Multi-agent collaborative perception has shown significant potential in improving environmental awareness, especially in complex and dynamic environments where it enables a more precise and comprehensive understanding. However, existing collaborative perception systems still face several challenges, particularly during feature fusion, where issues such as information redundancy and noise interference arise. To address these challenges, this paper introduces the Dual-Attention Weighting Collaborative Perception (DAW-CoPe) framework. The framework integrates dilated convolutional techniques within the Multi-Scale Spatial Attention Module (MSAM), allowing for the capture of multi-dimensional object features across different scales and the effective integration of richer contextual information, thereby enhancing the model’s perceptual capabilities. Furthermore, the incorporation of local convolutions strengthens the model’s ability to extract fine-grained features, improving its capacity to capture local details during feature fusion and reducing the risk of losing important information. To further enhance perception accuracy, we introduce a dual-attention weighting (DAW) mechanism: a same-level, learnable gating module that jointly reweights channel semantics and multi-scale spatial cues, thereby emphasizing informative channels and critical spatial regions. By suppressing redundant and noisy signals, this mechanism delivers substantial gains in performance and robustness across perception tasks. Experimental results demonstrate that DAW-CoPe outperforms existing state-of-the-art methods on three public datasets, significantly boosting the accuracy, reliability, and adaptability of collaborative perception systems. The code associated with this research will be made publicly available athttps://github.com/HIT-K/DAW-CoPe

Multi-agent collaborative perception can mitigate individual blind spots by sharing and integrating sensory data, thereby providing a more comprehensive understanding of the surrounding environment. However, challenges remain, particularly in feature fusion, where issues such as redundancy, localization errors, and the trade-off between perceptual accuracy and real-time performance must be addressed. In this paper, we propose the Triple-Attention Collaborative Perception (TA-CoPe) framework, which employs hybrid channel attention, frequency attention, and spatial attention to enhance feature representation across these dimensions, optimizing the fusion and selection of multi-dimensional features. Additionally, a dynamic gating mechanism with adaptive weight learning assigns varying weights to each module, dynamically optimizing their contributions to the final output, thus improving both the accuracy and robustness of feature fusion. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art methods on two public datasets in multi-agent perception tasks.

Integrated sensing and communication (ISAC) enables several emerging applications while suffering severe security issues. In this article, the novel illegal sensing suppression (ISS) is proposed to protect the privacy of the specific legitimate user in the presence of an adversary with the sensing capacity in the ISAC system. The target detection and the direction of arrival estimation as common sensing tasks are investigated to deteriorate the sensing performance at the adversary side. To this end, we propose the missed detection probability maximization and the Cramér-Rao bound (CRB) maximization-based ISS model. This model also ensures the achievable rate and the legitimate sensing CRB toward the adversary at the ISAC base station side. The beamforming optimization algorithms are developed based on the alternating optimization and semidefinite relaxation techniques in order to address the nonconvex issue in the formulated ISS models and explore the rate-ISS tradeoff in the ISAC system. We further propose the closed-form solution for the single user case in the target detection suppression scenario. Numerical results demonstrate the necessity of the ISS and the ISS gain of proposed algorithms compared with other baseline schemes.

With the development of the Internet of Things, cross-view geolocation of satellite images and Unmanned Aerial Vehicles (UAVs) aerial images is gaining attention due to its significant potential in denied-navigation environments under Global Navigation Satellite System (GNSS). However, inadequate feature representation between views, as well as uncertainties in positional offsets and distance scales, constitute key challenges in this field. Existing research primarily focuses on extracting comprehensive and fine-grained feature information. However, effective feature representation and alignment are equally critical. Therefore, a novel visual localization algorithm based on Multi-scale Feature Fusion on improved Transformer architecture (MFFT) is proposed. The algorithm combines Adaptive semantic Feature extraction based on Improved ASPP (AFIA) model and Spatial Pyramid based on Multi-scale Subsampling (SPMS) model, and has strong robustness. MFFT cooperatively uses AFIA and SPMS to successfully achieve an effective balance between feature representation and alignment. Experimental results show that, on the common benchmark data set DenseUAV, compared with the most advanced algorithms in the same field, the R@1 of MFFT algorithm is improved by at least 2.23% and the SDM@1 is improved by at least 1.64%, thus achieving the most advanced cross-view matching performance, verifying that the proposed algorithm has strong competitiveness in UAV visual self-localization tasks, and opening up a new technical path for future UAV autonomous navigation and diversified mission execution in complex environments.

Based on current theoretical insights and ideas in salient object detection in optical remote sensing images (RSI-SOD), the utilization of boundaries typically involves enhancing object features through boundary-guided assistance. However, this initial approach overlooks the intrinsic connections between regions and boundaries, which are essential for capturing rich contextual features and refining boundary details from a global perspective, thereby providing robust support for the mutual enhancement of information. To further capture the complex relationships between regions and boundaries, we propose a novel RSI-SOD network, the Multi-Strategy Region and Boundary Interaction Network (MRBINet), which employs multiple strategies to analyze potential key information of the objects. Specifically, we design the Boundary-Region Split Module (BRS) to decompose image information and obtain features of regions and boundaries. Based on the obtained information, the Boundary-Guided Reasoning Refinement Module (BGRR) and the Multi-Source Dynamic Cross-Attention Module (MDC) are employed to explore intrinsic connections between regions and boundaries through graph reasoning and interactive attention, respectively, during the SOD process, effectively constraining diffuse regional information through robust boundary guidance. Finally, we utilize the Collaborative Enhancement Cascade Decoder Module (CECD) to recombine and reinforce the refined regions and boundaries, aiming to restore the intrinsic features of the target object. Extensive experiments on three benchmark datasets demonstrate that our method outperforms classical approaches, showing competitive performance. Furthermore, the intrinsic features enhancement of region and boundary contributes to the performance improvement of the RSI-SOD method. The code and results for this work can be found at https://github.com/JieZzzoo/MRBINet.

The collaborative exchange of information between multiple agents effectively addresses the limitations of individual perception. However, in practical applications, agents often lack the ability to estimate uncertainty for detected targets, which can negatively impact decision-making and control processes. As a result, uncertainty estimation is crucial for multi-agent systems. In this paper, we introduce Evidence Regression for Collaborative Perception (ER-CoPe), a novel and efficient collaborative perception framework that employs evidential deep learning to explicitly quantify and manage aleatoric and epistemic uncertainties. ER-CoPe incorporates an Evidence Regression (ER) module that models bounding box parameter uncertainties using a Normal-Inverse Gamma distribution, effectively eliminating the computational overhead associated with traditional repeated inference-based uncertainty quantification. Additionally, we propose gradient regularization to mitigate the gradient vanishing issue in regions of high uncertainty and introduce uncertainty regularization to address evidence shrinkage. These novel regularization techniques are integrated into a multi-level uncertainty loss function we designed to maintain training stability. Furthermore, we propose a multi-task loss function that dynamically balances detection accuracy and uncertainty estimation during the training process. Extensive experiments conducted on two widely-used collaborative perception datasets, OPV2V and V2X-Set, validate that ER-CoPe achieves state-of-the-art performance, significantly improving both the reliability and efficiency of multi-agent collaborative perception systems, making it particularly suitable for autonomous driving applications. The code will be released at https://github.com/HIT-K/ER-CoPe

