Multi-view point cloud registration remains a significant challenge in 3D computer vision due to the sensitivity to outliers. In this paper, we propose a clustering multi-view point cloud registration algorithm based on kernel mean p-power error (KMPE) loss function. Firstly, the 3D point clouds are clustered by using the K-means algorithm, where the centroids of the clusters are regarded as the model point cloud for multi-view point cloud registration. Secondly, the model point cloud is used to estimate the rigid transformation of each point cloud sequentially. Considering that the KMPE loss function can efficiently suppress outliers, a robust point cloud rigid registration optimization model based on the KMPE loss function is established. The Levenberg-Marquardt (LM) algorithm is adopted to optimize the optimization model to obtain the optimal rigid transformation between each point cloud and the model point cloud. Finally, the clustering and KMPE-based rigid transformation estimation are iteratively and alternatively applied to all point clouds to realize multi-view point cloud registration. Experimental results demonstrate that the proposed algorithm can effectively suppress the influence of outliers on the multi-view point cloud registration of, and can achieve high accuracy of multi-view point cloud registration.

Intended to boost the simplification precision of 3D point clouds and alleviate the cavity issue caused by overemphasis on feature components, this study puts forward a 3D point cloud simplification method that combines partition strategy and information, with the simplification rate being controllable. Using the partitioning strategy, the original point cloud is divided into three regions: edge points, feature points, and non-feature points. and the three regions are simplified respectively, and the points with a large amount of information are retained by calculating and updating the mutual information amount of each point in each region. The results demonstrate that the proposed method can effectively enhance the accuracy of simplified point clouds across various types of datasets, and maintain good simplification accuracy even when the simplification rate is large.

Mobile devices have increasingly integrated with numerous deep learning-based visual applications, such as object classification and recognition models. While these models perform well in controlled environments, their effectiveness declines in real-world environment due to out-of-distribution (OOD) data not seen during training. Existing methods for detecting OOD data often compromise normal data recognition and require extensive training on unattainable OOD data. To address these issues, we propose$\mathtt {POD}$, a framework designed to enhance mobile visual applications by providing high-precision OOD detection without affecting original model performance. In the offline phase,$\mathtt {POD}$generates OOD detectors from any classification model by analyzing model's neuron responses to various data types. In the online phase, it continuously adjusts decision boundaries by integrating results from both the original model and the detector. Evaluated on two public datasets and one self-collected dataset across various popular classification models,$\mathtt {POD}$significantly improves OOD detection performance while maintaining the accuracy of original models.

With the recent development of AIoT (AI+IoT), crowdsourcing has emerged as a promising paradigm for distributed problem solving and business practice. Crowdsourcing entails posting tasks on a dedicated Web platform, enabling networked workers to choose preferred tasks on a first-come, first-served basis, typically of the same type to ensure high assignment accuracy. However, existing crowdsourcing task assignment methods do not take into account the potential fatigue of workers for similar tasks. In this article, we propose a task assignment architecture using a (TA-GAE), which comprehensively considers the relationship between the occupation and skills of workers and potential tasks, facilitating an accurate assignment of a wide variety of tasks to workers. The proposed architecture consists of three modules, The Graph Creation module analyzes the potential connections between tasks based on worker evaluations and constructs an initial task graph that represents these connections. The gravity-based graph autoencoder module is inspired by Newton’s law of universal gravitation. We analogize the tasks on the crowdsourcing platform to masses in the universe and calculate the mutual attractive force between two tasks to quantify their correlation. The Hybrid Task Assignment module recommends task lists to workers by combining traditional collaborative filtering and content-based task assignment strategies. The experimental results demonstrate that the proposed architecture outperforms several state-of-the-art methods and achieves a diversity rate of over 40% across four data sets: 1) fliggy trip; 2) MovieLens 1M; 3) library; and 4) survey.

