The vision of sustainable 6G connectivity infrastructure, from space to ground, critically relies on green communication protocols that can efficiently operate within the constraints of wireless power transfer (WPT). Backscatter communication emerges as a cornerstone for such protocols, yet its potential is hindered by the inability to adapt to the dynamic spectrum and energy conditions inherent to WPT-powered networks. This paper presents ChannelDance, an edge-assisted adaptive hopping system for Bluetooth Low Energy (BLE) backscatter, architected specifically for green and sustainable connectivity in WPT-enabled environments. By leveraging real-time excitation channel intelligence from a low-latency edge server, ChannelDance dynamically configures the tag modulation clock, enabling robust and spectrally agile frequency hopping. This agility is paramount for maintaining reliable communication links amidst the interference and intermittent energy supply characteristic of integrated WPT systems. Our prototype demonstrates a median hopping success rate of 93% across 40 channels, a 3.5× goodput gain with channel optimization, and the ability to establish connections with commodity BLE devices under hopping conditions. ChannelDance thus establishes a foundational green communication primitive for future sustainable 6G networks, where seamless coexistence with energy transfer signals is not a feature but a fundamental requirement.

Information security is a non-negligible issue for wireless transmission. Covert communication provides high security by concealing the transmitted signals within environmental noise. However, existing solutions suffer from low transmission efficiency. Ambient backscatter, concealing data within ubiquitous ambient signals, provides a promising way to achieve high-efficiency covert communication. In this paper, we propose CoScatter, an efficient covert transmission system based on OFDM WiFi backscatter. Current studies rely on redundant modulation, resulting in low throughput. This paper is to increase throughput and shorten transmission time, thereby reducing exposure risk. This is the first work to realize single-sample level demodulation, efficiently eliminating the redundancy, increasing the throughput, and reducing the transmission time. We discover that the main obstacles are the additional phase offsets introduced by three independent wireless channels in backscatter systems. Based on this, we design a new backscatter channel equalization procedure to remove the channel influences while preserving all the covert information embedded by the tag, realizing an efficient covert transmission. Evaluation results show that Coscatter achieves a throughput exceeding 15.7 Mbps, which is around 64x of that of RapidRider, and 16x of that of Tscatter. Consequently, the exposure risk of CoScatter is reduced to 1/64 of that of RapidRider and 1/16 of that of Tscatter.

Improving transmission efficiency is a key objective in OFDM WiFi backscatter systems. A promising direction is sub-symbol-level tag modulation, which embeds more tag data within each OFDM symbol. However, we observe that fine-grained tag modulation is coupled with channel variation, which distorts the cascade structure between the two channels, transmitter-to-tag and tag-to-receiver, making the conventional channel estimation method in WiFi ineffective. Although recent systems have explored new channel estimation methods, their accuracy is limited and the modulation redundancy remains necessary. To address this problem, we present Fascatter, a high-throughput OFDM WiFi backscatter system that enables single-sample-level tag modulation without modulation redundancy. The key enabler is a new channel estimation method that independently estimates the two channels at per-subcarrier granularity. We construct channel observations from the LTF fields and reference symbols, and accurately solve the two channels through matrix decomposition. We further introduce polynomial smoothing and multi-symbol fine-tuning modules to improve estimation robustness. Experimental results demonstrate that the channel estimation results are close to the actual channel responses, and our method shows robust performance under a variety of complex channel conditions. In particular, Fascatter achieves a throughput of up to 15.9 Mbps, which is at least 3.2× over state-of-the-art systems. © 2026 IEEE.

IoT streaming applications serve as critical drivers for the artificial intelligence of things (AIoT), characterized by their sensitivity to delays and resource requirements. Placing these application tasks on cooperative edge systems efficiently utilizes edge computing and network resources, enhancing data processing efficiency. However, existing placement methods often oversimplify the network environments or overlook dependencies of tasks, leading to suboptimal performance in real-world scenarios. In addition, the varying Quality of Service (QoS) requirements for IoT streaming applications emphasize the need to make different optimization decisions for applications with different QoS requirements. To address these challenges, we propose DAPNet, a neural adaptive dependent task placement method for IoT streaming applications. We model dependent tasks in an IoT streaming application as a directed acyclic graph (DAG) and then formalize the dependent task placement problem as a multi-objective optimization problem to maximize the IoT streaming application’s QoS. DAPNet adapts resource allocation to meet varying QoS requirements. It uses a deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) to optimize task placement and resource allocation for IoT streaming applications in dynamic network environments. Simulations with real-world datasets were conducted, comparing our approach with state-of-the-art methods across two network environments, evaluating completion time, energy consumption, and QoS. Results show that our method outperforms existing approaches in all three metrics.