Image retrieval technology has become increasingly important in modern society. Particularly, instance-level image retrieval not only enables fast and accurate image retrieval but also plays a significant role in the field of Visual place recognition (VPR). NetVLAD's (Vector of locally aggregated descriptors) global feature extraction method exhibits excellent performance in the field of image retrieval. Nevertheless, the NetVLAD method is characterized by a long training time, a low recall rate, and a slow method for extracting features. To address this problem, we propose an end-to-end model based on a novel deep neural network for global feature extraction. It exhibits lower computation complexity for high-dimensional features, accelerates feature extraction, and improves the recall rate of image retrieval. We present the following two principal contributions. First, we introduce attention aggregation module based on self-attention mechanism that combines the positional information and confidence scores of local features. And then it enhances local features using the self-attention mechanism. Second, we present the global feature extraction module, At-tnVLAD, based on the principles of the NetVLAD method. It employs a cross-attention mechanism in place of convolution, reducing the number of trainable parameters without affecting the computational complexity. The experimental results indicate that the proposed method can not only speed up training convergence and feature extraction but also improve recall rate.

In the realm of optical remote sensing imagery, tackling the intricate task of detecting salient objects poses challenges that expose limitations in prevailing CNN- and transformer-based methodologies, particularly in dealing with intricate object topologies. To address this, we present a novel solution, the recurrent adaptive graph reasoning network (RAGRNet), tailored to capturing patterns of salient objects within complex and irregular remote sensing images. For the first time, our network orchestrates simultaneous modeling of both object regions and boundaries, ushering in a comprehensive comprehension of their intrinsic correlations. This pioneering approach involves the deployment of a multigranularity boundary-focused module (MGBF), which effectively crafts enriched boundary features. Going further, the recurrent adaptive graph reasoning (RAGR) module scrutinizes the semantic interplay between region and boundary representations, thereby fortifying the learning process of intricately structured object features. Concluding the process, a specialized decoder, termed the heterogeneous semantic cooperative interconnection (HSCI), systematically fuses region and boundary information in a progressively unified manner. This intricate interplay harnesses their synergistic complementarity to bolster the Precision and sophistication of modeling salient objects. In rigorous evaluations across diverse datasets, our proposed network outperforms 38 state-of-the-art (SOTA) methodologies, a testament to its remarkable efficacy. Code and results will be available athttps://github.com/JieZzzoo/RAGRNet.

Reconfigurable intelligent surface (RIS) is a programmable metasurface composed of sub-wavelength meta-atoms and a controller. Recent research works have shown that multi-layer RISs can form over-the-air neural networks. This enables support for a variety of tasks, including image recognition, mobile communication coding-decoding, and real-time multi-beam focusing. In this work, we present a RISs-based over-the-air joint source channel coding (JSCC) scheme, named as AirJSCC, where multi-layer RISs are used to realize wave-based complex-valued neural networks (CVNNs). AirJSCC transforms the computation process inherited in JSCC into the transmission of wireless signals through RISs, and thus many benefits, such as light-speed computation and high parallel processing capability, can be achieved. To the best of our knowledge, this is the first deep JSCC scheme that incorporates RISs and CVNNs. Simulation results demonstrate that AirJSCC achieves better image reconstruction performance compared with the baseline scheme, even under low signal-to-noise ratio (SNR) and limited bandwidth, and exhibits robustness against varying channel conditions.

Nowadays, blind guiding service is highly required by visually impaired people for a normal outdoor life. However, the tactile pavements fail to work for blind guiding when they are covered or blocked, and even become barriers for normal people who are riding bicycles, sliding the trolley cases and so on. Recently, distributed optical fiber vibration sensing (DOFVS) is widely used for electric fence and human activity recognition. In this paper, to our best knowledge, we first propose the optical fiber pavement to replace the tactile pavement for outdoor blind guiding, and provide the positioning method based on phase-sensitive optical time domain reflectometer ($\Phi$-OTDR). By extracting critical gait features from the output intensity signal, we succeed to make accurate classification between the blind pedestrians and the normal ones. Based on signal autocorrelation analysis in both time and space domains, the locations of each target person on the optical fiber pavement are accurately estimated. The experiment results show that our proposed method achieves highly accurate recognition of blind pedestrians and provides their localization estimations accurately.

Aiming to improve the accuracy of visual indoor positioning, an efficient deep multi-patch network based image deblurring algorithm (DMPID) is proposed to eliminate the effect of blurred images on the positioning accuracy. Meanwhile, the self-sorting visual word based image retrieval algorithm and the block based improved eight-point method are also proposed in this paper to improve the retrieval accuracy and positioning accuracy. The proposed image deblurring algorithm adopts the deep multi-patch network and the weight selective sharing scheme to acquire the deblurred images. Then, we propose the self-sorting visual word and add two additional elements representing the relative spatial information into every obtained feature point to utilize the intrinsic relationships between extracted features and physical positions in order to improve the retrieval accuracy. Meanwhile, the visual word filtering is also employed to eliminate redundant visual words and reduce the time consumption of image retrieval. Finally, the position estimation of query camera can be achieved by the epipolar constraint based on the improved eight-point method. Simulation results and performance analysis show that the proposed method can restore the image details effectively and improve the accuracy of visual indoor positioning.

The advance of 5G has brought great convenience and opportunities for the application of visual indoor positioning in the Internet of Things, augmented reality, emergency rescue and other important fields, but its positioning performance still needs to be improved. In order to increase the accuracy and efficiency of the vision-based indoor localization system, we propose hierarchical image retrieval algorithm based on Bag-of-Visual-Word and eight-point algorithm with feature clouds for position estimation which can effectively refine the efficiency and accuracy of image retrieval and location estimation. The experimental results show that the proposed method can dramatically improve the real-time performance and accuracy of the visual positioning system.

Aiming at the problem of cumulative error in the relative position estimation, a location drift detection method is proposed in this paper. This method calculates the location drift distance in the relative position estimation to realize the correction for the camera position and ensure that the positioning error is within a reasonable range. Firstly, this paper selects the inlier points of all estimated feature points based on the fitted line. Secondly, these inlier points are adopted to estimate the drift distance in order to reduce the estimation error. Finally, the drift threshold is set, and the positioning system will switch the relative position estimation to the absolute position estimation when the drift distance is larger than the drift threshold. Experimental analysis and simulation results show that the proposed method in this paper can effectively control the positioning error.

