Broad adoption of Online Travel Platforms (OTPs) has led to increasing interest in accurately predicting users' hotel purchase behavior, with price being a key influencer in user decision-making and receiving significant focus. In examining the hotel purchasing process, we identify a pervasive trend that users make extensive price comparisons before making decisions. Existing research primarily focuses on a hotel's own price, neglecting the complex dynamics of market-driven price competition. In this paper, we propose the concept of Marketplace-oriented Hotel Price Competitiveness (MHPC) to model a hotel's pricing competitiveness within the marketplace. Being independent of specific user preferences, MHPC can be applied to and improve various downstream operations in the online hotel industry, such as hotel ranking and pricing, ultimately benefiting hoteliers, users, and OTPs. Furthermore, a novel Hotel Price Competitiveness-aware Purchase Prediction Model (HP3M) is constructed by incorporating MHPC and demand dynamics into a multi-task learning framework, featuring three distinct submodules to encompass the tri-dimensional facets of MHPC. Extensive offline and online experiments demonstrate HP3M's effectiveness in predicting hotel purchase probability and enhancing the performance of hotel ranking and pricing compared to the state-of-the-art methods. HP3M has been fully deployed on Fliggy, a leading OTP in China, serving thousands of hoteliers and tens of millions of users.

Compared to traditional map-based, goal-driven navigation methods , deep reinforcement learning (DRL)-based goal-driven navigation for mobile robots offers the advantage of not relying on prior map information, it enables autonomous decision-making through continuous interaction with the environment. However, DRL-based goal-driven navigation faces significant challenges in terms of low generalization ability and learning inefficiency. In this paper, we propose a DRL approach , variational sparsity Q network (VSQN), which leverages variational inference and transfer learning to achieve efficient goal-driven navigation. The variational inference framework models weight uncertainty within the network, thereby enhancing the agent’s generalization capability. Furthermore, a hierarchical learning network framework is adopted, and transfer learning is employed to incorporate prior knowledge from a pre-trained model into new navigation tasks . This enables the agent to rapidly adapt to novel tasks without the need for fine-tuning after selecting an optimal sub-goal. This improves the agent’s initial performance in previously unseen navigation tasks . The experimental results indicate that the proposed method achieves a success rate (SR) of 76% and a success weighted by inverse path length (SPL) of 0.52 in previously unencountered environments and target locations within the grid environment , and an SR of 81% with an SPL of 0.30 in the AI2-THOR environment. These findings demonstrate that the method substantially enhances the agent’s generalization capability.

Human activity recognition (HAR) is an important task in the field of human-computer interaction. Given the penetration of WiFi devices in our daily lives, HAR using WiFi channel state information (CSI) is a more cost-efficient and comfortable approach. However, most existing approaches ignore the correlation between CSI sub-carriers, which makes their models inefficient and need to rely on deeper and more complex networks to further improve performance. To solve these problems, we propose a reconstructed WiFi CSI phase based HAR approach (WiPhase), which contains a two-stream model to fuse both temporal features and sub-carrier correlation features of reconstructed CSI phase. Specifically, a gated pseudo-Siamese network (GPSiam) is designed to capture the temporal features of the reconstructed sparse CSI phase integration representation (CSI-PIR), and a dynamic resolution based graph attention network (DRGAT) is designed to capture the nonlinear correlation between CSI sub-carriers by the reconstructed CSI phase graph. Furthermore, dendrite network (DD) makes the final decision by combining the features output from GPSiam and DRGAT. Experimental results show that WiPhase outperforms the existing state-of-the-art approaches.

Through-wall physiological parameter monitoring has significant application potential in non-intrusive health surveillance for individuals living alone and in post-disaster rescue operations. To address challenges such as multipath effects and clutter interference, this paper proposes a method for through-wall physiological signal reconstruction based on ultra-wideband signal time-frequency spectrogram. The method effectively mitigates multipath effects and improves the signal-to-noise ratio using Singular Value Decom-position (SVD) and the Least Squares method. It further employs Ensemble Empirical Mode Decomposition (EEMD) and time-frequency spectrogram for signal decomposition and reconstruction, accurately estimating both respiration and heart rates. Ablation experiments demonstrate that this method enhances monitoring accuracy stability and improves signal reliability.

Cerebral stroke is a disease with a high incidence and disability rate. As its most common sequela, upper limb motor dysfunction seriously affects patients' daily activities when it occurs. Traditional rehabilitation training requires patients to go to medical institutions, which is limited due to the lack of human resources. In recent years, the development of virtual reality has provided a novel technology for rehabilitation training, which has attracted more attention in the medical field. In order to overcome the limitations of traditional rehabilitation approaches, this paper develop a virtual reality upper limb rehabilitation training assistance system based on wearable devices and Unity 3D engine. This system combines monotonous training movements in rehabilitation training with virtual games, achieving interaction between human arms and virtual training scenes. The system consists of two upper body training games. It has entertainment, sustainability and real-time feedback functions, providing users with an immersive gaming experience and meeting their training demands for different parts of the upper limbs.

Dynamic pricing, which suggests the optimal prices based on the dynamic demands, has received considerable attention in academia and industry. On online hotel booking platforms, room demand fluctuates due to various factors, notably hotel popularity and competition. In this paper, we propose a dynamic pricing approach with popularity and competitiveness-aware demand learning. Specifically, we introduce a novel demand function that incorporates popularity and competitiveness coefficients to comprehensively model the price elasticity of demand. We develop a dynamic demand prediction network that focuses on learning these coefficients in the proposed demand function, enhancing the interpretability and accuracy of price suggestion. The model is trained in a multi-task framework that effectively leverages the correlations of demands among groups of similar hotels to alleviate data sparseness in room-level occupancy prediction. Comprehensive experiments conducted on real-world datasets validate the superiority of our method over state-of-the-art baselines in both demand prediction and dynamic pricing. Our model has been successfully deployed on a popular online travel platform, serving tens of millions of users and hoteliers.

