Smartphone-based positioning is highly promising due to its widespread availability. However, its reliance on global navigation satellite systems (GNSS) leads to accuracy degradation in complex environments such as urban canyons. While integrating GNSS with inertial sensor-based pedestrian dead reckoning (PDR) can improve performance, challenges such as cumulative positioning errors as well as reduced accuracy in step length and heading estimation due to mixed phone poses remain unresolved during GNSS outages. To address these issues, a Long-Term Evolution (LTE) signal-aided GNSS/PDR system is proposed to improve the applicability to complex environments and mixed poses. Firstly, based on the analysis of propagation characteristics of LTE signal, we establish the relationship between the reference signal received power (RSRP) variations and positions to extract position, step length parameter and heading observations. Additionally, theoretical accuracy of the position observations is derived and its influencing factors are analyzed, providing a basis for the design of subsequent availability assessment strategy. Secondly, multiple observations from GNSS are extracted. Thirdly, after compensating for PDR heading offset caused by pose switching, we employ an unscented Kalman filter (UKF) model to fuse PDR with multi-observation from GNSS and LTE signals. An availability assessment strategy is designed to adaptively adjust filter parameters to exclude moments when GNSS or LTE signals are unavailable, which can mitigate the step length and heading errors induced by environments and poses effectively. Experiments conducted at three sites demonstrate that the introduction of LTE signal significantly reduces step length and heading errors, thereby improving positioning performance.

Integrating Long-Term Evolution (LTE) signal which are readily available through smartphones and stable in indoor environments with Pedestrian Dead Reckoning (PDR) provides a low-cost and accurate solution for indoor positioning. Existing LTE-based indoor positioning methods typically rely on fingerprinting, where databases are built using offline-collected data or regression models. These approaches, however, often ignore the influence of building structure on signal propagation, which limits database accuracy and degrades positioning performance. In fact, some studies have applied propagation models to improve database efficiency and accuracy. However, they primarily focus on signals from indoor stations such as Wi-Fi, and generally use fixed model parameters, making them sensitive to environmental changes and device heterogeneity. To overcome these limitations, this paper introduces an indoor positioning method that integrates LTE signal with PDR, aided by real-time inversion of propagation model parameters. Firstly, the spatial distribution characteristics of LTE signal are analyzed, then a suitable propagation model is selected. Secondly, the model parameters are inversely estimated in real time to enhance environmental and device adaptability. The propagation model is then applied for database construction, and position observations are extracted through fingerprint matching. Meanwhile, the proximity method is introduced to maintain positioning performance as well as support parameter inversion with reliable position references. Finally, the observations extracted from LTE signal are combined with PDR to achieve positioning. Experimental results demonstrate that the proposed method enhances the adaptability of the fingerprint databases to device and environmental changes without requiring prior data, thereby improving positioning performance.

Collaborative content dissemination enables vehicles to directly access content from surrounding nodes through Vehicle-to-Everything (V2X) technologies, such as Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication. This approach significantly alleviates the downlink traffic burden on cellular network base stations caused by repeated downloading of popular content while addressing security challenges in electric vehicle (EV) charging operations, such as payment fraud and data tampering. A key yet unresolved challenge in content dissemination and EV charging is incentivizing vehicles to participate in collaborative processes voluntarily. Existing incentive mechanisms mainly rely on centralized architectures, which are vulnerable to single-point attacks and trust issues in third-party platforms. To overcome these limitations, we propose the PCD-DB (Popular Content Dissemination using DAG-based Blockchain) scheme, which uses a Directed Acyclic Graph (DAG)-based blockchain to incentivize V2X collaboration for dual applications: improving content dissemination efficiency and ensuring secure EV charging transactions. Our novel framework establishes a decentralized incentive system where vehicles act as content propagators or charging service providers, depending on their service capabilities. We define the propagation and charging capabilities of vehicles and use contract theory to design hierarchical contracts tailored to heterogeneous vehicle roles. Numerical results show that our decentralized incentive mechanism significantly improves the efficiency and profitability of vehicle content dissemination while ensuring the security of electric vehicle charging transactions, outperforming existing benchmark methods.