In power limited wireless sensor networks (WSNs), receiving, storing then utilizing energy from radio frequency (RF) signal has been a valid solution to prolong the lifetime. In this paper, we analyze the technique of Simultaneous Wireless Information and Power Transfer (SWIPT) in clustered WSNs, where some nodes are selected as the relay, collecting energy from the signal to help transmit information from the source to the destination. We design an energy-efficient transmission (e-Trans) so that the system can take a full utilization of energy to transmit data. And we aim to achieve the maximal system energy efficiency by jointly optimizing the source transmitting power, relay selection scheme and relay power splitting ratio. We first formulate the e-Trans design problem as a constrained optimization problem. Then, by transforming the fractional objective function into an equation, we propose a novel algorithm to solve the problem and achieve the optimal system energy efficiency. Simulation results illustrate performance of the proposed transmission design.

Simultaneous Localization and Mapping (SLAM) has been a hot research direction in the field of mobile robots since it was proposed. ORB-SLAM is a typical algorithm in feature point SLAM system. However, because the ORB-SLAM can only provide sparse map construction, it can only be used for positioning. Just using ORB-SLAM to handle the positioning problem make the calculation complexity higher and reduce the real-time performance. Because of the complicated calculation, the ORB-SLAM is not practical to the embedded devices with low computing power. Therefore, we propose a monocular SLAM algorithm with high real-time performance based on Kalman filter. Our proposed algorithm reduces the time consumption of the system by modifying the posture optimization algorithm. Our algorithm provides accurate and efficient positioning for upper-level applications. We use Kalman filter modeling to optimize the pose of the camera. The experimental results show that compared with traditional algorithm, our proposed algorithm can effectively improve the real-time performance and reduces the time consumption of the system.

In our society, realizing intelligent positioning in indoor environments is important to build a smart city. Currently, mutual positioning requirements in the unknown indoor environment are growing fast. However, in such environment, we can obtain neither outdoor radio signal nor the indoor images in advance for online positioning. Therefore, how to achieve mutual positioning becomes an interesting problem. In this paper, we propose a vision‐based mutual positioning method in an unknown indoor environment. First, two users take images of the unknown indoor environment, use semantic segmentation network to identify the semantic targets contained in the images, and upload the generated semantic sequence to the user shared database in real time. Then, every time two users reupload a semantic sequence due to a change of location, it is necessary to retrieve whether another user has uploaded the same semantic sequence in the shared database. If the retrieval is successful, it means that two users have seen the same scene. Finally, two users select a target from the two user images taken based on the same scene to establish a three‐dimensional coordinate system, respectively, calculate their own position coordinates in this coordinate system, and realize mutual positioning through position coordinate sharing. Experiment results show that our proposed method can successfully realize mutual positioning between two users in an unknown indoor environment, while ensuring high positioning accuracy.

As an emerging paradigm, supplying power by radio frequency signal has been a key technology for the wireless powered communication network (WPCN) to prolong the lifetime. This paper considers a multiple input multiple output (MIMO) system where users are charged only by one source. The source is equipped with multiple antennas while each user with one antenna. Besides receiving information as the traditional way, the source has the capacity to transfer energy with beamforming, which can be harvested by users to store for information transmission in the later. However, the unknown channel state information (CSI), low energy efficiency, and various demands of transmitting volume jointly raise inaccurate, wasteful, and flexible conditions in transmitting design. On the other hand, energy and spectrum efficient solutions are indispensable to the success of Internet of Things (IoT). In this case, we put forward a novel design of downlink energy transfer, uplink information transmission, and channel estimation to achieve a practical efficient transmission. By jointly optimizing the source antenna number, power allocation, energy beamforming vectors, and each phase time of channel estimation, energy harvest, and information transmission, we aim to achieve the optimized system energy efficiency with constraints of signal‐to‐noise ratio (SNR), data transmission volume, and transmitting power. Based on fractional programming and Lagrangian dual functions, we also put forward a distributed iterative algorithm to solve the formulated problem optimally. Simulation results verify the convergence of our proposed algorithm and illustrate the relationship between variables of antenna number, data volume requirement, pathloss factor and system performance of sum‐throughput, energy efficiency, and user fairness. Our proposed transmitting design can achieve the optimized energy efficiency, whose upper bound is improved by appropriate massive antenna employment.

Satellite location in a leader-follower satellite cluster is very important to control the satellite cluster topology. Fingerprint localization is a promising method because its simple and low cost requirement for the satellite hardware. However, the received signal strength is always changing in a complex way due to the relative movements of the satellites, which will make the radio map dynamic. Therefore, in this paper we propose to calculate the dynamic radio map based on the satellite cluster ephemeris. In detail, we propose a coarse-fine localization method for localizing the leader-follower satellite cluster. In the offline phase, we calculate the sparse radio map in several moment of the ephemeris so as to achieve coarse localization and small size of the dynamic radio map. Dense radio map is calculated in the satellite online for the fine localization. We finally use the fine localization for trajectory tracking to further improve the localization accuracy. The numerical results demonstrate that the proposed method not only yields a high localization accuracy, but also significantly reduces the space and time complexity.

In order to accurately realize the ablation treatment of liver tumors, it is necessary to design a surgical treatment plan for radio frequency ablation (RFA) based on computed tomography (CT) image. Currently, as a popular deep learning method, U-net network is widely used for image segmentation. However, traditional U-net network cannot achieve to segment the liver tumor from the CT images. In order to accomplish the tumor segmentation in CT images and make the design on the RFA treatment, in this paper we propose an improved U-net network. We use two deep convolutional neural networks based on U-net and improve the overall performance of the segmentation network by improving the network model. On the basis of liver tumor segmentation, we finally accomplish the design of RFA for liver tumor removal. Simulation results show that our proposed method can effectively achieve liver tumor segmentation and obtain a good Dice coefficient, thus providing help for the surgical design of RFA for liver tumors.

2D image-based and 3D structure-based are the most two popular vision-based indoor positioning methods. Though 2D method has better efficiency for image retrieval, the number of stored images is usually too many to achieve fast 2D-2D matching, and it cannot obtain high positioning accuracy either. In contrast, 3D method has smaller database and can achieve much higher positioning accuracy with the help of 3D information. However, it needs to construct 3D point cloud map of the positioning environment and spend too much time on 2D-3D matching. Therefore, in order to improve the positioning accuracy and the retrieval efficiency, we propose a vision-based indoor positioning method by joint use 2D images and 3D point cloud map in this paper. In offline stage, we select key images into the offline database based on the 3D point cloud map that is built by RGB-D simultaneous localization and mapping (SLAM), and establish the mapping relation between 2D image local feature points and 3D points. In online stage, we utilize the rough-fine matching strategy to obtain 2D-3D matching pairs between local feature points of the user query image and 3D points in the 3D point cloud map. The user position is finally estimated by the efficient perspective-n-point (EPnP) algorithm. Performance analysis and simulation results indicate that our proposed method can not only achieve high positioning accuracy but also speed up the matching process.

