Development of Face Recognition on Raspberry Pi for Security Enhancement of Smart Home System

Nowadays, there is a growing interest in the smart home system using Internet of Things. One of the important aspect in the smart home system is the security capability which can simply lock and unlock the door or the gate. In this paper, we proposed a face recognition security system using Raspberry Pi which can be connected to the smart home system. Eigenface was used the feature extraction, while Principal Component Analysis (PCA) was used as the classifier. The output of face recognition algorithm is then connected to the relay circuit, in which it will lock or unlock the magnetic lock placed at the door. Results showed the effectiveness of our proposed system, in which we obtain around 90% face recognition accuracy. We also proposed a hierarchical image processing approach to reduce the training or testing time while improving the recognition accuracy

Open-Source Face Recognition Frameworks: A Review of the Landscape

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Security and the smart city: A systematic review

The implementation of smart technology in cities is often hailed as the solution to many urban challenges such as transportation, waste management, and environmental protection. Issues of security and crime prevention, however, are in many cases neglected. Moreover, when researchers do introduce new smart security technologies, they rarely discuss their implementation or question how new smart city security might affect traditional policing and urban planning processes. This systematic review explores the recent literature concerned with new ‘smart city’ security technologies and aims to investigate to what extent these new interventions correspond with traditional functions of security interventions. Through an extensive literature search we compiled a list of security interventions for smart cities and suggest several changes to the conceptual status quo in the field. Ultimately, we propose three clear categories to categorise security interventions in smart cities: Those interventions that use new sensors but traditional actuators, those that seek to make old systems smart, and those that introduce entirely new functions. These themes are then discussed in detail and the importance of each group of interventions for the overall field of urban security and governance is assessed

Real-Time Object Detection with Automatic Switching between Single-Board Computers and the Cloud

We present a wireless real-time object detection system utilizing single-board devices, cloud computing platforms and web-streaming. Currently, most inference applications stat- ically perform tasks either on local machines or remote cloud servers. However, devices connected through cellular technolo- gies face volatile network conditions, compromising detection performance. Furthermore, while the limited computing power of single-board computers degrade detection correctness, exces- sive power consumption of machine learning models used for inference reduces operation time. In this paper, we propose a dynamic system that monitors embedded device’s wireless link quality and battery level to decide on detecting objects locally or remotely. The experimental results show that our dynamic offloading approach could reduce devices’ energy usage while achieving high accuracy, real-time object detection. Index Terms—Machine learning, WebRTC, object detection

Edge Computing for Extreme Reliability and Scalability

The massive number of Internet of Things (IoT) devices and their continuous data collection will lead to a rapid increase in the scale of collected data. Processing all these collected data at the central cloud server is inefficient, and even is unfeasible or unnecessary. Hence, the task of processing the data is pushed to the network edges introducing the concept of Edge Computing. Processing the information closer to the source of data (e.g., on gateways and on edge micro-servers) not only reduces the huge workload of central cloud, also decreases the latency for real-time applications by avoiding the unreliable and unpredictable network latency to communicate with the central cloud

Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded Devices

open2noThe application of Artificial Intelligence to the industrial world and its appliances has recently grown in popularity. Indeed, AI techniques are now becoming the de-facto technology for the resolution of complex tasks concerning computer vision, natural language processing and many other areas. In the last years, most of the the research community efforts have focused on increasing the performance of most common AI techniques—e.g., Neural Networks, etc.—at the expenses of their complexity. Indeed, many works in the AI field identify and propose hyper-efficient techniques, targeting high-end devices. However, the application of such AI techniques to devices and appliances which are characterised by limited computational capabilities, remains an open research issue. In the industrial world, this problem heavily targets low-end appliances, which are developed focusing on saving costs and relying on—computationally—constrained components. While some efforts have been made in this area through the proposal of AI-simplification and AI-compression techniques, it is still relevant to study which available AI techniques can be used in modern constrained devices. Therefore, in this paper we propose a load classification task as a case study to analyse which state-of-the-art NN solutions can be embedded successfully into constrained industrial devices. The presented case study is tested on a simple microcontroller, characterised by very poor computational performances—i.e., FLOPS –, to mirror faithfully the design process of low-end appliances. A handful of NN models are tested, showing positive outcomes and possible limitations, and highlighting the complexity of AI embedding.openAndrea Agiollo; Andrea OmiciniAndrea Agiollo; Andrea Omicin

Social, Private, and Trusted Wearable Technology under Cloud-Aided Intermittent Wireless Connectivity

There has been an unprecedented increase in the use of smart devices globally, together with novel forms of communication, computing, and control technologies that have paved the way for a new category of devices, known as high-end wearables. While massive deployments of these objects may improve the lives of people, unauthorized access to the said private equipment and its connectivity is potentially dangerous. Hence, communication enablers together with highly-secure human authentication mechanisms have to be designed.In addition, it is important to understand how human beings, as the primary users, interact with wearable devices on a day-to-day basis; usage should be comfortable, seamless, user-friendly, and mindful of urban dynamics. Usually the connectivity between wearables and the cloud is executed through the user’s more power independent gateway: this will usually be a smartphone, which may have potentially unreliable infrastructure connectivity. In response to these unique challenges, this thesis advocates for the adoption of direct, secure, proximity-based communication enablers enhanced with multi-factor authentication (hereafter refereed to MFA) that can integrate/interact with wearable technology. Their intelligent combination together with the connection establishment automation relying on the device/user social relations would allow to reliably grant or deny access in cases of both stable and intermittent connectivity to the trusted authority running in the cloud.The introduction will list the main communication paradigms, applications, conventional network architectures, and any relevant wearable-specific challenges. Next, the work examines the improved architecture and security enablers for clusterization between wearable gateways with a proximity-based communication as a baseline. Relying on this architecture, the author then elaborates on the social ties potentially overlaying the direct connectivity management in cases of both reliable and unreliable connection to the trusted cloud. The author discusses that social-aware cooperation and trust relations between users and/or the devices themselves are beneficial for the architecture under proposal. Next, the author introduces a protocol suite that enables temporary delegation of personal device use dependent on different connectivity conditions to the cloud.After these discussions, the wearable technology is analyzed as a biometric and behavior data provider for enabling MFA. The conventional approaches of the authentication factor combination strategies are compared with the ‘intelligent’ method proposed further. The assessment finds significant advantages to the developed solution over existing ones.On the practical side, the performance evaluation of existing cryptographic primitives, as part of the experimental work, shows the possibility of developing the experimental methods further on modern wearable devices.In summary, the set of enablers developed here for wearable technology connectivity is aimed at enriching people’s everyday lives in a secure and usable way, in cases when communication to the cloud is not consistently available

Transforming traffic surveillance: a YOLO-based approach to detecting helmetless riders through CCTV

CCTV systems, while ubiquitous for traffic surveillance in Indonesian roadways, remain underutilized in their potential. The integration of AI and Computer Vision technologies can transform CCTV into a valuable tool for law enforcement, specifically in monitoring and addressing helmet non-compliance among motorcycle riders. This study aims to develop an intelligent system for the accurate detection of helmetless motorcyclists using image analysis. The approach relies on deep learning, involving the creation of a dataset with 764 training images and 102 testing images. A deep convolutional neural network with 23 layers is configured, trained with a batch size of 10 over ten epochs, and employs the YOLO method to identify objects in images and subsequently detect helmetless riders. Accuracy assessment is carried out using the mean Average Precision (mAP) method, resulting in a notable 82.81% detection accuracy for riders without helmets and 75.78% for helmeted riders. The overall mAP score is 79.29%, emphasizing the system's potential to substantially improve road safety and law enforcement effort