Static Link Research Articles

An Ensemble Approach to Improve the Performance of Real Time Data Stream Classification

In the era of the Internet of Things (IoT), data stream mining has gained importance to make accurate and profitable decisions. Various techniques are used to gain insight into data streams, including classification, clustering, pattern mining, etc. Data are subject to changes over time. When this happens, predictive models that assume a static link between input and output variables may perform poorly or even degrade, which is called concept drift. This study proposes an ensemble architecture designed to improve performance and effectively detect concept drift in stream data classification. Using an ensemble approach, the proposed architecture incorporates three classifiers to improve accuracy and robustness against concept drift. The proposed architecture provides drift detection that ensures the model's continued performance by enabling it to be quickly modified to changing data distributions. Through comprehensive testing, the performance of the proposed algorithm was compared with existing methods, and the results demonstrate its superiority in terms of classification accuracy, precision, and recall and drift detection capabilities.

DaE2: Unmasking malicious URLs by leveraging diverse and efficient ensemble machine learning for online security

Over 5.44 billion people now use the Internet, making it a vital part of daily life, enabling communication, e-commerce, education, and more. However, this huge Internet connectivity also raises concerns about online privacy and security, particularly with the rise of malicious Uniform Resource Locators (URLs). Recently, conventional ensemble models have attracted attention due to their notable benefits of reducing the variance in models, enhancing predictive performance, improving prediction accuracy, and demonstrating high generalization potential. But, its application in addressing the challenge of malicious URLs is still an open problem. These URLs often hide behind static links in emails or web pages, posing a threat to individuals and organizations. Despite blacklisting services, many harmful sites evade detection due to inadequate scrutiny or recent creation. Hence, to improve URL detection, a Diverse and Efficient Ensemble (DaE2) machine learning algorithm was developed using four ensemble models, that is, AdaBoost, Bagging, Stacking, and Voting to classify URLs. After preprocessing, the experimental result shown that all models achieved over 80 % accuracy, with AdaBoost reaching 98.5 % and Stacking offering the fastest runtime. AdaBoost and Bagging also delivered strong performance, with F1 scores of 0.980 and 0.976, respectively.

Data driven origin–destination matrix estimation on large networks—A joint origin–destination-path-choice formulation

This paper presents a novel approach to data-driven time-dependent origin–destination (OD) estimation using a joint origin–destination-path choice formulation, inspired by the well-known equivalence of doubly constraint gravity models and multinomial logit models for joint O–D choice. This new formulation provides a theoretical basis and generalizes an earlier contribution. Although including path choice increases the dimensionality of the problem, it also dramatically improves the quality of the data one can directly use to solve it (e.g. measured path travel times versus coarse centroid-to-centroid travel times); and opens up possibilities to combine different assimilation techniques in a single framework: (1) fast shortest path set computation using static (e.g. road type) and dynamic (speed, travel time) link properties; (2) predicting a “prior OD matrix” using the resulting path-shares and (estimated or measured) production and attraction totals; and (3) scaling/constraining this prior using link flows (informative of demand). If the resulting system of equations has insufficient rank, we use principal component analysis to reduce the dimensionality, solve this reduced problem, and transform that solution back to a full OD matrix. Comprehensive tests and sensitivity analysis on 7 networks with different sizes and characteristics give an empirical underpinning of the extended equivalence principle; demonstrate good accuracy and reliability of the OD estimation method overall; and suggest that the method is robust with respect to major assumptions and contributing factors.

Emergent order in adaptively rewired networks.

We explore adaptive link change strategies that can lead a system to network configurations that yield ordered dynamical states. We propose two adaptive strategies based on feedback from the global synchronization error. In the first strategy, the connectivity matrix changes if the instantaneous synchronization error is larger than a prescribed threshold. In the second strategy, the probability of a link changing at any instant of time is proportional to the magnitude of the instantaneous synchronization error. We demonstrate that both these strategies are capable of guiding networks to chaos suppression within a prescribed tolerance, in two prototypical systems of coupled chaotic maps. So, the adaptation works effectively as an efficient search in the vast space of connectivities for a configuration that serves to yield a targeted pattern. The mean synchronization error shows the presence of a sharply defined transition to very low values after a critical coupling strength, in all cases. For the first strategy, the total time during which a network undergoes link adaptation also exhibits a distinct transition to a small value under increasing coupling strength. Analogously, for the second strategy, the mean fraction of links that change in the network over time, after transience, drops to nearly zero, after a critical coupling strength, implying that the network reaches a static link configuration that yields the desired dynamics. These ideas can then potentially help us to devise control methods for extended interactive systems, as well as suggest natural mechanisms capable of regularizing complex networks.

