Cortical Layer-Dependent Signaling in Cognition: Three Computational Modes of the Canonical Circuit.

The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity as a model, which has been recently revolutionized by genetic approaches in mice. However, cognitive processes such as recall and imagery require top-down generative computation. The question of whether the layered module operates similarly in top-down generative processing as in bottom-up sensory processing has become testable by advances in the layer identification of recorded neurons in behaving monkeys. This review examines recent advances in laminar signaling in these two computations, using predictive coding computation as a common reference, and shows that each of these computations recruits distinct laminar circuits, particularly in layer 5, depending on the cognitive demands. These findings highlight many open questions, including how different interareal feedback pathways, originating from and terminating at different layers, convey distinct functional signals.

Directional dipole dice enabled by anisotropic chirality.

Directional radiation and scattering play an essential role in light manipulation for various applications in integrated nanophotonics, antenna and metasurface designs, quantum optics, etc. The most elemental system with this property is the class of directional dipoles, including the circular dipole, Huygens dipole, and Janus dipole. A unified realization of all three dipole types and a mechanism to freely switch among them are previously unreported, yet highly desirable for developing compact and multifunctional directional sources. Here, we theoretically and experimentally demonstrate that the synergy of chirality and anisotropy can give rise to all three directional dipoles in one structure at the same frequency under linearly polarized plane wave excitations. This mechanism enables a simple helix particle to serve as a directional dipole dice (DDD), achieving selective manipulation of optical directionality via different "faces" of the particle. We employ three "faces" of the DDD to realize face-multiplexed routing of guided waves in three orthogonal directions with the directionality determined by spin, power flow, and reactive power, respectively. This construction of the complete directionality space can enable high-dimensional control of both near-field and far-field directionality with broad applications in photonic integrated circuits, quantum information processing, and subwavelength-resolution imaging.

Transient Oscillations of Neural Firing Rate Associated With Routing of Evidence in a Perceptual Decision.

To form a perceptual decision, the brain must acquire samples of evidence from the environment and incorporate them in computations that mediate choice behavior. While much is known about the neural circuits that process sensory information and those that form decisions, less is known about the mechanisms that establish the functional linkage between them. We trained monkeys of both sexes to make difficult decisions about the net direction of visual motion under conditions that required trial-by-trial control of functional connectivity. In one condition, the motion appeared at different locations on different trials. In the other, two motion patches appeared, only one of which was informative. Neurons in the parietal cortex produced brief oscillations in their firing rate at the time routing was established: upon onset of the motion display when its location was unpredictable across trials, and upon onset of an attention cue that indicated in which of two locations an informative patch of dots would appear. The oscillation was absent when the stimulus location was fixed across trials. We interpret the oscillation as a manifestation of the mechanism that establishes the source and destination of flexibly routed information, but not the transmission of the information per se Significance Statement It has often been suggested that oscillations in neural activity might serve a role in routing information appropriately. We observe an oscillation in neural firing rate in the lateral intraparietal area consistent with such a role. The oscillations are transient. They coincide with the establishment of routing, but they do not appear to play a role in the transmission (or conveyance) of the routed information itself.

The macroscale routing mechanism of structural brain connectivity related to body mass index.

Understanding the brain's mechanisms in individuals with obesity is important for managing body weight. Prior neuroimaging studies extensively investigated alterations in brain structure and function related to body mass index (BMI). However, how the network communication among the large-scale brain networks differs across BMI is underinvestigated. This study used diffusion magnetic resonance imaging of 290 young adults to identify links between BMI and brain network mechanisms. Navigation efficiency, a measure of network routing, was calculated from the structural connectivity computed using diffusion tractography. The sensory and frontoparietal networks indicated positive associations between navigation efficiency and BMI. The neurotransmitter association analysis identified that serotonergic and dopaminergic receptors, as well as opioid and norepinephrine systems, were related to BMI-related alterations in navigation efficiency. The transcriptomic analysis found that genes associated with network routing across BMI overlapped with genes enriched in excitatory and inhibitory neurons, specifically, gene enrichments related to synaptic transmission and neuron projection. Our findings suggest a valuable insight into understanding BMI-related alterations in brain network routing mechanisms and the potential underlying cellular biology, which might be used as a foundation for BMI-based weight management.

Infrared routing and switching with tunable spectral bandwidth using arrays of metallic nanoantennas.

We study infrared routing and switching with tunable spectral bandwidth using in-plane scattering of light by flat Au nanoantenna arrays. The base dimensions of these nanoantennas are approximately 250 by 850 nm, while their heights vary from 20 to 150 nm. Our results show that, with the increase in height, the arrays become more efficient scatterers while their spectra broaden within the 1-1.6µm range. Our findings demonstrate that such processes strongly depend on the incident light polarization. For a given polarization, the incident light is efficiently scattered in only two opposite directions along the plane of the arrays, with insignificant transmission. Switching such a polarization by 90∘, however, suppresses this process, allowing the light to mostly pass through the arrays with minimal scattering. These unique characteristics suggest a tunable beam splitter application in the 1-1.6µm range and even longer wavelengths.