The demand for indoor location services is growing rapidly. Various indoor positioning techniques have been proposed. As a supplement, public FM radio signal based methods can provide a possible solution for infrastructure-free indoor positioning which benefits from its wide coverage. However, existing research mainly focuses on algorithm development, with a notable absence of theoretical performance analysis. To bridge this gap, this paper firstly analyzed the FM signal indoor positioning performance by deriving the Fisher information. Our findings indicate that: FM RSS theoretically has difficulties in two-dimensional (2D) indoor positioning and exhibits directional differences in one-dimensional (1D) performance. Thus, when both directions are used for localization simultaneously, the direction with blurred features will significantly affect the overall accuracy of 2D positioning. Besides, there exists a problem of finding effective FM positioning region in large-scale scenes. To address these issues: First, the method for finding positioning region within effective FM signal environmental field with PDR aided is designed. Second, focusing on the problem of 2D positioning, a new strategy is suggested that takes the 1D characteristics of FM RSS by employing the split-direction accuracy estimation and parameter adjustment. To begin with, three improved lightweight accuracy estimation indicators are adopted to assess the FM signal positioning performance. Then, the split-direction adaptive FM fingerprinting method is proposed, which integrates the FM RSS signal and PDR-constrained space metrics by using the accuracy estimation results in different directions. Practical experiments are conducted in three large-scale scenes, and the results demonstrated an improvement of 86.6% and 33.4% compared to traditional 2D fingerprinting and fusion methods, respectively.

Autonomous pedestrian localization involving complex motion modes is important for applications, such as firefighting and counter-terrorism in global navigation satellite system denied environments. Inertial navigation based on Zero Velocity Update is a crucial method, and adaptive zero velocity detector (AZVD) is a key part to improve performance. However, current AZVD still face challenges in adaptability to different motion speeds and complex motion modes (such as crawling), as well as high-computational load. To address these issues, this article proposes an AZVD that represents the generalized likelihood ratio test statistics within a time window as a ratio relative to the minimum statistic and sets thresholds based on these ratios (relative statistics). First, we theoretically analyze the method based on relative statistics. To ensure that the time window for data segmentation includes zero-velocity phases, and the minimum statistic corresponds to the zero-velocity phase, we divide the algorithm into two steps: 1) design a gait cycle (GC) detection suitable for single support motions and segment the data according to the GC. For motions where the GC cannot be detected, a designed time window is used for data segmentation and 2) for motions where steps are detectable, we restrict the zero-velocity phase to a specific range within the GC; for motions where the GC are undetectable, no restriction is imposed. Then, the threshold is set based on the relative statistics. Finally, through experiments involving multiple individuals and various motion speeds and motion modes, the adaptability of the proposed algorithm is validated.

Among various indoor positioning methods, fingerprinting-based localization technology which utilizes ubiquitous radio signals has been widely studied due to advantages, such as low cost and high accuracy. To tackle the issues of time-consuming fingerprint database construction and enhance its applicability in unknown environments, radio signal simultaneous localization and mapping (Radio-SLAM) technology is utilized to automate the construction process of fingerprint database (also known as radio map). Nevertheless, existing Radio-SLAM methods mainly employ regression models for radio map construction, neglecting the influence of building structures on signal propagation, which leads to limited applicability in nonrepetitive path scenarios. Moreover, existing methods usually fail to generate indoor floorplans. To address these problems, this article integrates pedestrian dead reckoning (PDR) with frequency modulation (FM) radio signal from outdoor stations and Wi-Fi signal from indoor access points (APs) to achieve simultaneous localization and rough indoor floorplan mapping. First, windows, corners, and APs are selected as landmarks. Utilizing the impact of building structures on signal propagation, landmark detection is achieved through extracting the unique attenuation patterns of received signal strength indicator (RSSI). Second, the parameters of the propagation model containing landmark positions are inverted, based on which rough floorplan and large-area radio map are automatically constructed. Finally, PDR and fingerprinting localization results are fused. Experimental results demonstrate that the average positioning errors for landmarks are less than 2 m. Compared with the commonly used Gaussian process regression model, the proposed method significantly improves the accuracy of the radio map, thereby enhancing the localization performance.