A Flexible Topology Control Strategy for Mega-Constellations via Inter-Satellite Links Based on Dynamic Link Optimization

In large-scale satellite constellations, the efficiency of inter-satellite communication is paramount. Traditional topology control strategies, such as the Manhattan configuration, provide stable links but can result in indirect communication paths, affecting the efficiency of information transfer. This paper addresses this issue by proposing an innovative “3 + 1” dynamic topology control scheme. The scheme retains three static links determined by the relative angular velocity and acceleration while introducing a dynamic link based on distance and angular velocity constraints to optimize the link duration and overall network communication efficiency. To address the complexity of matching dynamic links, this paper introduces an elite strategy-based maximum weighted matching algorithm for general graphs. Compared to traditional greedy algorithms, our proposed algorithm significantly improves the link duration and topological stability. Through simulation experiments comparing communication delays between Xiamen and Los Angeles, our results show that the proposed dynamic link scheme substantially reduces the average delay, enhancing the efficiency and flexibility of inter-satellite communication. This research not only extends the duration of inter-satellite links but also provides new perspectives and methodologies for further studies on inter-satellite topology control strategies.

Automated fiber switch with path verification enabled by an AI-powered multi-task mobile robot

As the capacity of optical transport networks undergoes significant growth, there is an ongoing discussion on how to effectively leverage both spectral and spatial degrees of freedom to scale future network capacity. This paper presents an artificial intelligence (AI)-powered multi-task robot comprising a collaborative robotic arm and a mobile robotic base designed for optical network automation. The robot demonstrates the capability of direct fiber switching, establishing static fiber links that consume zero power and have minimal insertion loss from fiber connectors. As a precautionary measure before physically switching fiber cables, the robot performs path verification by detecting robot-driven events using real-time coherent receivers, aiming to avoid accidental unplugging. Additionally, the robot showcases its mobility by efficiently navigating between different network racks and rooms while executing various tasks. Implementing the automation of network operations using robots has the potential to reduce both capital and operational expenditures.

Load-optimization in Reconfigurable Data-center Networks: Algorithms and Complexity of Flow Routing

Emerging reconfigurable data centers introduce unprecedented flexibility in how the physical layer can be programmed to adapt to current traffic demands. These reconfigurable topologies are commonly hybrid, consisting of static and reconfigurable links, enabled by e.g., an Optical Circuit Switch (OCS) connected to top-of-rack switches in Clos networks. Even though prior work has showcased the practical benefits of hybrid networks, several crucial performance aspects are not well understood. For example, many systems enforce artificial segregation of the hybrid network parts, leaving money on the table. In this article, we study the algorithmic problem of how to jointly optimize topology and routing in reconfigurable data centers, in order to optimize a most fundamental metric, maximum link load. The complexity of reconfiguration mechanisms in this space is unexplored at large, especially for the following cross-layer network-design problem: given a hybrid network and a traffic matrix, jointly design the physical layer and the flow routing in order to minimize the maximum link load. We chart the corresponding algorithmic landscape in our work, investigating both un-/splittable flows and (non-)segregated routing policies. A topological complexity classification of the problem reveals NP-hardness in general for network topologies that are trees of depth at least two, in contrast to the tractability on trees of depth one. We moreover prove that the problem is not submodular for all these routing policies, even in multi-layer trees. However, networks that can be abstracted by a single packet switch (e.g., nonblocking Fat-Tree topologies) can be optimized efficiently, and we present optimal polynomial-time algorithms accordingly. We complement our theoretical results with trace-driven simulation studies, where our algorithms can significantly improve the network load in comparison to the state-of-the-art.

Investigating Black-Box Function Recognition Using Hardware Performance Counters

This paper presents new methods and results for recognising black-box program functions using hardware performance counters (HPC), where an investigator can invoke and measure function calls. Important use cases include analysing compiled libraries, e.g. static and dynamic link libraries, and trusted execution environment (TEE) applications. We develop a generic approach to classify a comprehensive set of hardware events, e.g. branch mis-predictions and instruction retirements, to recognise standard benchmarking and cryptographic library functions. This includes various signing, verification and hash functions, and ciphers in numerous modes of operation. Three architectures are evaluated using off-the-shelf Intel/X86-64, ARM, and RISC-V CPUs. Next, we show that several known CVE-numbered OpenSSL vulnerabilities can be detected using HPC differences between patched and unpatched library versions. Further, we demonstrate that standardised cryptographic functions within ARM TrustZone TEE applications can be recognised using non-secure world HPC measurements, applying to platforms that insecurely perturb the performance monitoring unit (PMU) during TEE execution. High accuracy was achieved in all cases (86.22–99.83%) depending on the application, architectural, and compilation assumptions. Lastly, we discuss mitigations, outstanding challenges, and directions for future research.