Aiming at the problem of existing bag-of-visual words based image retrieval algorithm, such as poor stability and low retrieval accuracy, a bag-of-visual words based improved image retrieval algorithm (IBVW) is proposed, which extracts features from the images in the database. The approximate K-means algorithm is adopted to cluster the image features into visual words and store them in the database. And then, the feature extraction and the inverted index in the online stage are implemented on the query image to find the images with high similarity in the database. At last, the best matching image is acquired through the similarity calculation, voting scheme and homography based matching algorithm. Simulation results and performance analysis show that the accuracy of our retrieval algorithm is improved by about 10% compared with existing methods for vision indoor positioning.

Quick and accurate floor identification in a multistory building is a challenging task for 3D indoor positioning. The performances of the available methods are low accuracy or even unworkable in the large and complex urban environment due to the noisy received data. Furthermore, the relationship between received data from different reference points is not considered to make the floor identification better. Therefore, in this paper, we focus on improving floor identification accuracy and propose a novel floor identification method. For better describing the property of the signal propagating difference coming from the same base station to different floors, we analyze both the channel characteristics and geographic characteristics and then select five important parameters for floor identification. Based on these parameters, a floor identification method is proposed. We use Denoising Autoencoder (DAE) on these parameters for noise reduction and feature extraction. Then, we use the Long Short-Term Memory (LSTM) on the denoised features for floor identification, which can better explore and utilize data feature relationship. Based on the real cellular network data, the experiment results show that our proposed floor identification method is very accurate for different structural buildings, which outperforms the traditional methods.

Different from the traditional device-to-device (D2D) communication, vehicle-to-everything (V2X) communication is characterized by the high mobility of vehicles, which introduces the fast variations of the channel state information (CSI) such that the accurate CSI is difficult to be obtained. Unlike other previous work which focuses on the perfect CSI, in this article, we investigate the joint power control and resource allocation problem for the D2D-based V2X communication in a more practical case where the CSI is imperfect. We aim at maximizing the sum ergodic capacity of all vehicular user equipments (VUEs) with imperfect CSI under the minimum signal-to-interference-plus-noise-ratio (SINR) requirements of cellular user equipments (CUEs) and the outage probability constraints of VUEs. We derive an approximate closed-form expression of VUE's ergodic capacity based on Jensen's inequality and propose a simple and efficient lower bound-based power control approach to solve the power control problem with low computational complexity. Based on the optimal power allocation results, we further propose an enhanced Gale-Shapley algorithm to solve the spectrum resource allocation problem, which is tailored to the scenario where each VUE can reuse spectrum resources of multiple CUEs but the resource of each CUE can be shared with one VUE at most and the maximum number of reuse CUEs for each VUE is limited by the maximum transmit power of the VUE. The numerical results verify the tightness of the proposed algorithm and demonstrate the proposed algorithm can enhance the sum ergodic capacity of VUEs efficiently and effectively.

The immense diffusion of smart mobile devices increase the usage of WiFi as a state-of-the-art wireless communication standard dramatically, and it also renders fingerprinting method for high-accuracy indoor localization possible. However, the fundamental challenge of existing fingerprinting localization is that the complicated indoor spatial structure brings great difficulty to exploit the relationship between received signal strength (RSS) distribution and the process of fingerprint matching. To address this problem, in this paper, we focus on integrating the signal pathloss model into distance metric learning, and a novel cost similar to the Large Margin Nearest Neighbor (LMNN) is designed to conduct offline metric learning procedure. The proposed metric learning, which is pathloss model practically considered to learn the mapping between signal strength distribution and physical space structure, aims to obtain a reasonable distance matrix for k Nearest Neighbor (KNN) based indoor localization. Experiment results corroborate that the proposed metric learning method can further improve the indoor localization accuracy compared with the conventional methods and its effectiveness is validated extensively in various cases for testing.

Device-to-Device (D2D) technology based proximity services (ProSe) has been recently proposed to support Vehicle-to-Everything (V2X) communication. In this paper, we propose a joint resource scheduling scheme that caters to both distributed and centralized resource scheduling for D2D-based V2X communication under different network load conditions. In the proposed scheme, Vehicle User Equipments (V-UEs) can operate in either the scheduled resource allocation mode (mode 3) or the autonomous resource selection mode (mode 4). We then further divide mode 3 into dedicated mode and reuse mode. Since safety-critical information is very important for V2X communication, we guarantee the Quality of Service (QoS) of V2X communication by considering both ProSe Per- Packet Priority (PPPP) and communication link quality of V2X messages. Our objective is to maximize the overall information value of all V-UEs by joint resource scheduling of different resource allocation modes under different network load conditions while satisfying the minimum signal to interference noise ratio (SINR) requirements of both Pedestrian User Equipments (P-UEs) and V-UEs. Finally, extensive simulation results are presented to evaluate the efficacy of the proposed scheme.

Wireless local area network (WLAN) fingerprint-based indoor positioning system has a wide application prospect because it supplies good positioning performance without the requirement of additional hardware installations. However, huge labor and time are needed to establish the fingerprint database called radio map. To address this problem, we propose an efficient radio map building method using tensor completion. The cost of the proposed method is reduced by lessening the number of reference points (RPs) to be measured. Due to the strong correlation between data, estimating received signal strength (RSS) at unmeasured reference points can be formulated as a low rank tensor completion problem. And the issue can be transformed into a convex optimization problem by substituting rank operation with trace norm operation. We solve the problem by employing the alternating direction method of multipliers (ADMM) algorithm. The experiment results indicate that the proposed method can not only reduce the effort of radio map building remarkably, but also achieve high positioning accuracy.

Interference can be utilized for energy harvest in simultaneous wireless information and power transfer (SWIPT) system. Therefore, we design a beamforming scheme for multiuser multiple-input single-output (MISO) downlink SWIPT system consisting of a multi- antenna base station (BS), and a set of single- antenna users. In the system, a novel joint beamforming and receiving scheme is designed for BS and users, where the former transmits either energy or information via time switching (TS), and users receive via hybrid TS and power splitting (TSPS). The aim is to minimize the total transmission power at BS by jointly designing energy beamforming, information beamforming vectors, transmit TS ratios and receive PS ratios under their given capacity constraints and harvested power constraints. The utility functions are non-concave and involved constraints are non-convex, so these problems are computationally troublesome. First, we investigate the sufficient and necessary condition for our problem. Next, we apply the semidefinite relaxation (SDR) to solve this problem. We also prove that our algorithm is indeed tight, in which case achieves its optimum solution. Simulation results confirm that the proposed scheme can significantly reduce the total power consumption over conventional alternatives.

Currently, monocular localization as a category of vision-based indoor localization methods has attracted more attention because of its application to indoor navigation and augmented reality. In a typical monocular localization system, the absolute position estimation is utilized to acquire the initial position of the query camera, and then the relative position estimation is employed to achieve subsequent camera positions. However, accumulative errors, i.e., localization drifts, caused by the relative position estimation seriously affect the localization performance. Therefore, a line model that contains a fitted line segment and some visual features is introduced, and the line model is used to seek inliers of the estimated features for the drift estimation. Based on the pre-constructed dense 3D map, a line model-based drift estimation method is presented to monitor accumulative errors. As a switching mechanism, the proposed method determines when the relative position estimation should switch to the absolute position estimation to correct user positions. Compared with the existing monocular localization methods, the proposed drift estimation method significantly reduces the accumulative errors, and the average errors are limited within a desirable range by giving a proper drift threshold. Experimental results demonstrate that the average localization errors of the proposed method are limited within 30 centimeters in various scenes by a 50-centimeters drift threshold.

