An Embedded Fuzzy Logic Based Application for Density Traffic Control System

The control of density traffic at cross junction road usually manned by human efforts or implementation of automatic traffic light system. This system seem and proves to be inefficient with some challenges. The major constraints of this traffic control are as a result of the inability of most traffic control systems to assign appropriate waiting time for vehicles based on the lane density. Also with little or no consideration for pedestrians, emergency and security agents priorities. In view of this, an intelligent density traffic control system using  (fuzzy logic) which is capable of providing priority to the road users based on the density and emergency situations was developed and presented in this paper. This system will obtain the approximate amount of vehicle and presence of pedestrians respectfully on each lane with help of Infrared Sensors (IR) and siren detection system for emergency and security road users. The working principle of this system depending on the logic inputs rules given into the processing unit by the (sensors, S1 and S2) which helps the system to generates a timing sequence that best suit the number of vehicles and pedestrians available on the lane at point in time

An Embedded Fuzzy Logic Based Application for Density Traffic Control System

The control of density traffic at cross junction road usually manned by human efforts or implementation of automatic traffic light system. This system seem and proves to be inefficient with some challenges. The major constraints of this traffic control are as a result of the inability of most traffic control systems to assign appropriate waiting time for vehicles based on the lane density. Also with little or no consideration for pedestrians, emergency and security agents priorities. In view of this, an intelligent density traffic control system using  (fuzzy logic) which is capable of providing priority to the road users based on the density and emergency situations was developed and presented in this paper. This system will obtain the approximate amount of vehicle and presence of pedestrians respectfully on each lane with help of Infrared Sensors (IR) and siren detection system for emergency and security road users. The working principle of this system depending on the logic inputs rules given into the processing unit by the (sensors, S1 and S2) which helps the system to generates a timing sequence that best suit the number of vehicles and pedestrians available on the lane at point in time

A Nonlinear Fuzzy Controller Design Using Lyapunov Functions for an Intelligent Greenhouse Management in Agriculture

The importance of agronomists in large-scale production of food crops under considerate environmental weather conditions cannot be overemphasized. However, emerging global warming is a threat to food security due to its effect on soil depletion and ecosystem degradation. In this work, the design of the proposed intelligent context is to observe, model and simulate greenhouse control system activity towards the management of the farm crop growth as the affected salient environmental parameters. Characteristically, temperature and humidity are the major factors that determine the crop yield in a greenhouse but the case of a dry air environment or beyond 300C−350C of high air humidity will affect crop growth and productivity. A Mamdani technique of fuzzy logic controller with non-linear consequent is used for intelligent greenhouse design in the LABVIEW virtual environment. This approach is used to mimic the human thought process in the system control by setting some logical rules that guide the greenhouse functions. For the system stabilization achievement, a direct method of Lyapunov functions was proposed. The simulation model result shows that, the average temperature of 18.50C and humidity 65% is achieved for a decent environment of crop growth and development during winter. However, the average temperature and humidity achieved during summer is 27.50C&70% respectively. For every season that is beyond 30.50Cand75% of temperature and humidity will require automation of roof opening and water spilled

PERFORMANCE EVALUATION OF MULTIPLE TRANSFORM WATERMARKING SYSTEM FOR PRIVACY PROTECTION OF MEDICAL DATA USING PSNR AND NC

This paper presents the performance evaluation of a developed multiple transform watermarking system for privacy protection of medical data using Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NC). The PSNR was used to evaluate the imperceptibility of the system, while the NC was used to evaluate the robustness of the system under the various attacks include: Gaussian noise, pepper and salt noise, sharp enhancing, image cutting, image compression, low pass filter and image rotation. The obtained result showed that the similarity between original image and watermarked image has PSNR of 52.4595dB as compared to the existing system of 50.0285dB. This indicates that the proposed scheme can conceal the watermark better, and as well retains the image quality

ETEASH-An Enhanced Tiny Encryption Algorithm for Secured Smart Home

The proliferation of the "Internet of Things" (IoT) and its applications have affected every aspect of human endeavors from smart manufacturing, agriculture, healthcare, and transportation to homes. The smart home is vulnerable to malicious attacks due to memory constraint which inhibits the usage of traditional antimalware and antivirus software. This makes the application of traditional cryptography for its security impossible. This work aimed at securing Smart home devices, by developing an enhanced Tiny Encryption Algorithm (TEA). The enhancement on TEA was to get rid of its vulnerabilities of related-key attacks and weakness of predictable keys to be usable in securing smart devices through entropy shifting, stretching, and mixing technique. The Enhanced Tiny Encryption Algorithm for Smart Home devices (ETEASH) technique was benchmarked with the original TEA using the Runs test and avalanche effect. ETEASH successfully passed the Runs test with the significance level of 0.05 for the null hypothesis, and the ETEASH avalanche effect of 58.44% was achieved against 52.50% for TEA. These results showed that ETEASH is more secured in securing smart home devices than the standard TEA

Advancing human nutrition without degrading land resources through modeling cropping systems in the Ethiopian highlands