Locality of contacts determines the subdiffusion exponents in polymeric models of chromatin.

Loop extrusion by motor proteins mediates the attractive interactions in chromatin on the length scale of megabases, providing the polymer with a well-defined structure and at the same time determining its dynamics. The mean-square displacement of chromatin loci varies from a Rouse-like scaling to a more constrained subdiffusion, depending on cell type, genomic region, and time scale. With a simple polymeric model, we show that such a Rouse-like dynamics occurs when the parameters of the model are chosen so that contacts are local along the chain, while in the presence of nonlocal contacts we observe subdiffusion at short time scales with exponents smaller than 0.5. Such exponents are independent of the detailed choice of the parameters and build a master curve that depends only on the mean locality of the resulting contacts. We compare the loop-extrusion model with a polymeric model with static links, showing that also in this case only the presence of nonlocal contacts can produce low-exponent subdiffusion. We interpret these results in terms of a simple analytical model.

Study of the generalized curves of the static and cyclic deformation, damage and fracture

Individual deformation diagrams of all materials on metal, non-metal, or composite base under static and cyclic load link stresses and strains. These diagrams are obtained in standard tensile, compression, torsion or bending tests of laboratory samples with the registration of forces and deformations of their working parts upon loading. The diagram for a single static deformation in the stress-strain coordinates in this case covers both the region of elastic deformations and the region of elastic-plastic deformation, when deformations are localized in the neck of the loaded sample up to the moment of its destruction at a critical stress level. It is shown that linear, fractional-linear and power approximation of the obtained deformation curve are widely used in the description of the obtained deformation diagrams. Direct experiments, the theory of dislocations and the statistical theory of strength confirm the priority of power approximation of considered diagrams. At the same time for all construction materials the generalized deformation diagram in relative coordinates is described by a single power equation with the individual hardening parameter which is determined experimentally or theoretically using the dependences linking data on the module of elasticity, yield stresses, strength, and ultimate plasticity of the material. Diagrams of a cyclic elastoplastic deformation in the form of loops of plastic hysteresis are recorded by analogy with static tension diagrams with «stress – strain» axes of in conditional and true relative values. The generalized deformation diagrams for a single static and cyclic loading form a scientific basis for construction of the generalized fatigue curve on the basis of deformation fracture criterion for a wide range of cycles to failure. An effective solution to the problems of strength and service life for the most complex engineering objects such as nuclear reactors, aircraft, rocket and space systems can be achieved through introduction of generalized deformation and fracture diagrams into consideration and corresponding calculations. Their significance will especially increase in the design and implementation of new unique science-intensive facilities.

Review of Path Selection Algorithms with Link Quality and Critical Switch Aware for Heterogeneous Traffic in SDN

Software Defined Networking (SDN) introduced network management flexibility that eludes traditional network architecture. Nevertheless, the pervasive demand for various cloud computing services with different levels of Quality of Service requirements in our contemporary world made network service provisioning challenging. One of these challenges is path selection (PS) for routing heterogeneous traffic with end-to-end quality of service support specific to each traffic class. The challenge had gotten the research community's attention to the extent that many PSAs were proposed. However, a gap still exists that calls for further study. This paper reviews the existing PSA and the Baseline Shortest Path Algorithms (BSPA) upon which many relevant PSA(s) are built to help identify these gaps. The paper categorizes the PSAs into four, based on their path selection criteria, (1) PSAs that use static or dynamic link quality to guide PSD, (2) PSAs that consider the criticality of switch in terms of an update operation, FlowTable limitation or port capacity to guide PSD, (3) PSAs that consider flow variabilities to guide PSD and (4) The PSAs that use ML optimization in their PSD. We then reviewed and compared the techniques' design in each category against the identified SDN PSA design objectives, solution approach, BSPA, and validation approaches. Finally, the paper recommends directions for further research.