Dynamic pricing algorithms have been widely studied to manage hotel and platform revenue over online travel platforms (OTPs). For better dynamic pricing, the accurate estimation of the market demand and the market competitiveness are crucial. However, the existing approaches obtain a pricing strategy tailored to each specific scenario using data only from that scenario. They are not considering the shared information between different scenarios, i.e., the data from different scenarios are not fully utilized. So we propose a Multi Scenario Pricing model (MSP) with a novel sharing structure design that leverages cross-scenario and specific information to capture more accurate market demand and competitiveness. Specifically, the model structure explicitly separates information into shared components as market demand and specific information as scenario-wise price competitiveness to prevent domain seesaw. To capture the inherent correlation between listings in different scenarios, an attention network named Price Competitiveness Representation Extraction (PCRE) is well-designed. Meanwhile, traditional metrics are skewed towards model that tends to reduce the price regardless of sample distribution. Thus we propose new offline evaluation metrics that shift attention with sample distribution to avoid biased pricing strategies, which is proved to be more closely related to actual business revenue. Our proposed MSP shows superiority under both offline and online experiments on real-world datasets. The multi-scenario industry dataset and our code are available. To the best of our knowledge, it will be the first real-industry multi-scenario pricing data.

Mobile charging is feasible to deal with the energy-constrained problem in wireless rechargeable sensor networks (WRSNs). The mobile chargers (MCs) are usually employed to charge the sensors sequentially according to the charging schemes. Existing studies assume that each sensor should be charged to its maximum energy capacity or to a fixed upper threshold before the next one can be charged. However, they neglect to control the charging time adaptively of each sensor according to the charging demand. Hence, in this paper, we assume that the charging time of each sensor can be controlled, and we study the joint optimization of charging sequence and charging time problem (JCSCT). Correspondingly, we propose a novel deep reinforcement learning with hybrid action space approach for JCSCT (DRLH-JCSCT), which utilizes deep q-network (DQN) to generate the charging sequence, and adopts deep deterministic policy gradient (DDPG) to determine the charging time. An attention-based encoder–decoder model is integrated in the actor network of DDPG, and a modified bi-directional gate recurrent unit network (MBGRU) is utilized as the decoder. We also design a novel reward function to evaluate the quality of the charging actions. Simulations demonstrate the improved charging performance of the proposed approach, with a longer network lifetime and fewer failed sensors compared with the existing mobile charging scheduling approaches.

Human activity recognition (HAR) is a key technology in the field of human–computer interaction. Unlike systems using sensors or special devices, the WiFi channel state information (CSI)-based HAR systems are noncontact and low cost, but they are limited by high computational complexity and poor cross-domain generalization performance. In order to address the above problems, a reconstructed WiFi CSI tensor and deep learning based lightweight HAR system (Wisor-DL) is proposed, which firstly reconstructs WiFi CSI signals with a sparse signal representation algorithm, and a CSI tensor construction and decomposition algorithm. Then, gated temporal convolutional network with residual connections is designed to enhance and fuse the features of the reconstructed WiFi CSI signals. Finally, dendrite network makes the final decision of activity instead of the traditional dense layer. Experimental results show that Wisor-DL is a lightweight HAR system with high recognition accuracy and satisfactory cross-domain generalization ability.

In recent years, WiFi-based device-free localization (DFL) has attracted attentions due to the rapid development of location-based applications. The localization performance of data-driven DFL models highly relies on the quality of the fingerprint. However, it is difficult to obtain high-quality fingerprints in cluttered environments due to various uncertainties, such as environmental dynamics. To mitigate effects of uncertainties, this article proposes a variance-constrained local–global modeling method to enhance the localization performance. To be specific, the collected channel state information data from a specific environment are first divided into several groups depending on their statistical characteristics using the clustering method, and the local–global modeling mechanism is then implemented based on the extreme learning machine, a kind of noniterative single hidden layer feedforward neural network, to achieve the good representation of the whole environment. During the local–global modeling process, the proposed method is to simultaneously minimize the output weights of the neural network, training errors of the DFL model, and intragroup variances of the grouped data, which makes the created DFL model robust in cluttered environments and not sensitive to uncertainties. Comprehensive experiments in several scenarios, including different indoor environments, device positions and heights, target's orientations, and body shapes, are performed. Experimental results indicate that the proposed method could achieve better localization performance than selected baseline methods, demonstrating the effectiveness of the proposed variance-constrained local–global modeling mechanism in DFL.

Broad adoption of online travel platforms (OTPs) has led to increasing focus on hotel dynamic pricing algorithms, which directly affect the revenue of platform and hotels. Existing approaches, which directly model the correlation between price and occupancy, have limitations in improving occupancy prediction accuracy while ensuring interpretability for dynamic pricing. Moreover, these methods struggle to address the significant data sparsity issue in hotel pricing scenarios. To overcome these limitations, we propose a novel Causality-driven Hotel Dynamic Pricing Model (CANDY) that captures the essential causal relationship between price and occupancy, enhancing occupancy prediction accuracy and interpretability for dynamic pricing. Specifically, we decompose confounders into three orthogonal groups of factors: characteristic factors, competitive factors, and temporal factors, and design submodules to capture the features of each dimension. To address the treatment bias and sample imbalance issues faced by existing causal inference methods in hotel pricing scenarios, we propose a novel data augmentation method based on the monotonic relationship between price and occupancy, and further design a multi-task learning framework tailored to multi-valued treatment scenarios, simultaneously alleviating the data sparsity issue. Both offline and online experiments demonstrate the effectiveness of CANDY in occupancy prediction and dynamic pricing. CANDY has been successfully deployed to provide price suggestion service at Fliggy, a leading OTP in China, serving thousands of hotel operators.