Food shortage in sub-Saharan Africa is generally considered a function of limited access to food, with little thought to nutritional quality. Analyzing household production of nutrients across farming systems could be valuable in guiding the improvement of those systems. An optimization model was employed to analyze the scenario of human nutrition and cropland allocation in enset (Enset ventricosum)/root crop-based and cereal-based systems of the Ethiopian Highlands. The type and amount of nutrients produced in each system were analyzed, and an optimization model was used to analyze which cropping strategies might improve the nutritional quality of the household using existing resources. Both production systems were in food deficit, in terms of quantity and quality of nutrients, except for iron. The energy supply of resource-poor households in the enset/root crop-based system was only 75% of the recommended daily dietary allowance (RDA) of the World Health Organization (WHO), whereas resource-rich farmers were able to meet their energy, protein, zinc, and thiamine demands. Extremely high deficiency was found in zinc, calcium, vitamin A, and vitamin C, which provided only 26.5%, 34%, 1.78%, and 12%, of the RDA, respectively. The RDA could be satisfied if the land area occupied by enset, kale, and beans were expanded by about 20%, 10%, and 40%, respectively, at the expense of maize and sweet potato. The cereal-based system also had critical nutrient deficits in calcium, vitamin A, and vitamin C, which provided 30%, 2.5%, and 2% of the RDA, respectively. In the cereal system, the RDA could be fully satisfied by reducing cropland allocated to barley by about 50% and expanding the land area occupied by faba beans, kale, and enset. A shift from the cereal/root crop-dominated system to a perennial-enset dominated system would decrease soil erosion by improving the crop factor by about 45%. This shift would also have a very strong positive impact on soil fertility management. However, any policy suggestions for change in cropland allocation should be done through negotiations with households, communities, and district stakeholders

A SCHEDULING-BASED ALGORITHM FOR LOW ENERGY CONSUMPTION IN SMART AGRICULTURE PRECISION MONITORING SYSTEM

The worldwide connectivity design of embedded wireless sensor-based Internet of Things (IoT) using 6LoWPAN platform has significantly increases in industry. However, the development of smart technology in agriculture precision for remote control, monitoring and sensing usually encounter some challenges in the IoT architecture, these include: high power consumption, interoperability, security, and end-to-end communication. In this paper, the authors developed a smart agriculture precision monitoring system using scheduling-based algorithm techniques to accomplished low energy consumption during the system operations. The IEEE 802.11 b/g/n technology (ESP 8266) was used as IoT gateway, ATmega 2560 development chip, with other wireless sensor nodes (such as soil pH meter, soil moisture and DHT 11 for temperature and humidity). The experimental sensor nodes simulation results in Cooja Contiki show the level at which packet generation, packet loss and throughput decline due to large packet transmission during end-to-end communication. Also, the laboratory experimentation results of the proposed scheduling algorithm implementation show that more than 58% of energy was saved during packet transmission from user end to the cloud database

Secure edge computing vulnerabilities in smart cities sustainability using petri net and genetic algorithm-based reinforcement learning

The Industrial Internet of Things (IIoT) revolution has emerged as a promising network that enhanced information dissemination about the city's resources. This city's resources are wirelessly connected to different constrained devices (such as sensors, robotics, and actuators). However, the communication of this wireless information is threatened by several malicious attacks, cyber-attacks, and hackers. This is due to unsecured IIoT networks that were exposed as a potential back door entry point for the attacks. Consequently, this study aims to develop a security framework for the smart cities’ sustainability edge computing vulnerabilities using Petri Net and Genetic Algorithm-Based Reinforcement Learning (GARL). First, a common trust model for addressing information outflows in the network using a distributed authorization algorithm is proposed. This algorithm is implemented on a secure framework modeling in Petri Net called secure trust-aware philosopher privacy and authentication (STAPPA) for mitigation of the privacy breach in the networks. Genetic Algorithm-based Reinforcement Learning (GARL) is used to optimize the search, detect anomalies, and shortest route during the agent learning in the environment. The detection and accuracy rate results obtained over a secure framework using reinforcement learning are 98.75, 99, 99.50, 99.75, and 100% during simulation in the network environment. The average sensitivity of the detection rate is 1.000, while the average specificity outcome is 0.868. The result of the GARL simulation model obtained shows the best distance of 238.84 * 10−3 fitness when the search space is optimized by reducing the number of chromosomes to 10 in the model. These approaches help to detect anomalies and prevent unauthorized users from accessing edge computing components in the city architecture

Crypto Hash Algorithm-Based Blockchain Technology for Managing Decentralized Ledger Database in Oil and Gas Industry

This research work proposes a method for the securing and monitoring of petroleum product distribution records in a decentralized ledger database using blockchain technology. The aim of using this technique is to secure the transaction of distributed ledgers in a database and to protect records from tampering, fraudulent activity, and corruption by the chain participants. The blockchain technology approach offers an efficient security measure and novel advantages, such as in the transaction existence and distribution ledger management between the depot, transporter, and retailing filling station. Others advantages are transparency, immunity to fraud, insusceptibility to tampering, and maintaining record order. The technique adopted for this secure distributed ledger database is crypto hash algorithm-1 (SHA-1)-based public permissioned blockchain and telematics, while this telematics approach is an embedded system integrated into an in-vehicle model for remote tracking of geolocation (using Global Positioning System (GPS)), monitoring, and far-off data acquisition in a real-time. The scope of the data in the secure distributed ledger database (using blockchain) developed are identification (ID) of the tanker operator, Depot name, Source station ID, Destination station ID, Petroleum product volume, Transporter ID, and Geographic automobiles location. This system proved to be efficient, secure, and easy to maintain as it does not permit any individual for records tampering, but supports agreement of ~75% of participants in the chain to make changes

Prediction of Call Drops in GSM Network using Artificial Neural Network

Global System for Mobile communication is a digital mobile system that is widely used in the world. Over the years, the number of subscribers has tremendously increased, the quality of service (Call Drop Rate) became an issue to consider as many subscribers were not satisfied with the services rendered. In this paper, we present the Artificial Neural Network approach to predict call drop during an initiated call. GSM parameters data for the prediction were acquired using TEMS Investigations software. The measurements were carried out over a period of three months. Post analysis and training of the parameters was done using the Artificial Neural Network to have an output of “0” for no-drop calls and “1” for drop calls. The developed model has an accuracy of 87.5% prediction of drop call. The developed model is both useful to operators and end users for optimizing the network