In this paper, we investigate the resource allocation problem for D2D-based Vehicle-to- Everything (V2X) communication. There are two resource allocation modes for V2X sidelink communication: autonomous resource selection and scheduled resource allocation which is further divided into 2 categories-the dedicated mode and the reuse mode. Considering that each V2X message is arranged with the independent ProSe Per-Packet Priority (PPPP) and V2X sidelinks require stringent latency and reliability, therefore, in this paper, we propose a new joint autonomous resource selection and scheduled resource allocation approach to maximize the sum of the information values of all users. Our proposed scheme not only considers the user's PPPP but also takes into account the user's communication reliability, while guaranteeing the quality of service (QoS) of both Pedestrian user equipments (P-UEs) and Vehicle user equipments (V-UEs). A comprehensive performance evaluation is conducted to demonstrate the efficacy of the proposed scheme.

Recently, indoor localization technology has attracted the attention of many scholars and IT industry enterprises. The main problem that exists in the current algorithms is time latency and localization accuracy. We propose a fast image retrieval algorithm for vision-based indoor localization system. The proposed algorithm is based on PCA, Linear Regression and GIST when an image database is built. Gist feature of database image is processed as training data set. Through PCA the key information is extracted, then we fit a model by linear regression. We compare the performance of our algorithm with C-GIST algorithm. It will be also demonstrated that our proposed algorithm takes an average of 31.5 percent less time for image retrieval in coarse matching step, and the accuracy is better than C-GIST. The most prominent contribution is the results of our algorithm can be directly applied to the localization of a region of narrower width, and the latency of the algorithm have no relation to the size of the visual database.

WLAN indoor positioning system has a wide application prospect because it is entirely based on the network infrastructure and mobile terminals which are both prevalent in our daily life without the need of additional equipment. However, indoor multipath channel, signal randomly blocking, unstable transmission power would no doubt cause interference to the propagation of signal, which introduces noise to the radio map built in the offline phase, and further degrades the positioning accuracy. Therefore, in this paper, we propose a radio map noise reduction method by using Hankel matrix. Based on the special structure of Hankel matrix, we could effectively separate the noise from the signal. We perform the noise reduction separately on each Hankel matrix coming from different signal vectors but not the entire radio map. The experiment results indicate that the proposed method could achieve better noise reduction on the radio map and contribute good positioning performance.

In recent years, received signal strength (RSS) based indoor localization system using WLAN has attracted considerable attention. However, signal strength variations across diverse devices becomes a major problem in this system, especially in the crowdsourcing based localization system. In this paper, the linear regression algorithm is proposed to solve this problem automatically. First of all, the problem of device diversity and the adverse effects caused by this problem are analyzed. Then the intrinsic relationship between different RSS values collected by different devices is mined by the linear regression algorithm. The problem of device diversity will be handled by this algorithm. In crowdsourcing systems, when the major problem is eliminated, a unique radio-map can be created in the offline phase and the user's location can be estimated by a localization algorithm in the online phase. Experimental results show that the proposed method results in a higher reliability and localization accuracy.

Currently, WiFi positioning system based on fingerprint technology is becoming the first option for worldwide indoor positioning service. However, the biggest obstacle is the cost of radio map building when calibrating the reference point absolute position within the floor plan. An efficient method is to make use of pedestrian dead reckoning (PDR) by sampling the PDR trajectory as the reference point position. Unfortunately, it is valid only when the start point or/and the end point is known. Without knowing this information, the PDR trajectory sampling position is merely a relative position but not an absolute one, which could not be used to build the radio map, especially in the crowdsourcing way. In this paper, we propose an effective method to transform PDR trajectory position sampling from a relative one to an absolute one by matching PDR trajectory with the major possible trajectories within the floor plan. We introduce image processing method to match the PDR trajectory with all the major possible trajectories based on Hough transform, Harris corner detection. The simulation results show that the proposed method could succeed to provide the absolute position from the PDR trajectory, and offer a good radio map for localization in a crowdsourcing way.

Currently, with the rapid development of 4G, most urban outdoor areas have been well covered with LTE signal, which provides the possibility to use LTE on land for positioning navigation system as a substitute of global navigation satellite system (GNSS). Because of cost and implementation difficulty, Minimization of Drive Tests (MDT) was proposed in LTE Release 10. It utilizes the LTE user equipment to feedback the measurement report of reference signal receiving power (RSRP) for the network optimization, which makes the LTE fingerprint-based positioning system possible. However, due to the mobile diversity, user habit and complicated channel, the user equipment (UE) RSRP difference is a non-ignorable problem that affects the positioning accuracy both in the online phase and offline phase. Therefore, in this paper, we propose an UE RSRP difference elimination method based on Multidimensional Scaling (MDS) algorithm. By using the relative RSRP difference between each two mobile terminals, we eliminate the RSRP difference both in the offline phase and online phase. We implemented the proposed method in a dense urban area and evaluated its performance. The experiment results indicated that the proposed method could effectively eliminate the UE RSRP difference and improve the positioning accuracy for LTE fingerprint-based positioning system.

Because of power consumption in free space, the received radio frequency signal is weak, which leads to a lack of energy collection in simultaneously wireless information and power transfer system, namely SWIPT system. The PS receiving mode is commonly used in SWIPT system, but the circuit design of PS receiving mode is complex, and the power of received radio frequency signal is small. If the power is divided once again, it must lead to a less energy collection. Meanwhile, less energy collection will affect information transmission, and it's not benefit to the improvement of system sum rate. Aiming at above problems, this paper proposes a system sum rate optimization problem based on zero-forcing beamforming and time switching mode for SWIPT system. Beamforming technology is suitable for multi-antenna system, which can not only increase the signal intensity in the direction of antenna array, but also can reduce the intensity of interference signal. As a result, the system sum rate of communication network is improved. The receiving TS mode means that a unit time is divided into two time slots, one is energy time slot, and the other is information time slot. In the two time slot, the received radio frequency signal is all used for information demodulation or energy collection. In this paper, a system sum rate optimization problem is established, it's a complex problem and it can be solved by Lagrange relaxation method. The simulation results show that when transmission power of base station is certain, the method proposed in this paper can make the SWIPT system's sum rate and power collection increase, so as to optimize the rate-energy (R-E) curve.