Recent advances in WiFi-based device-free localization (DFL) mainly focus on stationary scenarios and ignore the environmental dynamics, hindering the large-scale implementation of the DFL technique. In order to enhance the localization performance in nonstationary environments, in this article, a novel multidomain collaborative extreme learning machine (MC-ELM)-based DFL framework is proposed. Specifically, the whole environment is first divided into several subdomains depending on the distributions of the collected data using a clustering algorithm, and a corresponding number of local DFL models are then built to represent these subdomains separately. Finally, a global DFL model is achieved by seamlessly integrating all the local DFL models in a global optimization manner. The created MC-ELM-based DFL model also can be incrementally updated with sequentially coming data without retraining to track the environmental dynamics. Extensive experiments in several indoor environments demonstrate the robustness and generalization of the proposed MC-ELM-based DFL framework.

In this paper, we propose a novel elastic demand function that captures the price elasticity of demand in hotel occupancy prediction. We develop a price elasticity prediction model (PEM) with a competitive representation module and a multi-sequence fusion model to learn the dynamic price elasticity from a complex set of affecting factors. Moreover, a multi-task framework consisting of room- and hotel-level occupancy prediction tasks is introduced to PEM to alleviate the data sparsity issue. Extensive experiments on real-world datasets show that PEM outperforms other state-of-the-art methods for both occupancy prediction and dynamic pricing. PEM model has been successfully deployed at Fliggy and shown good performance in online hotel booking services.

In the past decade, great progresses have been made in WiFi-based device-free localization (DFL). However, some challenging issues still hinder the large-scale implementation of DFL techniques, mainly including fingerprint vanishing and environmental dynamics. In order to enhance the localization performance in cluttered environments, in this article, a modified hierarchical framework for DFL is designed, which consists of several functional modules. Specifically, the collected data are first divided into several subsets in the spatiotemporal separation module. Next, the raw data are mapped to another feature space to mitigate the effects of fingerprint vanishing with the help of a deep neural network. In the distributed modeling module, local DFL models are built to separately represent the subsets. Additionally, probability distributions of local DFL models are calculated to estimate and control the effects of the noise. Finally, a global DFL model is built by integrating all the local DFL models with the embedding of the probability distribution information of those local DFL models. In this manner, the localization performance in cluttered environments could be significantly enhanced by the proposed hierarchical framework. Comprehensive experiments in several indoor environments demonstrate the robustness and generalization performance of the proposed hierarchical framework.

Ubiquitous WiFi signals not only provide fundamental communications for a large number of Internet of Things devices, but also enable to estimate target’s location in a contactless manner. However, most of the existing device-free localization (DFL) methods only utilize the time dynamics of the received WiFi signals, leading to inaccurate DFL in the cluttered indoor environments. Because different layouts of environments and deployments of WiFi devices cause the different mathematical distributions of the data collected from the cluttered indoor environments. In this article, a multiple kernel representation-based extreme learning machine (ELM) is proposed, named integrated multiple kernel ELM (IMK-ELM), for strengthening the localization performance in the cluttered indoor environments utilizing spatiotemporal information. In the proposed IMK-ELM-based DFL, the whole data set is first divided into several subsets depending on their mathematical distributions through the$K$-means clustering algorithm, and then a corresponding number of local DFL models are built for all the subsets to capture both the time dynamics and spatial properties of the data. Finally, a global DFL model is achieved by seamlessly integrating all the local DFL models due to the consistency mechanism. In addition, the Fresnel zone sensing theory is utilized for helping understand and explain the essence of indoor DFL. Comprehensive experiments indicate that the proposed IMK-ELM-based DFL outperforms state-of-the-art methods in the cluttered indoor environments.

Device-free localization (DFL) is becoming one of the attractive techniques in wireless sensing field, due to its advantage that the target does not need to be attached to any electronic device. However, most of the existed approaches for DFL can only obtain satisfactory localization performance in specific small area, they cannot function well when implemented to complex and large area. In order to tackle this issue, in this article, a hierarchical framework is developed for large-scale DFL based on data and knowledge twin driven integration, which consists of two phases, including the offline training phase and the online localization phase. In the offline training phase, the complex and large monitoring area is first divided into some subdomains by the K-means clustering algorithm, and then training corresponding number of broad learning (BL)-based DFL models for each subdomain using the Fresnel phase difference as the fingerprints. Meanwhile, a class-specific cost regulation extreme learning machine (CCR-ELM) classifier is also trained for determining the attribution of the reference points, which can alleviate the impacts of imbalanced data distribution on classification results. In the online localization phase, the attributions of the testing points are first judged through the trained CCR-ELM classifier, after that, estimating the target's location in the corresponding subdomains using BL-based DFL models. The validity of the proposed hierarchical framework is evaluated both in small and larger areas, respectively.

