Enhanced source location privacy and prolonged network lifetime are imperative for WSNs-the skin of IoT. To address these issues, a novel technique named source location privacy with enhanced privacy ...and network lifetime (SLP-E) is proposed. It employs a reverse random walk followed by a walk on annular rings, to create divergent routing paths in the network, and finally, min-hop routing together with the walk on dynamic rings to send the packets to the base station (BS). The existing random walk-based SLP approaches have either focused on enhancing only privacy at the cost of network lifetime (NLT) or have aimed at improving the amount of privacy without degrading the network lifetime performance. Unlike these schemes, the objectives of the proposed work are to simultaneously improve the safety period and network lifetime along with achieving uniform privacy. This combination of improvements has not been considered so far in a single SLP random walk-based scheme. Additionally, this study investigates for the first time the impact of the sensors' radio range on both privacy strength and network lifetime metrics in the context of SLP within WSNs. The performance measurements conducted using the proposed analytical models and the simulation results indicate an improvement in the safety period and network lifespan. The safety period in SLP-E increased by 26.5%, 97%, 123%, and 15.7% when compared with SLP-R, SRR, PRLPRW, and PSSLP techniques, respectively. Similarly, the network lifetime of SLP-E increased by 17.36%, 0.2%, 83.41%, and 13.42% when compared with SLP-R, SRR, PRLPRW, and PSSLP techniques, respectively. No matter where a source node is located within a network, the SLP-E provides uniform and improved privacy and network lifetime. Further, the simulation results demonstrate that the sensors' radio range has an impact on the safety period, capture ratio, and the network lifetime.
The application of the Internet of Things (IoT), vehicles to infrastructure (V2I) communication and intelligent roadside units (RSU) are promising paradigms to improve road traffic safety. However, ...for the RSUs to communicate with the vehicles and transmit the data to the remote location, RSUs require enough power and good network quality. Recent advances in technology have improved lithium-ion battery capabilities. However, other complementary methodologies including battery management systems (BMS) have to be developed to provide an early warning sign of the battery's state of health. In this paper, we have evaluated the impact of the received signal strength indication (RSSI) and the current consumption at different transmission frequencies on a static battery-based RSU that depends on the global system for mobile communications (GSM)/general packet radio services (GPRS). Machine learning (ML) models, for instance, Random Forest (RF) and Support Vector Machine (SVM), were employed and tested on the collected data and later compared using the coefficient of determination (R2). The models were used to predict the battery current consumption based on the RSSI of the location where the RSUs were imposed and the frequency at which the RSU transmits the data to the remote database. The RF was preferable to SVM for predicting current consumption with an R2 of 98% and 94%, respectively. It is essential to accurately forecast the battery health of RSUs to assess their dependability and running time. The primary duty of the BMS is to estimate the status of the battery and its dynamic operating limits. However, achieving an accurate and robust battery state of charge remains a significant challenge. Referring to that can help road managers make alternative decisions, such as replacing the battery before the RSU power source gets drained. The proposed method can be deployed in other remote WSN and IoT-based applications.
Fire monitoring in local urban markets within East Africa (EA) has been seriously neglected for a long time. This has culminated in a severe destruction of life and property worth millions. These ...rampant fires are attributed to electrical short circuits, fuel spillages, etc. Previous research proposes single smoke detectors. However, they are prone to false alarm rates and are inefficient. Also, satellite systems are expensive for developing countries. This paper presents a fuzzy model for early fire detection and control as symmetry’s core contribution to fuzzy systems design and application in computer and engineering sciences. We utilize a fuzzy logic technique to simulate the performance of the model using MATLAB, using six parameters: temperature, humidity, flame, CO, CO2 and O2 vis-à-vis the Estimated Fire Intensity Prediction (EFIP). Results show that, using fuzzy logic, a significant improvement in fire detection is observed with an overall accuracy rate of 95.83%. The paper further proposes an IoT-based fuzzy prediction model for early fire detection with a goal of minimizing extensive damage and promote intermediate fire suppression and control through true fire incidences. This solution provides for future public safety monitoring, and control of fire-related situations among the market community. Hence, fire safety monitoring is significant in providing future fire safety planning, control and management by putting in place appropriate fire safety laws, policies, bills and related fire safety practices or guidelines to be applied in public buildings, market centers and other public places.
Traffic operation efficiency is greatly impacted by the increase in travel demand and the increase in vehicle ownership. The continued increase in traffic demand has rendered the importance of ...controlling traffic, especially at intersections. In general, the inefficiency of traffic scheduling leads to traffic congestion, resulting in a rise in fuel consumption, exhaust emissions, and poor quality of service. Various methods for time series forecasting have been proposed for adaptive and remote traffic control. The prediction of traffic has attracted profound attention for improving the reliability and efficiency of traffic flow scheduling while reducing congestion. Therefore, in this work, we studied the problem of the current traffic situation at Muhima Junction one of the busiest junctions in Kigali city. Future traffic rates were forecasted by employing long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) models, respectively. Both the models’ performance criteria for adequacy were the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The results revealed that LSTM is the best-fitting model for monthly traffic flow prediction. Within this analysis, we proposed an adaptive traffic flow prediction that builds on the features of vehicle-to-infrastructure communication and the Internet of Things (IoT) to control traffic while enhancing the quality of service at the junctions. The real-time actuation of traffic-responsive signal control can be assured when real-time traffic-based signal actuation is reliable.
