Smart Surveillance System Using Computer Vision and Internet of Things

2020 ◽  
Vol 17 (1) ◽  
pp. 68-73
Author(s):  
M. Hemaanand ◽  
V. Sanjay Kumar ◽  
R. Karthika
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Tracking System ◽  
Single Shot ◽  
Stream Data ◽  
Advanced Technique ◽  
The Real ◽  
Smart Surveillance ◽  
Bounding Boxes

With the evolution of technology ensuring people for their safety and security all around the time constantly is a big challenge. We propose an advanced technique based on deep learning and artificial intelligence platform that can monitor the people, their homes and their surroundings providing them a quantifiable increase in security. We have surveillance cameras in our homes for video capture as well as security purposes. Our proposed technique is to detect and classify as well as inform the user if there is any breach in security of the classified object using the cameras by implementing deep learning techniques and the technology of internet of things. It can serve as a perimeter monitoring and intruder alert system in smart surveillance environment. This paper provides a well-defined structure for live stream data analysis. It overcomes the challenge of static closed circuit cameras television as it serves as a motion based tracking system and monitors events in real time to ensure activities are limited to specific persons within authorized areas. It has the advantage of creating multiple bounding boxes to track down the objects which could be any living or non-living thing based on the trained modules. The trespasser or intruder can be efficiently detected using the CCTV camera surveillance which is being supported by the real-time object classifier algorithm at the intermediate module. The proposed method is mainly supported by the real time object detection and classification which is implemented using Mobile Net and Single shot detector.

scholarly journals Internet of Things (IoT) Authentication and Access Control by Hybrid Deep Learning Method - A Study

2021 ◽  
Vol 2 (4) ◽  
pp. 236-245
Author(s):  
Joy Iong Zong Chen ◽  
Kong-Long Lai
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Control Device ◽  
Security And Privacy ◽  
The Real ◽  
Intelligent Technique ◽  
History Of ◽  
Iot Devices ◽  
Future Direction

In the history of device computing, Internet of Things (IoT) is one of the fastest growing field that facing many security challenges. The effective efforts should have been made to address the security and privacy issues in IoT networks. The IoT devices are basically resource control device which provide routine attract impression for cyber attackers. The IoT participation nodes are increasing rapidly with more resource constrained that creating more challenging conditions in the real time. The existing methods provide an ineffective response to the tasks for effective IoT device. Also, it is an insufficient to involve the complete security and safety spectrum of the IoT networks. Because of the existing algorithms are not enriched to secure IoT bionetwork in the real time environment. The existing system is not enough to detect the proxy to the authorized person in the embedding devices. Also, those methods are believed in single model domain. Therefore, the effectiveness is dropping for further multimodal domain such as combination of behavioral and physiological features. The embedding intelligent technique will be securitizing for the IoT devices and networks by deep learning (DL) techniques. The DL method is addressing different security and safety problems arise in real time environment. This paper is highlighting hybrid DL techniques with Reinforcement Learning (RL) for the better performance during attack and compared with existing one. Also, here we discussed about DL combined with RL of several techniques and identify the higher accuracy algorithm for security solutions. Finally, we discuss the future direction of decision making of DL based IoT security system.

scholarly journals AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiang Yu ◽  
Chun Shan ◽  
Jilong Bian ◽  
Xianfei Yang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Computing Environment ◽  
The Real ◽  
Edge Node ◽  
Model Based

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning

2022 ◽  
Vol 25 (3) ◽  
pp. 28-33
Author(s):  
Francesco Restuccia ◽  
Tommaso Melodia
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Software Defined Radio ◽  
Wireless Systems ◽  
Physical World ◽  
Wireless Protocols ◽  
Key Innovation ◽  
Custom Made ◽  
Time Waveform

Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.

Prediction of Crime Scene Objects Using Deep Learning

2021 ◽  
Vol 9 (VIII) ◽  
pp. 857-864
Author(s):  
Vibhavari B Rao
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Computer Architecture ◽  
Real Time ◽  
Surveillance System ◽  
Crime Scene ◽  
Detection Model ◽  
Detection Algorithms ◽  
Performance Metric ◽  
Smart Surveillance

