Link Quality Prediction Research Articles

LSTM based link quality confidence interval boundary prediction for wireless communication in smart grid

The smart grid will play an important role in the future city to support the diversified energy supply. Wireless communication, the most cost-effective alternative to the traditional wire-lines, promises to provide ubiquitous bi-direction information channel for smart grid devices. However, due to the complex environment that smart grid devices located in, the wireless link is easily been interfered with and therefore appears strong stochastic features. Considering different smart grid application traffics have different and strict reliability requirements, the confidence interval lower boundary is more suitable to represent the worst-case reliability of the stochastic wireless link quality and trustworthy for judging whether the link quality is qualified for the next transmission. In this paper, we propose a Long-Short-Term-Memory (LSTM) based link quality confidence interval lower boundary prediction for the smart grid. According to the analysis of the characteristics of the wireless link, we employ the wavelet denoising algorithm to decompose the signal-to-noise ratio time series into the deterministic part and the stochastic part for training two LSTM neural networks. Then, the deterministic part and the variance of the stochastic part are predicted respectively. Lastly the confidence interval boundary is calculated. To verify the performance of the proposed LQP method, real-world experiments are carried out and the results show that our method is more accurate and trustworthy in comparison with other link quality prediction methods.

RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid

In the application of wireless sensor networks (WSNs) to smart grid, real-time and accurate wireless link quality prediction (LQP) is important to determine which link is reliable enough to undertake the communication task. However, the existing LQP methods are neither suitable to describe the dynamic stochastic features of link quality nor to ensure the validity of prediction results. In this paper, a random-vector-functional-link-based LQP (RVFL-LQP) algorithm is proposed. The algorithm selects the signal-to-noise ratio (SNR) as the link quality metric and decomposes the raw SNR sequence into the time-varying sequence and the stochastic sequence according to the analysis of wireless link characteristics. Then, the RVFL network is used to establish the prediction model of the time-varying sequence and the variance of the stochastic sequence. Lastly, the probability-guaranteed interval boundary of SNR is predicted, and the validity and practicability of prediction results are evaluated by comparative experiments and real-world application, respectively.

A Link Quality Prediction Method for Wireless Sensor Networks Based on XGBoost

Link quality is an important factor for nodes selecting communication links in wireless sensor networks. Effective link quality prediction helps to select high quality links for communication, so as to improve stability of communication. We propose the improved fuzzy C-means clustering algorithm (SUBXBFCM) and use it to adaptively divide the link quality grades according to the packet reception rate. The Pearson correlation coefficient is employed to analyse the correlation between the hardware parameters and packet reception rate. The averages of the received signal strength indicator, link quality indicator and the signal to noise ratio are selected as the inputs of the link quality estimation model based on the XGBoost (XGB_LQE). The XGB_LQE is constructed to estimate the current link quality grade, which takes the classification advantages of XGBoost. Based on the estimated results of the XGB_LQE, the link quality prediction model (XGB_LQP) is constructed by using the XGBoost regression algorithm, which can predict the link quality grade at the next moment with historical link quality information. Experiment results in single-hop scenarios of square, laboratory, and grove show that the SUBXBFCM algorithm is effective at dividing the link quality grades compared with the normal division methods. Compared with link quality prediction methods based on the Support Vector Regression and 4C, XGB_LQP makes better predictions in single-hop wireless sensor networks.

An active and incremental learning framework for the online prediction of link quality in robot networks

This paper presents a comprehensive framework for the active and incremental learning of link quality (LQ) in robot networks. Mobile robots need foresight into the quality of their wireless links in order to proactively optimize routing, plan mobility routes, avoid disconnects, and make other network optimizations. However, the task of predicting LQ is nontrivial. Many environmental factors that influence the quality of radio wave propagation are dynamic, and thus, robots must continually learn and update their prediction models while they operate online. Prior work in the field uses online learning algorithms for predicting LQ, but this approach is costly in terms of energy and network capacity because of the need for a consistent stream of LQ labels to be transmitted from the receiver to the transmitter. Hence, this paper introduces a framework to reduce these overhead expenses by incorporating active learning to selectively label only a portion of the samples from the data stream. The framework also uses incremental training batches to conserve labeling resources, and updates the batches using change detection and forgetting mechanisms to mitigate concept drift. Experimental results reveal that the framework reduces label queries by up to 21.5% and prediction error by up to 9% after periods of concept drift.

