To address the dynamic characteristics of data transmission across multiple data centers, enhance the utilization of network resources and the quality of data transmission, and prevent critical data loss, a dynamic priority-based optimization method for grouped scheduling of data transmission paths in multi-data centers is proposed. Based on the IEEE 802.1lp standard, this method categorizes data frames into four access categories according to their importance. It calculates the significance of different categories of data frames and the urgency of transmission tasks to generate a dynamic priority queue for data transmission tasks. On this basis, the transmission probability of data frames with varying priorities is computed. Using the A3C-optimized MPTCP algorithm as the core, the optimal set of transmission paths under different network conditions is determined. By incorporating the minimum round-trip delay algorithm and evaluating path transmission quality, transmission packets are scheduled and allocated within the selected path set, thereby achieving optimized grouped scheduling of data transmission paths for multi-data centers. Test results demonstrate that the proposed method effectively calculates data frame priorities and transmission task urgency, generating a prioritized data transmission queue for the data center. After scheduling optimization, the average link utilization exceeds 0.933; the data transmission rate remains above 685.5 kB/s under various network conditions, and the bandwidth utilization reaches above 88.66%.

Telesurgery offers a solution to the uneven distribution of surgical resources by enabling telesurgery through surgical robotic systems. This study aimed to describe the technical feasibility and preliminary short-term safety of urologic telesurgery using Chinese surgical systems (CSS). This single-center exploratory descriptive case series evaluated urologic telesurgery using CSS between January 2023 and October 2025. A total of 6 consecutive eligible telesurgeries were included: 3 robot-assisted radical prostatectomies (RARP) and 1 robot-assisted partial nephrectomy (RAPN) were performed with the Edge MP1000 (MP1000) system, and 1 RAPN and 1 robot-assisted radical cystectomy (RARC) were performed with the KangDuo SR-2000 (KD-SR-2000) system. Six locally performed RARP cases using the da Vinci Xi (DV-Xi) system during the same study period were retrospectively collected as a limited contextual reference for the RARP subgroup. The primary outcome was technical success, defined as completion without conversion. Secondary descriptive outcomes included perioperative safety events, 30-day Clavien-Dindo complications (CDC), network performance, and, in the RARP subgroup, positive surgical margin (PSM) rate, estimated blood loss (EBL), operative time, suture-per-stitch time, and 4-week urinary continence recovery after catheter removal. All six telesurgeries were completed without intraoperative conversion or major complications. In the RARP subgroup, remote MP1000 procedures were summarized alongside locally performed DV-Xi procedures as a limited contextual reference. PSM rate and early urinary continence recovery showed no obvious unfavorable signal in this small contextual cohort, whereas operative time and suture-per-stitch time were longer, and EBL was numerically higher, in remote procedures. Mean Network round-trip latency for telesurgery ranged from 6.13 ± 0.71 ms (Harbin-Harbin, 2km) to 54.12 ± 0.58 ms (Harbin-Hangzhou, 2200km), with no frame loss. In this small selected case series, urologic telesurgery using CSS was technically feasible and preliminary safety under stable telecommunication conditions.

Telerobotic surgery allows expert surgical care delivery across large distances using robotic systems and high-speed, low-latency networks. However, concerns regarding technical safety, clinical workflow and outcomes - particularly in settings without on-site surgical expertise - limit its global adoption. To evaluate the feasibility, safety, efficacy, workflow and technical requirements of long-distance telerobotic cholecystectomy in India using the indigenous SS Innovations (SSI) MantraSync robotic system and Multiprotocol Label Switching (MPLS)-based dedicated connectivity. Two female patients underwent an elective telerobotic cholecystectomy with the surgeon located at Gurugram and the patient at Guwahati, separated by ~ 1950 km. A pre-established surgical workflow with a modified surgical safety checklist was followed. A strict contingency protocol, multilayered network security and backup conversion plans were implemented. Technical requirements included a dedicated 40 Mbps MPLS line, ultra-low latency video and control data paths and the RASCOW2 protocol for stable streaming. Perioperative and network parameters were prospectively recorded. Both surgeries were completed successfully without intraoperative complications, conversions or technical failures. Console times were 51 and 30 mins, blood loss was minimal (10-15 mL), Visual Analogue Scale pain score at 24 h was 1-2 and uneventful discharge occurred on day 2. Network transmission latency was 35-40 ms, round-trip latency 250-260 ms, with jitter <10 ms and packet loss <0.1%. All values were within recommended global benchmarks for telerobotic safety (<320 ms round-trip). Functional handover to local control was verified and could be achieved in 2-3 min if needed. Telerobotic cholecystectomy using the SSI MantraSync platform and a robust network infrastructure is safe, feasible and effective, with technical and clinical outcomes comparable to in-person robotic surgery. Adherence to multidisciplinary workflow, safety checklists and redundancy protocols is critical. These results support expanded adoption of telerobotic surgery in resource-limited and remote environments, enabling broader access to advanced surgical care.