WiFi-based gesture recognition has emerged as a promising alternative to computer vision, enabling seamless integration and enhanced interaction in human-computer interaction systems. Simultaneously identifying users during gesture recognition is vital for improving security and personalization. However, existing WiFi-based dual-task recognition approaches often rely on handcrafted features, which hinder precision and introduce delays in cross-domain scenarios. To address these challenges, we propose WiDual, a real-time system for cross-domain gesture recognition and user identification using WiFi signals. By integrating spatial and channel attention mechanisms, WiDual adaptively extracts crucial features for dual-task recognition. The system employs Channel State Information (CSI) visualization to convert WiFi signals into images, facilitating efficient feature extraction and minimizing information loss and latency. Furthermore, a collaborative module fuses gesture and user identity features, enhancing recognition performance. Experimental evaluations on a public dataset with six gestures and six users across diverse environments demonstrate WiDual's effectiveness. It achieves 96% accuracy in cross-domain gesture recognition and 91.27% in user identification. Compared to state-of-the-art methods, WiDual improves user identification accuracy by 26%, gesture recognition by 8%, and reduces processing time sixfold, showcasing its potential for real-time applications.

Mobile applications based on machine learning (ML) are increasingly relying on offloading to the edge devices for low-latency, resource-efficient computation. Applying serverless computing for these ML applications on the edge offers a promising solution for handling dynamic workloads while meeting user-specified latency service-level objectives (SLOs). However, existing serverless frameworks, with their coarse-grained data parallelism and rigid model partitioning, are inadequate for ML inference on widely adopted edge System-on-Chip (SoC) devices. This paper presents FluidEdge, an edge-native serverless inference framework. FluidEdge identifies bottleneck operators in ML models and addresses them through a novel fine-grained intra-function latency-sensitive auto-scaling approach that dynamically scales inference bottlenecks during online serving. Additionally, it employs inter-function scaling to further prevent latency SLO violations and leverages the unified memory of edge SoCs for efficient data sharing during inference. Experimental results demonstrate that FluidEdge achieves a 37.4% latency improvement and 67.3%-87.6% SLO violation reduction compared to best-performed state-of-the-art serverless inference frameworks.

OFDM WiFi backscatter with uncontrolled ambient signals is a promising approach for realizing passive Internet of Things (IoT) systems. However, existing OFDM backscatter systems are often limited by coarse modulation granularity, typically constrained to the packet or OFDM symbol levels, and commonly require dual receivers for data demodulation. To address these challenges, we propose DFTScatter, a novel sub-symbol level backscatter system that utilizes a single receiver for demodulation. DFTScatter leverages the Discrete Fourier Transform (DFT) shift theorem to achieve sub-symbol level modulation through frequency domain cyclic shifts. This method enhances data transmission efficiency and operates within a single-symbol bandwidth, thereby optimizing spectrum utilization. Additionally, we introduce a single-receiver decoding technique that exploits invariant frequency domain information of the reflected signals for accurate demodulation. Extensive experiments demonstrate that DFTScatter outperforms existing methods and effectively operates with various ambient OFDM WiFi signals, including WiFi 3/4/5/6, paving the way for scalable, low-cost IoT ecosystems in smart cities, healthcare, industrial automation, and beyond.

