Modified Echo State Network-enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs

Minimizing energy consumption is one of the major challenges in wireless sensor networks (WSNs) due to the limited size of batteries and the resource constrained tiny sensor nodes. Energy harvesting in wireless sensor networks (EH-WSNs) is one of the promising solutions to minimize the energy consumption in wireless sensor networks for prolonging the overall network lifetime. However, static energy harvesting in individual sensor nodes is normally limited and unbalanced among the network nodes. In this context, this paper proposes a modified echo state network (MESN) based dynamic duty cycle with optimal opportunistic routing (OOR) for EH-WSNs. The proposed model is used to act as a predictor for finding the expected energy consumption of the next slot in dynamic duty cycle. The model has adapted a whale optimization algorithm (WOA) for optimally selecting the weights of the neurons in the reservoir layer of the echo state network towards minimizing energy consumption at each node as well as at the network level. The adapted WOA enabled energy harvesting model provides stable output from the MESN relying on optimal weight selection in the reservoir layer. The dynamic duty cycle is updated based on energy consumption and optimal threshold energy for transmission and reception at bit level. The proposed OOR scheme uses multiple energy centric parameters for selecting the relay set oriented forwarding paths for each neighbor nodes. The performance analysis of the proposed model in realistic environments attests the benefits in terms of energy centric metrics such as energy consumption, network lifetime, delay, packet delivery ratio and throughput as compared to the state-of-the-art-techniques

Towards Physical Layer Security for Internet of Vehicles: Interference Aware Modelling

The physical-layer security (PLS) of wireless networks has witnessed significant attention in next-generation communication systems due to its potential toward enabling protection at the signal level in dense network environments. The growing trends toward smart mobility via sensor-enabled vehicles are transforming today’s traffic environment into Internet of Vehicles (IoVs). Enabling PLS for IoVs would be a significant development considering the dense vehicular network environment in the near future. In this context, this article presents a PLS framework for a vehicular network consisting a legitimate receiver and an eavesdropper, both under the effect of interfering vehicles. The double-Rayleigh fading channel is used to capture the effect of mobility within the communication channel. The performance is analyzed in terms of the average secrecy capacity (ASC) and secrecy outage probability (SOP). We present the standard expressions for the ASC and SOP in alternative forms, to facilitate analysis in terms of the respective moment generating function (MGF) and characteristic function of the joint fading and interferer statistics. Closed-form expressions for the MGFs and characteristic functions were obtained and Monte Carlo simulations were provided to validate the results. Approximate expressions for the ASC and SOP were also provided, for easier analysis and insight into the effect of the network parameters. The results attest that the performance of the considered system was affected by the number of interfering vehicles as well as their distances. It was also demonstrated that the system performance closely correlates with the uncertainty in the eavesdropper’s vehicle location

W-GUN: Whale Optimization for Energy and Delay centric Green Underwater Networks

Underwater Sensor Networks (UWSNs) has witnessed significant R&D attention in both academia and industries due to its growing application domain such as border security, freight via sea or river, natural petroleum production, etc. Considering the deep underwater oriented access constraints, energy centric communication for lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network without giving much attention to the realistic impact of underwater network environments resulting in degraded performance. Towards this end, this paper presents an adapted whale optimization algorithm-based energy and delay centric green UWSNs framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale centric optimization in relay node selection. Firstly, an underwater relay- node optimization model is mathematically derived focusing on whale and wolf optimization algorithms for incorporating realistic underwater characteristics. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm. Thirdly, a complete work-flow of the W-GUN framework is presented with the optimization flowchart. The comparative performance evaluation attests the benefits of the proposed framework as compared to the state-of-the-art techniques considering various metrics related to underwater network environments

Physiological Tremor Filtering without Phase Distortion for Robotic Microsurgery

All existing physiological tremor filtering algorithms, developed for robotic microsurgery, use nonlinear phase prefilters to isolate the tremor signal. Such filters cause phase distortion to the filtered tremor signal and limit the filtering accuracy. We revisited this long-standing problem to enable filtering of the physiological tremor without any phase distortion. We developed a combined estimation-prediction paradigm that offers zero-phase type filtering. The estimation is achieved with the mathematically modified recursive singular spectrum analysis algorithm, and the prediction is delivered with the standard extreme learning machine. In addition, to limit the computational cost, we developed two moving window versions of this structure, which are appropriate for real-time implementation. The proposed paradigm preserved the natural phase of the filtered tremor. It achieved the key performance index of error limitation below 10\mum, yielding the estimation accuracy larger than 70%, at a time delay of 36 ms only. Both moving window versions of the proposed approach restricted the computational cost considerably while offering the same performance. It is the first time that the effective estimation of the physiological tremor is achieved, without any prefiltering and phase distortion. This proposed method is feasible for real-time implantation. Clinical translation of the proposed paradigm can significantly enhance the outcome in hand-held surgical robotics

