The privacy of medical data and resource restrictions in the Internet of Things (IoT) nodes prohibit medical users from utilizing disease detection (DD) services offered by the health cloud in the mobile healthcare network (MHN). Also, health clouds may need the DD procedures to be private. Therefore, the essential requirements for MHN DD services are (i) performing accurate and fast DD without jeopardizing the privacy of health clouds and medical users and (ii) reducing the computational and transmission overhead (energy-consumption) of the green IoT devices while performing privacy-preserving DD. The outsourced privacy-preserving DD is available in the literature based on popular tree-based machine learning schemes such as a random forest. However, these schemes utilize energy-hungry public-key encryption schemes in IoT nodes at medical users for privacy preservation. This work proposes an energy-efficient, fully homomorphic modified Rivest scheme (FHMRS) for the proposed privacy-preserving random forest classification (PRFC). A secure integer comparison protocol is also developed for reducing processing time and energy consumption for users while performing outsourced PRFC. The implementation results and security analysis show that the proposed schemes guarantee better energy efficiency for MHN green IoT devices without compromising privacy than the existing tree-based schemes.

Voltage references are crucial for various mixed-signal and RF systems in the analog market, including IoT, wireless technologies, and body-area networks. In recent technological advancements, the increasing demand for low-power, low-cost, and energy-efficient solutions has driven the need for sub-1V voltage reference circuits. This paper presents the design and development of a sub 1-V voltage reference circuit for ultra-low power applications. The MOSFET-only circuit proposed here is compatible with CMOS fabrication in the 180 nm node. The voltage reference circuit achieves a stable 0.406V reference with a low line sensitivity (LS) of only 0.2%/V over a wide source voltage range from 648mV to 3.6V. It exhibits a temperature coefficient (TC) of 32 ppm/°C within the temperature range of −40°C to 105°C with a power consumption of 9.9nW at room temperature. The performance analysis includes a comprehensive assessment considering all process corners and statistical analysis through Monte Carlo simulations. Furthermore, a comparison with state-of-the-art references validates the effectiveness of the proposed circuit.

Conventional modeling and simulation of$n$-output dc–dc converters requires$(n+1) \times (n+1)$matrix computations. This approach increases the modeling approach’s complexity and increases the design and simulation time required for the modeling process. A state separate modeling methodology is proposed where each state of the dc–dc converter is considered separately and combined with the help of a multiplexer. The proposed modeling approach is modular and thus improves the scalability to multiple outputs. The proposed methodology aids the designer in designing and modeling multioutput dc–dc converters faster, enabling fast prototyping. The proposed model outperforms the existing mathematical models in terms of computation time. The output voltage variation to duty cycles has a root mean square error in between 0.08 and 0.22 V.

Adopting mobile healthcare network (MHN) services such as disease detection is fraught with concerns about the security and privacy of the entities involved and the resource restrictions at the Internet of Things (IoT) nodes. Hence, the essential requirements for disease detection services are to (i) produce accurate and fast disease detection without jeopardizing the privacy of health clouds and medical users and (ii) reduce the computational and transmission overhead (energy consumption) of the IoT devices while maintaining the privacy. For privacy preservation of widely used neural network– (NN) based disease detection, existing literature suggests either computationally heavy public key fully homomorphic encryption (FHE), or secure multiparty computation, with a large number of interactions. Hence, the existing privacy-preserving NN schemes are energy consuming and not suitable for resource-constrained IoT nodes in MHN. This work proposes a lightweight, fully homomorphic, symmetric key FHE scheme (SkFhe) to address the issues involved in implementing privacy-preserving NN. Based on SkFhe, widely used non-linear activation functions ReLU and Leaky ReLU are implemented over the encrypted domain. Furthermore, based on the proposed privacy-preserving linear transformation and non-linear activation functions, an energy-efficient, accurate, and privacy-preserving NN is proposed. The proposed scheme guarantees privacy preservation of the health cloud’s NN model and medical user’s data. The experimental analysis demonstrates that the proposed solution dramatically reduces the overhead in communication and computation at the user side compared to the existing schemes. Moreover, the improved energy efficiency at the user is accomplished with reduced diagnosis time without sacrificing classification accuracy.

This study proposes a secure and privacy‐preserving protocol for outsourcing health data processing operations during the emergency in the mobile healthcare network. The proposed protocol provides a practical solution to utilise smartphone resources at both remote and nearby for processing the overwhelming personal health information (PHI) of a user in healthcare emergency opportunistically and securely. The patients with symptoms matching with those of the user in an emergency are considered as opportunities to minimise the privacy disclosure of the user. Opportunities at both remote and nearby are exploited with the help of a base station in the 4G network. Moreover, novel and efficient outsourced privacy access control schemes are developed to minimise the power drain of the user in an emergency without compromising his privacy. The outsourced privacy access control is facilitated through the design of innovative schemes for outsourced attribute‐based access mechanism and an outsourced privacy‐preserving scalar product computation. Detailed performance evaluations through implementations on Raspberry Pi 3B + board and simulations using NS3 network simulator and Scyther tool confirm the efficiency of the proposed protocol in providing highly reliable PHI processing and transmissions with reasonably low delay and energy consumption while maintaining user privacy.

Fast Fourier Transform(FFT) is one of the widely used algorithms in almost all signal processing applications in various contexts. Efficient hardware architectures to implement FFT tailored for a typical application scenario is always challenging and requires a lot of analysis. In this paper an architecture for a 64×64 FFT is proposed for a real time video motion estimation application. Global motion estimation for video data employing phase correlation has many advantages such as its ability to estimate larger motions with low computational complexity in addition to spectrum insensitivity and immunity to noise and illumination variation. The major computational block that decides the efficiency of the phase correlation algorithm is FFT. The proposed FFT architecture is designed to meet the real-time requirements of a video stabilization module employing phase correlation for the motion estimation where the videos are assumed to be captured at a rate of 30 frames per second or above. The proposed architecture was implemented on a Kintex-7 FPGA board and met the required specifications.

This paper presents the design of a processor for the computation of 8×8 two dimensional Discrete Cosine Transform (2D-DCT) in analog domain. In the implementation, current mode modules are used. Suitable architectures for analog implementation with optimal power and hardware usage have been selected for the implementation of the DCT processor design. A digital design of the DCT processor using components such as flip flops, multiplexers which are inferred from the hardware description in behavioral model is done for performance comparison. The analog design is found to be suitable for power-constrained applications whereas digital design is suitable for accuracy-specific applications.

Through a reaction-diffusion based model we have studied modulation ratio (MR) in layer 2/3 cells in cat primary visual cortex. The reaction-diffusion based developmental model is based on resource limited competition and diffusive cooperation among neighboring cells. We have obtained receptive fields (RFs) and responses of binocular cells in layer 2/3. The ON-OFF subregion overlap in RF is quantified. We observe a significant correlation between RF ON-OFF subregion overlap and MR. For almost all the cells in layer 2/3 MR1 and behave as simple cells. Based on modulation index from spiking response our modelled layer 2/3 cell population has 76% complex cells and 24% simple cells. We find that local lateral connections have less influence on MR but improve the tuning characteristics of cells significantly.