Efficient Security and Authentication for Edge-Based Internet of Medical Things

Internet of Medical Things (IoMT)-driven smart health and emotional care is revolutionizing the healthcare industry by embracing several technologies related to multimodal physiological data collection, communication, intelligent automation, and efficient manufacturing. The authentication and secure exchange of electronic health records (EHRs), comprising of patient data collected using wearable sensors and laboratory investigations, is of paramount importance. In this article, we present a novel high payload and reversible EHR embedding framework to secure the patient information successfully and authenticate the received content. The proposed approach is based on novel left data mapping (LDM), pixel repetition method (PRM), RC4 encryption, and checksum computation. The input image of size $M imes N$ is upscaled by using PRM that guarantees reversibility with lesser computational complexity. The binary secret data are encrypted using the RC4 encryption algorithm and then the encrypted data are grouped into 3-bit chunks and converted into decimal equivalents. Before embedding, these decimal digits are encoded by LDM. To embed the shifted data, the cover image is divided into $2 imes 2$ blocks and then in each block, two digits are embedded into the counter diagonal pixels. For tamper detection and localization, a checksum digit computed from the block is embedded into one of the main diagonal pixels. A fragile logo is embedded into the cover images in addition to EHR to facilitate early tamper detection. The average peak signal to noise ratio (PSNR) of the stego-images obtained is 41.95 dB for a very high embedding capacity of 2.25 bits per pixel. Furthermore, the embedding time is less than 0.2 s. Experimental results reveal that our approach outperforms many state-of-the-art techniques in terms of payload, imperceptibility, computational complexity, and capability to detect and localize tamper. All the attributes affirm that the proposed scheme is a potential candidate for providing better security and authentication solutions for IoMT-based smart health

Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes

The development of multimedia and deep learning technology bring new challenges to steganography and steganalysis techniques. Meanwhile, robust steganography, as a class of new techniques aiming to solve the problem of covert communication under lossy channels, has become a new research hotspot in the field of information hiding. To improve the communication reliability and efficiency for current real-time robust steganography methods, a concatenated code, composed of Syndrome–Trellis codes (STC) and cyclic redundancy check (CRC) codes, is proposed in this paper. The enhanced robust adaptive steganography framework proposed is this paper is characterized by a strong error detection capability, high coding efficiency, and low embedding costs. On this basis, three adaptive steganographic methods resisting JPEG compression and detection are proposed. Then, the fault tolerance of the proposed steganography methods is analyzed using the residual model of JPEG compression, thus obtaining the appropriate coding parameters. Experimental results show that the proposed methods have a significantly stronger robustness against compression, and are more difficult to be detected by statistical based steganalytic methods

Overlooked Influence of Indian Hemp (Cannabis sativa) Cultivation on Soil Physicochemical Fertility of Humid Tropical Agroecosystems: Lowland Soils

One agricultural practice that may be depleting plant nutrients in wetland soils of the humid tropics is cultivation of Indian hemp (Cannabis sativa), also called Marijuana. Though Nigerian Law, adopted from International Conventions on narcotics, prohibits handling of any part of cannabis plant, it is still illicitly cultivated. This practice may be undermining the quality of wetland agroecosystems. To support these concerns with empirical data, the influence of Cannabis cultivation on soil physicochemical fertility of wetland agroecosystems was assessed at a representative location in southwestern Nigeria. The study compared four land-use options; land not used for Cannabis cultivation (NUC), land currently under Cannabis cultivation (CCC), farmlands converted from Cannabis to alternative use (CAU), and Cannabis farmlands abandoned or seized (ABS). Soil data from the pedogenetic horizons under these land-use options were averaged and analysed. There were significant differences in soil bulk density, with low values in NUC (1.36 Mg m–3) < medium values in CCC (1.55 Mg m–3) < high values in both CAU and ABS (1.62-1.66 Mg m–3). The highest value in the ABS (1.66 M  m–3) is slightly above the critical limit (1.60 Mg m–3) for root growth. Soil compaction in Cannabis farmland thus worsened even after discontinuation of cultivation. Soil pH, soil organic C, total N, exchangeable Ca, exchangeable Mg, apparent and effective cation exchange capacity also differed thus NUC ≥ CCC ≥ CAU ≥ ABS, while base saturation showed an inverse trend. Available P was, however, higher in CCC (14.32 mg kg–1) than the rest, with lowest values in ABS (5.83 mg kg–1). Micronutrients (Mn, Zn and Cu), excluding Fe which was unaffected, followed the trend of soil pH. It is concluded that continuous cultivation of Cannabis in humid tropical lowlands compacts the soil and drains soil nutrients except available P whose status is rather elevated. The practice thus poses a threat to food security and ecological well-being.&nbsp