Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios.

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node's energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node's residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10-20% compared to the benchmark algorithms.

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks.

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.

An Optimization Framework for Data Collection in Software Defined Vehicular Networks.

A Software Defined Vehicular Network (SDVN) is a new paradigm that enhances programmability and flexibility in Vehicular Adhoc Networks (VANETs). There exist different architectures for SDVNs based on the degree of control of the control plane. However, in vehicular communication literature, we find that there is no proper mechanism to collect data. Therefore, we propose a novel data collection methodology for the hybrid SDVN architecture by modeling it as an Integer Quadratic Programming (IQP) problem. The IQP model optimally selects broadcasting nodes and agent (unicasting) nodes from a given vehicular network instance with the objective of minimizing the number of agents, communication delay, communication cost, total payload, and total overhead. Due to the dynamic network topology, finding a new solution to the optimization is frequently required in order to avoid node isolation and redundant data transmission. Therefore, we propose a systematic way to collect data and make optimization decisions by inspecting the heterogeneous normalized network link entropy. The proposed optimization model for data collection for the hybrid SDVN architecture yields a 75.5% lower communication cost and 32.7% lower end-to-end latency in large vehicular networks compared to the data collection in the centralized SDVN architecture while collecting 99.9% of the data available in the vehicular network under optimized settings.

Enhancing Handover for 5G mmWave Mobile Networks Using Jump Markov Linear System and Deep Reinforcement Learning.

The Fifth Generation (5G) mobile networks use millimeter waves (mmWaves) to offer gigabit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G. This in turn causes too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity, and avert unnecessary HO, we propose an HO scheme based on a jump Markov linear system (JMLS) and deep reinforcement learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time-varying behaviors. We combine the two techniques to account for time-varying, abrupt, and irregular changes in mmWave link behavior by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduce training sample size. Thus, the JMLS-DRL platform formulates intelligent and versatile HO policies for 5G. When compared to a signal and interference noise ratio (SINR) and DRL-based HO scheme, our HO scheme becomes more reliable in selecting reliable target links. In particular, our proposed scheme is able to reduce wasteful HO to less than 5% within 200 training episodes compared to the DRL-based HO scheme that needs more than 200 training episodes to get to less than 5%. It supports longer dew time between HOs and high sum rates by ably averting unnecessary HOs with almost half the HOs compared to a DRL-based HO scheme.

Automatic Assessment of Aphasic Speech Sensed by Audio Sensors for Classification into Aphasia Severity Levels to Recommend Speech Therapies.

Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by specific speech therapies based on the presence or absence of identified characteristics in aphasic speech in order to give more specific treatment to the patient. In the aphasia severity level classification process, we experiment on different speech feature extraction techniques, lengths of input audio samples, and machine learning classifiers toward classification performance. Aphasic speech is required to be sensed by an audio sensor and then recorded and divided into audio frames and passed through an audio feature extractor before feeding into the machine learning classifier. According to the results, the mel frequency cepstral coefficient (MFCC) is the most suitable audio feature extraction method for the aphasic speech level classification process, as it outperformed the classification performance of all mel-spectrogram, chroma, and zero crossing rates by a large margin. Furthermore, the classification performance is higher when 20 s audio samples are used compared with 10 s chunks, even though the performance gap is narrow. Finally, the deep neural network approach resulted in the best classification performance, which was slightly better than both K-nearest neighbor (KNN) and random forest classifiers, and it was significantly better than decision tree algorithms. Therefore, the study shows that aphasia level classification can be completed with accuracy, precision, recall, and F1-score values of 0.99 using MFCC for 20 s audio samples using the deep neural network approach in order to recommend corresponding speech therapy for the identified level. A web application was developed for English-speaking aphasia patients to self-diagnose the severity level and engage in speech therapies.

A Comprehensive Open-Source Simulation Framework for LiFi Communication.

Light Fidelity (LiFi) is a new candidate for wireless networking that utilizes the visible light spectrum and exploits the existing lighting infrastructure in the form of light-emitting diodes (LEDs). It provides point-to-point and point-to-multipoint communication on a bidirectional channel at very high data rates. However, the LiFi has small coverage, and its optical gain is closely related to the receiver's directionality vis-à-vis the transmitter, therefore it can experience frequent service outages. To provide reliable coverage, the LiFi is integrated with other networking technologies such as wireless fidelity (WiFi) thus forming a hybrid system. The hybrid LiFi/WiFi system faces many challenges including but not limited to seamless integration with the WiFi, support for mobility, handover management, resource sharing, and load balancing. The existing literature has addressed one or the other aspect of the issues facing LiFi systems. There are limited free source tools available to holistically address these challenges in a scalable manner. To this end, we have developed an open-source simulation framework based on the network simulator 3 (ns-3), which realizes critical aspects of the LiFi wireless network. Our developed ns-3 LiFi framework provides a fully functional AP equipped with the physical layer and medium access control (MAC), a mobility model for the user device, and integration between LiFi and WiFi with a handover facility. Simulation results are produced to demonstrate the mobility and handover capabilities, and the performance gains from the LiFi-WiFi hybrid system in terms of packet delay, throughput, packet drop ratio (PDR), and fairness between users. The source code of the framework is made available for the use of the research community.

