Abstract Millimeter-wave radar has shown significant potential in privacy-preserving human activity recognition. However, the lack of diverse radar datasets across various scenarios poses a challenge to the robustness and generalization of deep learning models. To address this limitation, existing works mainly focus on synthesizing micro-doppler data from video, range-doppler data, which provides an extra dimension, has been overlooked due to challenges caused by signal offsets. In this paper, we propose a comprehensive approach for synthesizing range-doppler data from videos by leveraging computer vision techniques and principles of camera imaging. Furthermore, we implement a map enhancement and classification model to facilitate human activity recognition. Our approach is validated on a custom dataset, where the proposed range-doppler synthesis method and classification model achieve an accuracy of 97.3% for activity recognition tasks. This performance is comparable to that of vision-based HAR methods, demonstrating the effectiveness of our proposed scheme in achieving privacy-preserving human activity recognition.

With the rapid development of the Internet of Things (IoT), data centers are facing growing performance demands for flow deadline requirements, other to the low latency and high throughput. To address congestion issues that affect the performance of data center networks, this paper proposes D2C4, a deadline-aware data-credit coupled congestion control scheme. By tightly coupled data-credit integration using an ECN-based feedback loop, D2C4 minimizes credit waste while a credit-driven scheduler meets IoT applications’ flow deadlines. The key innovations of D2C4 include: 1) Data-credit coupling for performance gains; 2) ECN-based credit rate control reducing waste; 3) Deadline-sensitive credit-based flow scheduling to meet flow deadline demands. We conduct extensive experiments of D2C4 through small-scale DPDK-based tests and large-scale OMNeT++ simulations. Experimental results show that D2C4 outperforms existing protocols in flow completion time, throughput, deadline miss ratio, and packet drop.

Motivated by the pursuit of greater efficiency, companies, such as Amazon and JD, are shifting toward a warehouse-distribution integration model to optimize logistics operations. In general full-link logistics scenarios, the collaboration between warehouses and sorting centers managed by different enterprises leads to data silos, posing challenges in securely sharing information and accurately predicting delivery times across the entire logistics network. Current delivery time prediction methods often overlook the heterogeneity of logistics networks and face data sharing constraints. We aim to address these issues by facilitating secure internode relationship analysis and leveraging distinct spatio-temporal characteristics to enhance efficiency. However, challenges remain in overcoming data isolation while maintaining protection and integrating diverse node characteristics for optimized modeling. To address these challenges, we propose the federated heterogeneous graph transformer (Fed-HGT) framework. This framework includes a federated training module that integrates local and central gradients by exchanging node representations and model parameters between logistics nodes and the central server. Additionally, it features a federated prediction module where local nodes compute time representations using their local data and transmit these to the central server. The central server then uses these representations to make accurate full-link delivery time predictions. Our method was evaluated on a dataset from a major e-commerce platform in China, demonstrating significant performance improvements over existing solutions.

As an important branch of IoT applications, Human activity recognition (HAR) is widely used in daily life, particularly through vision-based methods. However, vision-based HAR has serious privacy issues. How to better and low-cost protect the privacy of users who have already installed the relevant devices is a problem that needs to be solved. To address this challenge, we can solve it by transforming video to privacy-preserving mmWave data. Existing studies have primarily focused on synthesizing micro-Doppler data from video, but there is a lack of methods for synthesizing range-Doppler data. Thus, we present a comprehensive method for synthesizing range-Doppler data from videos and subsequently utilize this synthetic data for HAR. Experimentally, we deploy our range-Doppler synthesis method and classification model on a custom dataset. Experimental results indicate that the model trained with synthetic data achieves accuracy on the custom dataset by 95.7%, which is comparable to the accuracy of vision-based HAR works, and demonstrate that the scheme proposed in this paper achieves privacy-preserving HAR.