Satellite Communication (SatCom) offers internet connectivity where traditional infrastructures are too expensive to deploy. When using satellites in a geostationary orbit, the distance from Earth forces a round-trip time of at least 550 ms. Coupled with the constrained capacity of the physical link, this challenges the traditional internet access quality we are used to. In this paper, we present a complete passive characterization of the traffic carried by an operational SatCom provider. With this unique vantage point, we observe the performance of the SatCom technology, as well as the usage habits of subscribers in different countries in Europe and Africa. We highlight the implications of such technology on Internet usage and functioning, and we pinpoint technical challenges due to the CDN and DNS resolution issues, while discussing possible optimizations that the ISP could implement to improve the service offered to SatCom subscribers. We complete the characterization of the adoption and performance of newer protocols with a focus on IPv6 and QUIC.

This study examines how variations in Maximum Transmission Unit (MTU) affect network performance in a Software-Defined Networking (SDN) architecture using the Ryu controller, Mininet, and Open vSwitch. Experiments were conducted in a virtualized Ubuntu 18.04 environment with four MTU settings 500, 1000, 1500, and 2000 bytes and performance was evaluated through repeated measurements of Round Trip Time (RTT), throughput, jitter, and packet loss. All scenarios produced 0% packet loss, enabling focused analysis on latency, efficiency, and temporal stability. The results indicate that MTU size has a non-linear influence on SDN behavior. The 1000-byte MTU yielded the lowest RTT (10.9 ms), while larger and smaller values introduced higher delay. Throughput peaked at 1500 bytes (11.4 Mbps) but declined sharply at 2000 bytes, reflecting inefficiencies in processing oversized packets. Jitter showed a distinct pattern, remaining low at 500 bytes, increasing at mid-range MTUs, and decreasing again at 2000 bytes, suggesting sensitivity to internal buffering and queue dynamics. Overall, MTU values between 1000 and 1500 bytes offer the most balanced performance across latency, throughput, and jitter. These findings highlight the need for careful MTU selection to optimize the operational stability and efficiency of SDN-based networks.

ABSTRACT In recent decades, indoor positioning systems have progressed rapidly to service various areas, including navigation systems, tracking devices, and autonomous vehicle management. In contrast to the outdoor positioning system, where the global navigation satellite system is a known typical technology, there is no standard indoor environment, despite the availability of miscellaneous connections of wireless networks, including a new lower-power version of Bluetooth, Radiofrequency (RF), Wi-Fi, RFID, vlc, and Ultrasonic. Within these technologies, the Ultrasonic position system (UPS) provides an outstanding implementation option because of its high precision and cheap cost. Although UPS provides several benefits, it faces obstacles from propagation circumstances like multipath and Doppler effect. Consequently, although various ultrasonic systems have been developed in recent decades, the issues remain an active and open subject. In this survey article, a comparison will be made between current scientific studies that will be categorized into several categories based on the method used to determine the location of objects as time-of-flight (ToA), round-trip-time (RTT), received-signal-strength (RSSI), and aoa. Furthermore, this study aims to assess the precision and efficacy of existing positioning systems comprehensively. The survey endeavours to present a thorough overview of critical attributes of alternative systems, encompassing their respective positioning methodologies, techniques, and measurement algorithms. Additionally, we will appraise these systems' constraints, considering factors such as precision, noise levels, and detection methodologies. Advanced simulation tools will be employed to replicate diverse scenarios to facilitate this evaluation.