Federated learning (FL) is an emerging distributed and privacy-preserving machine learning framework. However, the performance of traditional FL methods is seriously impaired by the real-world data, which appear to be non-IID. The recent clustered federated learning (CFL) methods eliminate the impact of non-IID data by grouping clients with similar data distribution into the same cluster. Unfortunately, existing CFL methods heavily rely on the pre-setting of the cluster number, failing to achieve adaptive client clustering. We also experimentally observe that imbalanced data largely degrade their correctness of client clustering. In this paper, we present a novel CFL method without manual intervention, named AutoCFL, which can eliminate both effects of non-IID and imbalanced data simultaneously. To deal with imbalanced data, the local training adjustment strategy adaptively adjusts the number of local training epochs for each client. To further improve the clustering correctness and adaptability, the weighted voting-based client clustering strategy automatically groups each client into an appropriate cluster. Extensive experiments are conducted to evaluate the design of AutoCFL with three popular datasets under various data settings. Experimental results demonstrate that AutoCFL outperforms state-of-the-art methods, e.g., on average improving model accuracy by 9.24%, while reducing communication costs by 4.67 in an adaptive manner.

Clustered federated learning (CFL) has proved to be an effective way to alleviate the non-IID (not independently and identically distributed) data challenge, which severely restricts the wider application of federated learning. However, existing approaches either lack adaptability,i.e., they require an additional number of clusters as a guide when clustering, or lack effectiveness in terms of communication. In this paper, we explore the differences in the ability of different layers in a model to represent non-IID data, and propose a hierarchical CFL approach, namedHiCFL, which considers both adaptivity and communication efficiency. The improvement of communication efficiency is due to our proposed novel concept of model stability, which characterizes the variation of model weights during training. Based on model stability,HiCFLcan find the proper time to bi-partition the clusters of mobile devices in a hierarchical manner more quickly. We conduct extensive experiments based on popular datasets with various non-IID data settings. The results show thatHiCFLachieves excellent performance effectiveness and efficiency. Compared to state-of-the-art approaches,HiCFLcan improve the model accuracy by$2.0\% \sim 9.0\%$, while reducing the communication overheads by$27.3\% \sim 80.6\%$.

Air quality monitoring is important to the green development of smart cities. Several technical challenges exist for intelligent, high-precision monitoring, such as computing overhead, area division, and monitoring granularity. In this article, we propose a fine-grained air quality monitoring system based on visual inspection analysis embedded in unmanned aerial vehicle (UAV), referred to as AQMon . This system employs a lightweight neural network to obtain an accurate estimate of atmospheric transmittance in visual information while reducing computation and transmission overhead. Considering that air quality is affected by multiple factors, we design a dynamic fitting approach to model the relationship between scattering coefficients and PM2.5 concentration in real time. The proposed system is evaluated using public datasets and the results show that AQMon outperforms four existing methods with a processing time of 13.8 ms.

Gesture detection based on radio frequency signals has gained increasing popularity in recent years due to several benefits it has brought, such as eliminating the need to carry additional devices and providing better privacy. In traditional methods, significant breakthroughs have been made to improve recognition accuracy and scene robustness, but the limited computing power of edge devices (the first-level equipment to receive signals) and the requirement of fast response for detection have not been adequately addressed. In this article, we propose a lightweight Wi-Fi gesture recognition system, referred to as WiFine, which is designed and implemented for deployment on low-end edge devices without the use of any additional high-performance services in the process. Toward these goals, we first design algorithms for phase difference selection and amplitude enhancement, respectively, to tackle the problem of data drift caused by user change. Then, we design a cross-dimension fusion method to extract features of finer granularity from information of different dimensions, thus solving the precision problem of feature granularity. Finally, we design a lightweight neural network architecture by leveraging redundancy to reduce computational cost while ensuring satisfactory recognition accuracy. Extensive experimental results show that the proposed system achieves fast recognition of various actions with an accuracy up to 96.03% in 0.19 seconds.