Modelling of Chest Wall Motion for Cardiorespiratory Activity for Radar-Based NCVS Systems.

Chest wall motion can provide information on critical vital signs, including respiration and heartbeat. Mathematical modelling of chest wall motion can reduce an extensive requirement of human testing in the development of many biomedical applications. In this paper, we propose a mathematical model that simulates a chest wall motion due to cardiorespiratory activity. Chest wall motion due to respiration is simulated based on the optimal chemical-mechanical respiratory control-based mechanics. The theory of relaxation oscillation system is applied to model the motion due to cardiac activity. The proposed mathematical chest wall model can be utilized in designing and optimizing different design parameters for radar-based non-contact vital sign (NCVS) systems.

Design and Implementation of a Self-Powered Smart Water Meter.

Smart cities require interactive management of water supply networks and water meters play an important role in such a task. As compared to fully mechanical water meters, electromechanical water meters or fully electronic water meters can collect real-time information through automatic meter reading (AMR), which makes them more suitable for smart cities applications. In this paper, we first study the design principles of existing water meters, and then present our design and implementation of a self-powered smart water meter. The proposed water meter is based on a water turbine generator, which serves for two purposes: (i) to sense the water flow through adaptive signal processing performed on the generated voltage; and (ii) to produce electricity to charge batteries for the smart meter to function properly. In particular, we present the design considerations and implementation details. The wireless transceiver is integrated in the proposed water meter so that it can provide real-time water flow information. In addition, a mobile phone application is designed to provide a user with a convenient tool for water usage monitoring.

Visible Light Communication: A System Perspective-Overview and Challenges.

Visible light communication (VLC) is a new paradigm that could revolutionise the future of wireless communication. In VLC, information is transmitted through modulating the visible light spectrum (400⁻700 nm) that is used for illumination. Analytical and experimental work has shown the potential of VLC to provide high-speed data communication with the added advantage of improved energy efficiency and communication security/privacy. VLC is still in the early phase of research. There are fewer review articles published on this topic mostly addressing the physical layer research. Unlike other reviews, this article gives a system prespective of VLC along with the survey on existing literature and potential challenges toward the implementation and integration of VLC.

Multifunctional Hyperbolic Nanogroove Metasurface for Submolecular Detection.

Metasurface serves as a promising plasmonic sensing platform for engineering the enhanced light-matter interactions. Here, a hyperbolic metasurface with the nanogroove structure in the subwavelength scale is designed. This metasurface is able to modify the wavefront and wavelength of surface plasmon wave with the variation of the nanogroove width or periodicity. At the specific optical frequency, surface plasmon polaritons are tightly confined and propagated with a diffraction-free feature due to the epsilon-near-zero effect. Most importantly, the groove hyperbolic metasurface can enhance the plasmonic sensing with an ultrahigh phase sensitivity of 30 373 deg RIU-1 and Goos-Hänchen shift sensitivity of 10.134 mm RIU-1 . The detection resolution for refractive index change of glycerol solution is achieved as 10-8 RIU based on the phase measurement. The detection limit of bovine serum albumin (BSA) molecule is measured as low as 0.1 × 10-18 m (1 × 10-19 mol L-1 ), which corresponds to a submolecular detection level (0.13 BSA mm-2 ). As for low-weight biotin molecule, the detection limit is estimated below 1 × 10-15 m (1 × 10-15 mol L-1 , 1300 biotin mm-2 ). This enhanced plasmonic sensing performance is two orders of magnitude higher than those with current state-of-art plasmonic metamaterials and metasurfaces.

Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics.

The Coronavirus pandemic has created complex challenges and adverse circumstances. This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential public sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research focuses on emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to the COVID-19 reopening debate, to identify dominant sentiment trends associated with the push to reopen the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of textual data analytics, including textual data visualization and statistical validation, that tweets data from American Twitter users shows more positive sentiment support, than negative, for reopening the US economy. This research develops a novel sentiment polarity based public sentiment scenarios (PSS) framework, which will remain useful for future crises analysis, well beyond COVID-19. With additional validation, this research stream could present valuable time sensitive opportunities for state governments, the federal government, corporations and societal leaders to guide local and regional communities, and the nation into a successful new normal future.

0.2 λ0 Thick Adaptive Retroreflector Made of Spin-Locked Metasurface.

The metasurface concept is employed to planarize retroflectors by stacking two metasurfaces with separation that is two orders larger than the wavelength. Here, a retroreflective metasurface using subwavelength-thick reconfigurable C-shaped resonators (RCRs) is reported, which reduces the overall thickness from the previous record of 590 λ0 down to only 0.2 λ0 . The geometry of RCRs could be in situ controlled to realize equal amplitude and phase modulation onto transverse magnetic (TM)-polarized and transverse electric (TE)-polarized incidences. With the phase gradient being engineered, an in-plane momentum could be imparted to the incident wave, guaranteeing the spin state of the retro-reflected wave identical to that of the incident light. Such spin-locked metasurface is natively adaptive toward different incident angles to realize retroreflection by mechanically altering the geometry of RCRs. As a proof of concept, an ultrathin retroreflective metasurface is validated at 15 GHz, under various illumination angles at 10°, 12°, 15°, and 20°. Such adaptive spin-locked metasurface could find promising applications in spin-based optical devices, communication systems, remote sensing, RCS enhancement, and so on.