Flying small unmanned aerial vehicles (UAVs) in outdoor areas without GPS usually means strapping a heavy, power-hungry companion computer to the drone to handle visual navigation. For budget-friendly research setups under$700, this simply isn't practical. This paper explores a more cost-effective alternative: shifting all the heavy visual processing to a regular consumer laptop down on the ground. The drone itself only carries its flight controller, a basic camera, and a digital telemetry link. By pairing a MicroAir H7 flight controller (running PX4 v1.14) with a SIYI HM30 data link, we pass video to a compact machine learning model on the laptop. The system then sends pose estimates back to the drone at 18 Hz. Because the radio link introduces a 178 ms round-trip delay, the ML model has to be extremely fast—which is exactly why we kept the architecture so lightweight. During 240-meter outdoor test flights, the drone successfully held its position with an absolute trajectory error of less than 3.4%, all without draining the aircraft's battery for computing tasks.Key Words : GPS-denied navigation, off-board processing, PX4 autopilot, MAVLink, visual-inertial odometry, budget UAVs.

The proliferation of Wireless Sensor Networks (WSNs) in mission-critical applications has made them primary targets for sophisticated routing layer threats, specifically multi-point wormhole attacks that compromise data integrity through artificial low-latency tunnels. This project proposes an Autonomous Self-Rerouting for Multi-Wormhole Mitigation in Wireless Sensor Networks using XGBoost Ensemble Learning to transition network security from passive detection to active, autonomous resilience. Initially, the framework ingests real-time telemetry data, including Round Trip Time (RTT) and Hop-Count Symmetry, which is refined using an Adaptive Feature-Aware Noise Suppression (AFNS) Logic to eliminate environmental jitter and synchronization artifacts. The refined data is then processed by an XGBoost-based Ensemble Classifier, which performs high-dimensional feature extraction to isolate the subtle signatures of colluding malicious nodes. To minimize false positives caused by natural network congestion, a Symptom-Aware Trust Engine (DTE) is integrated to evaluate node reliability over multiple transmission cycles. Once a threat is validated, an Autonomous Mitigation Layer is triggered to logically prune malicious edges from the network topology. The system then utilizes a Cost-Aware Dijkstra’s Algorithm to recalculate secure alternative paths in real-time, ensuring zero-downtime communication. Experimental results demonstrate that the proposed integrated approach maintains a Packet Delivery Ratio (PDR) above 95% even during intense attack scenarios. Ultimately, this framework provides a robust, self-healing solution that significantly improves the reliability and longevity of secure WSN infrastructures

The Internet is evolving rapidly, and billions of devices are being connected to it at the edge. Performance at the edge matters, and the role of the congestion control mechanism is important. Since the emergence of the Bottleneck Bandwidth and Round-trip propagation time (BBR) algorithm by Google, active research has been going on between BBR and TCP Cubic. BBR-v3 is the third version since its inception, and it has tried to address many of the shortcomings of its earlier versions. The issue of fairness with Cubic and Reno flows, the bandwidth overestimation issue in multi-flow scenarios, high packet transmission rate in shallow buffers, and queuing delays and packet losses during/after its startup phase. Not much research work is yet available on BBR-v3 evaluation with Cubic, especially in a variety of connectivity scenarios, such as wired and wireless together. In this paper, we evaluate BBR-v3 with Cubic and have proposed BBR-n+ (BBR Smart Exit) that refines the generic BBR's (BBR-v3) startup exit by detecting receive window limitations and empirically evaluate its performance with the generic BBR (BBR-v3). The early exit issue of BBR-v3 from the startup phase when the congestion window is receiver-limited is probed, and the Smart Exit algorithm has been proposed. It is the continuation of our work on BBR-n, and we have evaluated it using various performance metrics. The role of modern AQMs has been explored with our testing on Common Applications Kept Enhanced (CAKE) and Flow Queue Controlled Delay (FQ_CoDel) AQMs. Through the experiments, we conclude that BBR-n+ in the receive window limitation test provides a 15-20% median throughput gain over BBR-v3 under different receiver window sizes. A 150 ms reduction in HTTP delay when compared with BBR-v3 and a ~ 300 ms reduction versus Cubic. 17% improvement in ping latency compared to BBR-v3 and 45% with Cubic in the wired scenario with a strenuous load of multiple streams. BBR-n+ outperforms BBR-v3 and Cubic in most of the tested scenarios, though it still exhibits limitations, particularly in achieving fairness when competing against Cubic flows when the number of concurrent streams is eight or more.