Recent years have witnessed significant advancements in O2O logistics, which require predicting the volume of shipments generated by customers, commonly referred to as customer value. The essence of accurately predicting customer value in O2O logistics involves analyzing both online buying habits and offline logistics operations. Existing customer value prediction efforts focus solely on online or offline features, making them unsuitable for O2O scenarios. In this paper, we investigate the integration of both online and offline features for customer value prediction, which faces challenges including (i) data silos issues between logistics platforms and e-commerce platforms, and (ii) feature heterogeneity between online and offline data. To address these challenges, we propose a Periodic Asynchronous Vertical Federated Learning framework with adaptive Feature Selection (PAVFL-FS), enabling logistics platforms to efficiently and accurately predict customer value in collaboration with the e-commerce platforms. PAVFL-FS consists of two components: (i) a periodic asynchronous vertical federated learning algorithm to handle data silos problem and enable efficient model training; (ii) a local feature selection algorithm based on stochastic gates to address the cross-platform feature heterogeneity. We have conducted experimental validation on a large-scale real-world dataset collected from a major logistics company in China, including over 2.4 million waybill records and over 6 million online sales records from more than 3,500 merchants. The experimental results demonstrate that PAVFL-FS achieves a mean absolute error of 0.52 in O2O logistics customer value prediction, outperforming 24.6% to the baseline.

The emergence of e-commerce with logistics service provides great convenience to people’s lives. However, platform usually receive seller claim for some reasons (e.g., damaged packages). Thus, it is important to predict seller claim risk in logistics scenarios. Existing solution for seller claim risk predict are challenging to address this problem due to two unique features including (i) multi-side collaborative risk factor caused by data sharing constraints of e-commerce and logistics platform, (ii) industry-specific risk factor caused by the dynamic similarity of sellers. To incorporate these new factors, we propose a novel seller claim risk prediction framework (Fed-SCRP) via federated multi-view learning, where we (i) design a federated multi-view learning to deal with data isolation problem, (ii) develop a STG-SRIM model and a series of information union transformers to capture the hybrid semantic embedding of dynamic seller features and industry-specific risk factor. We conduct a comprehensive evaluation of our method using datasets from major Chinese e-commerce and logistics platforms. Experimental results demonstrate that our method outperforms state-of-the-art baselines in various metrics.

To improve the efficiency of multi-agent systems in the Internet of Things, multi-agent reinforcement learning (MARL) has been extensively studied. Although transformer-based models now treat decision-making in MARL as a sequence problem and achieve advanced performance, the action of agents often depends on previous states without direction in information sharing. To address this issue, we propose a Bi-directional Sequence Inference framework for Multi-Agent Reinforcement Learning (BSI-MARL), consisting of three components: Action-Observation Processing Module, Sequence Inference Module, and Policy Optimization Module. The Action-Observation Processing Module defines the state space, action space, and reward function for the agents. Based on the Transformer model, BSI-MARL designs an encoder-decoder module for bi-directional sequence inference to generate action sequences for multi-agent decisions. Additionally, the policy gradient optimization module broadens the action sampling window, improving training efficiency. The experiments conducted on Mujoco-Half Cheetah and Google Research Football demonstrate that BSI-MARL exhibits excellent performance in multi-agent decision-making and good stability and generalization ability.

In the event of disasters, emergencies, or special circumstances, the military needs to deploy vehicle resources to meet the needs of emergency response and rescue work. This problem involves the reasonable allocation and utilization of limited vehicle resources to complete emergency tasks quickly and efficiently while reducing casualties and property losses. However, there are still some difficulties regarding the military vehicle deployment problem, including uneven resource deployment, low deployment efficiency, and insufficient logistics protection. Herein, a mixed-integer linear programming model is constructed to optimize the deployment problem of military vehicles to minimize the total transportation time and cost. Considering the complexity and uncertainty in military operations, this paper introduces a variety of constraints, particularly in terms of vehicle number, geographical limitation, and strategic vehicle. With the designed model, instances of problems of different scales are solved, and comparisons are drawn with the existing deployment methods. The results show that the proposed model can effectively reduce the time and cost required for vehicle deployment, while significantly improving the flexibility and adaptability of the deployment scheme. This study can not only provide a new optimization tool for army vehicle deployment but also be utilized for other similar types of emergency logistics deployment.