Massive flood damage has garnered a lot of social attention. Due to the tension between the strong demand for generalized models and the constrained capabilities of edge devices for hydrological surveillance, this study proposes an accessible distributed hydrological surveillance (HS) and computing system with integrated end-edge-cloud (iEEC) architecture to address the issue. In order to increase the inference efficiency of the edge servers (ES), we first develop a HS model with multiple exits, aiming to exploit its network structure and inference strategy. Then, using a collaborative scheduling algorithm, we construct the iEEC pathway to decide whether to undertake edge inference or cloud invocation. With a prototype system and a simulation tool, we eventually performed a numerical analysis of the system at various scales. The accuracy reached 94.3 %, the speed reached 30.3 frames per second (FPS), and it can better handle the occurrence of hard instances compared to state-of-the-art (SOTA) approaches.

Flood damage is a devastating natural disaster that requires effective hydro-surveillance (HS) systems. However, the limited capabilities of edge servers (ES) make it challenging to develop such systems. Our study proposes an accessible, distributed HS and computing system to address flood damage. To increase inference efficiency on ES, we develop a HS model and combine it with a collaborative scheduling algorithm to construct an integrated end-edge-cloud (iEEC) computing pathway. Our system achieves 94.3% accuracy and a speed of 30.3 frames per second (FPS), outperforming state-of-the-art (SOTA) approaches, and can handle hard instances. The prototype system and simulation tool demonstrate the effectiveness of our approach at various scales.

Edge intelligence (EI) migrates data and artificial intelligence (AI) to the “edge” of a network, enhancing the high-bandwidth and low-latency of wireless data transmission with the multiplier effect of 5G and AI, greatly improving the edges’ processing speed. Through integrating EI and computer vision technology, video surveillance systems in ITS can improve the processing capability of traffic information, which improves traffic efficiency and ensures traffic safety. Accordingly, first, we propose an edge intelligence-based improved-YOLOv4 vehicle detection algorithm, introducing an efficient channel attention (ECA) mechanism and a high-resolution network (HRNet) to enhance vehicle detection ability. Second, an edge intelligence-based improved DeepLabv3+ image segmentation algorithm is proposed, replacing the original backbone network with MobileNetv2 and using the softpool method, thus reducing the network size while improving the segmentation accuracy. Experimental results show that our proposed model has a higher average precision (AP) and can improve vehicle detection accuracy from 82.03% to 86.22%. The mean intersection over union (mIOU) of the image segmentation model improves from 73.32% to 75.63%.

The demand for indoor positioning-based services grows rapidly. Fingerprinting technology has been widely applied in indoor positioning since its high accuracy and low dependence on external devices. However, the main dilemma limiting its development is the time-consuming and laborious site survey in the offline stage. In this paper, we use FM radio signal as the navigation source, and present a completely online FM indoor fingerprint database construction and calibration method based on the propagation model. With the floorplan obtained only, FM data initially collected inside the building is utilized to estimate the model parameters and build the fingerprint database. In the stage of online calibration and positioning, PDR is used to calibrate the database and constrain the fingerprint matching range, then an EKF-based fusion method is developed to improve the positioning performance and ensure the accuracy of calibration. Considering that the little data collected at the initial walking stage may cause inaccurate estimation of model parameters, different calibration strategies are designed using the least square and characteristics of FM fingerprint distribution to increase the stability and accelerate the convergence speed of database error. Field experiments are conducted in the campus office building, and the results indicate the effectiveness of the proposed method.