Connected and autonomous vehicles produce a substantial amount of data that is essential for implementing advanced and intelligent features. Given the importance and the volume of in-vehicle data, storing it in the cloud for later extraction as critical evidence for vehicle digital forensics is a logical choice. However, ensuring the security of forensic data against tampering and forgery attacks throughout the process is a significant challenge. Existing solutions typically assume that vehicles will generate and upload the in-vehicle data to the cloud honestly. In reality, it may be necessary to prove whether the vehicle has uploaded authentic driving-related data in case of disputes about data authenticity. To address this issue, we propose an in-vehicle digital forensic scheme with public auditing, enabling anyone to perform a public auditing algorithm to check whether the data has been modified. The proposal is based on a process-oriented data integrity proof method that enables a vehicle to generate public verifiable integrity proof. Furthermore, we evaluated the practicality of our scheme by assessing its computational and communication overhead. In terms of computational cost, our proposed scheme demonstrates a power consumption of 0.0385 kWh per 100 km at a speed of 60 km/h. Regarding communication delay, our method exhibits a 50.1% decrease compared to similar approaches.

The security and privacy challenges faced by Vehicular Ad hoc Networks (VANETs) have led to the development of conditional privacy-preserving authentication (CPPA) schemes. Hardware security modules (HSMs) are seen as a promising solution for implementing these schemes while minimizing the burden on certificate storage. However, existing HSM-based CPPA schemes still have high computation overhead and do not meet the forward security requirements for system secret key (SSK) updates. To address these challenges, we propose an HSM-based lightweight CPPA scheme for VANETs that enjoy low computation costs. Most operations could be performed within the HSM before the message is ready to be signed, reducing real-time computation delay. The scheme also supports SSK updating using an identity-based batch multi-signature algorithm, which helps to provide forward security and vehicle revocation. Especially, the proposed SSK update scheme does not rely on any single trusted authority. Formal proof demonstrates that the proposed scheme satisfies the desired security notions. Our analysis shows that this scheme surpasses other similar ones in terms of efficiency when it comes to generating signatures.

WiFi-based human gesture recognition has recently enjoyed increasing popularity in the Internet of Things (IoT) scenarios. Simultaneously recognizing user identities and user gestures is of great importance for enhancing the system security and user quality of experience (QoE). State-of-the-art approaches that perform dual tasks suffer from increased latency or degraded accuracy in cross-domain scenarios. In this paper, we present WiDual, a dual-task system that achieves accurate cross-domain gesture recognition and user identification based on WiFi in a real-time manner. The basic idea of WiDual is to use the attention mechanism to adaptively explore cross-domain features worthy of attention for dual tasks. WiDual employs a CSI (Channel Statement Information) visualization method that transfers WiFi signals to images for further feature extraction and model training. In this way, WiDual mitigates the possible loss of useful information and excessive delays caused by extracting handcrafted features directly from the WiFi signal. Furthermore, WiDual utilizes a collaboration module to combine gesture features and user identity features to enhance the performance of dual-task recognition. We implement WiDual and evaluate its performance extensively on a public dataset including 6 gestures and 6 users performed across domains. Results show that WiDual outperforms state-of-the-art approaches, with 26% and 8% improvements on the accuracy of cross-domain user identification and gesture recognition respectively.

With the rapid advancement of smartphones and other mobile devices, an ever-increasing desire for wireless indoor localization has emerged. This technology is capable of determining the position of a user or device in an indoor setting and facilitating an array of captivating applications. Due to the low cost and wide availability of WiFi, WiFi-based indoor localization has received considerable attention and has become a prominent research focus in recent times. We have distilled that an ideal WiFi-based indoor localization system is anticipated to meet three criteria: high-accuracy, pervasiveness, and easy-deployment. Nevertheless, it is not a trivial task to satisfy all three criteria simultaneously. This document scrutinizes the key issues, basic models, and current methods for WiFi indoor localization with the objective of highlighting the underlying principles and challenges. Finally, this manuscript pinpoints the prospective research paths for WiFi indoor localization.