Gesture detection based on WiFi signals does not require users to carry additional equipment, and can better protect the privacy of users during the detection process, so it has received widespread attention. However, the existing work does not consider the actual deployment of the platform, and ignores the requirements for the computing power of the platform and the actual reasoning delay, resulting in many methods that are not suitable for the use of edge devices. In this paper, we propose a WiFi gesture detection system, named WiRN, which is fully deployed on edge devices and does not require the participation of additional computing devices. In WiRN, We have proposed solutions to related problems. First of all, in order to solve the problem of large differences in multiple phase differences obtained in different scenarios due to over-sensitive phases and to improve the robustness and universality of the system, we propose a multi-antenna-based phase difference selection algorithm to find the most suitable phase difference. Then, we fuse the amplitude and phase difference of different dimensions and obtain more fine-grained input data to solve the problem of the inability to deploy complex neural networks to fully extract features due to the limitation of edge device computing power, so that the input data contains richer feature information. In this way, for the first time, we will improve the accuracy of network classification from the data source. We evaluated the system through a series of experiments, and the results showed that under the premise of satisfying the real-time calculation of edge devices, we achieved the same accuracy as the existing complex network by using the simplest two-layer neural network. The recognition accuracy of about 93% is achieved in different environments.

Operating in unlicensed ISM bands, ZigBee devices often yield poor performance due to the interference from ever increasing wireless devices in the 2.4 GHz band. Our empirical results show that, a specific interference is likely to have different influence on different outbound links of a ZigBee sender, which indicates the chance of concurrent transmissions. Based on this insight, we propose Smoggy-Link, a practical protocol to exploit the potential concurrency for adaptive ZigBee transmissions under harsh interference. Smoggy-Link maintains an accurate link model to quantify and trace the relationship between interference and link qualities of the sender's outbound links. With such a link model, Smoggy-Link can translate low-cost interference information to the fine-grained spatiotemporal link state. The link information is further utilized for adaptive link selection and intelligent transmission schedule. We implement and evaluate a prototype of our approach with TinyOS and TelosB motes. The evaluation results show that Smoggy-Link has consistent improvements in both throughput and packet reception ratio under interference from various interferers.

Crowdsensing tasks are usually described by certain features or attributes, and the task assignment essentially performs a matching with respect to the worker or user's preference on these features. However, the existing matching strategy could lead to a misaligned task coverage problem, i.e., some popular tasks tend to enter workers' candidate task lists, while some less popular tasks could be always unsuccessfully assigned. To ensure task coverage after the assignment, the system may have to increase their biding costs to reassign such tasks, which causes a high operational cost of the crowdsensing system. To address this problem, we propose to migrate certain qualified workers to the less popular tasks for increasing the task coverage and meanwhile, optimize other performance factors. By doing this, other performance factors, such as task acceptance and quality, can be comparably achieved as recent designs, while the system cost can be largely reduced. Following this idea, this article presents cTaskMat, which learns and exploits workers' task preferences to achieve coverage-ensured task assignments. We implement the cTaskMat design and evaluate its performance using both real-world experiments and data set-driven evaluations, also with the comparison with the state-of-the-art designs.

Drowsiness detection is critical to driver safety, considering thousands of deaths caused by drowsy driving annually. Professional equipment is capable of providing high detection accuracy, but the high cost limits their applications in practice. The use of mobile devices such as smart watches and smart phones holds the promise of providing a more convenient, practical, non-invasive method for drowsiness detection. In this article, we propose a real-time driver drowsiness detection system based on mobile devices, referred to as dWatch, which combines physiological measurements with motion states of a driver to achieve high detection accuracy and low power consumption. Specifically, based on heart rate measurements, we design different methods for calculating heart rate variability (HRV) and sensing yawn actions, respectively, which are combined with steering wheel motion features extracted from motion sensors for drowsiness detection. We also design a driving posture detection algorithm to control the operation of the heart rate sensor to reduce system power consumption. Extensive experimental results show that the proposed system achieves a detection accuracy up to 97.1% and reduces energy consumption by 33%.