The current era witnesses the advent of the Internet of Things (IoT), a transformative force giving rise to various technological innovations, most notably connected objects. This paradigm shift ...catalyzes the widespread adoption of autonomous decision-making systems, particularly in sectors like agriculture, where the aim is to amplify productivity. As a result of the agricultural domain, mushrooms have concurrently emerged as a significant component of daily diets, offering additional vitamins and flavor. Despite their popularity, cultivating mushrooms in open environments poses challenges in maintaining optimal environmental conditions, prompting numerous research efforts. Recognizing the inconsistencies in existing approaches to safeguard vital parameters in mushroom farms, this paper introduces an innovative system utilizing intelligent sensors whose real-time records are managed based on the fuzzy sets concept. These sensors, encompassing the Capacitive Soil Moisture Sensor v1.2, DHT22 Sensor, Light Dependent Resistor (LDR sensor), and Passive Infrared Receiver (PIR sensor), collectively capture essential data for decision-making in mushroom farming. Employing fuzzy logic, the system addresses pivotal aspects such as substrate watering, environmental control, light management, and pest detection. Through experimental results, it becomes evident that the proposed system not only exemplifies the potential of IoT technologies in agriculture but also offers a comprehensive and efficient approach to real-time decision-making. By aggregating sensor data, the system proves instrumental in enhancing the quality and yield of mushroom crops, showcasing a promising trajectory for sustainable and technologically driven agricultural practices.
Monitoring System to Strive against Fall Armyworm in Crops Case Study: Maize in Rwanda Hanyurwimfura, Damien; Nizeyimana, Eric; Ndikumana, Faustine ...
2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Conference Proceeding
The food security has received much attention due the hunger that usually affects some countries in Africa. Pests are very dangerous to crops and reduce the harvest of affected crop and can cause ...risk if no technical methods are applied to strive against them. Fall armyworm (FAW) is one the pest that has attacked the priority stable crop of Rwanda (maize) since last year 2017. This reduced the expected harvest from this crop. The presence of this pest costs the government to use drones and other special forces to strive for it, but pest will remain a problem if there is no regular, automatic way to detect the pest and strive for it at the early days of attacking the crop. This paper introduces a design of a prototype of an automated system that will be able to detect the presence of a fall armyworm in the field. The system will use the new technology of Internet of Things (IoT) where sensors are used to identify the pest location. Once the presence of the pest is detected in the farm, the system will be able to give the information of the affected crop and notify the farmer through his/her mobile phone who will immediately react accordingly. Through the authorization from the farmer, the system will be able to pump pesticides to kill both larvae and eggs of fall armyworm on the affected crop. This automated system will save the farmer's time, as it will monitor the crops while the farmer is occupied with other activities. Lastly the system will help to increase the production of maize in Rwanda since the crop will be safe from the pest.
The shiitake mushroom is the most important culinary medicinal mushroom. Traditional and conventional approaches have been tried to increase the harvest of Shiitake mushrooms. However, the production ...of shiitake mushrooms is less because the previously proposed methods didn't yield satisfying results. This research provides a model based on fuzzy logic (FL) to improve the harvest of shiitake mushrooms by managing the environment of the cultivation farm namely the temperature, humidity, and soil moisture. The findings of our experiments with the proposed system proved the stability of the farm environmental parameters for 66.7% of the 24 hours and the harvest with the existing system was improved by 49% with the proposed method.
Every year, about 4 million people die from respiratory diseases. While early prediction would reduce this mortality rate, till now diagnosis is only done at hospitals involving costly diagnosis ...resources and scarce healthcare professionals. Ideally, regular noninvasive breath analysis check-ups at home would allow us to anticipate medical consultation. Considering developing country's contexts, existing commercial portable diagnosis kits under proprietary licenses are expensive and require internet connectivity to work. The study aimed to investigate the possibility of detecting respiratory diseases using edge AI. Thanks to recent advances in open source edge AI frameworks, this study presents a prototyping design of an offline STM32 based portable kit locally embedding a tiny Machine Learning (TinyML) trained model to predict respiratory disease. Evaluated on an open dataset of Chronic Obstructive Pulmonary Disease (COPD), the resulting real-time requirements of our edge AI model is 15.9 Kb of ROM and 1.5 Kb RAM and performs the inference in 1 ms. Results also show that the accuracy and peak memory for the model are affected by preprocessing, type of sensors and the number of sensors. In addition to the early detection of respiratory diseases, the proposed solution will be of great value in the process of mass collection of exhaled breath data to enable the training of efficient AI models for respiratory diseases.
Poultry farm control systems make the poultry farm a good environment for the welfare of the birds. Keeping the birds in a good state of welfare increases production in poultry farming. To achieve ...such, these control systems use a high amount of electrical energy. This paper proposes an algorithm that harnesses the relationship between humidity and temperature to control their actuators for a more energy-efficient operation. The algorithm uses psychrometric calculations on the edge to predict the resulting environmental parameters after operating the actuators then intelligently finds the best way to operate them. The algorithm was compared with the traditional algorithm and it shows that the proposed algorithm uses less electrical energy compared to the traditional algorithm. It also avoids operating the actuators in short bursts. It was then concluded that this algorithm is more efficient for use in smart poultry farms.
PDF