Accurate customer LifeTime Value (LTV) predictions are crucial for customer relationship management, especially in Supply Chain Platforms (SCP), which involve effectively managing the service resources in business decision-making. Previous LTV prediction methods usually rely on ample historical customer data, which is not available in the early stages of a customer's lifecycle. It makes the modeling of the historical customer data a difficult task due to the data sparsity. Besides, the long-tail distribution of customer LTV also brings new challenges to the prediction of LTV. To tackle the above issues, we propose CDLtvS, a novel Cross Domain method for customer Lifetime value prediction in SCP. It leverages rich cross-domain information from upstream platforms to enhance LTV predictions in downstream platforms. Firstly, CDLtvS pre-trains the customer representations by an LTV modeling framework named LtvS in source and target domains separately. Specifically, LtvS incorporates the Expert Mask Network (ExMN), which not only effectively models the long-tail distribution of LTV in single-domain but also resolves cross-domain learning model bias resulting from this distribution. Then, the various-level alignment mechanism is introduced to keep the consistency of knowledge transferring from source to target domains on both sparse and non-sparse data. Comprehensive experiments on real-world data from JD, one of the world's largest supply chain platforms, demonstrate that CDLtvS achieves a normalized mean average error of 0.3378 in LTV prediction, outperforming 16.3% to the baseline. Additionally, the improvements of ≥2.3% across various data sparsity levels (0% -- 80%) provide valuable insights into cross-domain LTV modeling.

Low-Power Wide-Area Networks (LPWANs), extensively utilized for connecting billions of IoT devices, encounter wireless interference challenges in unlicensed frequency bands. Cutting-edge research suggests employing Received Signal Strength Indication (RSSI) sequences for error detection to mitigate interference-related issues. Nevertheless, the effectiveness of this method significantly declines under low signal-to-noise ratios (SNRs). Additionally, long-range communication often results in low SNR received signals, sometimes even below the noise floor. Targeting this fundamental issue, this article proposes the LPWAN packet technique, broadly applicable across diverse scenarios through edge–cloud collaboration. On the edge side, we propose an innovative architecture that fully exploits spatial distribution and interference independence in the field. Rather than utilizing resource-intensive RSSI-based error detection, we leverage a lightweight coding scheme for error detection at the Long Range (LoRa) edge, forwarding correct frames to the cloud. On the cloud side, packet recovery is achieved utilizing group-weighted voting. We design and implement ECRLoRa with commercially available devices (SemTech’s SX1278 and SX1302 LoRa chipsets) and assess its performance in low SNR environments. Our thorough evaluation demonstrates that our approach attains a Packet Recovery Ratio of 96% with low SNR (i.e., below −10 dB), resulting in 1.8× throughput, 7.5× faster recovery time, and 4.92× average accuracy compared to state-of-the-art cloud-optimized application layer solutions.

Route prediction in instant delivery is still challenging due to the unique characteristics compared with conventional delivery services, such as strict deadlines, overlapped delivery time of multiple orders, and diverse individual preferences on delivery routes. Recently, development in the mobile Internet of Things (IoT) offers the opportunity to collect multi-sensor data with rich real-time information. Therefore, this study proposes a route prediction model called Roupid, which leverages multi-sensor data to improve the accuracy of route prediction in instant delivery. Specifically, we design a 3-Conversion Network-based route prediction framework to take full advantage of various information provided by multi-sensor data, including the encounter data sensed by Bluetooth low energy (BLE) beacons, active site data reported by smart handheld devices, and trajectory data detected by GPS. The 3-Conversion Network we propose is based on a deep neural network framework, which integrates an improved relational graph attention network with edge features (RGATE) to encode global information that couriers typically consider when planning routes. We evaluate our Roupid with real-world data collected from one of the largest instant delivery companies in the world, i.e., Eleme. Experimental results show that our Roupid outperforms other state-of-the-art baselines and offers up to 85.51% of the route prediction precision.

Artificial intelligence (AI) model inference performance in browsers is constrained, and transmitting data to the server consumes substantial transfer time by cloud computing. In this paper, we investigate the status quo of cloud and browser processing and explore model computation partitioning methods. Our study is rooted in WebGPU and employs the Tensorflow.js framework, encompassing seven AI models spanning computer vision, natural language processing, and automatic speech recognition domains. Leveraging the characteristics of neural network layers, we find a significant performance boost through a method that partitions AI models at layer granularity. We design a system called WebInf to partition AI models at layer granularity between the browser and server for faster inferencing-based adaptive model partitioning. WebInf supports diverse hardware, wireless networks, neural network structures, servers, and adaptive partitioning models for optimal inference performance. We evaluate WebInf on two laptops and servers, demonstrating that WebInf yields inference time improvements of 30% and 52%, respectively, when compared to separate inference execution in servers and browsers. The improvements can even peak at 33% and 69% respectively.