The crime rates today can inevitably put a civilian's life in danger. While consistent efforts are being made to alleviate crime, there is also a dire need to create a smart and proactive surveillance system. Our project implements a smart surveillance system that would alert the authorities in real-time when a crime is being committed. During armed robberies and hostage situations, most often, the police cannot reach the place on time to prevent it from happening, owing to the lag in communication between the informants of the crime scene and the police. We propose an object detection model that implements deep learning algorithms to detect objects of violence such as pistols, knives, rifles from video surveillance footage, and in turn send real-time alerts to the authorities. There are a number of object detection algorithms being developed, each being evaluated under the performance metric mAP. On implementing Faster R-CNN with ResNet 101 architecture we found the mAP score to be about 91%. However, the downside to this is the excessive training and inferencing time it incurs. On the other hand, YOLOv5 architecture resulted in a model that performed very well in terms of speed. Its training speed was found to be 0.012 s / image during training but naturally, the accuracy was not as high as Faster R-CNN. With good computer architecture, it can run at about 40 fps. Thus, there is a tradeoff between speed and accuracy and it's important to strike a balance. We use transfer learning to improve accuracy by training the model on our custom dataset. This project can be deployed on any generic CCTV camera by setting up a live RTSP (real-time streaming protocol) and streaming the footage on a laptop or desktop where the deep learning model is being run.

IoT Device Onboarding, Monitoring, and Management

2021 ◽  
pp. 227-255
Author(s):  
Selvaraj Kesavan ◽  
Senthilkumar J. ◽  
Suresh Y. ◽  
Mohanraj V.
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Secure Communication ◽  
Mutual Authentication ◽  
Modern World ◽  
Cloud Platform ◽  
Privacy And Security ◽  
The Real ◽  
Healthy Environment

In establishing a healthy environment for connectivity devices, it is essential to ensure that privacy and security of connectivity devices are well protected. The modern world lives on data, information, and connectivity. Various kinds of sensors and edge devices stream large volumes of data to the cloud platform for storing, processing, and deriving insights. An internet of things (IoT) system poses certain difficulties in discretely identifying, remotely configuring, and controlling the devices, and in the safe transmission of data. Mutual authentication of devices and networks is crucial to initiate secure communication. It is important to keep the data in a secure manner during transmission and in store. Remotely operated devices help to monitor, control, and manage the IoT system efficiently. This chapter presents a review of the approaches and methodologies employed for certificate provisioning, device onboarding, monitoring, managing, and configuring of IoT systems. It also examines the real time challenges and limitations in and future scope for IoT systems.

Applying Security to a Big Stream Cloud Architecture for the Internet of Things

2020 ◽  
pp. 1260-1284
Author(s):  
Laura Belli ◽  
Simone Cirani ◽  
Luca Davoli ◽  
Gianluigi Ferrari ◽  
Lorenzo Melegari ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Large Data ◽  
The Internet ◽  
Low Latency ◽  
Stream Data ◽  
Memory Processing ◽  
Cloud Architecture ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) is expected to interconnect billions (around 50 by 2020) of heterogeneous sensor/actuator-equipped devices denoted as “Smart Objects” (SOs), characterized by constrained resources in terms of memory, processing, and communication reliability. Several IoT applications have real-time and low-latency requirements and must rely on architectures specifically designed to manage gigantic streams of information (in terms of number of data sources and transmission data rate). We refer to “Big Stream” as the paradigm which best fits the selected IoT scenario, in contrast to the traditional “Big Data” concept, which does not consider real-time constraints. Moreover, there are many security concerns related to IoT devices and to the Cloud. In this paper, we analyze security aspects in a novel Cloud architecture for Big Stream applications, which efficiently handles Big Stream data through a Graph-based platform and delivers processed data to consumers, with low latency. The authors detail each module defined in the system architecture, describing all refinements required to make the platform able to secure large data streams. An experimentation is also conducted in order to evaluate the performance of the proposed architecture when integrating security mechanisms.

scholarly journals Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3930 ◽  
Author(s):  
Ayaz Hussain ◽  
Umar Draz ◽  
Tariq Ali ◽  
Saman Tariq ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Air Pollutants ◽  
Garbage Collection ◽  
Air Pollutant ◽  
Real Time Monitoring ◽  
Collection System ◽  
The Real ◽  
The Status

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

Design of High Accuracy Robot Welding Machine for a Cone Bottom

2014 ◽  
Vol 496-500 ◽  
pp. 1289-1292
Author(s):  
De Huan Tang ◽  
De Yang Luo
Keyword(s):  
Real Time ◽  
Tracking System ◽  
High Accuracy ◽  
Welding Robot ◽  
Robotic Welding ◽  
High Quality ◽  
The Real ◽  
Laser Tracking ◽  
Welding Machine

This paper designed a special welding machine for an aluminum cone bottom workpiece. This machine contains highly accurate positioner system, laser tracking system, and robotic welding devices. It is used to weld the transverse seams and the longitudinal seams of the workpiece. The interaction of welding robot with positioner and the real-time seam correcting can ensure high quality of welding.

Sign in / Sign up

Export Citation Format

Share Document