Mobile sensing is a promising sensing paradigm in the era of Internet of Things (IoT) that utilizes mobile device sensors to collect sensory data about sensing targets and further applies learning techniques to recognize the sensed targets to correct classes or categories. Due to the recent great success of deep learning, an emerging trend is to adopt deep learning in this recognition process, while we find an overlooked yet crucial issue to be solved in this paper - The size of deep learning models should be sufficiently large for reliably classifying various types of recognition targets, while the achieved processing delay may fail to satisfy the stringent latency requirement from applications. If we blindly shrink the deep learning model for acceleration, the performance cannot be guaranteed. To cope with this challenge, this paper presents a compact deep neural network architecture, namely cDeepArch. The key idea of the cDeepArch design is to decompose the entire recognition task into two lightweight sub-problems: context recognition and the context-oriented target recognitions. This decomposition essentially utilizes the adequate storage to trade for the CPU and memory resource consumptions during execution. In addition, we further formulate the execution latency for decomposed deep learning models and propose a set of enhancement techniques, so that system performance and resource consumption can be quantitatively balanced. We implement a cDeepArch prototype system and conduct extensive experiments. The result shows that cDeepArch achieves excellent recognition performance and the execution latency is also lightweight.

Mobile sensing is a promising sensing paradigm that utilizes mobile device sensors to collect sensory data about sensing targets and further applies learning techniques to recognize the sensed targets to correct classes or categories. Due to the recent great success of deep learning, an emerging trend is to adopt deep learning in this recognition process, while we find an overlooked yet crucial issue to be solved in this paper - The size of deep learning models should be sufficiently large for reliably classifying various types of recognition targets, while the achieved processing delay may fail to satisfy the stringent latency requirement from applications. If we blindly shrink the deep learning model for acceleration, the performance cannot be guaranteed. To cope with this challenge, this paper presents a compact deep neural network architecture, namely cDeepArch. The key idea of the cDeepArch design is to decompose the entire recognition task into two lightweight sub-problems: context recognition and the context-oriented target recognitions. This decomposition essentially utilizes the adequate storage to trade for the CPU and memory resource consumptions during execution. In addition, we further formulate the execution latency for decomposed deep learning models and propose a set of enhancement techniques, so that system performance and resource consumption can be quantitatively balanced. We implement a cDeepArch prototype system and conduct extensive experiments. The result shows that cDeepArch achieves excellent recognition performance and the execution latency is also lightweight.

Device-free passive detection is an emerging technology to detect whether there exist any moving entities in the areas of interest without attaching any device to them. It is an essential primitive for a broad range of applications including intrusion detection for safety precautions, patient monitoring in hospitals, child and elder care at home, and so forth. Despite the prevalent signal feature Received Signal Strength (RSS), most robust and reliable solutions resort to a finer-grained channel descriptor at the physical layer, e.g., the Channel State Information (CSI) in the 802.11n standard. Among a large body of emerging techniques, however, few of them have explored the full potential of CSI for human detection. Moreover, space diversity supported by nowadays popular multiantenna systems are not investigated to a comparable extent as frequency diversity. In this article, we propose a novel scheme for device-free PAssive Detection of moving humans with dynamic Speed (PADS). Both full information (amplitude and phase) of CSI and space diversity across multiantennas in MIMO systems are exploited to extract and shape sensitive metrics for accuracy and robust target detection. We prototype PADS on commercial WiFi devices, and experiment results in different scenarios demonstrate that PADS achieves great performance improvement in spite of dynamic human movements.

Smartphone theft is a non-negligible problem that causes serious concerns on personal property and privacy. The existing solutions to this problem either provide only functions like retrieving a phone, or require dedicated hardware to detect thefts. How to protect smartphones from being stolen at all times is still an open problem. In this paper, we propose iGuard, a real-time anti-theft system for smartphones. iGuard utilizes only the inertial sensing data from the smartphone. The basic idea behind iGuard is to distinguish different people holding a smartphone, by identifying the order of the motions during the `take-out' behavior andhoweach motion is performed. For this purpose, we design a motion segmentation algorithm to detect the transition between two motions from the noisy sensing data. We then leverage the distinct feature contained in each sub-segment of a motion to estimate the probability that the motion is performed by the smartphone owner himself/herself. Based on such pre-processed data, we propose a Markov based model to track the behavior of a smartphone user. According to this model, iGuard instantly alarms once the tracked data deviate from the smartphone owner's usual habit. We implement iGuard on Android and evaluate its performance in real environments. The experimental results show that iGuard is accurate and robust in various scenarios.