Sensor scheduling for energy-efficient collaborative target tracking in wireless sensor networks (WSNs) is an important problem to deal with the limited network resources. With the recent development and emerging applications of energy acquisition technologies, it has become possible to overcome the bottleneck of battery energy in WSNs using the energy harvesting devices, where theoretically the lifetime of the network could be extended to the infinite. However, the energy harvesting WSN also poses new challenges for sensor scheduling algorithm over the infinite horizon under the limited sensor energy harvesting capabilities. In this article, a novel multistep prediction-based adaptive dynamic programming (MSPADP) approach is proposed for collaborative target tracking in energy harvesting WSNs to schedule sensors over an infinite horizon, according to the ADP mechanism. The “action” module of MSPADP is designed to obtain the sensor scheduling for multiple steps starting from the current step, and implemented by the minimal-cost first search (MCFS) decision tree scheme, and the “critic network” module of MSPADP is iteratively performed to optimize the performance for the remaining infinite steps using neural network. Extended Kalman filter (EKF) is adopted to predict and estimate the target state. The performance index is defined by the tracking accuracy derived from EKF and the energy consumption predicted by the candidate sensor schedule. Theoretical analysis shows the optimality of MSPADP, and simulation results demonstrate its superior tracking performance compared with single-step prediction-based ADP (SSPADP), multistep prediction-based dynamic programming (MSPDP), and multistep prediction-based pruning (MSPP) sensor scheduling approaches. Note to Practitioners-Collaborative target tracking is a typical problem in wireless sensor networks (WSNs) where the sensors need to be scheduled to address the constraints of the limited network resources, such as sensor energy usually supplied by the battery. In the recent years, energy harvesting device has been developed and applied to WSNs to overcome the energy restriction. As the energy harvesting capabilities of the sensors are limited, sensor scheduling remains as a challenging problem and is studied in this article. A novel multistep prediction-based adaptive dynamic programming (MSPADP) approach is proposed for collaborative target tracking, by scheduling sensors for the current time step based on the predictions of the subsequent steps over an infinite horizon. It runs iteratively in two modules: obtaining the previous optimal multistep sensor scheduling and updating the remaining infinite-step performance. Simulation results show its superior tracking performance compared with single-step prediction-based ADP (SSPADP), multistep prediction-based dynamic programming (MSPDP), and multistep prediction-based pruning (MSPP) approaches, and lay a good foundation for the practical applications.

In this paper, an intelligent data-driven optimization scheme is proposed for finding the proper burden surface distribution, which exerts large influences on keeping blast furnace running smoothly in an energy-efficient state. In the proposed scheme, production indicators prediction models are first developed using a kernel extreme learning machine algorithm. To heel, burden surface decision is presented as a multiobjective optimization problem for the first time and solved by a modified two-stage intelligent optimization strategy to generate the initial setting values of burden surface. Furthermore, considering the existence of the approximation error of the created prediction models, feedback compensation is implemented to enhance the reliability of the results, in which an improved association rule mining method is developed to find the corrected values to compensate the initial setting values. Finally, we apply the proposed optimization scheme to determine the setting values of burden surface using actual data, and experimental results illustrate its effectiveness and feasibility.

Device-free localization (DFL) is an emerging wireless localization technique where the target does not equip with any electronic tag for communicating with the localization system. It has been found numerous practical applications such as human health and medical care with the help of location detection and behavior analysis, intrusion detection, security safeguard, and emergency rescue. In the DFL system based on the radio-frequency (RF) signal, the radio transmitters (RTs) and the radio receivers (RXs) are used as sensors to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements of the wireless links. In this paper, a novel geometric approach, CFP-PPOCS, is proposed for DFL, in which the DFL problem is formulated as a convex feasibility problem (CFP), and addressed by the parallel projections onto convex sets (PPOCS) algorithm. Experimental results demonstrate that the new approach can achieve better performance than the existing NOOLR and RTI approach.

Device-free localization (DFL) is an emerging wireless network target localization technique that does not need to attach any electronic device with the target. It is remaining as a challenging research problem due to the weak wireless signals and the uncertain wireless communication environment. In this paper, a novel Gaussian Process (GP) based wireless propagation model is proposed to describe the likelihood relationship between the target location and the changes of the RSS measurement for a wireless link. Sequentially Particle Filter (PF) is applied to the DFL for estimating the location of the target, after the GP model is trained using the experimental measurements of the link. Experimental results demonstrate that the proposed GP-PF algorithm can track the target with much better localization accuracy than the Support Vector Machine (SVM) based PF approach.

In this brief, an optimal control scheme based on adaptive dynamic programming (ADP) is developed to solve infinite-horizon optimal control problems of continuous-time complex-valued nonlinear systems. A new performance index function is established on the basis of complex-valued state and control. Using system transformations, the complex-valued system is transformed into a real-valued one, which overcomes Cauchy-Riemann conditions effectively. With the transformed system and the performance index function, a new ADP method is developed to obtain the optimal control law by using neural networks. A compensation controller is developed to compensate the approximation errors of neural networks. Stability properties of the nonlinear system are analyzed and convergence properties of the weights for neural networks are presented. Finally, simulation results demonstrate the performance of the developed optimal control scheme for complex-valued nonlinear systems.

In this paper, a finite-approximation-errors iterative adaptive dynamic programming (ADP) algorithm is proposed to solve optimal control problems for nonlinear systems with controller constraints. Considering the controller constraints, a nonquadratic performance index function is introduced. The finite-approximation-errors iterative ADP algorithm is used to obtain the iterative control law which makes the iterative performance index function reach the optimum. It shows that the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions. Finally, a simulation example is given to illustrate the performance of the proposed method.