In this paper, we propose a new joint mode selection and proportional fair scheduling scheme for Device-to-Device (D2D) communication in cellular networks. Our objective is to maximize the sum of all users' proportional fairness functions while guaranteeing the signal-to-interference-plus-noise ratio (SINR) requirements of both D2D and cellular links. In our scheme, we consider two communication mode options for D2D communication: the dedicated mode and the reuse mode. In order to improve the fairness of the D2D users (DUs), we allocate the dedicated radio resource to the D2D pair with a higher priority. Furthermore, we use the Hungarian Algorithm to implement the sub-optimal resource allocation for multiple D2D pairs in the reuse mode and their reuse cellular users (CUs). The simulation results demonstrate that both the system throughput and the system fairness experience significant increases in our proposed scheme.

In this paper, we propose an user-oriented graph based frequency allocation algorithm for downlink transmission in the heterogeneous network. As different users may use different communication service, they would have different data rate requirement. It is critical to fulfill all users' traffic demand before enlarging the system throughput. So, the optimization objective is simultaneously improving user satisfaction and guaranteeing proportionally fairness. In this paper, different traffic demands can be achieved by adjusting two factors: the users' signal-to- interference-plus-noise ratio (SINR) thresholds and the resource blocks allocated to them. We propose an improved graph based resource allocation algorithm to allocate enough resource to the users by dyeing each users several colors. To use the frequency resource efficiently and fulfill more users' satisfaction, we set different SINR threshold for each user by the genetic algorithm to build the optimal interference graph. Simulation results show that our algorithm provides high user satisfaction meanwhile guarantees a better fairness among users.

Currently, the indoor navigation has attracted lots of attention and been considered as the most prominent technology in the future. However, the biggest obstacle that hinders the promotion of the indoor navigation is the cost of building floor map. In the unknown indoor environment, it will take a lot of resources to acquire or build indoor floor map. Therefore, in this paper, in order to reduce the floor map building cost, we mainly propose to utilize the pedestrian dead reckoning (PDR) method through massive users' inertial measurement unit (IMU) data to build indoor floor map. We proposed a novel algorithm through the density analysis of massive crowdsourcing trajectories, and keep the most proper trajectories with the suitable hotspots to generate floor map. The proposed algorithm relies on the improved PDR algorithm based on the proposed indoor walking model. We have implemented the proposed algorithm in our lab and evaluated its performances. The simulation results show that the proposed algorithm could automatically generate the floor map in the unknown environments with lower cost, which would contribute a lot for the indoor navigation technology.

With the development of the economic and the popularity of smartphones, location-based service is receiving more and more attention. It can be used inside a building where GPS signals are often unavailable. Because of its low deployment cost, vision-based indoor localization is becoming popular in the complicated indoor environment. However, in order to increase the accuracy of indoor localization, the database should be as large as possible. But in online phase, the query image retrieving would be more time-consuming. Therefore, we propose a fast cluster-based GIST (C- GIST) image retrieval method to reduce the time overhead of image retrieval. Compared with the existing indoor localization system, the proposed method utilizing video data could reduce the computational complexity evidently, which is much more convenient. The experiment results show that the proposed method is applicable in the complicated indoor environment, whose localization error less than 2 meters is nearly 70%. The error performance of the proposed method is slightly worse than the traditional method. Nevertheless, the proposed method decreases the computational complexity of image retrieval significantly.

This paper considers an energy efficient (EE) maximization problem in a downlink cloud radio access network (CRAN), using the worst-case design criteria. We formulate the joint problem of robust EE and base station (BS) activation design by maximizing the worst-case EE under channel state information CSI uncertainty and per-BS power constraint, which is a nonconvex mixed-integer related fractional program and is difficult to solve. With the transformation of worst-case signal-to-interference-plus-noise ratio (SINR), we propose a single-stage branch-and-bound (BnB) algorithm to find the global optimal solution via solving a series of second-order cone programming (SOCP) problems for any given active BS set. Then, a heuristic BS activation (BSA) algorithm is proposed to choose active BSs successively to further improve the worst-case EE. Simulation results suggest that the BnB algorithm converges to the global optimum, and a higher EE can be achieved by selecting the active BSs properly.

This paper studies robust energy optimization for the cloud radio access network (C-RAN). The objective of this paper is to jointly minimize network power consumption through optimizing the base station (BS) mode, multi-user (MU)-BS association, and beamforming vectors given imperfect channel state information (CSI). To solve this non-trivial problem, we first transform the problem to a semi-definite programming (SDP) one using the S-lemma with the aid of the semi-definite relaxation technique, and then propose a SDP-based group sparse beamforming approach to solve it iteratively. Since the computational complexity of solving SDP problems is intractable, we propose to translate the uncertainty in the CSI to the uncertainty in its covariance matrix, and then recast the original problem as a mixed-integer second-order cone programming problem. We further propose a two-stage rank selection framework to determine the BS mode and MU-BS association separately and successively. Simulation results demonstrate the convergence of our proposed algorithms, and validate the effectiveness of the proposed algorithms in minimizing the network power consumption of the C-RAN.

Great efforts have been driven to improve the optimal decision for network selection, which is significant for vertical handover to meet user's rapidly increased requirements. Unfortunately, the instability of the basic decisive parameters is usually ignored, resulting in a high misjudgement ratio. Therefore, this paper proposed a novel parameter estimation algorithm to bridge the observed values and the inherent characters for the decisive parameters via Gaussian Hidden Markov Model. After trained by given parameter sequences in off-line phase, the parameter pre-estimated algorithm can reveal the parameters' inherent statuses in the on-line phase. Furthermore, the pre- estimated results can be regarded as normalized inputs for network selections. The simulation results show that the proposed algorithm could reduce the misjudgement ratio of the network selection algorithms, especially exhibiting a good performance for the low speed mobile users.

This paper investigates the power efficiency in downlink cooperative networks under spherical channel uncertainties, using a worst-case robust formulation. The aim of this paper is to jointly optimize the base station (BS) operation mode, mobile user (MU) association, and beamformings to achieve the robust network power gain. We formulate the problem as a mixed-integer non-convex problem with infinite number signal-to-interference-plus-noise ratio (SINR) and maximum transmit power constraints, which is nontrivial to solve even without this constraint. By firstly translating the uncertainty in CSI to the uncertainty in its covariance matrix, the SINR constraint spans a convex set using the Lagrangian based method. Due to the coupled integer variables, the complexity of exhaustive search grows exponentially with network size, motivating to develop low-complexity heuristic algorithms. We propose to solve the problem via a novel two-layer (TL) algorithm: inner and outer layer, successively. Specifically, the BS operation mode is determined successively in the outer layer using a Rank based Successive Search (RSS) method, while in the inner layer, an iterative Difference of Convex (DC) based algorithm is proposed to determine the MU association. Simulation results illustrate that the proposed algorithm can significantly reduce the total power consumption over existing alternatives.