Community group-buying is an emerging shopping model in which individuals within the same community collectively purchase daily necessities at lower prices. The logistics transportation in this scenario requires higher turnover rates and timeliness compared to traditional online shopping. Therefore, making efficient vehicle dispatch decisions is necessary to improve delivery efficiency and reduce transportation costs. However, uncertainties in orders and complex dependencies between multi-stage decisions in the community group-buying scenario challenge the scheduling of delivery vehicles. In this paper, we propose a vehicle dispatch system for community group-buying logistics based on a predict-then-optimize process, which incorporates a demand prediction module and a vehicle dispatch module. In the prediction module, we categorize goods and predict the total quantity of each category using a multi-layer perceptron (MLP). In the vehicle dispatch module, we model the vehicle dispatch process as a reinforcement learning problem and use an action masking mechanism to prune the policy search space. Experimental results demonstrate that the vehicle dispatch system achieves state-of-the-art performance compared to several popular baselines across three different datasets. Our system reduces transportation costs by at least 8.89% and improves decision efficiency by at least 10%. Moreover, it behaves close to the approximate optimal solution in most cases.

Recent researches have mitigated interference by utilizing cloud assistance or cloud-edge collaboration for Low-Power Wide-Area Networks. However, the issue of long interference recovery time prevents these methods from being well utilized in practical scenarios. In this paper, we propose a novel method, called FDR, for Edge-Cloud collaborative interference mitigation with Fuzzy Detection Recovery, which recovers errors in real-time. Our design (i) utilizes gateways and cloud servers and (ii) reduces data transmissions with fuzzy detection codes for real-time error recovery. In our design, each gateway detects and reports the fuzzy positions of errors to the cloud. Then the cloud restores packets with fuzzy detection results. FDR takes the advantage of both the computational ability of the cloud and the error detection benefit of each gateway. We design and implement FDR with commodity devices including LoRa SX1280 and the USRP-B210 platform. Experimental results show that FDR reduces recovery time by 78.53% compared with the state-of-art, and recovers interfered data packets accurately when the packet damage rate reaches 45.72%.

Compared with conventional delivery services, instant delivery usually provides a stricter constraint on delivery time (e.g., 30 minutes). To guarantee the quality of time constraint service, precisely predicting the courier’s actual route plays an important role in order dispatching. Most of the existing studies on route prediction are based on single-source data-set such as GPS trajectories or order waybills information, and are not significant to accurately predict the courier’s route. This paper focuses on fully leveraging multi-source data to improve the accuracy of route prediction, including the encounter data, active site report data and GPS trajectories. To achieve this, we propose a multi-source data fusion framework for route prediction. It consists of (i) a multi-source features extracting and fusion module to address the challenge of the heterogeneity of multisource data; (ii) a prediction module taking full advantage of features with different aspects of information containing noise. We evaluate our approach with real-world data collected from one of the largest instant delivery companies in China, i.e., Eleme. Experimental results show that the performance of our multisource data fusion-based prediction model outperforms other state-of-the-art baselines, and achieves a precision of 83.08% for route prediction.

Due to the rapid development of indoor location-based services, automatically deriving an indoor semantic floorplan becomes a highly promising technique for ubiquitous applications. To make an indoor semantic floorplan fully practical, it is essential to handle the dynamics of semantic information. Despite several methods proposed for automatic construction and semantic labeling of indoor floorplans, this problem has not been well studied and remains open. In this article, we present a system called SiFi to provide accurate and automatic self-updating service. It updates semantics with instant videos acquired by mobile devices in indoor scenes. First, a crowdsourced-based task model is designed to attract users to contribute semantic-rich videos. Second, we use the maximum likelihood estimation method to solve the text inferring problem as the sequential relationship of texts provides additional geometrical constraints. Finally, we formulate the semantic update as an inference problem to accurately label semantics at correct locations on the indoor floorplans. Extensive experiments have been conducted across 9 weeks in a shopping mall with more than 250 stores. Experimental results show that SiFi achieves 84.5% accuracy of semantic update.

