Fire Weather Index (FWI) formulation in Fire Danger Rating System (FDRS). Specifically, Drought Code (DC) is formulated using GWL, instead of temperature and rain in the existing model. From the GWL aggregated from the IoT system, the parameter is predicted using machine learning based on a neural network. The results show that the data monitored by the IoT system has a high correlation of 0.8 with the data released by METMalaysia, and the Mean Squared Error (MSE) between the predicted and real values of the ground water level of the two sensor nodes deployed through neural network machine learning are 0.43 and 12.7 respectively. This finding reveals the importance and feasibility of the ground water level used in the prediction of the tropical peatland fire weather index system, which can be used to the maximum extent to help predict and reduce the fire risk of tropical peatland.

5G is the next-generation mobile communication technology expected to deliver more excellent data rates than Long Term Evolution (LTE). It offers ultra-low latency and ultra-high dependability, allowing revolutionary services across sectors. 5G mm-Wave base stations may emit harmful radiofrequency electromagnetic fields (RF-EMF). However, RF-EMF concern raises health and safety. From our research, the RF-EMF prediction model lacks papers or publications. This study uses IEEE and ICNIRP standards for assessment and exposure limits. The measuring campaign analyses one sector of a 5G base station (5G-BS) operation on 29.5 GHz in Cyberjaya, Malaysia. This study proposes two prediction models. The first model predicts signal beam RF-EMF, and the second predicts base station RF-EMF. Each model contains three neural network techniques to forecast RF-EMF values: Approximate-RBFNN, Exact-RBFNN, and GRNN. The results are analysed and compared with the measured data, observing which algorithm is more accurate by calculating the RMSE of each algorithm. As a result, it can observe that the Exact-RBFNN algorithm is the best algorithm to predict the RF-EMF because it shows a good agreement with the measured value. Besides that, in 1-min duration, the difference between predicted and measured values reached 0.2 less channels. However, in 6-min and 30-min, it can observe more accurate results since the differences between values reached 0.1in the situations. In addition, the ICNIRP standard was used and compared with the result and validation value of the algorithms.

The occurrence of fires in tropical peatlands poses significant threats to their ecosystems. An Internet of Things (IoT) system was developed to measure and collect fire risk factors in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, to address this issue. In this paper, neural networks with different layers were employed to predict peatland forests’ Fire Weather Index (FWI). The neural network models used two sets of input parameters, consisting of four and nine fire factors. The predicted FWI values were compared with actual values obtained from the Malaysian meteorological department. The findings revealed that the five-layer neural network outperformed others in both the four-input and nine-input models. Specifically, the nine-input neural network achieved a mean square error (MSE) of 1.116 and a correlation of 0.890, surpassing the performance of the four-input neural network with the MSE of 1.537 and the correlation of 0.852. These results hold significant research and practical implications for precise peatland fire prevention, control, and the formulation of preventive measures.

The increasing urban population and the growing preference for private transportation have led to a significant rise in vehicle numbers, exacerbating traffic congestion and parking challenges. Cruising for parking not only consumes time and fuel but also contributes to environmental and energy inefficiencies. Smart parking systems have emerged as essential solutions to these issues, addressing everyday urban challenges and enabling the development of smart, sustainable cities. By reducing traffic congestion and streamlining parking processes, these systems promote eco-friendly and efficient urban transportation. This paper introduces a provenance-based smart parking system leveraging fog computing to enhance real-time parking space management and resource allocation. The proposed system employs a hierarchical fog architecture, with four layers architecture nodes for efficient data storage, transfer, and resource utilisation. The provenance component empowers users with real-time insights into parking availability, facilitating informed decision-making. Simulations conducted using the iFogSim2 toolkit evaluated the system across key metrics, including end-to-end latency, execution cost, execution time, network usage, and energy consumption in both fog and cloud-based environments. A comparative analysis demonstrates that the fog-based approach significantly outperforms its cloud-based counterpart in terms of efficiency and responsiveness. Additionally, the system minimises network usage and optimises space utilisation, reducing the need for parking area expansion. A real-world case study from SDU University Park validated the proposed system, showcasing its effectiveness in managing parking spaces, particularly during peak hours.

Peatland poses a severe environmental threat due to its potential for massive carbon emission during fires. Conventional Ground Water Level (GWL) monitoring in peatlands is labor-intensive and lacks real-time data, hindering effective management. To address this, this paper proposed an IoT system with neural network-based GWL prediction for real-time monitoring. By using atmospheric parameters, the neural network predicts GWL, allowing extra time for the responsible party to take the appropriate action to reduce the fire risk in peatland. The proposed neural network demonstrates promising results, with a Root Mean Square Error (RMSE) between 3.554 and 4.920, ensuring 99% accuracy within 14.760 mm range of the actual GWL. This finding underscores the novel approach of integrating IoT and neural networks for peatland GWL prediction, offering a significant advancement in real-time monitoring and fire risk mitigation strategies. The novelty lies in its capability to predict real-time GWL even in areas lacking the resources for conventional monitoring, using simple meteorological parameters.

This paper presents a theoretical overview of peatland forest fire management, focusing on the integration of IoT-based monitoring systems and Fire Weather Index (FWI) for improved fire prediction and mitigation. The paper examines the impact of wildfires on carbon loss in Southeast Asian peatland forests and reviews existing IoT solutions for monitoring environmental conditions, such as temperature, humidity, and soil moisture. Furthermore, the paper surveys the current advancements in FWI systems, highlighting the role of machine learning in improving the accuracy of fire risk predictions by analyzing large environmental datasets. The research discusses challenges related to integrating IoT with FWI systems and considers critical factors such as topography, altitude, and human activities. By synthesizing existing literature, this paper aims to outline the current state of research and propose directions for future work to enhance fire risk prediction and management in peatland ecosystems.