Image based indoor localization is an important problem with many useful application. This paper proposes an indoor localization system for performing fine localization and less latency with more priori information, including tile angel and the relative height between camera optical center and origin in reference coordinate system (RCS). The system is divided into two stages: offline stage and online stage. SURF(Speed-Up Robust Features) technique and learning line method are performed by establishing an image database in offline stage, which makes preparation for next stage. In online stage, position and direction angle's estimation are performed by homography matrix and learning line. Compared with the performance of other related literature works, our method reduces the storage overhead of database, which indicates less latency in actual applications. What will be also demonstrated is that the proposed system can keep error in sub-meter range and get the fine direction angle of the device.

With the progress of science and technology, mobile phone become a necessary carryon in our daily life. Location service get more and more people's attention. But in the indoor environment, due to the block of walls and other factors, traditional positioning systems are often not available such as GPS. Now the visual-based indoor localization have become a popular research in recent years. We proposed an improvement of the original RANSAC method and apply it in the indoor visual-based localization. In our improving proposed method, we define a matching quality function to measure the matching quality of a matching pair of images. Through matching quality, we choose the 4 best matching pair to calculate the projective transform matrix of two images and eliminate false matching pairs. The most important improvement of proposed algorithm is sorting the matching pairs according to matching quality instead of random testing. The experiment result shows that the proposed method significantly reduce the iterations and running time, at the same time, improves the stability of the algorithm.

Visual-based indoor localization have become a favored research area in recent years. It can be used inside a building where GPS signals are often not available. And due to its low deployment cost, visual-based indoor localization has been implemented in the complicated indoor environment. However, in order to increase the accuracy of indoor localization, the scale of image database should be as large as possible. The process of building a database that can be used for indoor localization is laborious. Thus, we propose a fast visual map building method for visual-based indoor localization, which takes fully use of convenience of video stream. Compared with existing image-based indoor localization system, the proposed system utilize video data is much more convenient. The experimental results show that the proposed method is applicable in the complicated indoor environment. The possibility of localization error less than 2 meters is nearly 70%. The error performance of the proposed method is slightly worse than the traditional method. Nevertheless, the proposed method can dramatically decrease the complexity of building a visual map.

As some public buildings have become large in spatial scale, people find it more and more difficult to know their actual location in these buildings. Generally in the indoor environment, to get location information is relatively more complex than that in the outdoor environment, for traditional outdoor localization methods do not perform well in indoor environment. Under this circumstances, image based indoor localization is a meaningful research topic under many application scenarios. In this paper, we presented a method for performing image based localization using smart phones in narrow corridors. In the first step, image fingerprint database is established, and then features of every image in the database are extracted. Users' query image is compared with the database to retrieve matched images and corresponding feature points. After that, epipolar geometry is utilized so as to calculate the actual location of query image. Besides, we make localization experiments in corridors and compare the result to that of localization method based on wireless local area network in the same environment. In our work, we achieve a slightly better localization accuracy over a set of 51 query images taken in an environment of narrow corridors than the accuracy attained by using wireless local area network based method with the same environmental conditions.

In this paper, a joint control method for the user association and almost blank sub-frame (ABSF) based interference coordination (IC) technique in heterogeneous cellular networks is proposed. The optimization objective is simultaneously improving system throughput and guaranteeing proportionally fairness. Usually, user and base station (UE-BS) association is performed before ABSF rate determination, which is suboptimal with respect to UE- BS association. We formulate the joint optimization of UE-BS association and ABSF rate as a combinatorial optimization problem. To deal with it, we proposed an iterative method to discover the ABSF rate, the optimal association and the optimal resource blocks(RBs) allocation that can maximum long-term average throughput. Besides, as we cannot know the interference before user-association and resource allocation, we proposed a graph-based BS clustering method to estimate the interference before user-association and resource allocation. Simulation results show the efficiency of our proposed scheme.

Currently, WiFi indoor positioning system based on IEEE 802.11 is widely attractive for its free infrastructure and high localization performance. However, due to working on-demand strategy in green WiFi scenario, the access points are not always available for mobile when radio map is built in the offline phase. Radio map with unknown received signal strength is not valid for positioning and usually be replaced by the minimum value, which leads to poor positioning performance. In This paper we propose a radio map recovery method based on inexact augmented Lagrange multiplier (IALM) algorithm, which achieves to precisely recover the missing received signal strength in the radio map for those access points unavailable in the offline. By solving the nuclear norm minimization, the IALM algorithm could not only recover the missing received signal strength, but also reduce the noise effectively. We have implemented the proposed method in our lab and evaluated its performances. The experiment results indicate the proposed method could precisely recover the radio map and achieve good positioning performance.

Advanced interference coordination and resource management schemes are important in heterogeneous wireless networks in order to achieve a high network capacity and good user experience. In this paper, we present a joint consideration of the sub-channel allocation and power distribution in MIMO two-tier networks where the femtocells form clusters of equal size. All of the BSs have a multi-antenna configuration utilizing zero-forcing beamforming. The optimization objective maximizes the long-term system throughput and guarantees the fairness among Femto users (FUEs) in different tiers at the same time. After that, a modified genetic algorithm (GA) is addressed to resolve the mixed-integer nonlinear programming problem involved. Each chromosome in the proposed GA is divided into an integer string for sub-channel allocation, and a real number strings for power distribution. In addition, new initialization, crossover and mutation operations are employed for this new type of chromosomes. Conducted simulations show that the proposed GA can increase the throughput of the system and guarantee the fairness among the FUEs.

Crowdsourcing allows for rapid deployment of indoor localization systems. However, compared to the conventional methods, crowdsourcing might collect fewer received signal strength (RSS) values, hence result in greater influence to outliers in RSS values. In this paper, we propose an algorithm to detect such outliers and to substitute them with more suitable RSS values. In particular, we investigate the relationship of RSS values between adjacent locations using a signal propagation model and show that the outliers can be corrected using a signal propagation model. We propose the Signal Propagation-based Outlier Reduction Technic (SPORT) for identifying and adjusting outlier values in both the offline training phase and the online localization phase. Experimental results show that SPORT greatly smoothens the radio map and improves the location accuracy.

In Wireless Local Area Network (WLAN) positioning system, the most popular solution for RSS-based positioning is the fingerprinting architecture. In this paper, we present a novel algorithm, known as Semi-supervised Discriminant Embedding (SDE), to reconstruct a radio map by using real-time signalstrength values received at random points. Instead of deploying dense reference points, our approach takes advantage of less labeled data and partial unlabeled data to transform into lowerdimensional feature signals. Through solving the objective functions optimization, with strong discriminative features in Receive Signal Strength (RSS) are retained in the low-dimensional space. We conducted experiments in our office area with a realistic WLAN environment. Compared to the traditional methods, the experimental results show that the proposed algorithm has considerable accuracy improvement in the same positioning environment. Furthermore, the results also show the size of training samples can be greatly reduced in the proposed algorithm in order to achieve the similar accuracy of traditional approaches. That is, the cost of collecting fingerprints in the offline stage and calibrating database in the online stage are thus reduced.