Due to the high complexity of artificial intelligence (AI) algorithms, performing the AI tasks on the resource-limited Internet-of-Things (IoT) devices has been proved to be inadvisable. Edge computing provides an effective computing paradigm for executing AI tasks, where large numbers of AI tasks can be offloaded to the edge servers. Most of the existing works focus on achieving efficient computing offload through improving the Quality of Service (QoS), such as reducing the average server-side delay. However, we show that those efforts are inefficient due to the heterogeneous impact of delays on users' Quality of Experience (QoE). Inspired by the observations, in this article, we reconsider the scheduling method from an orthometric perspective, i.e., improving the QoE by designing a QoE-aware service-enhancement strategy for edge AI applications. Besides, multiple AI algorithms are utilized in our service model to execute the same type of tasks concurrently, thus meeting users' heterogeneity requirements of accuracy and delays. Specifically, for the online arriving AI tasks, we optimize the task allocation and scheduling strategy according to the QoE sensitivity of each task. The model can be formulated as the mixed-integer nonlinear programming problem, which is known to be NP-hard. Hence, we then propose an efficient two-phase scheduling strategy for this problem. The results of comprehensive emulations validate that our model can effectively improve the average QoE of users and achieve a higher task completion ratio.

Changes in network state are a common source of instability in networks. An update event typically involves multiple flows that compete for network resources at the cost of rescheduling and migrating some existing flows. Previous network updating schemes tackle such flows independently, rather than as the entity of an update event. They only optimize the flow-level metrics for the flows involved in an update event. In this paper, we present an event-level abstraction of network update that groups flows of an update event and schedules them together to minimize the event completion time (ECT). We then study the scheduling problem of multiple update events for achieving high scheduling efficiency and preserving fairness. The designed least migration traffic first (LMTF) method schedules all update events in the FIFO order, but it avoids head-of-line blocking by randomly fine-tuning the queue order of some events. It can considerably reduce the update cost, the average, and tail ECTs of update events. In addition, we design a general parallel-LMTF (P-LMTF) method to guarantee fairness and further improve scheduling efficiency among update events. This improves the LMTF method by opportunistically updating multiple events simultaneously. The comprehensive evaluation results indicate that the average ECT of our approach is up to 10× faster than the flow-level scheduling method for network update events, and its tail ECT is up to 6× faster. Our P-LMTF method incurs a 75% reduction in the average ECT compared with FIFO when the network utilization exceeds 70%, and it achieves a 42% reduction in tail ECT.

The rapid development of mobile computing has prompted indoor navigation to be one of the most attractive and promising applications. Conventional designs of indoor navigation systems depend on either infrastructures or indoor floor maps. This article presents CloudNavi, a ubiquitous indoor navigation solution, which relies on the point clouds acquired by the 3D camera embedded in a mobile device. Particularly, CloudNavi first efficiently infers the walking trace of each user from captured point clouds and inertial data. Many shared walking traces and associated point clouds are combined to generate the point cloud traces, which are then used to generate a 3D path-map. Accordingly, CloudNavi can accurately estimate the location of a user by fusing point clouds and inertial data using a particle filter algorithm and then guiding the user to its destination from its current location. Extensive experiments are conducted on office building and shopping mall datasets. Experimental results indicate that CloudNavi exhibits outstanding navigation performance in both office buildings and shopping malls and obtains around 34% improvement compared with the state-of-the-art method.

Changes of network state are a common source of instability in networks. An update event typically involves multiple flows that compete for network resources at the cost of rescheduling and migrating some existing flows. Previous network updating schemes tackle such flows independently, rather than as the entity of an update event. They only optimize the flow-level metrics for the flows involved in an update event. In this paper, we present an event-level abstraction of network update which groups flows of an update event and schedules them together to minimize the event completion time (ECT). We then study the scheduling problem of multiple update events for achieving high scheduling efficiency and preserving fairness. The designed least migration traffic first (LMTF) method schedules all update events in the FIFO order, but avoids head-of-line blocking by randomly fine-tuning the queue order of some events. It can considerably reduce the update cost, the average, and tail ECTs of all update events. In addition, we design a general parallel-LMTF (P-LMTF) method to guarantee fairness and further improve scheduling efficiency among update events. It improves the LMTF method by opportunistically updating multiple events simultaneously. The comprehensive evaluation results indicate that the average ECT of our approach is up to 10× faster than the flow-level scheduling method for network update events, and its tail ECT is up to 6x faster. Our P-LMTF method incurs 75% reduction in the average ECT compared with FIFO when the network utilization exceeds 70%, and it achieves a 42% reduction in tail ECT.

