An Efficient Cryptosystem for Video Surveillance in the Internet of Things Environment

Hamza, Rafik; Hassan, Alzubair; Huang, Teng; Ke, Lishan; Yan, Hongyang

doi:10.1155/2019/1625678

Cited by 27 publications

(8 citation statements)

References 42 publications

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“…Observational findings confirmed the suggested cryptosystem's efficiency with respect to reliability and highlevel protection relative to many other modern image cryptographic algorithms. During most of the distribution of essential keyframes to customized WCE healthcare providers and gastroenterologists, Hamza et al [42] reported an effective cryptosystem for IoT-based monitoring systems.…”

Section: Preliminary Workmentioning

confidence: 99%

“…e outcomes of the proposed methodology are fairly reasonable as well as nearly ideal standards. We compared proposed methods with past referenced schemes such as [20,39,41,42,50,71]. We noticed that the referenced surveillance and encryption scheme is produced relative results to attain confidentiality of the security.…”

Section: Comparative Analysis Among the Monitoring And Surveillance S...mentioning

confidence: 99%

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Secure Smart Healthcare Monitoring in Industrial Internet of Things (IIoT) Ecosystem with Cosine Function Hybrid Chaotic Map Encryption

Khan

et al. 2022

Scientific Programming

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The technological progression is raised as a hybrid ecosystem with the industrial Internet of Things (IIoT). Among them, healthcare is also broadly unified with the Internet of Things to develop an industrial forthcoming system. Utilizing this type of system can be facilitating optimum patient monitoring, competent diagnosis, intensive care, and including the appropriate operation against the existing critical diseases. Due to enormous data theft or privacy leakage, security, and privacy towards patient-based informative data, the preservation of personal patients’ informative data has now become a necessity in the digitized community. The produced article is underlined on handsomely monitoring, perceptively extracted keyframe, and further processed lightweight cosine functions using hybrid way chaotic map keyframe image encryption. Initially, a regular concept of extracted keyframe is deployed to salvage meaningful detected frames by transmitting an alert autonomously to the administration. Then, lightweight cosine function for encryption is employed. This encryption incorporates keyframe exceedingly secure and safe from the outside world or any adversary. Our proposed methodology validates effectiveness throughout the IIoT ecosystem. The produced outcome is highly acceptable and has minimum execution time, robustness, and reasonably adopted cost-effective, secure parameter than any other (keyframes) image encryption methods. Furthermore, this methodology has optimally reduced bandwidth, essential communicating price, transmission cost, storage, and immediately judicious analysis of each occurred activity from the outside world or any adversary to remain secure and confident about the real patient-based data in the smartly developed environment.

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Section: Preliminary Workmentioning

confidence: 99%

Section: Comparative Analysis Among the Monitoring And Surveillance S...mentioning

confidence: 99%

Secure Smart Healthcare Monitoring in Industrial Internet of Things (IIoT) Ecosystem with Cosine Function Hybrid Chaotic Map Encryption

Khan

et al. 2022

Scientific Programming

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“…To increase distributed video surveillance systems' resiliency and intelligence, network bandwidth and reaction time between wireless cameras and cloud servers are being reduced in the Internet-of-things (IoT). In [9], an effective cryptosystem is used to create a safe IoT-based surveillance system. ere are three parts to it.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

He¹,

Gao²,

Zheng³

et al. 2021

wpt

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Companies that produce energy transmit it to any or all households via a power grid, which is a regulated power transmission hub that acts as a middleman. When a power grid fails, the whole area it serves is blacked out. To ensure smooth and effective functioning, a power grid monitoring system is required. Computer vision is among the most commonly utilized and active research applications in the world of video surveillance. Though a lot has been accomplished in the field of power grid surveillance, a more effective compression method is still required for large quantities of grid surveillance video data to be archived compactly and sent efficiently. Video compression has become increasingly essential with the advent of contemporary video processing algorithms. An algorithm’s efficacy in a power grid monitoring system depends on the rate at which video data is sent. A novel compression technique for video inputs from power grid monitoring equipment is described in this study. Due to a lack of redundancy in visual input, traditional techniques are unable to fulfill the current demand standards for modern technology. As a result, the volume of data that needs to be saved and handled in live time grows. Encoding frames and decreasing duplication in surveillance video using texture information similarity, the proposed technique overcomes the aforementioned problems by Robust Particle Swarm Optimization (RPSO) based run-length coding approach. Our solution surpasses other current and relevant existing algorithms based on experimental findings and assessments of different surveillance video sequences utilizing varied parameters. A massive collection of surveillance films was compressed at a 50% higher rate using the suggested approach than with existing methods.

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“…Hamza et al [23] addressed a new idea which is efficient cryptosystem to provide security into secure surveillance approach at the IoT ecosystem. Further explanation of the cryptosystem, he mentioned that this approach has three-fold.…”

Section: Related Workmentioning

confidence: 99%

“…In which our proposed method has gaining impetus in each mentioned area. We compared with other methods such as [11], [31], [23] and [24]. Each method has proposed good values and tried to cover confidentiality of the secure parameter of the image encryption.…”

Section: F Comparative Anlysis With Existing Surveilance Schemesmentioning

confidence: 99%

SMSH: Secure Surveillance Mechanism on Smart Healthcare IoT System With Probabilistic Image Encryption

Khan

Ahamad

et al. 2020

IEEE Access

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The Internet of Things is made of diverse networked things (i.e., smart, intelligent devices) that are consistently interconnected, producing meaningful data across the network without human interaction. Nowadays, the Healthcare system is widely interconnected with IoT environments to facilitate the best possible patient monitoring, efficient diagnosis, and timely operate with existing diseases towards the patients. Concerning the security and privacy of the patient's information. This paper is focused on Secure surveillance mechanism for a medical healthcare system with enabled internet of Things (sensors) by intelligently recorded video summary into the server and keyframes image encryption. This paper is twofold. Firstly, a well-organized keyframe extraction mechanism is called to extract meaningful image frames (detected normal/abnormal activities keyframe) by the visual sensor with an alert sent to the concerned authority in the healthcare system. Secondly, the final decision about the happened activity with extracted keyframes to keep highly secure from any adversary, and we propose an efficient probabilistic and lightweight encryption algorithm. Our proposed mechanism verifies effectiveness through producing results, robustness in nature, minimum execution time, and comparatively secure than other images (keyframes) encryption algorithms. Additionally, this mechanism can reduce storage, bandwidth, required transmission cost, and timely analysis of happened activity from any adversary with protecting the privacy of the patient's information in the IoT enabled healthcare system.

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An Efficient Cryptosystem for Video Surveillance in the Internet of Things Environment

Cited by 27 publications

References 42 publications

Secure Smart Healthcare Monitoring in Industrial Internet of Things (IIoT) Ecosystem with Cosine Function Hybrid Chaotic Map Encryption

Secure Smart Healthcare Monitoring in Industrial Internet of Things (IIoT) Ecosystem with Cosine Function Hybrid Chaotic Map Encryption

Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

SMSH: Secure Surveillance Mechanism on Smart Healthcare IoT System With Probabilistic Image Encryption

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