Among indoor positioning systems, Pedestrian dead reckoning (PDR) system has been widely used for less requirement for expensive infrastructure or laborious surveys. Heading estimation is one of the important parts of PDR. There are two main sources for heading estimation. The gyroscope-based method suffers from error accumulation problem, while the method using magnetometer is vulnerable to magnetic disturbances. Therefore, a novel heading estimation method using adaptive Unscented Kalman Filter (UKF) is proposed in this paper, which fuse accelerometer, magnetometer and gyroscope on the basis of motion mode recognition. ZARU (Zero angular rate update) is utilized to estimate gyro biases and correct the gyro output based on pedestrian still/walking classification. Straight feature is then applied on the basis of straight/turning classification. Magnetometer is finally used to further reduce heading error. In addition, the adaptive adjustment mechanism of filter parameters based on the quality evaluation of measurements is designed in this paper to improve the applicability of the method to different speeds and people. Experiments have been conducted by four experimenters at three sites using two smartphones. During the experiments, the phone is waist-mounted or handheld. The results show that the 3σ positioning error of the proposed method is reduced by more than 55% compared with the gyroscope-based method and magnetometer-based method.

Abstract In the IEEE 802.11 wireless LAN (WLAN), the fundamental medium access control (MAC) mechanism—distributed coordination function (DCF), only supports best‐effort service, and is unaware of the quality‐of‐service (QoS). IEEE 802.11e enhanced distributed channel access (EDCA) supports service differentiation by differentiating contention parameters. This may introduce the problem of non‐cooperative service differentiation. Hence, an incompletely cooperative EDCA (IC‐EDCA) is proposed in this paper to solve the problem. In IC‐EDCA, each node that is cooperative a priori adjusts its contention parameters (e.g., the contention window (CW)) adaptively to the estimated system state (e.g., the number of competing nodes of each service priority). To implement IC‐EDCA in current WLAN nodes, a frame‐analytic estimation algorithm is presented. Moreover, an analytical model is proposed to analyze the performance of IC‐EDCA under saturation cases. Extensive simulations are also carried out to compare the performances of DCF, EDCA, incompletely cooperative game, and IC‐EDCA, and to evaluate the accuracy of the proposed performance model. The simulation results show that IC‐EDCA performs better than DCF, EDCA, and incompletely cooperative game in terms of system throughput or QoS, and that the proposed analytical model is valid. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, a cooperative game is proposed to perform a fair and efficient resource allocation for the time division multiple access (TDMA) based cooperative communication networks. In the considered system, two selfish user nodes can act as a source as well as a potential relay for each other. A transmission node with energy limitation is willing to seek cooperative relaying only if the data-rate achieved through cooperation is not lower than that achieved without cooperation by consuming the same amount of energy. The cooperative strategy of a node can be defined as the number of data-symbols and power that it is willing to contribute for relaying purpose. We formulate this two-node fair and efficient resource sharing problem as a bargaining game. Since the Nash bargaining solution (NBS) to the game is computationally complex to obtain, a low-complexity algorithm to search the suboptimal NBS is proposed. Simulation results show that the NBS results are fair in that both nodes could experience better performance than if they work independently. And the NBS results are efficient in that the performance loss of the game to that of the maximal overall rate scheme is small while the maximal-rate scheme is unfair.

A pricing-based non-cooperative game is proposed to stimulate cooperation and perform spectrum allocation in multi-relay cooperative transmission networks. The authors construct a buyers' market competition model to consider that multiple relays are willing to share their spectrum resources with a single user. Both the benefits of the relays and the user are concerned in the game. First, according to the current user's demand, the relays as sellers compete with each other to determine the price of relaying that can maximise their profits. Then to maximise its utility, the user purchases the optimal amount of spectrum resources from each relay. The existence of the Nash equilibrium (NE), that is, the solution of the game, is proved. Even though the NE can be obtained in a centralised manner, a distributed algorithm to search for the NE is developed, which is more applicable in practical systems. Also, the convergence conditions of the algorithm are also analysed. Furthermore, the authors have also proved that the NE is not efficient when considering the total relays' profits. Thus, a general method to find the global optimal solution that maximises the total relays' profits is given. Simulation results show, by using the game, that a reasonable spectrum allocation can be performed between the relays and the user.

Low-medium-altitude platforms (LMAPs) are being actively researched and developed as a key solution to improve the performance and services of emergency communications. In order to provide higher capacity, throughput and quality of service guarantee to territorial users in emergency scenarios, a LMAP-based WiMAX system, AirWiMAX, is presented in this paper. Firstly, a hierarchical AirWiMAX topology is presented. Secondly, a joint radio resource allocation is carried out simultaneously at the time, frequency and power domain for the AirWiMAX downlink. This problem is modelled as a cooperative game in which a fairness criterion is enforced. Simulation results show that compared to the other two typical resource allocation algorithms, that is, the max-rate algorithm and the max–min fairness algorithm, the proposed algorithm achieves a good trade-off between the overall system throughput and the fairness.