Human Activity Detection Using Smart Wearable Sensing Devices with Feed Forward Neural Networks and PSO

Hospitals must continually monitor their patients’ actions to lower the chance of accidents, such as patient falls and slides. Human behavior is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating a static link that is used to influence human behavior is challenging, since it is hard to forecast how individuals will think or act in response to a certain event. Mobility tracking depends on intelligent monitoring systems that apply artificial intelligence (AI) applications referred to as “categories”. Because motion sensors, such as gyroscopes and accelerometers, output unconnected data that lack labels, event detection is a vital task. The fall feature parameters of tridimensional accelerometers and gyroscope sensors are presented and used, and the classification technique is based on distinguishing characteristics. This study focuses on the age-old problem of tracking turbulence in motion to improve detection precision. We trained the model, considering that detection accuracy is limited by factors such as the subject’s mass, velocity, and gait style. This is performed by employing an experimental dataset. When we used the sophisticated technique of particle swarm optimization (PSO) in combination with a four-stage forward neural network (4SFNN) to forecast four different types of turbulent motion, we observed that the total prediction accuracy was 98.615% accurate.

General solution scheme for the static link transmission model

Most static traffic assignment models, both in the literature and in practice, are neither capacity constrained nor storage constrained. They allow flows to exceed the link capacity and/or queues to exceed the link length. Recent studies in this area have resulted in novel approaches that do consider capacity constraints, resulting in residual queues, and sometimes even storage constraints, resulting in possible queue spillback. We build upon the results of these works and in particular on the model formulated in Bliemer and Raadsen (2020) that introduced a static network loading model formulation that is both capacity constrained as well as storage constrained. Their static network loading model formulation is derived from – and consistent with - the link transmission model, a well-established dynamic network loading model. It is referred to as the static link transmission model (sLTM) in this paper. This model considers a general concave fundamental diagram for each link and a general first order node model. It is well known that ignoring queue spillback can result in significant underestimation of path travel times. This is especially true for paths that do not traverse any of the bottleneck(s) directly, but that are affected by space occupied by queues that are spilling back. The prospect of being able to capture queuing and spillback effects in static assignment provides new opportunities for improving the modelling capabilities of this paradigm. In this paper, we propose a solution scheme to sLTM capable of finding a solution on large scale networks. This is the first time that an algorithm is proposed for solving an analytical static model with queue spillback. The inclusion of a node model in a static context - while enhancing the model's capabilities – generally results in the absence of a guaranteed convergent algorithm, and introducing spillback exacerbates the issue. Given the challenges of finding a stable solution, we discuss a base solution scheme and three (configurable) extensions. Further, we investigate algorithmic settings with respect to convergence and its impact on computational cost. A large-scale case study demonstrates the feasibility of the proposed scheme by finding solutions under the most challenging of conditions in a real-world setting. We show that improving convergence capabilities negatively affects computational efficiency. To this end, several potential improvements based on our findings are discussed. Lastly, we discuss the potential of this line of research in more general terms, highlighting strengths and weaknesses following our experiences.

Imagens, links e vídeos: argumentação em campanhas eleitorais no Twitter

This article presents an excerpt from our ongoing doctoral research, which aims to identify and analyze linguistic aspects and argumentative strategies in electoral campaign publica-tions on social media from the official profiles/pages of Jair Bolsonaro and Fernando Haddad - the 2018 Brazilian presidential candidates – and Ana Gomes and André Ventura – the 2021 Portuguese presidential candidates – based on the theoretical framework of Discourse Analysis. Here, we pro-vide an analysis of two of the 15 most relevant publications from each candidate on the Twitter social media posted in the 15 days immediately preceding the elections. We draw on Textual Lin-guistics, following the works of Adam (1997, 2001a, 2001b), Bronckart (1985), and Plantin (1996), to identify and analyze how the structure of the social network (Bossetta, 2018), as a textual support, as conceived by Maingueneau (2001), influences the materialization of techno-discourses (Paveau, 2021), which is reflected in the construction of textual sequences identified in the corpus, specifi-cally regarding the presentation of arguments or data (premises) in argumentative textual sequences in which multimodal compositions involving static images, links, and videos were preferred.