The augmented reality(AR) technology can enrich a person actual life, and is attracting more and more attention. However, the tradition methods have some problems such as high time delay, high deployment cost and low accuracy. These problems greatly hinder the AR technology to ubiquitous applications. In this paper, we design a AR system deployed on mobile devices, named SpeAR, which leveraging the feature matching algorithm to fast recognize the object with high accuracy. In SpeAR, we employ the depth camera embedded in the mobile device to obtain the distance between mobile device and object to use as a feature of this object image. For the accurately matching, the flutter-free algorithm will be designed to extract more accurate image feature. For the fast matching, the shrunken SURF(sSURF) is proposed to match images combining the distance feature. We have implemented a prototype system to evaluate the actual performance. The experiment results show that our solution achieves an 141ms delay in object recognition in this system.

Smartphone theft is a non-negligible problem that causes serious concerns on personal property, privacy, and public security. The existing solutions to this problem either provide only functions like retrieving a phone, or require dedicated hardware to detect thefts. How to protect smartphones from being stolen at all times is still an open problem. In this paper, we propose iGuard, a real-time anti-theft system for smartphones. iGuard utilizes only the inertial sensing data from the smartphone. The basic idea behind iGuard is to distinguish different people holding a smartphone, by identifying the order of the motions during the `take-out' behavior and how each motion is performed. For this purpose, we design a motion segmentation algorithm to detect the transition between two motions from the noisy sensing data. We then leverage the distinct feature contained in each sub-segment of a motion, instead of the entire motion, to estimate the probability that the motion is performed by the smartphone owner himself/herself. Based on such pre-processed data, we propose a Markov Chain based model to track the behavior of a smartphone user. According to this model, iGuard instantly alarms once the tracked data deviate from the smartphone owner's usual habit. We implement iGuard on Android and evaluate its performance in real environments. The experimental results show that iGuard is accurate and robust in various scenarios.

Device-free localization (DFL), which does not require any devices to be attached to target(s), has become an appealing technology for many applications, such as intrusion detection and elderly monitoring. To achieve high localization accuracy, most recent DFL methods rely on collecting a large number of received signal strength (RSS) changes distorted by target(s). Consequently, the incurred high energy consumption renders them infeasible for resource-constraint networks, such as wireless sensor networks. This paper introduces an energy-efficient framework for high-precision multi-target-adaptive device-free localization (E-HIPA). Compared with the existing methods, E-HIPA demands fewer transceivers, applies the compressive sensing (CS) theory to guarantee high localization accuracy with less RSS change measurements. The motivation behind the proposed E-HIPA is the sparse nature of multi-target locations in the spatial domain. Before taking advantage of this intrinsic sparseness, we theoretically prove the validity of the proposed CS-based framework problem formulation. Based on the formulation, the proposed E-HIPA primarily includes an adaptive orthogonal matching pursuit (AOMP) algorithm, by which it is capable of recovering the precise location vector with high probability, even for a more practical scenario with unknown target number. Experimental results via real testbed demonstrate that, compared with the previous state-of-the-art solutions, i.e., RTI, SCPL, and RASS approaches, E-HIPA reduces the energy consumption by up to 69 percent with meter-level localization accuracy.

Many emerging applications driven the fast development of the device-free localization (DfL) technique, which does not require the target to carry any wireless devices. Most current DfL approaches have two main drawbacks in practical applications. First, as the pre-calibrated received signal strength (RSS) in each location (i.e., radio-map) of a specific area cannot be directly applied to the new areas, the manual calibration for different areas will lead to a high human effort cost. Second, a large number of RSS are needed to accurately localize the targets, thus causes a high communication cost and the areas variety will further exacerbate this problem. This paper proposes FitLoc, a fine-grained and low cost DfL approach that can localize multiple targets over various areas, especially in the outdoor environment and similar furnitured indoor environment. FitLoc unifies the radio-map over various areas through a rigorously designed transfer scheme, thus greatly reduces the human effort cost. Furthermore, benefiting from the compressive sensing theory, FitLoc collects a few RSS and performs a fine-grained localization, thus reduces the communication cost. Theoretical analyses validate the effectivity of the problem formulation and the bound of localization error is provided. Extensive experimental results illustrate the effectiveness and robustness of FitLoc.

