IoT Raspberry Pi Based Smart Parking System with Weighted K-Nearest Neighbours Approach

Due to the limited availability of parking slots in parking areas, drivers often have difficulty finding an empty parking slot. The number of parking slots available at a particular location is usually less than the number of vehicles. Hence, drivers spend a lot of time looking for vacant parking slots, which eventually delays the completion of their tasks, such as paying bills, attending a meeting, or visiting a patient at the hospital, etc. There are a couple of parking guidance systems that have been highlighted by the other researchers, but most of them lack real-time, convenient guidance. This research proposed a smart parking guidance system made of an IoT Raspberry Pi combined with an Android application that makes use of the weighted k nearest neighbours for positioning the vehicle. This was achieved through the use of Wi-Fi signal strength indicator fingerprinting, allowing for real-time navigation and parking detection. In order to achieve real-time parking over the internet, Raspberry Pi hardware and the ThingSpeak IoT cloud with ultrasonic sensors are used in the proposed method. An Android application was involved in this parking detection system, which adopted IoT approaches to estimate the location of users in real-time and provide routes using route-finding techniques to assist drivers in finding their desired parking slots. Data from the sensors was processed and translated into the Raspberry Pi using the Python programming language. They were sent using the Message Telemetry Transport protocol to send parking data to the ThingSpeak IoT cloud in real-time. This data was displayed via the Android app. The user is then able to view each available parking slot, acquire the route, and be directed with high accuracy to the parking slots of their choice. In this study, advanced sensing and communication technologies were used together with the weighted k nearest neighbours algorithm for positioning and wayfinding in order to improve parking guidance accuracy. Based on the experimental results, the proposed system showed a lower average error rate of 1.5 metres in comparison to other positioning techniques, such as GPS, or other similar algorithms for positioning, such as maximum a posteriori, which have shown average errors of 2.3 metres and 3.55 metres, respectively, a potential increase of more than 35% from the previous error rate. Doi: 10.28991/CEJ-2023-09-08-012 Full Text: PD

A hybrid-based modified adaptive fuzzy inference engine for pattern classification

The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a hybrid Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data set. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the hybrid fuzzy clustering and Apriori algorithm technique, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a minimal set of rules. The proposed hybrid MAFIE is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher's Iris data set and shown to be very competitive

Glove-based Approach To Dynamic Signature Verification

The purpose of this research is to develop a novel, accurate and efficient on-line signature verification system

SVD-BASED SIGNATURE VERIFICATION TECHNIQUE USING DATA GLOVE

Data glove is a new dimension in the field of virtual reality environments, initially designed to satisfy the stringent requirements of modern motion capture and animation professionals. In this paper, we try to shift the implementation of data glove from motion animation towards signature verification problem, making use of the offered multiple degrees of freedom for each finger and for the hand as well. The proposed technique is based on the Singular Value Decomposition (SVD) in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, and thus account for most of the variation in the original data, so the effective dimensionality of the data can be reduced. Having identified data glove signature through its rth principal subspace, the authenticity can then be obtained by calculating the angles between the different subspaces. The SVD-signature verification technique is tested with large number of authentic and forged signatures, showing remarkable level of accuracy in finding the similarities between genuine samples as well as those differentiated between genuine-forgery trials

Enhancement of Glove-Based Approach to Dynamic Signature Verification by Reducing Number of Sensors

Data glove is a new dimension in the field of virtual reality environments, initially designed to satisfy the stringent requirements of modern motion capture and animation professionals. Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel online signature verification technique using Singular Value Decomposition (SVD) for signature classification and verification is presented The proposed technique is based on the S VD in finding r-singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, and thus account for most of the variation in the original data, so the effective dimensionality of the data can be reduced. Having identified data glove signature through its r-principal subspace, the authenticity is then can be obtained by calculating the angles between the different subspaces. In this paper we try to ponder a significant analysis of accuracy and performance of dynamic signature identification and verification using data glove with reduced number of sensors from 14 to 5 to achieve a significant level of accuracy. The SVD-based signature verification technique is appears to be promising with the best combination of selected 5 prominent sensors instead of select all the 14-seonsor based data sets and the best performance is shown to be able to produce 2.33% of Equal Error Rate (EER)

Guest editorials: P2P computing for 5G, beyond 5G (B5G) networks and internet-of-everything (IoE)

Due to the technology advancements, in recent days, most of the wireless devices are connected to machines (IoT) rather than human being, will lead to the growth in Smart Cities, Smart Homes, Smart Vehicles etc. Hence, the future customers will request for higher capacity, higher data rate with global connectivity. Hence, 5G and B5G networks encounter fundamental challenges for peer-to-peer IoT communications, including automated vehicle, robotic and other automatic systems, and computing infrastructures. Therefore Internet-of-Everything (IoE), integration of everything in a single hub is the related development. Other challenges predicted for future networks are to handle Ultra-dense cell networks, Reconfigurable Hardware, Networked VLC, Networking Intelligence and technological developments to empower the users with 100% Immersive Experiences

Simplified online signature verification through uncompromised electrode reduction in data gloves

The signature verification through data glove signals has overheads of cost, time and complexity in data dimensions. A novel approach to reduce channels by prudently selecting the most essential channels will result in reduced cost, processing time and low level complexity. The reduction in electrodes should not compromise the accuracy and security. A continuous search to find the most significant channels helps distinguishing them from less contributing channels by various experiments. Here in this work the comparison of zone based channel groups were conducted to classify the SVD signal features through LDA and the performances of thumb zone based channels were found better than the little finger zone in contributing genuine features by the results obtained from various levels of experiments

Virtual Reality Based Dynamic Signature Verification Using Data glove

Data glove is a new dimension in the field of virtual reality environments, initially designed to satisfy the stringent requirements of modern motion capture and animation professionals. In this paper we try to shift the implementation of data glove from motion animation towards signature verification problem, making use of the offered multiple degrees of freedom for each finger and for the hand as well. The proposed technique is based on the Singular Value Decomposition (SVD) in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, and thus account for most of the variation in the original data, so the effective dimensionality of the data can be reduced. Having identified data glove signature through its r-th principal subspace, the authenticity is then can be obtained by calculating the angles between the different subspaces. The SVD-signature verification technique is tested with large number of authentic and forgery signatures and shows remarkable level of accuracy in finding the similarities between genuine samples as well as the differenced between genuine-forgery trials

A Secured Fingerprint Authentication System

This study is a protection analysis of foremost privacy enhanced technologies for biometrics including watermark embedding technique and fixed digit encryption. A biometric authentication system is vulnerable to a mixture of attacks. These attacks are anticipated to either evade the security afforded by the system or to discourage the usual functioning of the system. Here, we briefly review some of the known attacks that can be encountered by a biometric system and some corresponding protection techniques. We explicitly focus on threats designed to extract information about the original biometric data of an individual from the stored data as well as the entire authentication system. We offer a biometric authentication scheme which uses two separate biometric features combined by watermark embedding with fixed digit encryption to obtain a non-unique identifier of the individual, in order to address security and privacy concerns. Moreover, we provide experimental results presenting the performance of the authentication system. In the client-server environment the transformed features and templates travel through insecure communication line like the internet or intranet. Our proposed technique causes security against eavesdropping and replay attacks on the internet or intranet, because the transmitted feature information and the templates are different every time