There are some challenges associated with the spectrum sensing for cognitive radio (CR), one main challenge of spectrum sensing is hardware requirements. The paper proposes the energy detection and a direct-conversion spectrum sensing receiver (DCSSR) to respond to the challenge of hardware requirements. However, the main problem is DC offsets of the DCSSR. A DC offset adaptive energy detection (DC-AED) algorithm is proposed to resolve the problem. The paper gives the theory analysis of DC-AED and simulates its detection performance. The simulation results show that DC-AED can remove the influence of DC offsets perfectly while the change period of DC offsets is larger than 64 times the detection length.

Vehicular communication, as a novel fundamental platform for providing wireless access, is drawing more and more attentions in recent years. IEEE 802.11p is a main radio access technology which supports communication for high mobility terminals. Due to the small coverage, road side unit (RSU) is usually deployed coupling with cellular network to achieve seamless mobility. In cellular/802.11p heterogeneous network, vehicular communication has the characteristics of short span of time associate with wireless local area network (WLAN). Besides that, the media access control (MAC) scheme of WLAN leads to the issue that network throughput decreases dramatically with the increasing of user quantity. Therefore, vehicular mobility and network load should be seriously taken into account with respect to the feasibility of vertical handoff decision. In response to these compelling problems, we propose an analytical model for estimating the average achievable individual throughput, and further the theoretically optimal handoff threshold. Simulation results are demonstrated under different conditions in terms of vehicle's velocity and user quantity. The work in this paper can be a reference for the issue of whether and when to execute handoff procedure in vehicular heterogeneous network.

This paper investigates the problem of network selection in access control for heterogeneous networks of WCDMA/WLAN. To optimize the network selection decision policy, an optimization equation is defined with the objective of maximizing the total rewards, and then a new Q-learning Based Network Selection (QBNS) mechanism is proposed to solve the equation. In the QBNS Algorithm, Q-learning algorithm is employed by taking both of the network capacity and the quality of service (QoS) requirements of users into account. In addition, the network states are analyzed by considering interference power of WCDMA subnet and channel busyness ratio of WLAN subnet. Simulation results show that the proposed QBNS scheme can obtain lower call blocking probability and much higher total reward performance than the traditional Semi-Markov Decision Process (SMDP) Algorithm.

As a very popular positioning system, WLAN positioning attracts widely researches and investigations throughout the world. It implements the fingerprint technique to realize indoor navigation. The fingerprinting technique which employs the KNN algorithm has to make use of RSS (Received Signal Strength) from the Access Points (APs) without any classification. However, not all of the APs provide the contributions but interference, which will not only burden the positioning system but also results in poor positioning accuracy. To increase the positioning accuracy and decrease the computation cost of Wireless Local Area Network (WLAN), a novel algorithm is proposed by implementing the KNN algorithm and Information Gain Theory to bridge the gap between the Access Point selection and positioning accuracy. The experiment results indicates that, the positioning accuracy is improved by 4.76% within 2m, and meanwhile the time for positioning is decreased by 23.81%, which means the proposed algorithm successfully achieves higher positioning accuracy with less computational cost. Besides, it is also proved in this paper that contrary to the traditional concept, more APs do not always mean higher positioning accuracy; on the opposite, in our experimental environment, a relatively small scale of APs can achieve higher positioning accuracy than that of large scale of APs.

As the most competitive and cost-efficient localization technology, the map-aided fingerprinting-assisted positioning has drawn a large body of attentions during the past decade. There are normally two phases, the off-line and on-line phases, and the construction of radio map in the off-line phase will significantly influence the location accuracy in the on-line phase. However, the radio map which describes the mapping relationship between the physical positions of the reference points (RPs) and the recorded radio signal strength (RSS) or signal to noise ratio (SNR) always involves the cumbersome collection work. Further, thousands of Wi-Fi access points (APs) remaining idle will also bring serious concerns of the power consumption in the future. Therefore, in response to these compelling problems, we present a preliminary analysis towards the probabilistic location methods, develop the green global-greedy position estimation (3GPE) and introduce entropy deduction as a new metric for performance evaluation. Finally, our analytical expressions and simulation results with respect to the simple circle model and ideal indoor Wi-Fi regular environment demonstrate these issues and challenges.

Location estimation using received signal strength (RSS) in pervasively available Wi-Fi infrastructures has been considered as a popular indoor positioning solution. However, accuracy deterioration due to uncertainty of RSS and offline manual calibration cost limit the deployment of Wi-Fi positioning systems. This paper proposes a signal perturbation technique to enhance existing support vector regression (SVR) based Wi-Fi positioning. By signal perturbation, more RSS training samples are generated, thus enhancing the generalization ability of SVR. In addition, access point (AP) selection method is applied to reduce the input dimension by discarding the redundant APs. The proposed method is compared with previous classical methods in a real wireless indoor environment. Experimental results show that the proposed method improves accuracy while reducing calibration cost.

A critical challenge for mobile ad hoc networks is the design of efficient routing protocols that are able to provide high bandwidth utilization and desired fairness in mobile wireless environment without any fixed communication establishments. While extensive efforts have already been devoted to providing optimization based, distributed congestion elusion schemes for efficient bandwidth utilization and fair allocation in both wired and wireless networks, a common assumption therein is fixed link capacities, which will unfortunately limit the application scope in mobile ad hoc networks where channels are ever changing. In this paper, an effective congestion elusion scheme is presented explicitly based on ant colony algorithm for mobile ad hoc networks, which will explore the optimal route between two nodes promptly, meanwhile forecast congestion state of the link. Accordingly, a new path will be found rapidly to have the flow spread around to relieve the congestion state. Compare with OLSR, the scheme proposed here will greatly reduce the packet loss ratio and the average end-to-end delay at the same time, which illustrate that it will make use of networking resource effectively.

The laborious collection of location fingerprints, that could also potentially change with time, remains a hurdle towards the widespread deployment of indoor and campus area positioning using WiFi. In this paper, we present a preliminary analysis of the complicated relationship between distance in signal space, the physical distance and location errors towards better guidelines for fingerprint collection. We introduce the idea of entropy of location fingerprints and investigate the relationships between physical and signal distances, entropy, and expected errors with positioning using location fingerprinting. We present results with no access point and one access point and consider variations in the density of reference points and the standard deviation of signal strength to illustrate the issues.

This paper proposes the optimal K nearest neighbors (KNN) positioning algorithm via theoretical accuracy criterion (TAC) in wireless LAN (WLAN) indoor environment. As far as we know, although the KNN algorithm is widely utilized as one of the typical distance dependent positioning algorithms, the optimal selection of neighboring reference points (RPs) involved in KNN has not been significantly analyzed. Therefore, in order to fill this gap, the optimal KNN positioning algorithm based on the best TAC is introduced. And this algorithm is beneficial to construct the reliable WLAN indoor positioning system and provide the efficient location based services (LBSs). The relationship among theoretical expectation accuracy, unit interval of neighboring RPs and dimensions of target location region is also revealed. Furthermore, the feasibility and effectiveness of optimal KNN positioning algorithm are verified based on the experimental comparisons respectively in the regular office room, straight corridors, static positioning and dynamic tracking situations.