Palm oil is the main cash crop of tropical Asia, and the implementation of LPWAN (low-power wide-area network) technologies for smart agriculture applications in palm oil plantations will benefit the palm oil industry in terms of making more revenue. This research attempts to characterize the LoRa 433 MHz frequency channels for the available spreading factors (SF7-SF12) and bandwidths (125 kHz, 250 kHz, and 500 kHz) for wireless sensor networks. The LoRa channel modeling in terms of path-loss calculation uses empirical measurements of RSS (received signal strength) in a palm oil plantation located in Selangor, Malaysia. In this research, about 1500 LoS (line-of-sight) and 300 NLoS (non-line-of-sight) propagation measurement data are collected for path-loss prediction modeling. Using the empirical data, a prediction model is constructed. The path-loss exponent for LoS propagation of the proposed prediction model is found to be 2.34 and 2.9 for 125–250 kHz bandwidth and 500 kHz bandwidth, respectively. Again, for the NLoS propagation links, the attenuation per trunk is found to be 7.58 dB, 7.04 dB, 5.35 dB, 5.02 dB, 5.01 dB, and 5 dB for SF7-SF12, and the attenuation per canopy is found to be 9.32 dB, 7.96 dB, 6.2 dB, 5.89 dB, 5.79 dB, and 5.45 dB for SF7-SF12. Moreover, the prediction model is found to be the better choice (mean RMSE 2.74 dB) in comparison to the empirical foliage loss models (Weissberger’s and ITU-R) to predict the path loss in palm oil plantations.

In Wireless Sensor Networks which are deployed in remote and isolated tropical areas; such as forest; jungle; and open dirt road environments; wireless communications usually suffer heavily because of the environmental effects on vegetation; terrain; low antenna height; and distance. Therefore; to solve this problem; the Wireless Sensor Network communication links must be designed for their best performance using the suitable electromagnetic wave behavior model in a given environment. This study introduces and analyzes the behavior of the LoRa pathloss propagation model for signals that propagate at near ground or that have low transmitter and receiver antenna heights from the ground (less than 30 cm antenna height). Using RMSE and MAE statistical analysis tools; we validate the developed model results. The developed Fuzzy ANFIS model achieves the lowest RMSE score of 0.88 at 433 MHz and the lowest MAE score of 1.61 at 433 MHz for both open dirt road environments. The Optimized FITU-R Near Ground model achieved the lowest RMSE score of 4.08 at 868 MHz for the forest environment and lowest MAE score of 14.84 at 868 MHz for the open dirt road environment. The Okumura-Hata model achieved the lowest RMSE score of 6.32 at 868 MHz and the lowest MAE score of 26.12 at 868 MHz for both forest environments. Finally; the ITU-R Maximum Attenuation Free Space model achieved the lowest RMSE score of 9.58 at 868 MHz for the forest environment and the lowest MAE score of 38.48 at 868 MHz for the jungle environment. These values indicate that the proposed Fuzzy ANFIS pathloss model has the best performance in near ground propagation for all environments compared to other benchmark models.

The novel coronavirus pandemic has brought our concern towards the necessity of digital infrastructure for remote monitoring of infected people as well as the people who are with close contact with them. Though, many nations have finished the clinical trials period of vaccination and started vaccination to people, still there are many uncertainties such as how these vaccines will work to the people with severe illness, which vaccine is more effective compared to others etc. Moreover, vaccination may not provide a full protection against the current situation since there are new variants of corona virus and the number of affected people increasing again. Therefore, a proper diseases detection and individual monitoring system to maintain a smart quarantine facility is still very much needed to minimise the rapid spread of covid-19 and other contagious virus. Here, wearable tools could play important roles to provide valuable information of tracking people such as real-time remote monitoring, contact tracing symptoms prediction etc. to establish and maintain a proper quarantine system. In this manuscript we proposed a geofencing system with a wristband, integrating it with IOT and GPS to trace Covid-19 patients inside a boundary. This design could be an effective quarantine solution as a home isolation system for covid-19 patients as well as the travellers who cross borders. The performance of our proposed system was measured with respect to received signal strength indication (RSSI), GPS accuracy and coverage.

Surface and groundwater resources are two important sources in meeting agricultural, urban, and industrial needs. Random supply of surface water resources has prevented these resources from being a reliable source of water supply at all times. Therefore, groundwater acts as insurance in case of water shortage, and maintaining the quality of these resources is very important. On the other hand, studying vulnerability and identifying areas prone to aquifer pollution seems necessary for the development and optimal management of these valuable resources. Identifying the vulnerabilities of the aquifer areas to pollution will lead to a greater focus on preserving those areas. Therefore, groundwater quality assessment was performed in this study using the groundwater quality index (GQI), and groundwater vulnerability to pollution was assessed using the DRASTIC index. GQI is developed based on the values of six quality parameters (Na +, Mg 2+, Ca 2+, SO 42–, Cl–, and TDS). The DRASTIC index is developed based on the values of seven parameters (depth to the water table, net recharge, aquifer media, soil media, topography, impact of vadose zone, hydraulic conductivity). The zoning of both indexes has been done using geographic information system (GIS) software. The results show that the GQI of the region was about 93, and its DRASTIC index was about 86. Therefore, the quality of aquifer groundwater is excellent, and its vulnerability to pollution is low.

In this paper a modified mathematical model based on the SIR model used which can predict the spreading of the corona virus disease (COVID-19) and its effects on people in the days ahead. This model considers all the death, infected and recovered characteristics of this disease. To determine the extent of the risk posed by this novel coronavirus; the transmission rate (R₀) is utilized for a time period from the beginning of spreading virus. Particularly it includes a novel policy to capture the Ro response in the virus spreading over time. The model estimates the vulnerability of the pandemic according to John H. Cochrane’s method with a prediction of new cases by estimating a time-varying R₀ to capture changes in the behavior of SIR model implies to new policy taken at different times and different locations of the world. This modified SIR model with the different values of R₀ can be applied to different country scenario using the real time data report provided by the authorities during this pandemic. The effective evaluation of R₀ can forecast the necessity of lockdown as well as reopening the economy.

Statement of Problems Smart farming is a revolutionary concept of modern science that denotes conducting farming activities using smart devices such as, IoT, robotics, drones, and AI. It can increase the quantity and quality of products significantly while optimizing the human labour. This paper explores different legal aspects of smart farming from the viewpoint of both the farm owner and the service provider. The purpose of this study is to analyse the legal issues and challenges arising out of smart farming and to suggest some recommendations to mitigate these issues. The study uses doctrinal legal research methodology followed by exploratory and analytical approach. Both primary and secondary sources are considered for identifying and interpreting data. The study finds that without specific, dedicated, and comprehensive legal regime it is impossible to govern the legal aspects of smart farming for any country. Therefore, it is recommended that every country needs a comprehensive legal regime to mitigate the legal issues and challenges of smart farming son that optimum benefit from smart farming can be attained and at the same time farm owners and service providers can be legally protected.