Mobile video analytics applications, such as smart driving, VR/AR, and video surveillance, have become increasingly popular due to the proliferation of mobile devices. These applications typically use compute-intensive Deep Neural Networks (DNNs) inference in real-time and require high accuracy. Recent studies have shown that edge computing can significantly improve the performance of these applications by offloading the computation, particularly neural network inference, from mobile devices to nearby edge servers. However, offloading continuous video streams to edge servers still faces the challenge of scarce and variable network bandwidth, resulting in high latency for mobile deep vision applications. Existing works often assume sufficient networks and powerful servers to offload all streaming computation to the edge, resulting in unsatisfactory performance in practical scenarios. In this paper, we propose EAVA, an adaptive Edge-Assisted framework on mobile devices designed for Video Analytics that considers a more practical edge situation with an unstable network environment and multiple DNN model choices. EAVA initially partitions video frame and combines mobile devices with powerful edge servers, allowing these frame partitions to parallel perform video analytics algorithms on local devices or edge servers. To handle the intricate network and inference model dynamics, EAVA trains a deep reinforcement learning model to optimize the Quality of Experience (QoE) for mobile deep vision applications, making adaptive configuration choices. Without relying on preconceived assumptions about the environment, EAVA makes optimal choices based on experiences. Finally, we implement and thoroughly evaluate the performance of EAVA using diverse real-world network traces, demonstrating its superior advantages over existing state-of-the-art solutions.

Audio sensing applications on embedded and mobile devices have recently enjoyed increasing popularity. Their performance can be significantly improved by edge computing which offloads computation-intensive tasks to edge servers through wireless links. The quality of wireless links is essential to offloading performance. However, existing edge computing solutions can hardly predict the link quality accurately and efficiently in a dynamic wireless environment, resulting in less optimal offloading decisions and unsatisfied user-perceived Quality of Experience (QoE). In this article, we present WiEdge, a distributed edge computing framework for audio sensing applications with accurate wireless link prediction. By combining cross-layer information extracted from recently received WiFi beacons, TCP-level statistics, and the past throughput observations, WiEdge can predict the throughput of wireless links accurately and efficiently in the near future. Based on the prediction, WiEdge makes optimal offloading decisions for QoE maximization. We formulate the offloading decision problem as a stochastic optimal control problem and propose an efficient solution based on model predictive control from the control-theoretic perspective. We implement WiEdge and evaluate its performance extensively in three representative real-world scenarios. Results show that WiEdge achieves high prediction accuracy and improves average normalized QoE by 2%, 11%, and 40% in three different scenarios, compared with state-of-the-art approaches.

How to accurately predict vacant QoS has become a fundamental issue for service-oriented downstream tasks. However, most QoS prediction approaches based on model learning fail to discriminatively capture the latent feature representations of a user and a service, since they either leverage the shallow neural network such as MLP or take advantage of insufficient location information. Moreover, collaborative relationships of similar neighborhood have not been fully taken into account together with prediction model learning. To address these issues, we propose a novel framework for adaptive QoS prediction named Neighborhood-based Collaborative Residual Learning (NCRL). Location-aware two-tower deep residual network is designed to achieve neural QoS prediction by extracting latent features of users and services, which are fed to generate similar neighborhood for collaborative prediction based on historical QoS invocations. They are integrally combined to perform adaptive QoS prediction. Extensive experiments are conducted based on a large-scale real-world QoS dataset called WS-DREAM with almost 2,000,000 historical QoS invocations. The results indicate that NCRL can remarkably outperform state-of-the-art competing baselines.

Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU) aiming to reduce the processing delay as well as the resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications resulting in suboptimal offloading policies. In this paper, we present FEVEC, a Fast and Energy-efficient VEC framework with the objective of making the optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirement. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decision and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC, where an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Furthermore, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on realistic and simulated vehicle trajectories verify that our proposed algorithm has a better performance compared with the state-of-the-art VEC mechanism.

