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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.

Ethernet is one of the widespread protocols residing in the second layer of the seven-layers Open Systems Interconnection (OSI) model. Ethernet offers various advantages which enable its widespread use in all types of network topology and becomes an essential part of computer and network architecture. Despite its features, Ethernet suffers from scalability issues where the increasing number of hosts in a single broadcast domain will significantly expand the broadcast traffic in the network. Since the emergence of software-defined networking (SDN), researchers exploited various attractive features of SDN to suppress the broadcast traffic. Although capable in addressing the scalability issue of Ethernet, the existing SDN based solutions are lacking of security mechanism, which may expose the network to various ARP based attacks. Owing to this issue, this paper proposes a floodless and secure mechanism to suppress broadcast traffic. In general, the proposed solution utilizes SDN architecture and accommodates a multistage security algorithm. The multistage security algorithm consists of three stages; each stage incorporates specific analysis to identify the packet status or behavior, and react accordingly based on its status. To demonstrate the efficiency of the proposed solution, several ARP based attack scenarios are generated and evaluated using Mininet emulator. The performance evaluation indicates that the true positive ratio for attack detection in the proposed solution is 57.14% for the first stage, 66.66% for the second stage, and in some cases may achieve 100% for the final stage.

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.

The IEEE 802.11ah standard relies on the conventional distributed coordination function (DCF) as a backoff selection method. The DCF is utilized in the contention-based period of the newly introduced medium access control (MAC) mechanism, namely restricted access window (RAW). Despite various advantages of RAW, DCF still utilizes the legacy binary exponential backoff (BEB) algorithm, which suffers from a crucial disadvantage of being prone to high probability of collisions with high number of contending stations. To mitigate this issue, this paper investigates the possibility of replacing the existing exponential sequence (i.e., as in BEB) with a better pseudorandom sequence of integers. In particular, a new backoff algorithm, namely Pseudorandom Sequence Contention Algorithm (PRSCA) is proposed to update the CW size and minimize the collision probability. In addition, the proposed PRSCA incorporates a different approach of CW freezing mechanism and backoff stage reset process. An analytical model is derived for the proposed PRSCA and presented through a discrete 2-D Markov chain model. Performance evaluation demonstrates the efficiency of the proposed PRSCA in reducing collision probability and improving saturation throughput, network throughput, and access delay performance.

With the rapid development of the mobile internet and the internet of things (IoT), the fifth generation (5G) mobile communication system is seeing explosive growth in data traffic. In addition, low-frequency spectrum resources are becoming increasingly scarce and there is now an urgent need to switch to higher frequency bands. Millimeter wave (mmWave) technology has several outstanding features--it is one of the most well-known 5G technologies and has the capacity to fulfil many of the requirements of future wireless networks. Importantly, it has an abundant resource spectrum, which can significantly increase the communication rate of amobile communication system. As such, it is now considered a key technology for future mobile communications. MmWave communication technology also has a more open network architecture; it can deliver varied services and be applied inmany scenarios. By contrast, traditional, all-digital precoding systems have the drawbacks of high computational complexity and higher power consumption. This paper examines the implementation of a new hybrid precoding system that significantly reduces both calculational complexity and energy consumption. The primary idea is to generate several sub-channels with equal gain by dividing the channel by the geometric mean decomposition (GMD). In this process, the objective function of the spectral efficiency is derived, then the basic tracking principle and least square (LS) techniques are deployed to design the proposed hybrid precoding. Simulation results show that the proposed algorithm significantly improves system performance and reduces computational complexity by more than 45% compared to traditional algorithms. [ABSTRACT FROM AUTHOR]