Masked Speech Perception with Bone Conduction Device, Contralateral Routing of Signals Hearing Aid, and Cochlear Implant Use in Adults with Single-Sided Deafness: A Prospective Hearing Device Comparison using a Unified Testing Framework.

INTRODUCTION: For the treatment of single-sided deafness (SSD), common treatment choices include a contralateral routing of signals (CROS) hearing aid, a bone conduction device (BCD), and a cochlear implant (CI). The primary aim of this study was to compare speech understanding in noise and binaural benefits in adults with postlingual SSD between preoperative unaided baseline, preoperative CROS and BCD trial devices, and CI, following recommendations from a consensus protocol. In addition, we investigated the effect of masker type on speech understanding. METHODS: This was a prospective study with twelve participants. Binaural effects of head shadow, squelch, summation, and spatial release from masking were assessed by measuring speech reception thresholds (SRTs) in five different spatial target-masker configurations using two different maskers: two-talker babble (TTB), and speech-shaped noise (SSN). Preoperatively, participants were assessed unaided and with CROS and BCD trial devices. After cochlear implantation, participants were assessed at 1, 3, and 6 months post-activation. RESULTS: For TTB, significant improvements in SRT with a CI relative to preoperatively unaided were found in all spatial configurations. With CI at 6 months, median benefits were 7.8 dB in SSSDNAH and 5.1 dB in S0NAH (head shadow), 3.4 dB in S0N0 (summation), and 4.6 dB in S0NSSD and 5.1 dB in SAHNSSD (squelch). CROS yielded a significant head shadow benefit of 2.4 dB in SSSDNAH and a significant deterioration in squelch of 2.5 dB in S0NSSD and SAHNSSD, but no summation effect. With BCD, there was a significant summation benefit of 1.5 dB, but no head shadow nor squelch effect. For SSN, significant improvements in SRT with CI compared to preoperatively unaided were found in three spatial configurations. Median benefits with CI at 6 months were: 8.5 dB in SSSDNAH and 4.6 dB in S0NAH (head shadow), 1.4 dB in S0N0 (summation), but no squelch. CROS showed a significant head shadow benefit of 1.7 dB in SSSDNAH, but no summation effect, and a significant deterioration in squelch of 2.9 dB in S0NSSD and 3.2 dB in SAHNSSD. With BCD, no binaural effect was obtained. Longitudinally, we found significant head shadow benefits with a CI in SSSDNAH in both maskers at all postoperative intervals and in S0NAH at 3 and 6 months post-activation. CONCLUSION: With a CI, a clear benefit for masked speech perception was observed for all binaural effects. Benefits with CROS and BCD were more limited. CROS usage was detrimental to the squelch effect.

Optimization-enabled deep learning model for disease detection in IoT platform.

Nowadays, Internet of things (IoT) and IoT platforms are extensively utilized in several healthcare applications. The IoT devices produce a huge amount of data in healthcare field that can be inspected on an IoT platform. In this paper, a novel algorithm, named artificial flora optimization-based chameleon swarm algorithm (AFO-based CSA), is developed for optimal path finding. Here, data are collected by the sensors and transmitted to the base station (BS) using the proposed AFO-based CSA, which is derived by integrating artificial flora optimization (AFO) in chameleon swarm algorithm (CSA). This integration refers to the AFO-based CSA model enhancing the strengths and features of both AFO and CSA for optimal routing of medical data in IoT. Moreover, the proposed AFO-based CSA algorithm considers factors such as energy, delay, and distance for the effectual routing of data. At BS, prediction is conducted, followed by stages, like pre-processing, feature dimension reduction, adopting Pearson's correlation, and disease detection, done by recurrent neural network, which is trained by the proposed AFO-based CSA. Experimental result exhibited that the performance of the proposed AFO-based CSA is superior to competitive approaches based on the energy consumption (0.538 J), accuracy (0.950), sensitivity (0.965), and specificity (0.937).

Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are essential in many areas, from healthcare to environmental monitoring. However, WSNs are vulnerable to routing attacks that might jeopardize network performance and data integrity due to their inherent vulnerabilities. This work suggests a unique method for enhancing WSN security through the detection of routing threats using feed-forward artificial neural networks (ANNs). The proposed solution makes use of ANNs' learning capabilities to model the network's dynamic behavior and recognize routing attacks like black-hole, gray-hole, and wormhole attacks. CICIDS2017 is a heterogeneous dataset that was used to train and test the proposed system in order to guarantee its robustness and adaptability. The system's ability to recognize both known and novel attack patterns enhances its efficacy in real-world deployment. Experimental assessments using an NS2 simulator show how well the proposed method works to improve routing protocol security. The proposed system's performance was assessed using a confusion matrix. The simulation and analysis demonstrated how much better the proposed system performs compared to the existing methods for routing attack detection. With an average detection rate of 99.21% and a high accuracy of 99.49%, the proposed system minimizes the rate of false positives. The study advances secure communication in WSNs and provides a reliable means of protecting sensitive data in resource-constrained settings.