Unmanned Aerial Vehicle Ad Hoc Networks (UAVANETs) are characterized by highly dynamic topology changes and unstable link conditions, which necessitate deep collaboration between transport-layer congestion control and network-layer routing decisions to ensure service quality. However, the existing layered architecture of Software-Defined Networking (SDN) results in a significant separation between routing information and congestion control mechanisms, rendering traditional protocols ineffective in handling severe performance fluctuations caused by highly dynamic route switching. The significant disconnect between network-layer route planning and transport-layer congestion control strategies in Software-Defined Unmanned Aerial Vehicle Ad Hoc Networks (SD-UAVANETs) leads to degraded transmission performance of BBR (Bottleneck Bandwidth and Round-trip propagation time) under high-dynamic route switching scenarios. As such, this paper proposes an in-band network telemetry (INT)-based cross-layer optimization scheme for BBR, named SDN-BBR. Firstly, a lightweight real-time route switching detection mechanism based on INT is designed. Secondly, a QoS inequality model before and after path switching is established, deriving the critical bandwidth of the new path and integrating it into the BBR algorithm to accelerate convergence and avoid congestion. Finally, the BBR state machine is redesigned to achieve cross-layer information fusion and coordinated control, thereby optimizing transmission performance. Experimental results show that the proposed scheme reduces convergence time by 69.8% and increases throughput by 73.9% in low-bandwidth to high-bandwidth switching scenarios; decreases packet loss rate by 86.8% and reduces delay by 8.3% in high-bandwidth to low-bandwidth switching scenarios; and improves throughput by 12.3%, lowers packet loss rate by 21%, and reduces delay by 7.9% in multi-traffic flow concurrent scenarios. The scheme significantly enhances the transmission performance of BBR in highly dynamic routing environments of SD-UAVANET.

Integrated optical sources operating at low repetition rates are a practical requirement in distributed fibre-optic sensing (DFOS) architectures, where the pulse period is set by the round-trip delay of long fibre links rather than by arbitrary design choices. Optomechanical oscillators offer a purely optical route to low-frequency signal generation, but experimental demonstrations on silicon photonic platforms have largely concentrated on higher-frequency operation or have relied on external electronic feedback to sustain oscillation. In this work, we demonstrate a silicon-photonic optomechanical oscillator operating in the low-megahertz regime as a self-sustained optical signal source without radio-frequency driving or electronic control. The device is implemented on a silicon-on-insulator platform using a racetrack resonator with a partially suspended waveguide. Stable self-oscillation is observed at a frequency of approximately 5.86 MHz. Time-domain measurements reveal a near-sinusoidal waveform, while frequency-domain spectra show a distinct mechanical resonance with a quality factor of about 1100. By adjusting the laser cavity detuning, the oscillator transitions from a single-tone oscillation state into nonlinear dynamical regimes characterized by pronounced harmonic generation. The evolution of the output spectrum follows the behavior expected from standard optomechanical models based on phase modulation of the intracavity field. Taken together, these results indicate that low-MHz optomechanical oscillators implemented within silicon photonics are experimentally accessible and can serve as compact optical signal primitives for DFOS-oriented integrated photonic systems.

This paper presents a lightweight MATLAB-based framework with a graphical interface for modeling, 3D simulation, trajectory generation, and experimental validation of a 6-DOF industrial robot. The platform integrates kinematic modeling using the rigidBodyTree structure, animated visualization, and both predefined and user-defined trajectory planning within a unified environment. A central aspect of the proposed approach is the implementation of a ROS-compatible TCP/IP communication protocol that avoids the need for a full ROS core installation while preserving compatibility with ROS-Industrial standards. This enables bidirectional data exchange between MATLAB and the robot controller within a simplified architecture. Communication performance tests indicate round-trip latency in the tens-of-milliseconds range and consistent StateServer update rates, supporting monitoring, trajectory execution, and digital twin synchronization in non-real-time conditions. Experiments conducted on an ABB IRB120 robot demonstrate a close correspondence between simulated and real motion, with RMSE below 0.0075 rad and MAE below 0.0065 rad across all joints. All data are stored in JSON format to support reproducibility and further analysis. By integrating simulation and real robot execution within a modular architecture, the proposed framework provides a practical tool for education, rapid prototyping, and experimental research in industrial robotics, while offering a basis for future extensions toward advanced control strategies and digital twin applications.

