A Novel Secure Occupancy Monitoring Scheme Based on Multi-Chaos Mapping

Smart building control, managing queues for instant points of service, security systems, and customer support can benefit from the number of occupants information known as occupancy. Due to interrupted real-time continuous monitoring capabilities of state-of-the-art cameras, a vision-based system can be easily deployed for occupancy monitoring. However, processing of images or videos over insecure channels can raise several privacy concerns due to constant recording of an image or video footage. In this context, occupancy monitoring along with privacy protection is a challenging task. This paper presents a novel chaos-based lightweight privacy preserved occupancy monitoring scheme. Persons’ movements were detected using a Gaussian mixture model and Kalman filtering. A specific region of interest, i.e., persons’ faces and bodies, was encrypted using multi-chaos mapping. For pixel encryption, Intertwining and Chebyshev maps were employed in confusion and diffusion processes, respectively. The number of people was counted and the occupancy information was sent to the ThingSpeak cloud platform. The proposed chaos-based lightweight occupancy monitoring system is tested against numerous security metrics such as correlation, entropy, Number of Pixel Changing Rate (NPCR), Normalized Cross Correlation (NCC), Structural Content (SC), Mean Absolute Error (MAE), Mean Square Error (MSE), Peak to Signal Noise Ratio (PSNR), and Time Complexity (TC). All security metrics confirm the strength of the proposed scheme

Multi-Chaos-Based Lightweight Image Encryption-Compression for Secure Occupancy Monitoring

With the advancement of camera and wireless technologies, surveillance camera-based occupancy has received ample attention from the research community. However, camera-based occupancy monitoring and wireless channels, especially Wi-Fi hotspot, pose serious privacy concerns and cybersecurity threats. Eavesdroppers can easily access confidential multimedia information and the privacy of individuals can be compromised. As a solution, novel encryption techniques for the multimedia data concealing have been proposed by the cryptographers. Due to the bandwidth limitations and computational complexity, traditional encryption methods are not applicable to multimedia data. In traditional encryption methods such as Advanced Encryption Standard (AES) and Data Encryption Standard (DES), once multimedia data are compressed during encryption, correct decryption is a challenging task. In order to utilize the available bandwidth in an efficient way, a novel secure video occupancy monitoring method in conjunction with encryption-compression has been developed and reported in this paper. The interesting properties of Chebyshev map, intertwining map, logistic map, and orthogonal matrix are exploited during block permutation, substitution, and diffusion processes, respectively. Real-time simulation and performance results of the proposed system show that the proposed scheme is highly sensitive to the initial seed parameters. In comparison to other traditional schemes, the proposed encryption system is secure, efficient, and robust for data encryption. Security parameters such as correlation coefficient, entropy, contrast, energy, and higher key space prove the robustness and efficiency of the proposed solution

Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication

Nano-scaled structures, wireless sensing, wearable devices, and wireless communications systems are anticipated to support the development of new next-generation technologies in the near future. Exponential rise in future Radio-Frequency (RF) sensing systems have demonstrated its applications in areas such as wearable consumer electronics, remote healthcare monitoring, wireless implants, and smart buildings. In this paper, we propose a novel, non-wearable, device-free, privacy-preserving Wi-Fi imaging-based occupancy detection system for future smart buildings. The proposed system is developed using off-the-shelf non-wearable devices such as Wi-Fi router, network interface card, and an omnidirectional antenna for future body centric communication. The core idea is to detect presence of person along its activities of daily living without deploying a device on person's body. The Wi-Fi signals received using non-wearable devices are converted into time-frequency scalograms. The occupancy is detected by classifying the scalogram images using an auto-encoder neural network. In addition to occupancy detection, the deep neural network also identifies the activity performed by the occupant. Moreover, a novel encryption algorithm using Chirikov and Intertwining map-based is also proposed to encrypt the scalogram images. This feature enables secure storage of scalogram images in a database for future analysis. The classification accuracy of the proposed scheme is 91.1%

A Lightweight Chaos-Based Medical Image Encryption Scheme Using Random Shuffling and XOR Operations