Precise Two-Photon Photodynamic Therapy using an Efficient Photosensitizer with Aggregation-Induced Emission Characteristics.

Two-photon photodynamic therapy (PDT) is able to offer precise 3D manipulation of treatment volumes, providing a target level that is unattainable with current therapeutic techniques. The advancement of this technique is greatly hampered by the availability of photosensitizers with large two-photon absorption (TPA) cross section, high reactive-oxygen-species (ROS) generation efficiency, and bright two-photon fluorescence. Here, an effective photosensitizer with aggregation-induced emission (AIE) characteristics is synthesized, characterized, and encapsulated into an amphiphilic block copolymer to form organic dots for two-photon PDT applications. The AIE dots possess large TPA cross section, high ROS generation efficiency, and excellent photostability and biocompatibility, which overcomes the limitations of many conventional two-photon photosensitizers. Outstanding therapeutic performance of the AIE dots in two-photon PDT is demonstrated using in vitro cancer cell ablation and in vivo brain-blood-vessel closure as examples. This shows therapy precision up to 5 µm under two-photon excitation.

The non-aqueous synthesis of shape controllable Cu(2-x)S plasmonic nanostructures in a continuous-flow millifluidic chip for the generation of photo-induced heating.

In this paper, a new method for synthesizing non-aqueous copper sulfide nanocrystals with different shapes and sizes using a homemade continuous-flow millifluidic chip is presented. Conventionally, the shape control of nanocrystals was accomplished using a surfactant-controlled approach, where directional growth is facilitated by selective passivation of a particular facet of the nanocrystals using surfactants. We demonstrate a "surfactant-free" approach where different sizes and shapes (i.e. spherical, triangular prism and rod) of plasmonic copper sulfide (Cu(2-x)S) nanocrystals can be fabricated by adjusting the flow rate and precursor concentrations. As continuous-flow synthesis enables uniform heating and easy variation of precursors' stoichiometries, it serves as an excellent incubation platform for nanoparticles due to its simplicity and high reproducibility. Transmission electron microscopy (TEM), fast Fourier transform (FFT) and X-ray diffraction (XRD) techniques were used to characterize the as-synthesized nanocrystals and revealed structures ranging from copper-deficient covellite (CuS), spionkopite (Cu1.39S), roxbyite (Cu1.75S), to copper-rich djurleite (Cu1.94S). The localized surface plasmon resonance (LSPR) peak of the nanocrystals can be tuned from 1115 to 1644 nm by simply varying the copper to sulfur molar ratio and flow rate. Furthermore, photothermal effects of Cu(2-x)S nanocrystals were also demonstrated to annihilate the RAW264.7 cells upon near infra-red laser irradiation.

Biodegradable charged polyester-based vectors (BCPVs) as an efficient non-viral transfection nanoagent for gene knockdown of the BCR-ABL hybrid oncogene in a human chronic myeloid leukemia cell line.

First-line therapy of chronic myelogenous leukemia (CML) has always involved the use of BCR-ABL tyrosine-kinase inhibitors which is associated with an abnormal chromosome called Philadelphia chromosome. Although the overall survival rate has been improved by the current therapeutic regime, the presence of resistance has resulted in limited efficacy. In this study, an RNA interference (RNAi)-based therapeutic regime is proposed with the aim to knockdown the BCR-ABL hybrid oncogene using small interfering RNA (siRNA). The siRNA transfection rates have usually been limited due to the declining contact probability among polyplexes and the non-adherent nature of leukemic cells. Our work aims at addressing this limitation by using a biodegradable charged polyester-based vector (BCPV) as a nanocarrier for the delivery of BCR-ABL-specific siRNA to the suspension culture of a K562 CML cell line. BCR-ABL siRNAs were encapsulated in the BCPVs by electrostatic force. Cell internalization was facilitated by the BCPV and assessed by confocal microscopy and flow cytometry. The regulation of the BCR-ABL level in K562 cells as a result of RNAi was analyzed by real-time polymerase chain reaction (RT-PCR). We observed that BCPV was able to form stable nanoplexes with siRNA molecules, even in the presence of fetal bovine serum (FBS), and successfully assisted in vitro siRNA transfection in the non-adherent K562 cells. As a consequence of downregulation of BCR-ABL, BCPV-siRNA nanoplexes inhibited cell proliferation and promoted cell apoptosis. All results were compared with a commercial transfection reagent, Lipofectamine2000™, which served as a positive control. More importantly, this class of non-viral vector exhibits biodegradable features and negligible cytotoxicity, thus providing a versatile platform to deliver siRNA to non-adherent leukemia cells with high transfection efficiency by effectively overcoming extra- and intra-cellular barriers. Due to the excellent in vitro transfection results from BCPV-siRNA, a newly developed biodegradable transfection agent, BCPV, is being probed for transfection performance in an animal model.