A Survey of Lightweight Cryptosystems for Smart Home Devices

A Smart Home uses interconnected network technology to monitor the environment, control the various physical appliances, and communicate with each other in a close environment. A typical smart home is made up of a security system, intercommunication system, lighting system, and ventilation system.  Data security schemes for smart homes are ineffective due to inefficiency cryptosystems, high energy consumption, and low exchange security. Traditional cryptosystems are less-applicable because of their large block size, large key size, and complex rounds. This paper conducts a review of smart homes, and adopts Ultra-Sooner Lightweight Cryptography to secure home door. It provides extensive background of cryptography, forms of cryptography as associated issues and strengths, current trends, smart home door system design, and future works suggestions. Specifically, there are prospects of utilizing XORed lightweight cryptosystem for developing encryption and decryption algorithms in smart home devices. The Substitution Permutation Network, and Feistel Network cryptographic primitives were most advanced forms of cipher operations with security guarantees. Therefore, better security, memory and energy efficiency can be obtained with lightweight ciphers in smart home devices when compared to existing solutions. In the subsequent studies, a blockchain-based lightweight cryptography can be the next springboard in attaining the most advanced security for smart home systems and their appliances.     &nbsp

Low-cost and Efficient Fault Detection and Protection System for Distribution Transformer

Distribution transformers are a vital component of electrical power transmission and distribution system. Frequent Monitoring transformers faults before it occurs can help prevent transformer faults which are expensive to repair and result in a loss of energy and services. The present method of the routine manual check of transformer parameters by the electricity board has proven to be less effective. This research aims to develop a low-cost protection system for the distribution transformer making it safer with improved reliability of service to the users. Therefore, this research work investigated transformer fault types and developed a microcontroller-based system for transformer fault detection and protection system using GSM (the Global System of Mobile Communication) technology for fault reporting. The developed prototype system was tested using voltage, current and temperature, which gave a threshold voltage higher than 220 volts to be overvoltage, a load higher than 200 watts to be overload and temperature greater than 39 degrees Celsius to be over temperature was measured. From the results, there was timely detection of transformer faults of the system, the transformer protection circuits were fully functional, and fault reporting was achieved using the GSM device. Overall, 99% accuracy was achieved. The system can thus be recommended for use by the Electricity Distribution Companies to protect distribution transformers for optimal performance, as the developed system makes the transformers more robust, and intelligent. Hence, a real-time distribution transformer fault monitoring and prevention system is achieved and the cost of transformer maintenance is reduced to an extent

Voice Recognition Systems for The Disabled Electorate: Critical Review on Architectures and Authentication Strategies

An inevitable factor that makes the concept of electronic voting irresistible is the fact that it offers the possibility of exceeding the manual voting process in terms of convenience, widespread participation, and consideration for People Living with Disabilities. The underlying voting technology and ballot design can determine the credibility of election results, influence how voters felt about their ability to exercise their right to vote, and their willingness to accept the legitimacy of electoral results. However, the adoption of e-voting systems has unveiled a new set of problems such as security threats, trust, and reliability of voting systems and the electoral process itself. This paper presents a critical literature review on concepts, architectures, and existing authentication strategies in voice recognition systems for the e-voting system for the disabled electorate. Consequently, in this paper, an intelligent yet secure scheme for electronic voting systems specifically for people living with disabilities is presented

Multi-layer Perceptron Model for Mitigating Distributed Denial of Service Flood Attack in Internet Kiosk Based Electronic Voting

Distributed Denial-of-Service (DDoS) flood attack targeting an Internet Kiosk voting environment can deprive voters from casting their ballots in a timely manner. The goal of the DDoS flood attack is to make voting server unavailable to voters during election process. In this paper, we present a Multilayer Perceptron (MLP) algorithm to mitigate DDoS flood attack in an e-voting environment and prevent such attack from disrupting availability of the vulnerable voting server. The developed intelligent DDoS flood mitigation model based on MLP Technique was simulated in MATLAB R2017a. The mitigation model was evaluated using server utilization performance metrics in e-voting. The results after the introduction of the developed mitigation model into the DDoS attack model reduced the server utilization from 1 to 0.4 indicating normal traffic. MLP showed an accuracy of 95% in mitigating DDoS flood attacks providing availability of voting server resources for convenient and timely casting of ballots as well as provide for credible delivery of electronic democratic decision making