The proliferation of mobile computing has prompted navigation to be one of the most attractive and promising applications. Conventional designs of navigation systems mainly focus on either indoor or outdoor navigation. However, people have a strong need for navigation from a large open indoor environment to an outdoor destination in real life. This article presents IONavi, a joint navigation solution, which can enable passengers to easily deploy indoor-outdoor navigation service for subway transportation systems in a crowdsourcing way. Any self-motivated passenger records and shares individual walking traces from a location inside a subway station to an uncertain outdoor destination within a given range, such as one kilometer. IONavi further extracts navigation traces from shared individual traces, each of which is not necessary to be accurate. A subsequent following user achieves indoor-outdoor navigation services by tracking a recommended navigation trace. Extensive experiments are conducted on a subway transportation system. The experimental results indicate that IONavi exhibits outstanding navigation performance from an uncertain location inside a subway station to an outdoor destination. Although IONavi is to enable indoor-outdoor navigation for subway transportation systems, the basic idea can naturally be extended to joint navigation from other open indoor environments to outdoor environments.

The proliferation of mobile computing has prompted navigation to be one of the most attractive and promising applications. Conventional designs of navigation systems mainly focus either indoor or outdoor navigation. However, people have a strong need for navigation from a large open indoor environment to an outdoor destination in real life. In this poster, we present a joint navigation system, named ioNavi. It can enable passengers to easily deploy indoor-outdoor navigation service for subway transportation systems in a crowdsourcing way, without comprehensive indoor localization systems. Any self-motivated passenger records and shares its individual walking trace and associated rich set of sensor readings, from a location inside a subway station to an uncertain outdoor destination within a given range, such as one kilometer. ioNavi further extracts navigation traces from shared individual traces, each of which is not necessary to be accurate and useful. A subsequent following user achieves indoor-outdoor navigation services by tracking a recommended navigation trace.

Distributed computing systems like MapReduce in data centers transfer massive amount of data across successive processing stages. Such shuffle transfers contribute most of the network traffic and make the network bandwidth become a bottleneck. In many commonly used workloads, data flows in such a transfer are highly correlated and aggregated at the receiver side. To lower down the network traffic and efficiently use the available network bandwidth, we propose to push the aggregation computation into the network and parallelize the shuffle and reduce phases. In this paper, we first examine the gain and feasibility of the in-network aggregation with BCube, a novel server-centric networking structure for future data centers. To exploit such a gain, we model the in-network aggregation problem that is NP-hard in BCube. We propose two approximate methods for building the efficient IRS-based incast aggregation tree and SRS-based shuffle aggregation subgraph, solely based on the labels of their members and the data center topology. We further design scalable forwarding schemes based on Bloom filters to implement in-network aggregation over massive concurrent shuffle transfers. Based on a prototype and large-scale simulations, we demonstrate that our approaches can significantly decrease the amount of network traffic and save the data center resources. Our approaches for BCube can be adapted to other servercentric network structures for future data centers after minimal modifications.

Recent work has demonstrated that path diversity is an effective way to improve the end-to-end performance of network applications. For every node pair in a full-mesh network with $(n)$ nodes, this paper presents a family of new approaches that efficiently identify an acceptable indirect path that has a similar to or even better performance than the direct path, hence considerably scaling the network at the cost of low per-node traffic overhead. In prior techniques, every node frequently incurs $(O(n^{1.5}))$ traffic overhead to probe the links from itself to all other nodes and to broadcast its probing results to a small set of nodes. In contrast, in our approaches, each node measures its links to only $(O(\sqrt{n}))$ other nodes and transmits the measuring results to $(O(\sqrt{n}))$ other nodes, where the two node sets of size $(O(\sqrt{n}))$ are determined by the partial sampling schemes presented in this paper. Mathematical analyses and trace-driven simulations show that our approaches dramatically reduce the per-node traffic overhead to $(O (n))$ while maintaining an acceptable backup path for each node pair with high probability. More precisely, our approaches, which are based on enhanced and rotational partial sampling schemes, are capable of increasing said probability to about 65 and 85 percent, respectively. For many network applications, this is sufficiently high such that the increased scalability outweighs such a drawback. In addition, it is not desirable to identify an outstanding backup path for every node pair in reality, due to the variable link quality.