This letter is concerned with source localization based on time-difference-of-arrival (TDOA) measurements from spatially separated sensors in a wireless sensor network (WSN). Most of the existing works adopt a centralized sensor pairing strategy, where one sensor node is chosen as the common reference. However, due to the bandwidth and power constraints of multihop WSNs, it is well known that this kind of centralized methods is energy consuming due to the need of single and multihop transmissions of raw measurement data. In this letter, we propose a decentralized in-network sensor pairing method to acquire TDOA measurements for source localization. It is proved that the proposed decentralized in-network sensor pairing method can result in the same Cramer-Rao-Bound (CRB) as the centralized one at a far less communication cost.

Source localization is an important application of wireless sensor networks (WSNs). Many types of sensors can be used for source localization, e.g., range sensors, bearing sensors and time-difference-of-arrival (TDOA) based sensors, etc. It is well known that relative sensor-source geometry can significantly affect the performance of any particular localization algorithm. Existing works in the literature mainly deal with geometry analysis for homogeneous sensors. However, in real applications, different types of sensors may be utilized for source localization. Hence, in this paper, we consider the optimal sensor placement problem in heterogeneous sensor networks (HSNs), where two types of sensors are deployed for source localization. Relative optimal sensor-source configurations with the minimum number of sensors for source localization are identified under the Doptimality criterion with potential extensions to the general case. Explicit characterizations of optimal sensor-source geometries are given for hybrid range and bearing sensors, hybrid bearing and TDOA based sensors as well as co-located hybrid range and bearing sensors, respectively. The results of this work can be applied to the sensor path planning problem for optimal source localization.

Source localization is an important application of wireless sensor networks (WSNs). Many types of sensors can be used for source localization, e.g. range-only sensors, bearing-only sensors and time-of-arrival (TOA) sensors, etc. It is well known that the relative sensor-source geometry can significantly affect the performance of any particular localization algorithm. Existing works in the literature mainly deal with the geometry analysis for a single type of sensors. However, in real applications, different types of sensors may be utilized for source localization simultaneously. Hence, in this paper, we consider the optimal sensor placement problem in heterogeneous sensor networks, where two types of sensors are deployed for source localization. Relative optimal sensor-source configurations with the minimum number of sensors for source localization, are identified under the D-optimality criterion with potential extensions to a general case. Explicit characterizations of optimal sensor-source geometries are given for hybrid range-only and bearing-only sensors as well as hybrid bearing-only and TOA sensors, respectively.

Robust stabilization of multiple coupled networked control system (NCSs) exchanging information over a shared communication network is addressed in this paper. Only one subsystem is assumed to access the network to transmit its state information at a time. We model such coupled NCSs with multiple packet transmission and packet dropout as a class of jump linear systems. A sufficient condition on robust stabilization of the NCSs with norm-bounded parameter uncertainties is derived in terms of linear matrix inequalities (LMIs). Constant stabilizing state feedback controllers can then be constructed by using the feasible solutions of some LMIs. A numerical example is given to illustrate the feasibility and effectiveness of the proposed approach.

Acoustic source localization based on time difference of arrival (TDOA) measurements from spatially separated sensors is an important problem in wireless sensor networks (WSNs). While extensive research works have been performed on algorithm development, limited attention has been paid in how to form the sensor pairs. In the literature, most of the works adopt a centralized sensor pairing strategy, where only one common sensor node is chosen as the reference. However, due to the multi-hop nature of WSNs, it is well known that this kind of centralized signal processing method is power consuming since raw measurement data is involved in the transmissions. To reduce the requirements for both network bandwidth and power consumptions, we propose an in-network sensor pairing method to collect the TDOA measurements while guaranteeing the quality of source localization. The solution involves finding a minimal sized dominating set (MSDS) for a graph of the muti-hop network. It has been proved that in-network sensor pairing can result in the same Cramer-Rao-Bound (CRB) as the centralized one but at a far more less communication cost. Furthermore, the structure of the proposed in-network sensor pairing coincides with the decentralized source localization, which is an important application of our method.

Due to the widespread deployment and low cost, WLAN has drawn much attention for indoor localization. In this poster, an efficient indoor localization algorithm, which utilizes the WLAN received signal strength from each Access Point (AP), has been proposed. The algorithm is based on the Extreme Learning Machine (ELM), a Single layer Feed-forward neural Network (SLFN). It is competitive fast in offline learning and online localization. Also, compared with existing fingerprinting approach, it does not need the fingerprinting database in the online phase, which can substantially reduce the required storage space of the terminal devices.

ABSTRACT Task allocation for wireless sensor networks with multiple concurrent applications (such as target tracking and event detection) requires sharing applications' tasks (such as sensing and computation) and available network resources. In this paper, we model the distributed task allocation problem for multiple concurrent applications by using a reverse combinatorial auction, in which the bidders (sensor nodes) are supposed to bid cost values (according to their available resources) for accomplishing the subset of the applications' tasks. Trust management schemes consist of a powerful tool for the detection of unexpected node behaviors (such as faulty or malicious). It is critical for participants (i.e., bidders and auctioneer) to estimate each other's trustworthiness before initiating the task allocation procedure. To address this issue, we introduce a real‐time trust management module for our auction system that is able to validate the reliable bid value and determine faulty nodes and malicious entities. The main objective of our task allocation scheme is to maximize the network lifetime by sharing tasks and network resources within applications, while enhancing the overall application quality of service (e.g., deadline). We also propose a heuristic two‐phase winner determination protocol to deal with the combinatorial reverse auction problem. Simulation results show that the proposed scheme offers the promising performance and efficiency. Copyright © 2012 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) are usually assigned tasks for a single application. Recently, the concept of shared sensor networks, which support multiple concurrent applications, has emerged, reducing the deployment and administrative costs, and increasing the usability and efficiency of the network. Supporting task allocation for multiple concurrent applications in sensor networks (such as target tracking, event detection, etc.) requires sharing applications' tasks (such as sensing, computation, etc.) and available network resources. In this paper, we model the distributed task allocation problem for multiple concurrent applications using a reverse combinatorial auction, in which the bidders (sensor nodes) bid the cost value (in terms available resources) for accomplishing the subset of the applications' tasks. The main objective is to maximize the network lifetime by sharing tasks and network resources among applications, while enhancing the overall application QoS (e.g., deadline). We also propose a heuristic two-phase winner determination protocol to solve the combinatorial reverse auction problem. Simulation results show that the proposed scheme offers efficiency and network scalability.

