Vehicular Ad hoc networks (VANETs) as spontaneous wireless communication technology of vehicles has a wide range of applications like road safety, navigation and other electric car technologies, ...however its practicability is greatly hampered by cyber-attacks. Due to message broadcasting in an open environment during communication, VANETs are inherently vulnerable to security and privacy attacks. However to address the cyber-security issues with optimal computation overhead is a matter of current security research challenge. So this paper designs a secure and efficient certificate-less aggregate scheme (ECLAS) for VANETs applicable in a smart grid scenario. The proposed scheme is based on elliptic curve cryptography to provide conditional privacy-preservation by incorporating usage of time validated pseudo-identification for communicating vehicles besides sorting out the KGC (Key Generation Center) escrow problem. The proposed scheme is comparatively more efficient to relevant related research work because it precludes expensive computation operations likes bilinear pairings as shown by the performance evaluation. Similarly, communication cost is within the ideal range to most related works while considering the security requirements of VANETs system applicable in a smart grid environment.
Air pollution is a critical problem in densely populated urban areas, with traffic significantly contributing. To mitigate the adverse effects of air pollution on public health and the environment, ...there is a growing need for the real-time monitoring and detection of pollution spikes in transportation. This paper presents a novel approach to using Internet of Things (IoT) edge networks for the real-time detection of air pollution peaks in transportation, specifically designed for innovative city applications. The proposed system uses IoT sensors in buses, cabs, and private cars. These sensors are equipped with air quality monitoring capabilities, including the measurement of pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and carbon dioxide (CO2). The sensors continuously collect air quality data and transmit them to edge devices within the transportation infrastructure. The data collected by these sensors are analyzed, and alerts are generated when pollution levels exceed predefined thresholds. By deploying this system within IoT edge networks, transportation authorities can promptly respond to pollution spikes, improving air quality, public health, and environmental sustainability. This paper details the sensor technology, data analysis methods, and the practical implementation of this innovative system, shedding light on its potential for addressing the pressing issue of transportation-related pollution. The proposed IoT edge network for real-time air pollution spike detection in transportation offers significant advantages, including low-latency data processing, scalability, and cost-effectiveness. By leveraging the power of edge computing and IoT technologies, smart cities can proactively monitor and manage air pollution, leading to healthier and more sustainable urban environments.
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.
This study investigates the use of machine-learning approaches to interpret Dissolved Gas Analysis (DGA) data to find incipient faults early in oil-impregnated transformers. Transformers are critical ...pieces of equipment in transmitting and distributing electrical energy. The failure of a single unit disturbs a huge number of consumers and suppresses economic activities in the vicinity. Because of this, it is important that power utility companies accord high priority to condition monitoring of critical assets. The analysis of dissolved gases is a technique popularly used for monitoring the condition of transformers dipped in oil. The interpretation of DGA data is however inconclusive as far as the determination of incipient faults is concerned and depends largely on the expertise of technical personnel. To have a coherent, accurate, and clear interpretation of DGA, this study proposes a novel multinomial classification model christened KosaNet that is based on decision trees. Actual DGA data with 2912 entries was used to compute the performance of KosaNet against other algorithms with multiclass classification ability namely the decision tree, k-NN, Random Forest, Naïve Bayes, and Gradient Boost. Investigative results show that KosaNet demonstrated an improved DGA classification ability particularly when classifying multinomial data.
Agriculture plays a key role in global food security. Agriculture is critical to global food security and economic development. Precision farming using machine learning (ML) and the Internet of ...Things (IoT) is a promising approach to increasing crop productivity and optimizing resource use. This paper presents an integrated crop and fertilizer recommendation system aimed at optimizing agricultural practices in Rwanda. The system is built on two predictive models: a machine learning model for crop recommendations and a rule-based fertilization recommendation model. The crop recommendation system is based on a neural network model trained on a dataset of major Rwandan crops and their key growth parameters such as nitrogen, phosphorus, potassium levels, and soil pH. The fertilizer recommendation system uses a rule-based approach to provide personalized fertilizer recommendations based on pre-compiled tables. The proposed prediction model achieves 97% accuracy. The study makes a significant contribution to the field of precision agriculture by providing decision support tools that combine artificial intelligence and domain knowledge.