DHGEEP: A Dynamic Heterogeneous Graph-Embedding Method for Evolutionary Prediction

Current graph-embedding methods mainly focus on static homogeneous graphs, where the entity type is the same and the topology is fixed. However, in real networks, such as academic networks and shopping networks, there are typically various types of nodes and temporal interactions. The dynamical and heterogeneous components of graphs in general contain abundant information. Currently, most studies on dynamic graphs do not sufficiently consider the heterogeneity of the network in question, and hence the semantic information of the interactions between heterogeneous nodes is missing in the graph embeddings. On the other hand, the overall size of the network tends to accumulate over time, and its growth rate can reflect the ability of the entire network to generate interactions of heterogeneous nodes; therefore, we developed a graph dynamics model to model the evolution of graph dynamics. Moreover, the temporal properties of nodes regularly affect the generation of temporal interaction events with which they are connected. Thus, we developed a node dynamics model to model the evolution of node connectivity. In this paper, we propose DHGEEP, a dynamic heterogeneous graph-embedding method based on the Hawkes process, to predict the evolution of dynamic heterogeneous networks. The model considers the generation of temporal events as an effect of historical events, introduces the Hawkes process to simulate this evolution, and then captures semantic and structural information based on the meta-paths of temporal heterogeneous nodes. Finally, the graph-level dynamics of the network and the node-level dynamics of each node are integrated into the DHGEEP framework. The embeddings of the nodes are automatically obtained by minimizing the value of the loss function. Experiments were conducted on three downstream tasks, static link prediction, temporal event prediction for homogeneous nodes, and temporal event prediction for heterogeneous nodes, on three datasets. Experimental results show that DHGEEP achieves excellent performance in these tasks. In the most significant task, temporal event prediction of heterogeneous nodes, the values of precision@2 and recall@2 can reach 30.23% and 10.48% on the AMiner dataset, and reach 4.56% and 1.61% on the DBLP dataset, so that our method is more accurate at predicting future temporal events than the baseline.

Demonstration of 100 Gbps coherent free-space optical communications at LEO tracking rates

Free-space optical communications are poised to alleviate the data-flow bottleneck experienced by spacecraft as traditional radio frequencies reach their practical limit. While enabling orders-of-magnitude gains in data rates, optical signals impose much stricter pointing requirements and are strongly affected by atmospheric turbulence. Coherent detection methods, which capitalize fully on the available degrees of freedom to maximize data capacity, have the added complication of needing to couple the received signal into single-mode fiber. In this paper we present results from a coherent 1550 nm link across turbulent atmosphere between a deployable optical terminal and a drone-mounted retroreflector. Through 10 Hz machine vision optical tracking with nested 200 Hz tip/tilt adaptive optics stabilisation, we corrected for pointing errors and atmospheric turbulence to maintain robust single mode fiber coupling, resulting in an uninterrupted 100 Gbps optical data link while tracking at angular rates of up to 1.5 deg/s, equivalent to that of spacecraft in low earth orbit. With the greater data capacity of coherent communications and compatibility with extant fiber-based technologies being demonstrated across static links, ground-to-low earth orbit links of Terabits per second can ultimately be achieved with capable ground stations.

A Collision Reduction Adaptive Data Rate Algorithm Based on the FSVM for a Low-Cost LoRa Gateway

LoRa (Long Range), a wireless communication technology for low power wide area networks (LPWANs), enables a wide range of IoT applications and inter-device communication, due to its openness and flexible network deployment. In the actual deployment and operation of LoRa networks, the static link transmission scheme does not make full use of the channel resources in the time-varying channel environment, resulting in a poor network performance. In this paper, we propose a more effective adaptive data rate (ADR) algorithm for low-cost gateways, we firstly analyze the impact of the different hardware parameters (RSSI, SNR) on the link quality and classify the link quality using the fuzzy support vector machine (FSVM). Secondly, we establish an end device (ED) throughput model and energy consumption model and design different adaptive rate algorithms, according to the different link quality considering both the link-level performance and the MAC layer performance. The proposed algorithm uses machine learning to classify the link quality, which can accurately classify the link quality using a small amount of data, compared to other adaptive rate algorithms, and the link parameter adaptation algorithm can maximize the throughput while ensuring the link stability, by considering the link-level performance and the MAC layer performance, compared to other algorithms. The results show that it outperforms the standard LoRaWAN ADR algorithm in both the single ED and the multi ED scenarios, in terms of the packets reception rate (PRR) and the network throughput. Compared to the LoRaWAN ADR in 32 multi-ED scenarios, the proposed algorithm improves the throughput by 34.12% and packets the reception rate by 26%, significantly improving the network throughput and the packets reception rate.