Operating in unlicensed ISM bands, ZigBee devices often yield poor throughput and packet reception ratio due to the interference from ever increasing wireless devices in 2.4 GHz band. Although there have been many efforts made for interference avoidance, they come at the cost of miscellaneous overhead, which oppositely hurts channel utilization. Our empirical results show that, a specific interference is likely to have different influence on different outbound links of a ZigBee sender, which indicates the chance of concurrent transmissions. Based on this insight, we propose Smoggy-Link, a practical protocol to exploit the potential concurrency for adaptive ZigBee transmissions under harsh interference. Smoggy-Link maintains an accurate link model to describe and trace the relationship between interference and link quality of the sender's outbound links. With such a link model, Smoggy-Link can obtain fine-grained spatiotemporal link information through a low-cost interference identification method. The link information is further utilized for adaptive link selection and intelligent transmission schedule. We implement and evaluate a prototype of our approach with TinyOS and TelosB motes. The evaluation results show that Smoggy-Link has consistent improvements in both throughput and packet reception ratio under interference from various interferer.

Device-free localization (DfL) techniques, which can localize targets without carrying any wireless devices, have attracting an increasing attentions. Most current DfL approaches, however, have two main drawbacks hindering their practical applications. First, one needs to collect large number of measurements to achieve a high localization accuracy, inevitably causing a high deployment cost, and the areas variety will further exacerbate this problem. Second, as the pre-obtained Received Signal Strength (RSS) from each location (i.e., radio-map) in a specific area cannot be directly applied to new areas for localization, the calibration process of different areas will lead to the high human effort cost. In this paper, we propose, FitLoc, a fine-grained and low cost DfL approach that can localize multiple targets in various areas. By taking advantage of the compressive sensing (CS) theory, FitLoc decreases the deployment cost by collecting only a few of RSS measurements and performs a fine-grained localization. Further, FitLoc employs a rigorously designed transfer scheme to unify the radio-map over various areas, thus greatly reduces the human effort cost. Theoretical analysis about the effectivity of the problem formulation is provided. Extensive experimental results illustrate the effectiveness of FitLoc.

The low-cost crystal oscillators embedded in wireless sensor nodes are prone to be affected by their working condition, leading to undesired variation of clock skew. To preserve synchronized clocks, nodes have to undergo frequent re-synchronization to cope with the time-varying clock skew, which in turn means excessive energy consumption. In this paper, we propose DualSync, a synchronization approach for low-power wireless networks under dynamic working condition. By utilizing time-stamp exchanges and local measurement of temperature and voltage, DualSync maintains an accurate clock model to closely trace the relationship between clock skew and the influencing factors. We further incorporate an error-driven mechanism to facilitate interplay between Inter-Sync and Self-Sync, so as to preserve high synchronization accuracy while minimizing communication cost. We evaluate the performance of DualSync across various scenarios and compare it with state-of-art approaches. The experimental results illustrate the superior performance of DualSync in terms of both accuracy and energy efficiency.

Passive localization is fundamental for many applications such as activity monitoring and real-time tracking. Existing received signal strength (RSS)-based passive localization approaches have been proposed in the literature, which depend on dense deployment of wireless communication nodes to achieve high accuracy. Thus, they are not cost-effective and scalable. This paper proposes the RSS distribution-based localization (RDL) technique, which can achieve high localization accuracy without dense deployment. In essence, RDL leverages the RSS and the diffraction theory to enable RSS-based passive localization in sensor networks. Specifically, we analyze the fine-grained RSS distribution properties at a variety of node distances and reveal that the structure of the triangle is efficient for low-cost passive localization. We further construct a unit localization model aiming at high accuracy localization. Experimental results show that RDL can improve the localization accuracy by up to 50%, compared to existing approaches when the error tolerance is less than 1.5 m. In addition, we apply RDL to facilitate the application of moving trajectory identification. Our moving trajectory identification includes two phases: an offline phase where the possible locations can be estimated by RDL and an online phase where we precisely identify the moving trajectory. We conducted extensive experiments to show its effectiveness for this application - the estimated trajectory is close to the ground truth.