Indoor positioning has emerged as a widely used application of Wi-Fi wireless networks. Fingerprinting techniques can provide a low-cost and high-accuracy localization solution by utilizing in-building communication infrastructures. However, existing fingerprinting localization algorithms are not resistant to outliers, for example, the accidental environment changes, access point (AP) attacks. Another drawback is that traditional K nearest neighbor (KNN) algorithm in the literature may not select the candidate reference points (RPs) correctly. In this paper, we propose a novel environmentally robust and attack resistant probabilistic fingerprinting localization method. In the offline phase, the distribution estimation of the signal strength is performed using probabilistic histogram method. Then in the online phase, a three-step location sensing method is proposed. In the first step, a simple and efficient outlier detection method named non-iterative “RANdom SAmple Consensus” (RANSAC) is run to detect and eliminate part of APs from which the signals measured are severely distorted by unexpected environment effects. In the second step, a novel region-based RP selection method which works like a “family of probability” is proposed to improve the possibility of the correctness of selection of the nearest RPs. In the final step, the location is obtained using a weighted-mean method. In the experiment section, we demonstrate the proposed method in our lab and find that the proposed strategies are resistant to outliers and can improve the localization accuracy effectively compared with existing methods.

Indoor positioning has emerged as a widely used application of Wi-Fi wireless networks. A region-based fingerprinting approach is presented for indoor positioning in Wi-Fi wireless networks. This proposed method compares the fingerprint of a Wi-Fi tag with that of a region-based group of reference points, instead of an individual reference point. With the fingerprinting position estimate obtained, and with an inertial measurement unit integrated with the Wi-Fi tag, a stochastic system model is adopted to track the target's position when it is in piecewise constant velocity motion in Wi-Fi wireless networks. The stochastic system model utilizes Wi-Fi fingerprinting position estimates as measurements and inertial sensing data as control inputs. Both simulation studies and experiment data have shown the positioning performance of the integrated mobile platform with improved accuracy, by using the proposed Wi-Fi and inertial sensing technologies.

Indoor positioning techniques based on radio fingerprints outstand over other localization methods because of their independence from radio propagation models and cost-effectiveness in terms of hardware and deployment requirements. However, their reported best achieved accuracy is bounded due to the random environmental changes which cause the inconsistency between the stored fingerprints and the current radio behavior. In order to overcome this limitation, we propose a cooperative localization scheme, whereby users exchange their real-time signal measurements in order to update and improve their estimated location. The update process relies on a modified version of the neural network structure of Self-Organizing Maps by considering the signal relationship between users. Performance evaluation results demonstrate accuracy improvement over the baseline fingerprinting technique while keeping the communication and complexity overheads low.

Deploying the sensor nodes at the best locations for random field reconstruction via sensor network is a fundamental task. One-dimensional random field is a stochastic process. In this paper, we first propose an optimal sensor deployment strategy for Wiener process estimation. The optimal locations for the deployed sensors are uniformly distributed in the field. In addition, we propose a suboptimal sensor deployment to estimate the Gaussian random field which is described as an Ornstein-Uhlenbeck process. We show that the suboptimal deployment strategy for Gaussian random field is also uniformly distributed. Several simulations show the performance of our proposed deployment strategies.

This paper exploits optimal position of the mobile sensor to improve the target tracking performance of wireless sensor networks and simultaneously localize both of the static sensor nodes and mobile sensor nodes when tracking the human motion. In our approach, mobile sensors collaborate with static sensors and move optimally to achieve the required detection performance. The accuracy of final tracking result is then improved as the measurements of mobile sensors have higher signal-to-noise ratios after the movement. Specifically, we can simultaneously localize the mobile sensor and static sensors position when localizing the human's position based on augmented extended Kaiman filters (EKF). In the algorithm, we develop a sensor movement optimization algorithm that achieves near-optimal system tracking performance. We also presented an sensor nodes management scheme in order to deduce the computation complexity when localizing the static sensor nodes. The effectiveness of our approach is validated by extensive simulations.

Upon the occurrence of a phenomenon of interest in a wireless sensor network, multiple sensors may be activated, leading to data implosion and redundancy. Data aggregation and/or fusion techniques exploit spatio-temporal correlation among sensory data to reduce traffic load and mitigate congestion. However, this is often at the expense of loss in Information Quality (IQ) of data that is collected at the fusion center. In this work, we address the problem of finding the least-cost routing tree that satisfies a given IQ constraint. We note that the optimal least-cost routing solution is a variation of the classical NP-hard Steiner tree problem in graphs, which incurs high overheads as it requires knowledge of the entire network topology and individual IQ contributions of each activated sensor node. We tackle these issues by proposing: (i) a topology-aware histogram-based aggregation structure that encapsulates the cost of including the IQ contribution of each activated node in a compact and efficient way; and (ii) a greedy heuristic to approximate and prune a least-cost aggregation routing path. We show that the performance of our IQ-aware routing protocol is: (i) bounded by a distance-based aggregation tree that collects data from all the activated nodes; and (ii) comparable to another IQ-aware routing protocol that uses an exhaustive brute-force search to approximate and prune the least-cost aggregation tree.