LoRa-LiSK: A Lightweight Shared Secret Key Generation Scheme for LoRa Networks

Physical-layer security (PLS) schemes use the randomness of the channel parameters, namely, channel state information (CSI) and received signal strength indicator (RSSI) to generate the secret keys. There has been limited work in PLS schemes in long-range (LoRa) wide-area networks (LoRaWANs) which hinder their widespread application. Limitations observed in existing studies include the requirement of high correlation between channel parameter measurements for secret key generation in the proposed schemes and the evaluation of the schemes has only been done in either fully indoor or outdoor environments. The real-world wireless sensor networks (WSNs) and LoRa use cases might not meet both requirements thus making the current PLS schemes inappropriate for these systems. By considering the limitations found in existing PLS schemes, this article proposes LoRA-LiSK, a practical and efficient shared secret key generation scheme for LoRa networks. Our proposed LoRa-LiSK scheme consists of several preprocessing techniques (timestamp matching, two sample Kolmogorov–Smirnov tests, and a Savitzky–Golay filter), multilevel quantization, information reconciliation using Bose–Chaudhuri–Hocquenghem (BCH) codes, and finally, privacy amplification using secure hash algorithm SHA-2. The LoRa-LiSK scheme is extensively evaluated on real WSN/IoT devices in practical application scenarios: 1) indoor to outdoor and 2) LoRa static and mobile outdoor links. It outperforms existing schemes by generating keys with channel parameter measurements of low correlation values (0.2–0.6), while still essentially achieving high key generation rates, and low key disagreement rates (10%–20%). The scheme updates a key in approximately 1 h using an application profile with high transmission rate compared to 3 h reported by existing works while still respecting the duty cycle regulation. It also incurs less communication overhead compared to the existing works.

Deep Reinforcement Learning Based Routing in IP Media Broadcast Networks: Feasibility and Performance

The Media Broadcast industry has evolved from Serial Digital Interface (SDI) based infrastructures to IP networks. While IP based video broadcast is well established in the data plane, the use of IP networks to transport media flows still poses challenges in terms of resource management and orchestration. SDN based orchestration architectures have emerged in the industry that use SDN to route the media flows of a broadcast service across the provider IP network. Several approaches to multimedia flow routing in IP based SDN networks have been proposed in the context of streaming applications over the Internet. These range from model based linear optimization solutions that have high complexity to simple shortest path based routing heuristics with either static or dynamic link costs. More recently model-free optimization methods such as Deep Reinforcement Learning (DRL) have been proposed for routing and Traffic Engineering of multimedia flows in SDN networks. The media broadcast scenario however has specific requirements, with services like Master Control Room operation and Live broadcasting of events, and it has been rarely addressed in the literature. In this work we propose a DRL based routing method for this scenario and compare it to static and dynamic link cost algorithms based on Dijkstra shortest paths. This is to our knowledge the first work to follow this approach in the context of Media Broadcast services in IP infrastructures. The algorithm is designed considering the specifications and capabilities of one of the leading SDN orchestrators in the market and considers the more common Service Level Agreement requirements in the industry. Three different DRL algorithms are implemented and compared and we evaluate them using a real service provider network topology. The results indicate that DRL based routing is applicable in real production scenarios and that it achieves considerable performance gains when compared to the static and dynamic link cost shortest path algorithms commonly used today.

Output Voltage and Resistance Assessment of Load-Independent-Voltage-Output Frequency Operating Inductive Wireless Power Transfer Link Utilizing Input DC-Side Measurements Only

The paper puts forward a method for predicting output voltage and resistance of a series-series (SS) compensated inductive wireless power transfer (IWPT) link operating at load-independent-voltage-output (LIVO) frequency. The link is a part of the static system (reported by the authors in earlier works), wirelessly delivering power into an enclosed compartment without any secondary-to-primary feedback. The proposed algorithm employs input DC-side quantities (which are slow-varying and nearly noise-free, thus measured utilizing low-cost, low-bandwidth sensors) only to monitor output DC-side quantities, required for protection and/or control. It is shown that high estimation accuracy is retained as long as system parameter values are known and the phasor-domain equivalent circuit is valid (i.e., upon continuous-conduction mode (CCM) of the diode rectifier, where the proposed methodology utilizes the recently revealed modified diode rectifier equivalent model for enhanced accuracy). Under light loading (i.e., in discontinuous conduction mode (DCM)), a nonlinear correction is combined with the proposed technique to retain accuracy. The proposed methodology is well-verified by application to a 400 V to 400 V, 1 kW static IWPT link by simulations and experiments.