Online machine learning algorithms to predict link quality in community wireless mesh networks

Accurate link quality predictions are key in community wireless mesh networks (CWMNs) to improve the performance of routing protocols. Unlike other techniques, online machine learning algorithms can be used to build link quality predictors that are adaptive without requiring a predeployment effort. However, the use of these algorithms to make link quality predictions in a CWMN has not been previously explored. This paper analyses the performance of 4 well-known online machine learning algorithms for link quality prediction in a CWMN in terms of accuracy and computational load. Based on this study, a new hybrid online algorithm for link quality prediction is proposed. The evaluation of the proposed algorithm using data from a real large scale CWMN shows that it can achieve a high accuracy while generating a low computational load.

A novel link quality prediction algorithm for wireless sensor networks

Ahead knowledge of link quality can reduce the energy consumption of wireless sensor networks. In this paper, we propose a cloud reasoning-based link quality prediction algorithm for wireless sensor networks. A large number of link quality samples are collected from different scenarios, and their RSSI, LQI, SNR and PRR parameters are classified by a self-adaptive Gaussian cloud transformation algorithm. Taking the limitation of nodes? resources into consideration, the Apriori algorithm is applied to determine association rules between physical layer and link layer parameters. A cloud reasoning algorithm that considers both short- and long-term time dimensions and current and historical cloud models is then proposed to predict link quality. Compared with the existing window mean exponentially weighted method, the proposed algorithm captures link changes more accurately, facilitating more stable prediction of link quality

Modeling link quality for high-speed railway wireless networks based on hidden Markov chain

In high-speed railway (HSR) wireless networks, the link quality is greatly time-dependent and location-varying. Due to the high randomness, it is challenging to predict the link quality in HSR wireless networks. In this paper, we firstly conducted a certain amount of field measurement campaigns of HSR wireless network link quality. A great number of practical datasets are collected regarding packet loss rate (PLR) and round-trip time (RTT). Then, we analyzed its changing pattern in different time scales, and further model the link quality of HSR wireless network using hidden Markov chain. Based on this, an improved algorithm was developed to simulate the variation of HSR wireless network link quality. Simulation results prove that the proposed model is capable of accurately reproducing the behavior of HSR wireless network link quality with regard to PLR and RTT. This work will offer new inspiration to the prediction of link quality for HSR wireless networks.

Efficient handoff based on link quality prediction for video streaming in urban transport systems

AbstractThe continuously increasing interest in developing efficient vehicle‐roadside communication networks to provide on‐board connectivity has recently brought to the definition of several solutions based on different wireless technologies. Among them, wireless local area network‐based solutions emerged as an attracting alternative to guarantee high‐quality connectivity enabling services, like video streaming, that require stringent quality of service guarantees. In this work, we propose a handoff procedure based on a forecasting model of the link quality for mobile routers operating in vehicle‐roadside wireless local area network‐based networks. First, a preliminary set of experiments is performed in a realistic environment to study the behavior of the wireless channel when mobility in urban environment is considered. Then, considering the hands‐on experience gained from the initial set of experiments, a novel handoff procedure is designed, which exploits a forecasting technique to predict link channel quality. The proposed procedure is then exploited in a cross‐layer manner to proactively reduce the number of transmitted layers during handoff in the case of real‐time video traffic based on H.264/SVC encoding. The proposal is assessed by means of simulation and compared with existing solutions. Results demonstrate that our proposal guarantees performance comparable with other algorithms. The advantage of predicting the handoff point is demonstrated by means of simulations employing a realistic video streaming traffic model, showing how the quality of experience perceived by end‐users can be improved through the adaptation of the traffic load. Copyright © 2016 John Wiley & Sons, Ltd.