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.

OPTORER: A Dynamic Routing and Touring Service for Indoors and Outdoor Tours.

This paper introduces a new routing and touring service both for outdoor and indoor places of touristic and cultural interest designed to be used in the wider area of Attica, Greece. This service is the result of the work performed in OPTORER (OPTORER: OPtimal rouTing and explOration of touRistic and cultural arEas of interest within Attica given personalized adaptive preferences, promoted underlying purpose, and interactive experience), project, and it aspires to offer a range of innovative and thematic routes to several specified points of interest in the selected area of Attica, encouraging the combination of indoor and outdoor routes in a single tour. The aim is to optimize the user experience while promoting specific, user-centric features, with safety and social welfare being a priority for every designed tour, resulting in enhancing the touristic experience in the area. Using a common smartphone device, as well as common wearable devices (i.e., smartwatches), the OPTORER service will provide an end-to-end solution by developing the algorithms and end-user applications, together with an orchestration platform responsible for managing, operating, and executing the service that produces and presents to the end user results derived from solving dynamically complex optimization problems.

Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network.

Computing resource measurement and computing routing are essential technologies in the computing first network (CFN), serving as its foundational elements. This paper introduces a Software Defined Computing First Network (SD-CFN) architecture. Building upon this framework, a Dynamic-Static Integrated Computing Resource Measurement Mechanism (DCRMM) is proposed, incorporating methods such as the entropy weight method and K-Means clustering. The DCRMM algorithm outperforms the Maximum-closest Static Algorithm (MSA) and Maximum Closest Dynamic Algorithm (MDA) in terms of node stability, node utilization, and node matching accuracy. Additionally, a Reinforcement Learning and Software Defined Computing First Networking Routing (RSCR) algorithm is presented as a software-defined computing routing solution within the SD-CFN. RSCR introduces a knowledge plane responsible for computing routing calculations. It comprehensively considers factors such as link latency, available bandwidth, and packet loss rate. Simulation experiments conducted on the GÉANT topology demonstrate that RSCR outperforms the OSPF algorithm in terms of link latency, packet loss rate, and throughput. DCRMM and RSCR offer innovative solutions for computing resource measurement and computing routing in computing first networks.

Multi-Hop Clustering and Routing Protocol Based on Enhanced Snake Optimizer and Golden Jackal Optimization in WSNs.

A collection of smaller, less expensive sensor nodes called wireless sensor networks (WSNs) use their sensing range to gather environmental data. Data are sent in a multi-hop manner from the sensing node to the base station (BS). The bulk of these sensor nodes run on batteries, which makes replacement and maintenance somewhat difficult. Preserving the network's energy efficiency is essential to its longevity. In this study, we propose an energy-efficient multi-hop routing protocol called ESO-GJO, which combines the enhanced Snake Optimizer (SO) and Golden Jackal Optimization (GJO). The ESO-GJO method first applies the traditional SO algorithm and then integrates the Brownian motion function in the exploitation stage. The process then integrates multiple parameters, including the energy consumption of the cluster head (CH), node degree of CH, and distance between node and BS to create a fitness function that is used to choose a group of appropriate CHs. Lastly, a multi-hop routing path between CH and BS is created using the GJO optimization technique. According to simulation results, the suggested scheme outperforms LSA, LEACH-IACA, and LEACH-ANT in terms of lowering network energy consumption and extending network lifetime.

Incentive Mechanism for Privacy-Preserving Collaborative Routing Using Secure Multi-Party Computation and Blockchain.

Traffic congestion results from the spatio-temporal imbalance of demand and supply. With the advances in connected technologies, incentive mechanisms for collaborative routing have the potential to provide behavior-consistent solutions to traffic congestion. However, such mechanisms raise privacy concerns due to their information-sharing and execution-validation procedures. This study leverages secure Multi-party Computation (MPC) and blockchain technologies to propose a privacy-preserving incentive mechanism for collaborative routing in a vehicle-to-everything (V2X) context, which consists of a collaborative routing scheme and a route validation scheme. In the collaborative routing scheme, sensitive information is shared through an off-chain MPC protocol for route updating and incentive computation. The incentives are then temporarily frozen in a series of cascading multi-signature wallets in case vehicles behave dishonestly or roadside units (RSUs) are hacked. The route validation scheme requires vehicles to create position proofs at checkpoints along their selected routes with the assistance of witness vehicles using an off-chain threshold signature protocol. RSUs will validate the position proofs, store them on the blockchain, and unfreeze the associated incentives. The privacy and security analysis illustrates the scheme's efficacy. Numerical studies reveal that the proposed incentive mechanism with tuned parameters is both efficient and implementable.