Smart Rice Precision Farming Schemes in Sub-Saharan Africa: Process and Architecture

Smart farming integrates information, communication, and control technologies in agricultural practices. Recently, crop enterprise management through smart precision farming technologies are antidotes to uncontrollable soil and environmental factors compounded by climate change. Farm production planning utilizes enormous data generated from the field by human agents and IoT devices, but is often unreliable and inaccurate. These cause low yield, high losses, inferior quality of farm produce, overuse or underuse of fertilizers, increased costs, and inefficient farm management. Traditionally, analyzing rice cropping yields is time-inefficient and tasking, which led to quicker IoT adoption. Aside insufficient data sharing infrastructure, data privacy problem is widespread The blockchain technology is useful for verifying the reliability, accuracy, and authenticity of IoT data generated from fields for the production planning. In the future, dynamic systems (smart rice farming) and model-based control systems can be applied to understand the physical process and valuable factors of production. This paper provides a comprehensive state-of-the-art process and architectural survey on impacts of uncontrollable environmental factors, smart precision framework, security and privacy architectures or solutions for improving rice crop production. Again, a new taxonomy is developed to guide researchers, advance the course of rice production, and improve yields across sub-Saharan Africa

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

Traffic Violation Detection System Using Image Processing

Over the last three decades, the global population of human beings has increased at an exponential rate, resulting in an equal rise in the number of vehicles owned and used globally. Vehicle traffic is a major economic component in both urban and rural areas, and it requires proper management and monitoring to ensure that this mass of vehicles coexists as smoothly as possible. The amount of vehicular traffic on roads around the world, with Nigeria as a case study, results in varying degrees of traffic rule violations, especially red light jumping.  To arrest offenders and resolve the weaknesses and failures of human traffic operators who cannot be everywhere at once, efficient traffic violation and number plate recognition systems are needed. There are several methods for reading characters, which can be alphabets, numbers, or alphanumeric. To minimize processing time and computational load on the machine, this research proposed k-Nearest Neighbour for plate number character recognition. The system was developed and evaluated. From the result, the localization of license plate regions within an image was 92 percent accurate, and character recognition was 73 percent accurate

Intelligent Cattle Detection and Recognition System Using ANN-Fourier Descriptor Techniques

Reoccurring Fulani-farmer crisis in Nigeria as a result of the destruction of farmland and arable crops by cattle during grazing has led to a high rate of loss of life and properties. To address this problem, this research work proposed a shape- based ANN-Fourier descriptor cattle recognition system. Twenty samples of each of cattle, deer, dog, elephant, horse, and camel were obtained from the UCL database. Colour space conversion and a simple image binarization were performed to obtained image segmentation. Edges of the images were extracted, and Discrete Fourier Transform was applied on the edges to obtain the Fourier descriptors. Ten Fourier descriptors were used to train feed-forward back propagation artificial neural networks for cattle recognition systems. The effect of increasing database size, the number of images used for training and testing, and threshold value were investigated. It was observed that there was no effect of increasing the size of the database on the system performance. For better accuracy, sensitivity, specificity, and precision, it was observed that a good threshold value must be carefully chosen and 75% of the image must be used for training and 25% for testing respectively. With twenty image samples, each for cattle, deer, camel, dog, horse, and elephant in the database, fifteen out of twenty samples were used for training, five for testing, and the threshold value of 0.6, 98.7% sensitivity, 98.8% specificity, 98.8% accuracy and 94.5% precision were achieved. This result provides a good method for cattle detection and recognition system