Optimization of Face Recognition System Based on Azure IoT Edg

Abstract

With the rapid development of artificial intelligence, face recognition systems are widely used in daily lives. Face recognition applications often need to process large amounts of image data. Maintaining the accuracy and low latency is critical to face recognition systems. After analyzing the two-tier architecture "client-cloud" face recognition systems, it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded, and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network. This paper proposes a flexible and efficient edge computing accelerated architecture. By offloading part of the computing tasks to the edge server closer to the data source, edge computing resources are used for image preprocessing to reduce the number of images to be transmitted, thus reducing the network transmission overhead. Moreover, the application code does not need to be rewritten and can be easily migrated to the edge server. We evaluate our schemes based on the open source Azure IoT Edge, and the experimental results show that the three-tier architecture "Client-Edge-Cloud" face recognition system outperforms the state-of-art face recognition systems in reducing the average response time.

Blockchain Security Attacks, Challenges, and Solutions for the Future Distributed IoT Network

“…In fact, even just the terms ''DTL'' and ''blockchain'' are confusing. In short, there is a general lack of technical understanding among consumers, business firms, and authorities [10], [94], [95], including in areas such as 1) the blockchain job market, 2) DTL, 3) smart contracts that require that the business logic nature in ledgers be automatically executed, 4) knowing where to look to find the necessary talent, and 5) investing in blockchain jobs regardless of the demand for new talent.…”

Future Smart Connected Communities to Fight COVID-19 Outbreak

“…A smart testing booth can include multiple sensors like infrared large-scale body temperature sensor, no contact oxygen level sensors with Red, Green, and Blue (RGB) camera, RFID scanners and AI assisted smart cameras. Single person at a time can enter one side of the glass-walled testing booth and will be identified through RFID tag based wearable device or optimized face recognition algorithm [32] . In-built sensors in the booth can record person’s body temperature and oxygen level including other PGHD which will be stored in medical cloud.…”

“…Here, we present a smart sanitizing scenario which uses IoT technology. For instance, when a user will walk up to the home from the driveway and before he/she even reaches the front door, the door unlocks using optimize face recognition application [32] on the edge, then user will walk inside the home. User’s phone will connect with home Wi-Fi network, and will show the presence of the user at home.…”

IoT-RECSM—Resource-Constrained Smart Service Migration Framework for IoT Edge Computing Environment

“…end if 7: end while 8: return f For a migrating service S v i ,j , the migration delay delay i,n of every candidate node v n ∈ E h can be predicted by the equation: size(S v i ,j )/NMF(ENN, v i , v n ). The Node Selection Model will choose an edge node with minimal delay as the destination dest for S v i ,j , by Equation (6).…”

“…In ECP, the edge layer is composed of many edge nodes, and it can provide supplementary capabilities for cloud centers. Presently, the ECP has been used into some smart services [5][6][7][8][9][10][11][12][13][14][15][16], because it can complete complex context-awareness and data analytics in real-time, and reduce the service latency and the whole network traffic [17].…”