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks.

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks.

5G cellular networks are already more than six times faster than 4G networks, and their packet loss rate, especially in the Internet of Vehicles (IoV), can reach 0.5% in many cases, such as when there is high-speed movement or obstacles nearby. In such high bandwidth and high packet loss network environments, traditional congestion control algorithms, such as CUBIC and bottleneck bandwidth and round-trip propagation time (BBR), have been unable to balance flow fairness and high performance, and their flow rate often takes a long time to converge. We propose a congestion control algorithm based on bottleneck routing feedback using an in-network control mode called bottleneck routing feedback (BRF). We use SDN technology (OpenFlow protocol) to collect network bandwidth information, and BRF controls the data transmission rate of the sender. By adding the bandwidth information of the bottleneck in the option field in the ACK packet, considering the flow fairness and the flow convergence rate, a bandwidth allocation scheme compatible with multiple congestion control algorithms is proposed to ensure the fairness of all flows and make them converge faster. The performance of BRF is evaluated via Mininet. The experimental results show that BRF provides higher bandwidth utilization, faster convergence rate, and fairer bandwidth allocation than existing congestion control algorithms in 5G cellular networks.

Fine-Grained Boundary Conditions in Field-Based Routing.

In the realm of industrial wireless mesh networks, an efficient routing protocol is highly demanded to play a crucial role in ensuring that packets are efficiently directed along shorter and congestion-free routes toward gateways. Field-based routing has emerged as a promising solution to tackle these network challenges. This routing approach draws inspiration from physics and employs a differential equation to model its behavior in finding efficient routes. Given the fundamental significance of boundary conditions in physics, where they play an essential role in shaping the solutions to the equation, exploring the impact of boundary conditions on field-based routing behavior within network domains becomes highly significant. However, despite their influence, the impact of boundary conditions has remained unexplored in existing studies on field-based routing. In this context, our work explores the boundary condition problem and introduces new advanced fine-grained boundary conditions for field-based routing. We demonstrate the superior performance of our proposed scheme, highlighting the substantial role of boundary conditions in network behavior. Our work holds significant value in that it explores the boundary condition problem, an aspect largely overlooked in previous research, and provides a viable solution, underscoring its crucial importance in routing enhancement.

Multiple-Junction-Based Traffic-Aware Routing Protocol Using ACO Algorithm in Urban Vehicular Networks.

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average.

An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs.

In the fields of industrial production or safety monitoring, wireless sensor networks are often content with unreliable and time-varying channels that are susceptible to interference. Consequently, ensuring both transmission reliability and data accuracy has garnered substantial attention in recent years. Although multipath routing-based schemes can provide transmission reliability for wireless sensor networks, achieving high data accuracy simultaneously remains challenging. To address this issue, an Energy-efficient Multipath Routing algorithm balancing data Accuracy and transmission Reliability (EMRAR) is proposed to balance the reliability and accuracy of data transmission. The multipath routing problem is formulated into a multi-objective programming problem aimed at optimizing both reliability and power consumption while adhering to data accuracy constraints. To obtain the solution of the multi-objective programming, an adaptive artificial immune algorithm is employed, in which the antibody initialization method, antibody incentive calculation method, and immune operation are improved, especially for the multipath routing scheme. Simulation results show that the EMRAR algorithm effectively balances data accuracy and transmission reliability while also saving energy when compared to existing algorithms.

Energy-Efficient and Highly Reliable Geographic Routing Based on Link Detection and Node Collaborative Scheduling in WSN.

Energy efficiency and data reliability are important indicators to measure network performance in wireless sensor networks. In existing research schemes of routing protocols, the impact of node coverage on the network is often ignored, and the possibility that multiple sensor nodes may sense the same spatial point is not taken into account, which results in a waste of network resources, especially in large-scale networks. Apart from that, the blindness of geographic routing in data transmission has been troubling researchers, which means that the nodes are unable to determine the validity of data transmission. In order to solve the above problems, this paper innovatively combines the routing protocol with the coverage control technique and proposes the node collaborative scheduling algorithm, which fully considers the correlation characteristics between sensor nodes to reduce the number of active working nodes and the number of packets generated, to further reduce energy consumption and network delay and improve packet delivery rate. In order to solve the problem of unreliability of geographic routing, a highly reliable link detection and repair scheme is proposed to check the communication link status and repair the invalid link, which can greatly improve the packet delivery rate and throughput of the network, and has good robustness. A large number of experiments demonstrate the effectiveness and superiority of our proposed scheme and algorithm.

ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV.

A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.