Time series analysis to predict link quality of wireless community networks

Community networks have emerged under the mottos “break the strings that are limiting you”, “don't buy the network, be the network” or “a free net for everyone is possible”. Such networks create a measurable social impact as they provide to the community the right and opportunity of communication. As any other network that mixes wired and wireless links, the routing protocol must face several challenges that arise from the unreliable nature of the wireless medium. Link quality tracking helps the routing layer to select links that maximize the delivery rate and minimize traffic congestion. Moreover, link quality prediction has proved to be a technique that surpasses link quality tracking by foreseeing which links are more likely to change its quality. In this work, we focus on link quality prediction by means of a time series analysis. We apply this prediction technique in the routing layer of large-scale, distributed, and decentralized networks. We demonstrate that it is possible to accurately predict the link quality in 98% of the instances, both in the short and the long terms. Particularly, we analyse the behaviour of the links globally to identify the best prediction algorithm and metric, the impact of lag windows in the results, the prediction accuracy some time steps ahead into the future, the degradation of prediction over time, and the correlation of prediction with topological features. Moreover, we also analyse the behaviour of links individually to identify the variability of link quality prediction between links, and the variability of link quality prediction over time. Finally, we also present an optimized prediction method that considers the knowledge about the expected link quality values.

Fuzzy Support Vector Regression-Based Link Quality Prediction Model for Wireless Sensor Networks

Fuzzy Support Vector Regression-Based Link Quality Prediction Model for Wireless Sensor Networks

Navigation Data-Assisted Opportunistic Spectrum Scheduling for Network-Based UAV Systems: A Parallel Restless Bandits Formulation

A myriad of applications and services of information and communication technology (ICT) has been developed in the past decades, within which networks are the fundamental architecture to connect people and things. As one of the network-based ICT applications, recently, unmanned aerial vehicle (UAV) cooperation systems have attached much attention due to their successful applications in complex military and civilian missions. In this paper, we propose a navigation data-assisted optimal opportunistic spectrum access scheme for wireless communications in heterogeneous UAV networks, to achieve maximized long-term data rate by flexibly scheduling the spectrum subbands. Thanks to the navigation data that is always available locally at the entities in the network, rough prediction of wireless link quality can be obtained to assist subband allocations. Furthermore, to formulate the optimal spectrum scheduling problem, we develop a parallel restless bandits model, which can reduce the NP-hard optimization problem to simply selecting the link with the minimum index in the online stage. Extensive simulation results are also presented to demonstrate the significant performance improvement of the proposed scheme compared to the existing one.

Short-term link quality prediction using nonparametric time series analysis

Wireless link quality prediction (LQP) is the foundation for proactive operations and is therefore a key technique in alleviating network performance degradation. However, accurate LQP is difficult because of the dynamic nature of wireless environments. A recent study found that fluctuations in intermediate quality links often show dynamics on a sub-second granularity, making the task even more challenging. In order to leverage the intermediate links, as well as fine-tune upper-layer protocols, we propose to use nonparametric modeling in nonlinear time series analysis that predicts short-term link quality online. Unlike existing studies, we do not define any new experimental or hypothetical models, or train models using a set of training data. Functional-coefficient autoregression is employed to predict the link dynamics at high time resolutions. We apply our approach and a local linear regression-based LQP (a typical parametric modeling approach) to both NS-2 simulation and empirical packet traces. The results indicate that the proposed method has much higher prediction accuracy and convergence speed than the local linear regression-based LQP under dynamic network conditions.