Medical images possess significant importance in diagnostics when it comes to healthcare systems. These images contain confidential and sensitive information such as patients’ X-rays, ultrasounds, computed tomography scans, brain images, and magnetic resonance imaging. However, the low security of communication channels and the loopholes in storage systems of hospitals or medical centres put these images at risk of being accessed by unauthorized users who illegally exploit them for non-diagnostic purposes. In addition to improving the security of communication channels and storage systems, image encryption is a popular strategy adopted to ensure the safety of medical images against unauthorized access. In this work, we propose a lightweight cryptosystem based on Henon chaotic map, Brownian motion, and Chen’s chaotic system to encrypt medical images with elevated security. The efficiency of the proposed system is proved in terms of histogram analysis, adjacent pixels correlation analysis, contrast analysis, homogeneity analysis, energy analysis, NIST analysis, mean square error, information entropy, number of pixels changing rate, unified average changing intensity, peak to signal noise ratio and time complexity. The experimental results show that the proposed cryptosystem is a lightweight approach that can achieve the desired security level for encrypting confidential image-based patients’ information

A Novel Privacy Approach of Digital Aerial Images Based on Mersenne Twister Method with DNA Genetic Encoding and Chaos

Aerial photography involves capturing images from aircraft and other flying objects, including Unmanned Aerial Vehicles (UAV). Aerial images are used in many fields and can contain sensitive information that requires secure processing. We proposed an innovative new cryptosystem for the processing of aerial images utilizing a chaos-based private key block cipher method so that the images are secure even on untrusted cloud servers. The proposed cryptosystem is based on a hybrid technique combining the Mersenne Twister (MT), Deoxyribonucleic Acid (DNA), and Chaotic Dynamical Rossler System (MT-DNA-Chaos) methods. The combination of MT with the four nucleotides and chaos sequencing creates an enhanced level of security for the proposed algorithm. The system is tested at three separate phases. The combined effects of the three levels improve the overall efficiency of the randomness of data. The proposed method is computationally agile, and offered more security than existing cryptosystems. To assess, this new system is examined against different statistical tests such as adjacent pixels correlation analysis, histogram consistency analyses and its variance, visual strength analysis, information randomness and uncertainty analysis, pixel inconsistency analysis, pixels similitude analyses, average difference, and maximum difference. These tests confirmed its validity for real-time communication purposes

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity

A new color image encryption technique using DNA computing and Chaos-based substitution box

In many cases, images contain sensitive information and patterns that require secure processing to avoid risk. It can be accessed by unauthorized users who can illegally exploit them to threaten the safety of people’s life and property. Protecting the privacies of the images has quickly become one of the biggest obstacles that prevent further exploration of image data. In this paper, we propose a novel privacy-preserving scheme to protect sensitive information within images. The proposed approach combines deoxyribonucleic acid (DNA) sequencing code, Arnold transformation (AT), and a chaotic dynamical system to construct an initial S-box. Various tests have been conducted to validate the randomness of this newly constructed S-box. These tests include National Institute of Standards and Technology (NIST) analysis, histogram analysis (HA), nonlinearity analysis (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), bit independence criterion strict avalanche criterion (BIC-SAC), bit independence criterion nonlinearity (BIC-NL), equiprobable input/output XOR distribution, and linear approximation probability (LP). The proposed scheme possesses higher security wit NL = 103.75, SAC ≈ 0.5 and LP = 0.1560. Other tests such as BIC-SAC and BIC-NL calculated values are 0.4960 and 112.35, respectively. The results show that the proposed scheme has a strong ability to resist many attacks. Furthermore, the achieved results are compared to existing state-of-the-art methods. The comparison results further demonstrate the effectiveness of the proposed algorithm

A novel symmetric image cryptosystem resistant to noise perturbation based on S8 elliptic curve S-boxes and chaotic maps

The recent decade has seen a tremendous escalation of multimedia and its applications. These modern applications demand diverse security requirements and innovative security platforms. In this manuscript, we proposed an algorithm for image encryption applications. The core structure of this algorithm relies on confusion and diffusion operations. The confusion is mainly done through the application of the elliptic curve and S8 symmetric group. The proposed work incorporates three distinct chaotic maps. A detailed investigation is presented to analyze the behavior of chaos for secure communication. The chaotic sequences are then accordingly applied to the proposed algorithm. The modular approach followed in the design framework and integration of chaotic maps into the system makes the algorithm viable for a variety of image encryption applications. The resiliency of the algorithm can further be enhanced by increasing the number of rounds and S-boxes deployed. The statistical findings and simulation results imply that the algorithm is resistant to various attacks. Moreover, the algorithm satisfies all major performance and quality metrics. The encryption scheme can also resist channel noise as well as noise-induced by a malicious user. The decryption is successfully done for noisy data with minor distortions. The overall results determine that the proposed algorithm contains good cryptographic properties and low computational complexity makes it viable to low profile applications