Abstract In this paper, we consider the problem of stimulating cooperation and resource allocation in cooperative transmission networks. We formulate this problem as a sellers' market competition where a relay is willing to share its resource with multiple users. We use a Stackelberg game to jointly consider the benefits of the relay and the users. Firstly, the relay determines the price of relaying according to the user demand. Secondly, the users purchase the optimal amount of resources to maximize their utilities. Although the Nash equilibrium, i.e., the solution of the game, can be obtained in a centralized manner, we develop a distributed algorithm to search the Nash equilibrium, which is more applicable in practical systems. Also, the convergence conditions of the algorithm are analyzed. Simulation results show, by using the distributed algorithm, the relay and the users could determine what price should ask for and how much bandwidth should buy, respectively. Copyright © 2010 John Wiley & Sons, Ltd.

In IEEE 802.11 WLANs, the fundamental MAC mechanism - DCF, only supports best-effort service, and is unaware of QoS. IEEE 802.11e EDCA supports service differentiation by differentiating contention parameters. This may introduce the problem of selfish traffics with rational nodes. Hence, game-theoretic EDCA (G-EDCA) is proposed in this paper to solve the problem. Moreover, a simple and run-time estimation mechanism is presented to implement G-EDCA in current WLANs. Extensive simulations are also carried out to compare the performances of DCF, EDCA and G-EDCA. The simulation results show that G-EDCA performs better than DCF and EDCA in terms of system throughput and QoS.

The location of mobile terminals has received considerable attention in the recent years. The performance of mobile location systems is limited by errors primarily caused by nonline-of-sight (NLOS) propagation conditions. We investigate the NLOS error identification and correction techniques for mobile user location in wireless cellular systems. Based on how much a priori knowledge of the NLOS error is available, two NLOS mitigation algorithms are proposed. Simulation results demonstrate that with the prior information database, the location estimate can be obtained with good accuracy even in severe NLOS propagation conditions.

The location of mobile terminals has received considerable attention in the recent years. The performance of mobile location systems is limited by errors primarily caused by nonline-of-sight (NLOS) propagation conditions. In this paper, we investigate the NLOS error identification and correction techniques for mobile user location in wireless cellular systems. Based on how much a priori knowledge of the NLOS error is available, two NLOS mitigation algorithms are proposed. Simulation results demonstrate that, with the prior information database, the location estimate can he obtained with good accuracy even in severe NLOS propagation conditions.

This paper proposes a mobile user location scheme for wideband code-division multiple-access (CDMA) wireless communication systems. To achieve high location accuracy and low cost of the mobile receiver, the location scheme combines the time difference of arrival (TDOA) measurements from the forward link pilot signals with the angle of arrival (AOA) measurement from the reverse link pilot signal. High chip rates in wideband CDMA systems facilitate accurate TDOA measurements, and a smart antenna used at the home base station (BS) can provide accurate AOA measurement in a macrocell environment. A two-step least square location estimator is developed based on a linear form of the AOA equation in the small error region. Numerical results demonstrate that the proposed hybrid TDOA/AOA location scheme gives a much higher location accuracy than TDOA only location, when the number of base stations is small and/or when the TDOA measurements have a relatively poor accuracy.

We investigate the non-line-of-sight (NLOS) propagation identification and correction for time difference of arrival (TDOA) based mobile user location in wireless communication systems. Based on the defined TDOA residual, an NLOS base station identification algorithm is proposed. Different choices of the reference location for the residual calculation are compared via simulation. To correct the NLOS error with a certain distribution, we propose a maximum likelihood (ML) estimator for TDOA location systems. Simulation results demonstrate that the proposed NLOS recovering algorithm performs better than that using only LOS measurements, especially when the number of available base stations is small and/or the LOS base stations have an undesirable geometric layout.