Indoor wireless localization has emerged as a key wireless network technology and has been used for a variety of applications. In this paper, we examine the possibility to use one RF-based fingerprint system for indoor wireless localization, and show that there is room for improvement in its location sensing approach. We propose an indoor wireless localization solution by improving a fingerprinting localization algorithm with Multidimensional Scaling (MDS). In our approach, we configure RFID readers to receive signal strengths from both RFID tags and reference points, and use a fingerprinting localization algorithm for initial location estimation. We preprocess the received signal strength information to obtain the pairwise distances' estimation between the RFID tags and the reference points. Having estimated the pairwise squared distances, we apply MDS to reconstruct the RFID tags' distribution, and we subsequently use Procrustes analysis to refine the previously obtained fingerprinting location estimation. Simulation results show that our proposed localization algorithm improves the localization accuracy of the fingerprinting approach under different wireless network conditions.

Applications for wireless sensor networks may be decomposed into the deployment of tasks on different sensor nodes in the network. Task allocation algorithms assign these tasks to specific sensor nodes in the network for execution. Given the resource-constrained and distributed nature of wireless sensor networks (WSNs), existing static (offline) task scheduling may not be practical. Therefore there is a need for an adaptive task allocation scheme that accounts for the characteristics of the WSN environment such as unexpected communication delay and node failure. In this paper, we focus on task allocation in WSNs which is performed with the aim of achieving a fair energy balance amongst the sensor nodes while minimizing delay using a market-based architecture. In this architecture, nodes are modeled as sellers communicating a deployment price for a task to the consumer. To address this task allocation problem, proposed price formulation is used as it continuously adapts to changes of the availabilities of resources. This scheme also accommodates for the node failure during task assignment. The centralized and distributed message exchanged mechanisms between the nodes (sellers) and task allocator (consumer) are proposed to determine the winner among the sellers with the goal of reducing overhead and energy consumption. Simulation results show that, compared with a static scheduling scheme with an objective in energy balancing, the proposed scheme adapts to new environmental changes and uncertain network condition more dynamically and achieves a much better performance on energy balancing.

This paper considers stabilization of network controlled systems (NCSs) with multiple-packet transmission. For NCSs node devices acting over a limited communication channel, we are particularly interested in the case that only one packet containing part of the state information can be transmitted at a time. Even if a state feedback is intended for the original system, control such a system is an output feedback problem. For NCSs transmitted in a periodic manner, sufficient conditions on stability and stabilization of the NCSs are presented. For NCSs transmitted in a stochastic manner, sufficient conditions on the mean square stabilization of NCSs are developed. Finally, numerical examples are given to show that the unstable system can be stabilized with part of state information transmitted over the network channel.

Recent advances in wireless communication and sensor technologies have opened up a new paradigm in healthcare industry. More and more attentions in the wireless body area network (WBAN) have been received recently. However, the limited network resources constrain the application development in WBAN. In this paper, we present a service-specific middleware architecture to bridge the gap between the application development and the underlying network sensor devices. The proposed middleware is able to provide functions including network initialization and registration, service announcement, sensor actuation and control for flexible multiple modalities data acquisition, and real-time network service management. The middleware has been implemented and tested with a healthcare monitoring test-bed using Imperial College sensor nodes.

Multi-source localization is an open and challenging research problem in the energy-based wireless sensor network (WSN) of acoustic sensors. Classic maximum likelihood (ML) algorithm can not work well due to the high computation demand. An expectation maximization (EM) algorithm was proposed in our previous paper to approximate the optimal solution with lower computational complexity. However that algorithm is centralized in nature in which all the observed data from each sensor node must be sent to the fusion centre. It has several drawbacks including the relying on the centre which may be damaged or shut down, poor scalability with the increasing of network size, and the high communication overhead requirement when a sensor is far away from the centre. In this paper we propose a distributed EM algorithm for multi-source localization in energy-based WSN. It can achieve satisfactory localization accuracy with significantly low communication and computation cost.

Most of the target tracking algorithms proposed for wireless sensor networks (WSNs) so far have been relying on sensors of single modality. To integrate multiple sensing modalities (e.g., by using the proximity sensors and ranging sensors together) to improve the tracking performance, the tracking algorithm shall be capable to deal with non-linear and non-Gaussian nature of the tracking problem. In this paper, we present a particle filter algorithm for multi-modality target tracking in WSNs. Simulation results show that the proposed algorithm can provide a good balance among sensor costs and tracking accuracy.

Online secondary path modeling is used to deal with varying secondary path in active control systems. However, instability may happen when there is fast "significant" changing due to the tracking speed limitation of online secondary path modeling. In this paper a scheme involving analogue compensator is proposed to be used in the secondary path with fast "significant" changing. The function of the analogue compensator is analyzed and the guideline for design is given. Simulations have been carried out to compare the performance of the proposed scheme and the existing online secondary path modeling approach. The result shows that the proposed scheme is more effective for fast "significant" secondary path variation.