In low-latency edge-intelligence scenarios such as autonomous driving and industrial edge analytics, the processing of large-scale sensor data imposes extremely stringent requirements on communication latency. However, the high overhead of the traditional TCP protocol makes it difficult to satisfy such demands, while the semantic gap between the high-performance RoCE protocol and the standard Socket API prevents existing applications from directly exploiting its advantages. To address this problem, this paper proposes TransBridge, a lightweight user-space communication middleware that transparently bridges TCP and RoCE. Its design is realized through three key innovations: a transparent user-space compatibility architecture that enables unmodified Socket-based applications to benefit from RoCE performance; a microsecond-level low-latency transmission engine that bypasses kernel and protocol stack overhead; and a lightweight lock-free resource management mechanism based on a decentralized peer-to-peer architecture and deferred buffer updates. Experiments on a real RoCE network show that TransBridge significantly outperforms mainstream schemes: it achieves an average round-trip latency of 5.926 μs for 16 B messages and a throughput of 20.254 Gbps for 16 KB messages; in the Fast DDS application-level evaluation, it achieves a throughput of 188 Mbps and an average round-trip latency of about 150 μs. The results indicate that TransBridge can provide transparent and effective RoCE acceleration for existing Socket-based applications in resource-constrained edge environments.

Modern particle accelerator facilities require sophisticated control systems capable of managing thousands of process variables in real-time while ensuring high availability, scalability, and ease of maintenance. This paper presents EPIK8S (EPICS on Kubernetes), a framework that leverages Kubernetes container orchestration to deploy, manage, and scale EPICS (Experimental Physics and Industrial Control System) infrastructure for accelerator control systems. A central design principle is that the entire control system configuration for a beamline — comprising IOCs, services, and infrastructure — is captured in a single YAML file, which is processed through a Jinja2 templating layer to produce device-specific IBEK runtime configurations. This three-tier architecture radically simplifies IOC management, enables complete change tracking via Git, and eliminates an entire class of manual configuration errors. The framework introduces a GitOps-based approach using ArgoCD for continuous deployment, providing declarative configuration management and automated synchronisation. We describe the architecture and implementation, and report operational experience from three INFN facilities: the SPARC_LAB photoinjector, the DAFNE Beam Test Facility (BTF), and the ELI-NP gamma beam system in Romania. Performance metrics — including dedicated Channel Access round-trip latency measurements comparing bare-metal, pod-to-pod, and external-to-cluster scenarios — and lessons learned from over two years of production operation demonstrate significant improvements in deployment efficiency, maintainability, and reliability compared to traditional bare-metal EPICS deployments, with containerisation overhead well within acceptable bounds for accelerator control applications.

Delays in prostate cancer diagnostic evaluation negatively affect patient experience and may adversely affect oncologic outcomes. We evaluated the effect of a telehealth-based rapid access program (RAP) for men referred with elevated PSA on diagnostic timeliness and patient access. We implemented an advanced practice provider-led, telehealth-based RAP within a statewide referral center. Men referred for elevated PSA were evaluated using standardized triage and biopsy decision algorithms. Outcomes in the and preimplementation (October 2023-January 2024) and postimplementation (February 2024-October 2025) cohorts were compared. Primary outcomes included time from referral to initial consultation, MRI, and biopsy. Secondary outcomes assessed patient access, including travel distance, time, and cost savings. Detection rates of Gleason Grade Group ≥ 2 were assessed as a balancing measure. There were 120 men in the preimplementation cohort and 700 men evaluated through RAP. The median time from referral to (1) initial consultation decreased from 63 to 18 days, (2) MRI from 120 to 51 days, and (3) biopsy from 161 to 71 days, representing a 55.9% reduction in overall diagnostic interval (all P < .001). Detection rates of clinically significant prostate cancer were similar in the preimplementation and postimplementation cohorts (34.0% vs 39.4%, respectively; P = .06). The median round-trip travel distance and time avoided were 58 miles and 66 minutes, respectively, resulting in a median cost savings of $48.86 per patient. Implementation of a telehealth-based RAP significantly reduced diagnostic intervals for men referred with elevated PSA while maintaining diagnostic quality. This scalable model demonstrates how telehealth-enabled workflow redesign can enhance timeliness and access.

This paper addresses the challenges of Direct Recursive Generalized Predictive Control (DR-GPC) in Space Robot Teleoperation (SRT) under complex, uncertain and large time delays. Firstly, a rigorous DR-GPC mathematical model is developed, incorporating time delays into the control term and revising its original formulation. By adjusting the forward delay to match the control output's round-trip delay, DR-GPC is adapted to SRT systems with asymmetric forward and backward delays, whose rationality is rigorously explained via block diagram transformation in an event-driven framework. Furthermore, mathematical induction is employed to analyze DR-GPC's prediction accuracy, theoretically demonstrating that enhanced prediction efficiency does not compromise accuracy–filling a theoretical gap and elucidating DR-GPC's recursive prediction mechanism, which also lays a foundation for future improvements. Finally, simulations confirm DR-GPC's effectiveness in SRT system control.