Although agriculture remains the dominant economic activity in many countries around the world, in recent years this sector has continued to be negatively impacted by climate change leading to food ...insecurities. This is so because extreme weather conditions induced by climate change are detrimental to most crops and affect the expected quantity of agricultural production. Although there is no way to fully mitigate these natural phenomena, it could be much better if there is information known earlier about the future so that farmers can plan accordingly. Early information sharing about expected crop production may support food insecurity risk reduction. In this regard, this work employs data mining techniques to predict future crop (i.e., Irish potatoes and Maize) harvests using weather and yields historical data for Musanze, a district in Rwanda. The study applies machine learning techniques to predict crop harvests based on weather data and communicate the information about production trends. Weather data and crop yields for Irish potatoes and maize were gathered from various sources. The collected data were analyzed through Random Forest, Polynomial Regression, and Support Vector Regressor. Rainfall and temperature were used as predictors. The models were trained and tested. The results indicate that Random Forest is the best model with root mean square error of 510.8 and 129.9 for potato and maize, respectively, whereas R2 was 0.875 and 0.817 for the same crops datasets. The optimum weather conditions for the optimal crop yield were identified for each crop. The results suggests that Random Forest is recommended model for early crop yield prediction. The findings of this study will go a long way to enhance reliance on data for agriculture and climate change related decisions, especially in low-to-middle income countries such as Rwanda.
Transient stability and supply disturbances are common yet unwelcome phenomena in power distribution systems, particularly in sub-Saharan Africa. The growing demand for greater reliability and ...dependability in power delivery has aroused the interest of researchers and renewed the pursuit of advanced technological solutions for fault detection and location determination at medium and low-voltage levels. The length of the distribution network typically ranges from hundreds to thousands of kilometers. In this regard, the management of distribution networks, including the identification of faulty segments, is a significant recurrent challenge facing power-system operators. With the ever-expanding distribution network and regulatory demands for service reliability, the challenge for network operators is daunting. However, the deployment of IoT technologies in the energy distribution infrastructure would significantly accelerate the detection and location of faults, thus transforming the electricity delivery service into one that is responsive, communicative, attractive, and robust. This study proposes, designs, and implements a reasonably priced LoRaWAN-based IoT platform for monitoring distribution networks. The study was conducted in Nakuru County, Kenya on an actual and active distribution network owned and managed by Kenya Power Company. Experimental results showed that a trigger is generated at the network-monitoring center in about 100 ms of the occurrence of a fault on the distribution network, thus enabling quick, prompt, and immediate commencement of reparative action. Furthermore, practical evaluation has shown that this platform is well adapted for the context of developing countries where budgetary constraints and cost prohibitions hinder the upgrade of the legacy grid into fully-fledged smart entities.
The ability to estimate soil quality has great value for agriculture, especially for low-incomeregions with minimal agricultural and financial resources. This prediction provides users ...withinformation that is useful in determining whether the soil is suitable for a specific crop, such aspotato (Solanum tuberosum). Farmers in Rwanda lack information on soil quality. There arenot enough soil laboratories to perform the requisite measurements of NPK, pH, and organiccarbon, nor are there enough experts to analyze the data and provide farmers with timelyresults. The prime objective of the proposed study is to develop a predictive framework thatcan estimate soil quality for the ideal cultivation of potato (Solanum tuberosum) considering acase study of Rwanda. In this study, bootstrapping is used to augment the small soil dataset,and fuzzy logic is used to label soil data into four classes of soil suitability, with verification ofthe labeling by soil experts. Several machine learning methods are then tested on the labeleddata, resulting in the classification of suitability for the augmented dataset and an assessment oftheir performance as a way to support experts in predicting soil quality. All machine learningmethods applied were viable, with the best performance achieved using an artificial neuralnetwork. The quantified outcome showed that the adoption of a neural-network-based schemehas an average accuracy of 32% in contrast to other learning schemes. However, 70%-80%accuracy was achieved upon the adoption of fuzzy logic. KCI Citation Count: 0
Traditional Intrusion Detection Systems (IDSs) are known for generating large volumes of alerts despite all the progress made over the last few years. The analysis of a huge number of raw alerts from ...large networks is often time consuming and labour intensive because the relevant alerts are usually buried under heaps of irrelevant alerts. Vulnerability based alert management approaches have received considerable attention and appear extremely promising in improving the quality of alerts. They filter out any alert that does not have a corresponding vulnerability hence enabling the analysts to focus on the important alerts. However, the existing vulnerability based approaches are still at the preliminary stage and there are some research gaps that need to be addressed. The act of validating alerts may not guarantee alerts of high quality because the validated alerts may contain huge volumes of redundant and isolated alerts. The validated alerts too lack additional information needed to enhance their meaning and semantic. In addition, the use of outdated vulnerability data may lead to poor alert verification. In this paper, we propose a fast and efficient vulnerability based approach that addresses the above issues. The proposed approach combines several known techniques in a comprehensive alert management framework in order to offer a novel solution. Our approach is effective and yields superior results in terms of improving the quality of alerts.
► We propose a comprehensive framework to manage IDS alerts. ► We construct a dynamic threat profile to improve the accuracy of alerts. ► Improves the quality of alerts by filtering out the unnecessary alerts. ► Eliminates the redundant and isolated alerts after alert verification process. ► Introduces alert metrics to improve the semantics of alerts.
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