The Internet of Vehicles (IoV) was proposed as an approach to enable intelligent traffic management and enhance road safety. In order to achieve the intended objective of improving road safety, vehicles are required to constantly broadcast messages to the traffic management infrastructure as well as to other vehicles in the vicinity. Cybersecurity protection of the IoV system is critical as security attacks on IoV and safety-related messages could be life threatening. In this connection, it is essential to ensure the authenticity of IoV messages. Whereas, from the angle of privacy protection, it is undesirable to directly authenticate the identities of vehicles that send the IoV messages. To cope with these conflicting requirements, researchers proposed the notion of conditional anonymous authentication, which aims to authenticate message senders anonymously. When necessary, a trusted third party, named tracer, will be allowed to reveal the true identities of malicious vehicles who sent fake messages. However, existing security techniques, including pseudonyms and group signatures typically assume that the tracer is trusted. This assumption may not be desirable in situations when a curious tracer may reveal the identities of honest vehicles in the IoV system. To address this challenge, this article proposes a privacy-preserving authentication scheme with abuse-resistant tracing. Compared with existing conditional anonymous authentication schemes, our scheme prevents a single tracer from revealing the identity of vehicles. Besides, the tracing key is generated in a distributed manner, and hence no single authority in the system can reveal the true identity of a vehicle.

The emerging IoT stream processing is a key enabling technology for the time-critical IoT applications, which often require high accuracy and low latency. Existing stream processing engines are insufficient to meet these requirements, since they could not integrate and respond timely to variable network conditions in the dynamic wireless environment. Recent efforts focusing on adaptive streaming support user-specified policies to adapt to the variable network conditions. However, those manual-policies can hardly achieve optimal performance across a broad set of network conditions and quality of experience (QoE) objectives. In this paper, we present a Reinforcement Learning-based Adaptive streaming system (RL-Adapt) that is capable of generating adaption policies using RL-strategy and providing declarative APIs for efficient development. RL-Adapt trains a neural network model that can automatically select the optimal policy based on the observed network conditions. RL-Adapt does not rely on pre-defined models or assumptions on the environment. Instead, it learns to make decisions solely through observations of the resulting performance of past decisions. We implemented RL-Adapt and evaluated its performance extensively in three representative real-world IoT applications. Our results show that RL-Adapt outperforms the state-of-the-art scheme, with 20% improvements on average QoE.

Mobile advertising plays a vital role in the mobile app ecosystem. A major threat to the sustainability of this ecosystem is click fraud, i.e., ad clicks performed by malicious code or automatic bot problems. Existing click fraud detection approaches focus on analyzing the ad requests at the server side. However, such approaches may suffer from high false negatives since the detection can be easily circumvented, e.g., when the clicks are behind proxies or globally distributed. In this paper, we present AdSherlock, an efficient and deployable click fraud detection approach at the client side (inside the application) for mobile apps. AdSherlock splits the computation-intensive operations of click request identification into an offline procedure and an online procedure. In the offline procedure, AdSherlock generates both exact patterns and probabilistic patterns based on URL (Uniform Resource Locator) tokenization. These patterns are used in the online procedure for click request identification and further used for click fraud detection together with an ad request tree model. We implement a prototype of AdSherlock and evaluate its performance using real apps. The online detector is injected into the app executable archive through binary instrumentation. Results show that AdSherlock achieves higher click fraud detection accuracy compared with state of the art, with negligible runtime overhead.

Most sensor networks employ dynamic routing protocols so that the routing topology can be dynamically optimized with environmental changes. The routing behaviors can be quite complex with increasing network scale and environmental dynamics. Knowledge on the routing path of each packet is certainly a great help in understanding the complex routing behaviors, allowing effective performance diagnosis and efficient network management. We propose PAT, a universal sensornet path tracing approach. PAT includes an intelligent path encoding scheme that allows efficient decoding at the PC side. To make PAT more scalable, we propose techniques to accurately estimate the degree information by exploiting timing information, allowing more compact path encoding. Moreover, we employ subpath concatenation to infer excessively long paths with a high recovery probability. We propose an analytical model to quantify the benefits of PAT with varying network scale, network density, routing dynamics and packet delivery performance. We evaluate PAT's performance using testbed experiments, trace-driven study, and extensive simulations. Results show that PAT significantly outperforms existing approaches.