Streaming video dominates traffic on today's Internet. For many content types, users do not watch the entire video, but rather skip around within the video. When a user skips to a time not yet buffered by the client software, current systems stall the video playback while fetching the relevant chunk. Meanwhile, end-user bandwidth continues to increase with increased deployment of fiber-to-the-home and 5G services, as CDN appliances inside of ISPs drive down round-trip time (RTT). This paper introduces StallFreeSeek (SFS), which can provide nearly stall free seek for users by proactively prefetching the latency-sensitive portion of future video chunks without increasing buffer waste. SFS carefully rechunks the video such that the bandwidth overhead for prefetching is small and the buffer can be refilled after user seek without stalling video playback. We evaluated SFS using various video genres, different models of user seeks using over fifty thousand simulated video sessions and nearly two thousand real-world video sessions. In these evaluations, SFS consistently outperforms Dash.js in QoE, stall time, and buffer waste. For example, in our ABR experiments on real-world network performance traces, SFS improved QoE by 25 percentage points, reduced seek-related stall times by 79%, while reducing buffer waste by 44%.

Round-trip time (RTT) is a key latency indicator for quality-of-service (QoS) control and task orchestration in cloud–edge systems. However, RTT is highly time-varying due to congestion dynamics, routing changes, and fluctuating traffic conditions, motivating short-term prediction to enable proactive decision making. This paper investigates a hybrid neuro-fuzzy baseline for RTT prediction implemented using an Adaptive Neuro-Fuzzy Inference System (ANFIS) with subtractive-clustering-based initialization to avoid rule explosion in high-dimensional inputs. A controlled dataset was generated in Mininet using a dumbbell topology with injected delays (1–1000 ms). In total, 100,000 raw RTT records were collected (100 RTT measurements per run across 1000 runs) and aggregated into 1000 supervised samples paired with TCP-state features. Experiments followed a unified and reproducible protocol with a fixed 60/10/30 train/validation/test split, train-only feature standardization, train-only target normalization with inverse transformation for reporting, and validation-based checkpoint selection. The ANFIS baseline (radius ????=0.5r=0.5, 19 rules) achieved RMSE/ MAE/ MAPE/ ????2 of 1191.63/ 751.02/ 0.001921/ 0.999996 on validation and 1207.23/ 664.70/ 0.001311/ 0.999996 on testing. Training required 546.91 s, while inference remained lightweight (0.0846 s for 100 validation samples and 0.1493 s for 300 test samples). Diagnostic analyses using learning curves, parity plots, residual inspection, and empirical error distributions further supported the strong agreement between predicted and observed RTT values. These results indicate that ANFIS with subtractive clustering can deliver accurate and low-latency RTT prediction suitable for QoS-aware orchestration pipelines where training can be performed offline.

Indoor positioning in hospitals is challenging because global navigation satellite systems signals are unavailable and existing solutions struggle with complex indoor propagation and high maintenance requirements. Fingerprinting-based methods using Wi-Fi, Bluetooth Low Energy (BLE), or magnetic field depend on extensive site surveys, while time or angle-based systems such as ultra-wide band, angle of arrival, and Wi-Fi round trip time require additional infrastructure. Recent machine learning approaches improve performance but remain limited by Pedestrian Dead Reckoning (PDR) drift and unstable spatial representations. This study proposes an AI-generated spatial pattern matching framework that integrates an AI-based PDR model with BLE Received Signal Strength Indicator (RSSI) to construct a user RSSI surface. Spatial similarity between user-generated patterns and the pre-built radio map is evaluated using Surface Correlation (SC), and a bi-directional candidate generation strategy with SC-based heading correction is employed to mitigate inertial drift. Experiments in a real hospital setting show that the proposed method achieves robust and accurate localization even in complex indoor environments where conventional fingerprinting and PDR techniques often fail. The results indicate that combining AI-driven inertial modeling with SC-based spatial pattern matching offers a practical and infrastructure-friendly solution for hospital indoor positioning.

Clinical and Technical Outcomes of Transcontinental Urological Telesurgery: A Series of 11Cases.