In classical code division multiple access (CDMA) based multi-channel medium access control (MAC) protocols for mobile ad hoc networks (MANETs), numerous exchanges of neighborhood information are required for code assignment such that nodes within a two-hop separation will adopt different transmission codes and therefore avoid the hidden terminal problem (HTP). However, such expensive communication overhead for code assignment is not desirable since it will cause an under-utilization of bandwidth, energy inefficiency and longer delays, which can significantly degrade the network performance. In this paper, a novel location-aware multi-channel MAC protocol is presented for a large-scale dense MANETs based on a scalable two-phase coding scheme, where the first-phase code is used for differentiating adjacent cells and the second-phase code is employed for distinguishing nodes in one specific cell. A node knows its first-phase code from its location information and requests its second-phase code from its cell leaden. This protocol eliminates the HTP during data transmission without the periodical exchange of neighborhood information. Furthermore, the mechanism of collision resolution in the control channel is described. The performance of the proposed protocol is analyzed in terms of the control overhead and delay. The theoretical results are confirmed by extensive simulations and it is shown that the new protocol significantly outperforms the classical CDMA-based multi-channel MAC protocols

This paper proposes a novel joint clustering and multi-channel medium access control (MAC) protocol for mobile ad hoc networks, based on a scalable two-phase coding scheme. It employs the first-phase codes for differentiating the clusters and the second-phase codes for distinguishing the nodes in a specific cluster. The proposed protocol effectively integrates the procedure of code assignment with dynamic clustering and henceforth substantially reduces the control overhead of code assignment in a code division multiple access (CDMA) based multi-channel MAC protocol while simultaneously combating the hidden terminal problem. Furthermore, the confliction detection and resolution mechanism for the allocation of the first-phase codes as well as the collision avoidance mechanism for the allocation of the second-phase codes in the control channel are also introduced. Analytical framework and extensive simulation results, in terms of control overhead and delay, are provided and compared to the traditional distributed CDMA based multichannel MAC algorithms with or without clustering.

Tracking is an essential capability in many wireless sensor network (WSN) applications. Due to the probabilistic nature of the target movement and the limited detection region and miss detection of sensor, this existing single sensor sensing scheme may result in tracking failure when a scheduled sensor fails to detect the target. We present an adaptive multi-sensor scheduling algorithm for collaborative target tracking in WSNs to improve the tracking reliability and power efficient. This proposed scheme determines the sampling intervals based on the predicted tracking accuracy, and selects a number of sensors to form a temporary tasking group for the next time step, based on a specified detection probability. One of the tasking group members is selected to be the leader node, which acts as a local temporary fusion center

Based on the optimal fusion algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion reduced-order Kalman filter with scalar weights is presented for discrete-time stochastic singular systems with multiple sensors and correlated noises. It has higher accuracy than any local filter does. Compared with the distributed fusion filter weighted by matrices, it has lower accuracy but has reduced computational burden. Computation formula of cross-covariance matrix of the filtering errors between any two sensors is given. An example with three sensors shows the effectiveness

Sensor scheduling is essential to collaborative target tracking in Wireless Sensor Networks (WSNs). In this paper, we present a Multi-step Adaptive Sensor Scheduling algorithm (MASS) by selecting the next tasking sensor and its associated sampling interval based on the prediction of tracking accuracy and energy cost over a finite horizon of steps. MASS adopts alternative tracking mode for each prediction step, i.e., the fast tracking mode (FTM) or the tracking maintenance mode (TMM) dependent on whether the estimated or predicted tracking accuracy is satisfactory. The Best Sensor Schedule Sequence (BSSS) is found by searching and comparing the Candidate Sensor Schedule Sequences (CSSSs) at two levels, i.e., the logical tracking mode level which is simplely defined on multi-step tracking modes and the physical quantity performance level by considering the tradeoff between tracking accuracy and energy cost. MASS employs the extended Kalman filter (EKF) algorithm to predict the tracking accuracy and an energy consumption model to predict the energy cost. Simulation results show that, compared with the traditional non-adaptive sensor scheduling algorithm and the single-step adaptive sensor scheduling algorithm, MASS can achieve fast tracking speed and superior energy efficiency without degrading the tracking accuracy.

This paper introduces a novel joint clustering and multi-channel medium access control (MAC) protocol for mobile ad hoc networks (MANETs) based on a scalable two-phase coding scheme, where the first-phase code is used for differentiating the clusters and the second-phase code is employed to distinguish the nodes in a specific cluster. It mitigates the hidden terminal problem (HTP) during data transmission and efficiently incorporates the procedure of code assignment with adaptive clustering. We also introduce the connection detection and confliction resolution mechanisms for the allocation of the first-phase code in the control channel. Simulation results show that substantial improvement in terms of control overhead can be achieved by the proposed protocol over the traditional distributed code division multiple access (CDMA) based multi-channel MAC algorithms with clustering or without clustering

We present a novel location-aware multi-channel medium access control (MAC) protocol for large-scale dense mobile ad hoc networks (MANETs) based on a scalable two-phase coding scheme, where the first-phase code is used to differentiate adjacent cells and the second-phase code is employed to distinguish nodes in one specific cell. It eliminates the hidden terminal problem (HTP) during data transmission without requiring periodical exchange of neighborhood information. Furthermore, we introduce a collision resolution mechanism in the control channel. We analyze the performance of the proposed protocol in terms of control overhead. Its theoretical results are confirmed by simulations and it is demonstrated that the new protocol significantly outperforms the classical code division multiple access (CDMA) based multi-channel MAC protocols.