For a long history, stampede is one of the high potential disaster when thousands of people gathered. Current monitoring systems, however, can only detect the presence of a small number of sparsely located targets, rather than to monitor the change of people flow where there are large number of dense crowd in the environment. This paper presents DanSen, a low-cost people flow monitoring system for sensing the potential danger using the existing wifi infrastructures. Inspired by the dynamic light scattering (DLS) theory, the designed DanSen calculates the correlations between the initial channel state information (CSI) data and all the history CSI data to monitor the changes of people flow and also estimates the sharpness of the changes. By doing so, DanSen can be utilised to perceive the potential danger. Real-world experimental results illustrate the advantage and effectiveness of DanSen.

Many emerging applications and the ubiquitous wireless signals have accelerated the development of Device Free localization (DFL) techniques, which can localize objects without the need to carry any wireless devices. Most traditional DFL methods have a main drawback that as the pre-obtained Received Signal Strength (RSS) measurements (i.e., fingerprint) in one area cannot be directly applied to the new area for localization, and the calibration process of each area will result in the human effort exhausting problem. In this paper, we propose FALE, a fine-grained transferring DFL method that can adaptively work in different areas with little human effort and low energy consumption. FALE employs a rigorously designed transferring function to transfer the fingerprint into a projected space, and reuse it across different areas, thus greatly reduce the human effort. On the other hand, FALE can reduce the data volume and energy consumption by taking advantage of the compressive sensing (CS) theory. Extensive real-word experimental results also illustrate the effectiveness of FALE.

This poster introduces MoveLoc, a system that locates a moving object without carrying any devices from detecting Doppler shifts reflected off the moving object. It works even if the object walking or running in different directions without any training. MoveLoc does not require the user to carry any communication devices, yet its accuracy exceeds current moving object localization systems using Radio Signal Strength(RSS). We implement the system and evaluate the performance on moving person by experiments. Experimental result shows that MoveLoc achieves an average location accuracy of 0.69 meters and reduces the equipment deployment density compared with other known wireless moving object localization approaches using RSS.

Without relying on devices carried by the target, device-free localization (DFL) is attractive for many applications, such as wildlife monitoring. There still exist many challenges for DFL for multiple targets without dense deployment of sensor nodes. To fit the gap, in this paper, we propose a multi-target localization method based on compressive sensing, named LCS. The key observation is that given a pair of nodes, the received signal strength (RSS) will be different when a target locates at different locations. Taking advantage of compressive sensing in sparse recovery to handle the sparse property of the localization problem, (i.e., the vector which contains the number and location information of k targets is an ideal k-sparse signal), we presented a scalable compressive sensing based multiple target counting and localization method i.e., LCS, and rigorously justify the validity of the problem formulation. The results from our realistic deployment in a 12m×12m open space are promising. For 12 people with 24 nodes, the worst localization error ratio and counting error ratio of our LCS is no more than 8.3% and 33.3% respectively.

The Radio Signal Strength Indicator (RSSI)-based localization algorithm is an effective solution for passive object localization. However, the localization accuracy of the existing methods highly depends on the transceiver distance and deployment density. Generally speaking, to obtain higher accuracy, we need a denser sensor node deployment, which results in a higher deployment cost and more communication overheads. In this paper, we investigate this problem based on extensive measurements. According to the measurement results, we propose to localize objects using an RSSI distribution based localization (RDL) model to identify the object location by different RSSI distributions of the communicating links. Experimental results show that the RDL method can achieve higher localization accuracy with less sensor nodes.

In this demo, we design a monitoring system based on sensor network for zoologists to research the activity budget of Rhinopithecus roxellana. We designed the hardware platform of the system for wild deployment and presented an analysis of the relationship between environmental factors and survival conditions of Rhinopithecus roxellana. The system can locate and track Rhinopithecus roxellana by individual identification. The prototype has been deployed in Wildlife Reserve of Qiling Mountain, China.