A practical framework for 802.11 MIMO rate adaptation

The emergence of MIMO antennas and channel bonding in 802.11n wireless networks has resulted in a huge leap in capacity compared with legacy 802.11 systems. This leap, however, adds complexity to optimizing transmission. Not only does the appropriate data rate need to be selected, but also the MIMO transmission technique (e.g., Spatial Diversity or Spatial Multiplexing), the number of streams, and the channel width. Incorporating these features into a rate adaptation (RA) solution requires a new set of rules to accurately evaluate channel conditions and select the appropriate transmission setting with minimal overhead. To address these challenges, our contributions in this work are two-fold. First, we propose a practical link metric that accurately captures channel conditions in MIMO 802.11n environments, and we call this metric diffSNR. Using diffSNR captured from real testbed environments, we build performance models that accuractely predict link quality in 95.5% of test cases. Practicality and deployability are guaranteed with diffSNR as it can be measured on all off-the-shelf MIMO WiFi chipsets. Second, we propose ARAMIS (Agile Rate Adaptation for MIMO Systems), a standard-compliant, closed-loop RA solution that jointly adapts rate and bandwidth, and we utilize the diffSNR-based 802.11n performance models within ARAMIS’s framework. ARAMIS adapts transmission rates on a per-packet basis; we believe it is the first closed-loop, 802.11 RA algorithm that simultaneously adapts rate and channel width. We have implemented ARAMIS with diffSNR on Atheros-based devices and deployed it on our 15-node testbed. Our experiments show that ARAMIS accurately adapts to a wide variety of channel conditions with negligible overhead. Furthermore, ARAMIS outperforms existing RA algorithms in 802.11n environments with up to a 10-fold increase in throughput.

Towards sensitive link quality prediction in ad hoc routing protocol based on grey theory

A mobile ad hoc network (MANET) is a self-organizing network of mobile nodes which can form a dynamic topology. All the nodes generally have a limited transmission range and move freely throughout the network. So implementing a good routing protocol to react the link state changing is a critical issue in current research. From now on, a popular method is introducing a link quality evaluation (LQE) mechanism in routing algorithm. In this paper, we study the link quality evaluation scheme based on traffic-based metrics, such as packet reception ratio (PRR), and find two flaws (one is the assessment lagging and the other is the sample bias) which result in large handoff delay in routing protocols, especially when the PRR-like scheme is used in mobile environments. Therefore, we propose a link quality prediction (LQP) model to sense the link state by analyzing the Signal to Noise ratio of the neighbor link, and using grey theory to tackle the small sample size problem. Finally we set up mobile nodes test-bed to validate the scheme. Based on comparison of previous schemes, the LQP's performance is far better in handoff delay and packet loss rate.

Link Quality Prediction via a Neighborhood-Based Nonnegative Matrix Factorization Model for Wireless Sensor Networks

A core factor to consider when designing wireless sensor networks is the reliable and efficient transmission of massive data from source to destination. In practical situations, data transmission is often disrupted by link interference and interruption resulting in the data losses. Link quality prediction is an important approach to solve this problem. By estimating the link quality based on the past knowledge and information, link quality prediction is essential for routing decisions of future data transmission. Traditional link quality prediction algorithms are simply based on the statistical information of the links in the wireless sensor network. By introducing complex network theory and machine learning techniques, we propose a neighborhood-based nonnegative matrix factorization model to predict link quality in wireless sensor networks. Our model learns latent features of the nodes from the information of past data transmissions combing with local neighborhood structures of the underlying network topology and then estimates the link quality depending on the common latent features of the two nodes between the link. Extensive experiments on both real-world networks and simulation networks demonstrate the effectiveness and efficiency of our proposed model.

Temporal Adaptive Link Quality Prediction with Online Learning

Link quality estimation is a fundamental component of the low-power wireless network protocols and is essential for routing protocols in Wireless Sensor Networks (WSNs). However, accurate link quality estimation remains a challenging task due to the notoriously dynamic and unpredictable wireless environment. In this article we argue that, in addition to the estimation of current link quality, prediction of the future link quality is more important for the routing protocol to establish low-cost delivery paths. We propose to apply machine learning methods to predict the link quality in the near future to facilitate the utilization of intermediate links with frequent quality changes. Moreover, we show that, by using online learning methods, our adaptive link estimator (TALENT) adapts to network dynamics better than statically trained models without the need of a priori data collection for training the model before deployment. We implemented TALENT in TinyOS with Low-Power Listening (LPL) and conducted extensive experiments in three testbeds. Our experimental results show that the addition of TALENT increases the delivery efficiency 1.95 times on average compared with a 4B, state-of-the-art link quality estimator, as well as improves the end-to-end delivery rate when tested on three different wireless testbeds.