Real-time physiological tremor estimation using recursive singular spectrum analysis

Physiological hand tremor causes undesirable vibration of hand-held surgical instruments which results in imprecisions and poor surgical outcomes. Existing tremor cancellation algorithms are based on detection of the tremulous component from the whole motion; then adding an anti-phase tremor signal to the whole motion to cancel it out. These techniques are based on adaptive filtering algorithms which need a reference signal that is highly correlated with the actual tremor signal. Hence, such adaptive approaches use a non-linear phase filter to pre-filter the tremor signal either offline or in real-time. However, pre-filtering causes unnecessary delays and non-linear phase distortions as the filter has frequency selective delays. Consequently, the anti-phase tremor signal cannot be generated accurately which results in poor tremor cancellation. In this paper, we present a new technique based on singular spectrum analysis (SSA) and its recursive version, that is, recursive singular spectrum analysis (RSSA). These algorithms decompose the whole motion into dominant voluntary components corresponding to larger eigenvalues and oscillatory tremor components having smaller eigenvalues. By selecting a group of specific decomposed signals based on their eigenvalues and spectral range, both voluntary and tremor signals can be reconstructed accurately. We test the SSA and RSSA algorithms using recorded tremor data from five novice subjects. This new approach shows the tremor signal can be estimated from the whole motion with an accuracy of up to 85% offline. In real-time, tolerating a delay of ≈ 72ms, the tremor signal can be estimated with at least 70% accuracy. This delay is found to be one-tenth of the delay caused by a conventional linear-phase bandpass filter to achieve similar performance in real-time.Accepted versio

Multi-step prediction of physiological tremor with random quaternion neurons for surgical robotics applications

Digital filters are employed in hand-held robotic instruments to separate the concomitant involuntary physiological tremor motion from the desired motion of micro-surgeons. Inherent phase-lag in digital filters induces phase distortion (time-lag/delay) into the separated tremor motion and it adversely affects the final tremor compensation. Owing to the necessity of digital filters in hand-held instruments, multi-step prediction of physiological tremor motion is proposed as a solution to counter the induced delay. In this paper, a quaternion variant for extreme learning machines (QELMs) is developed for multi-step prediction of the tremor motion. The learning paradigm of the QELM integrates the identified underlying relationship from 3-D tremor motion in the Hermitian space with the fast learning merits of ELMs theories to predict the tremor motion for a known horizon. Real tremor data acquired from micro-surgeons and novice subjects are employed to validate the QELM for various prediction horizons in-line with the delay induced by the order of digital filters. Prediction inferences underpin that the QELM method elegantly learns the cross-dimensional coupling of the tremor motion with random quaternion neurons and hence obtained significant improvement in prediction performance at all prediction horizons compared with existing methods.Published versio

Inactivation of SARS-CoV‑2 and Other Human Coronaviruses Aided by Photocatalytic One-Dimensional Titania Nanotube Films as a Self-Disinfecting Surface

SARS-CoV-2 and its variants that continue to emerge have necessitated the implementation of effective disinfection strategies. Developing self-disinfecting surfaces can be a potential route for reducing fomite transmissions of infectious viruses. We show the effectiveness of TiO2 nanotubes (T_NTs) on photocatalytic inactivation of human coronavirus, HCoV-OC43, as well as SARS-CoV-2. T_NTs were synthesized by the anodization process, and their impact on photocatalytic inactivation was evaluated by the detection of residual viral genome copies (quantitative real-time quantitative reverse transcription polymerase chain reaction) and infectious viruses (infectivity assays). T_NTs with different structural morphologies, wall thicknesses, diameters, and lengths were prepared by varying the time and applied potential during anodization. The virucidal efficacy was tested under different UV-C exposure times to understand the photocatalytic reaction’s kinetics. We showed that the T_NT presence boosts the inactivation process and demonstrated complete inactivation of SARS-CoV-2 as well as HCoV-OC43 within 30 s of UV-C illumination. The remarkable cyclic stability of these T_NTs was revealed through a reusability experiment. The spectroscopic and electrochemical analyses have been reported to correlate and quantify the effects of the physical features of T_NT with photoactivity. We anticipate that the proposed one-dimensional T_NT will be applicable for studying the surface inactivation of other coronaviruses including SARS-CoV-2 variants due to similarities in their genomic structure