IoT Modes of Operations with Different Security Key Management Techniques: A Survey

Mohamed, Samah; Hassan, Ayman M.; Aslan, Heba K.

doi:10.18280/ijsse.110604

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…An approach for summarizing movies from IoT surveillance networks using deep CNNs and hierarchical weighted fusion is suggested by Muhammad et al [18] Naik and Thimmaiah [19], Mohamed et al [20], Bangare and Patil [21]. The suggested solution seeks to retain high accuracy in video summarization while lowering the high computational cost of processing video data in IoT surveillance networks.…”

Section: Related Workmentioning

confidence: 99%

A Robust Multi Descriptor Fusion with One-Class CNN for Detecting Anomalies in Video Surveillance

Chidananda,

Kumar

2023

IJSSE

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In the domain of computer vision and machine learning, Video Anomaly Detection (VAD) has emerged as a pivotal area of inquiry, particularly relevant to security, surveillance, and video analytics. The extraction of pertinent features from video data constitutes a foundational aspect of VAD, enabling the discernment of anomalous patterns and structures. Features such as motion, texture, shape, and aesthetics are extracted and tailored according to the exigencies of the application and the intrinsic properties of the video content. The amalgamation of multiple features is often requisite for refining the accuracy of anomaly detection systems. Given the inherent high dimensionality of video data, dimensionality reduction techniques have been employed to mitigate computational demands and enhance the precision of the anomaly detection process. The present study delineates a novel approach centered on the deployment of a One-Class Convolutional Neural Network (CNN). This network is exclusively trained on normal events to establish a baseline representation of typicality. During the evaluation phase, the network is tasked with predicting the normality or abnormality of new video segments against this established norm. Moreover, this work introduces a novel fused feature descriptor, referred to as the Multiple Feature Descriptor (MFD), which is designed to encapsulate the spatiotemporal attributes of video data effectively. The proposed methodology has been subjected to rigorous testing against publicly available datasets, where it has demonstrated superior performance, outstripping numerous contemporary state-of-the-art methods in both accuracy and computational efficiency.

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Section: Related Workmentioning

confidence: 99%

A Robust Multi Descriptor Fusion with One-Class CNN for Detecting Anomalies in Video Surveillance

Chidananda,

Kumar

2023

IJSSE

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“…To reduce damages and injuries, the FFSS provides real-time observation by alerting the fire station or neighborhood residents. The biggest challenge with WSN networks is to reduce power consumption for sustainable usage while optimizing the rate of data transmission, instead, an Low Power Wide Area (LPWA) technology-based network is recommended to decrease energy used [22]. A study on Track Bicycle Performance Monitoring using ZigBee [23] reduced power consumption drastically by putting the ZigBee into sleep mode for redundant and converged data, as well as putting the microcontroller in sleep mode when there was no sensor firing.…”

Section: Related Workmentioning

confidence: 99%

LoRa-Based IoT System for Emergency Assistance and Safety in Mountaineering

Rai Sharma,

Raghuwanshi,

Chandak

et al. 2023

IJSSE

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Mountaineering and trekking are outdoor activities that attract thousands of enthusiasts each year. These activities often take place in remote and isolated areas, where medical assistance is scarce, and rescue operations are challenging. When trekkers are injured in such areas, they face significant challenges in accessing help due to the harsh terrain, limited resources, and most notably due to lack of communication infrastructure. In the last century, an average of four people were killed each year on Mount Everest alone, but in the last decade, the number of deaths increased to an average of 6.5 annually. There is a need for an efficient, flexible, and economical solution for safety in mountaineering and other long-distance remote use cases where cellular networks prove ineffective. One of the promising technologies suitable for this application is the LoRa (long range) Network, which is used for communication in isolated areas such as wooded areas (forests) with more minor power consumption. Fast and low-effort localization can potentially increase the chances of saving injured individuals' lives. The proposed system developed a device made of a microcontroller, a Global Positioning System (GPS) module and an accelerometer module to gather trekker data, a LoRa module, and Bluetooth module to transmit data as well as a power supply, and an integrated mobile software application. The system successfully tested the functionality and reliability of an Internet of Things (IoT) network for tracking and alerting purposes, providing a simple, cost-effective system for safety assistance in case of emergencies. The system showed high accuracy in location tracking, long-range communication capability of up to 1 to 2 kilometers, and reliable performance in various environmental conditions.

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IoT Modes of Operations with Different Security Key Management Techniques: A Survey

Cited by 2 publications

References 33 publications

A Robust Multi Descriptor Fusion with One-Class CNN for Detecting Anomalies in Video Surveillance

A Robust Multi Descriptor Fusion with One-Class CNN for Detecting Anomalies in Video Surveillance

LoRa-Based IoT System for Emergency Assistance and Safety in Mountaineering

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