Low-power and multihop wireless networking is envisioned as a promising technology to achieve both energy efficiency and easy deployment for many Internet of Things (IoT) applications. Measuring packet-level path is crucial for managing large-scale multihop wireless networks. Packet-level path information encodes the routing path, a packet that takes through a network. The availability of packet-level path information can greatly facilitate many network management tasks. It is challenging to reconstruct packet-level paths using a small overhead, especially for large-scale networks. While there is a long list of existing path reconstruction algorithms, these algorithms focus on specific network scenarios, e.g., periodic monitoring networks or event detection networks. There lacks a unified model for systematically understanding and comparing the performance of these algorithms in different network scenarios. In this paper, we fill this gap by proposing an abstract model. Using this model, it is possible to derive a decision space for selecting the best algorithm for different networks. Furthermore, this model also guides us to devise better path reconstruction algorithms (cPathτ,cPaths,and cPathsT) with respect to path reconstruction ratio. Extensive experiments demonstrate the prediction power of our model as well as the advantages of our proposed algorithms. The results show that our algorithm (cPathsT) improves a path reconstruction ratio from 94.4%, 34.3%, and 30.8% to 98.9%, 99.9%, and 60.1% on average in three network scenarios, respectively, compared with the best state-of-the-art algorithms.

Multihop wireless networking is a key enabling technology for interconnecting a vast number of IoT devices. Measurement is fundamental to various network operations including management, diagnostics, and optimization. Out-of-band measurement approaches use external sniffers to monitor the network traffic passively, and they provide detailed information about the network. However, existing approaches do not carefully consider lossy and correlated links which are common in low-power wireless networks, resulting in unsatisfactory packet capture ratio and low measurement quality. In this paper, we present NetVision, a practical out-of-band measurement system with special consideration for sniffer deployment. By explicitly considering link quality and link correlation, we are able to achieve a high measurement quality while minimizing the deployment cost. We formulate the sniffer deployment problem as an optimization problem and propose efficient algorithms for solving this problem. We further design a set of instructions and APIs to simplify a variety of common measurement tasks. We implement NetVision on the TinyOS/TelosB platform and evaluate its performance extensively both in simulation and an indoor testbed with 80 TelosB nodes. Results show that NetVision is accurate, generic, and robust. Three typical case studies demonstrate that NetVision can facilitate various measurement and debugging tasks.

Network measurement, which provides detailed information about the behaviors of operational networks, is essential for network management in wireless sensor networks. In the literature, there have been many approaches focusing on measuring individual aspect of the network, e.g., per-packet routing path and per-hop delay. However, there lacks a general support for conducting different measurement tasks. When managing an operational network, a network operator often needs to switch the current measurement task to a different one, in order to diagnose the observed symptoms. In this paper, we propose TinySDM, a software-defined measurement architecture for WSNs. TinySDM provides a general support for conducting different measurement tasks. TinySDM defines a set of carefully selected hooks that allow the users to easily execute their own measurement tasks. In addition, TinySDM provides a C- like language called TinyCode Language (TCL) to enable easy customization of measurement tasks. By only transmitting the binary code of the measurement task, TinySDM significantly reduces the size of the disseminated data compared with existing reprogramming approaches. We implement TinySDM on the TinyOS/TelosB platform and evaluate its performance extensively in a testbed with 60 nodes. We also use TCL to implement four specific measurement tasks. Results show that TinySDM is flexible, efficient and easily programmable.

Wireless Sensor Networks (WSNs) have been successfully applied in many application areas. Understanding the wireless link performance is very helpful for both protocol designers and network managers. Loss tomography is a popular approach to inferring the per-link loss ratios from end-to-end delivery ratios. Previous studies, however, are usually targeted for networks with static or slowly changing routing paths. In this work, we propose Dophy, a Dynamic loss tomography approach specifically designed for dynamic WSNs where each node dynamically selects the forwarding nodes towards the sink. The key idea of Dophy is based on an observation that most existing protocols use retransmissions to achieve high data delivery ratio. Dophy employs arithmetic encoding to compactly encode the number of retransmissions along the paths. Dophy incorporates two mechanisms to optimize its performance. First, Dophy intelligently reduces the size of symbol set by aggregating the number of retransmissions, reducing the encoding overhead significantly. Second, Dophy periodically updates the probability model to minimize the overall transmission overhead. We implement Dophy on the Tiny OS platform and evaluate its performance extensively using large-scale simulations. Results show that Dophy achieves both high encoding efficiency and high estimation accuracy. Comparative studies show that Dophy significantly outperforms traditional loss tomography approaches in terms of accuracy.