LIPS: Link Prediction as a Service for data aggregation applications

A central component of the design of wireless sensor networks is reliable and efficient transmission of data from source to destination. However, this remains a challenging problem because of the dynamic nature of wireless links, such as interference, diffusion, and path fading. When the link quality gets worse, packets will get lost even with retransmissions and acknowledgments when internal queues become full. For example, in a well-known study to monitor volcano behavior (Werner-Allen et al., 2006), the measured data yield of nodes ranges from 20% to 80%. To address this challenge brought by unreliable links, in this paper, we propose the idea of LIPS, or Link Prediction as a Service. Specifically, we argue that it is beneficial for applications to be aware of link layer variations, so that they can take into account the future link quality estimates based on past measurements. In particular, we present a novel state space based approach for the link quality prediction, and demonstrate that it is possible to integrate this model into operating system interfaces, so that higher layer data aggregation protocols can directly exploit these interfaces to improve their performance. Our intensive evaluation indicates the state space based approach is accurate, stable and lightweight comparing to other strategies such as the autoregressive model (Liu and Cerpa, 2010). We carry out experiments based on the commonly used sensor node hardware including both link layer and operating system measurements.

A Link Quality Prediction Metric for Location based Routing Protocols under Shadowing and Fading Effects in Vehicular Ad Hoc Networks

The location-based routing protocol has been chosen as one of the efficient routing approaches in vehicular ad hoc networks in terms of low overhead and high scalability. Its critical advantage lies in performing a pathless routing such that a node having a packet forwards it to its neighbor node that provides the shortest physical distance to destination and this process continues until the packet reaches destination. The problem lies in that the link stability of the neighbors varies largely depending on the mobility of vehicles and the environmental factors that incur shadowing and fading effects. In this paper, we propose a new link quality prediction metric associated with location based protocols to improve the selection of next hop, that consider both the link quality assessment based on the transmission success rate and the link quality assessment based on the prediction of the future locations of vehicles. Simulation results are presented to demonstrate the efficiency of the proposed metric.

Data-driven link quality prediction using link features

As an integral part of reliable communication in wireless networks, effective link estimation is essential for routing protocols. However, due to the dynamic nature of wireless channels, accurate link quality estimation remains a challenging task. In this article, we propose 4C, a novel link estimator that applies link quality prediction along with link estimation. Our approach is data driven and consists of three steps: data collection, offline modeling, and online prediction. The data collection step involves gathering link quality data, and based on our analysis of the data, we propose a set of guidelines for the amount of data to be collected in our experimental scenarios. The modeling step includes offline prediction model training and selection. We present three prediction models that utilize different machine learning methods, namely, naive Bayes classifier, logistic regression, and artificial neural networks. Our models take a combination of PRR and the physical-layer information, that is, Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Link Quality Indicator (LQI) as input, and output the success probability of delivering the next packet. From our analysis and experiments, we find that logistic regression works well among the three models with small computational cost. Finally, the third step involves the implementation of 4C, a receiver-initiated online link quality prediction module that computes the short temporal link quality. We conducted extensive experiments in the Motelab and our local indoor testbeds, as well as an outdoor deployment. Our results with single- and multiple-senders experiments show that with 4C, CTP improves the average cost of delivering a packet by 20% to 30%. In some cases, the improvement is larger than 45%.

Route Optimization Using Adaptive Shrink Mechanism for MANET

In Mobile Ad hoc networks (MANET), routing protocols do not adapt to changing network topology. Unless there is a link failure, the route will not be updated even if another route with lesser hop count becomes available. In this paper, we propose a route optimization technique using adaptive shrinking mechanism. It initially uses the estimated geometrical distance (EGD) metric for route discovery and link quality prediction technique. The adaptive shrinking mechanism involves sending a shrink packet along with the data packets, based on the parameters link quality, traffic rate and link change rate. When the source want to send some data to destination, it verifies the above said parameters and then decides to send the shrink packet. It optimizes the existing routing protocol by reducing the delay and increasing the lifetime of the network. By simulation, it is shown that the proposed mechanism reduces the delay and packet drop while increasing the throughput.