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Two functions are essential and necessary for the wireless-powered communication network, which are energy beamforming and localization. On one hand, energy beamforming controls the wireless energy waves of the energy access point (E-AP) in order to activate the nodes for transmitting information. On the other hand, locating the nodes is important to network management and location-based services in the wireless power communication network (WPCN). For a large-scale network, cooperative localization that employs neighborhood nodes to participate in positioning unknown target nodes is highly accurate and efficient. However, how to use energy beamforming to achieve highly accurate localization is not fully investigated yet. In this article, we analyze the impacts of energy beamforming on the cooperative localization performance of WPCNs. We formulate the Fisher information matrix (FIM) and the corresponding Cramer-Rao lower bound (CRLB) for the full connected network and a single node, respectively. Then, we propose beamforming schemes to optimize the cooperative localization and the power consumption. For optimal localization problems, we derive the closed-form expression of the optimal energy beamforming. For the optimal energy efficiency problems, we propose semidefinite programming (SDP) solutions to achieve the minimum power consumption while using calibrations to approach the actual localization requirements. Further, we also analyze the impacts of channel uncertainty. Through extensive simulations, the results demonstrate the dominant factors of the localization performance, and the performance improvements of our proposed schemes, which outperform the existing power allocation schemes.

Analyzing blockchain protocols is a notoriously difficult task due to the underlying large scale distributed networks. To address this problem, stochastic model-based approaches are often utilized. However, the abstract models in prior work turn out not to be adoptable to consortium blockchains as the consensus of such a blockchain often consists of multiple processes. To address the lack of efficient analysis tools, we propose a queueing network-based method for analyzing consistency properties of consortium blockchain protocols in this article. Our method provides a way to evaluate the performance of the main stages in blockchain consensus. We apply our framework to the Hyperledger Fabric system and recover key properties of the blockchain network. Using our method, we analyze the security properties of the ordering mechanism and the impact of delaying endorsement messages in consortium blockchain protocols. Then an upper bound is derived of the damage an attacker could cause who is capable of delaying the honest players’ messages. Based on the proposed method, we employ analytical derivations to investigate both the security and performance features, and corroborate close agreement with measurements on a wide-area network testbed running the Hyperledger Fabric blockchain. With the proposed method, designers of future blockchains can provide a more rigorous analysis of their consortium blockchain schemes.

With the proliferation of compute-intensive and delay-sensitive mobile applications, large amounts of computational resources with stringent latency requirements are required on Internet-of-Things (IoT) devices. One promising solution is to offload complex computing tasks from IoT devices either to mobile-edge computing (MEC) or mobile cloud computing (MCC) servers. MEC servers are much closer to IoT devices and thus have lower latency, while MCC servers can provide flexible and scalable computing capability to support complicated applications. To address the tradeoff between limited computing capacity and high latency, and meanwhile, ensure the data integrity during the offloading process, we consider a blockchain scenario where edge computing and cloud computing can collaborate toward secure task offloading. We further propose a blockchain-enabled IoT-Edge-Cloud computing architecture that benefits both from MCC and MEC, where MEC servers offer lower latency computing services, while MCC servers provide stronger computation power. Moreover, we develop an energy-efficient dynamic task offloading (EEDTO) algorithm by choosing the optimal computing place in an online way, either on the IoT device, the MEC server or the MCC server with the goal of jointly minimizing the energy consumption and task response time. The Lyapunov optimization technique is applied to control computation and communication costs incurred by different types of applications and the dynamic changes of wireless environments. During the optimization, the best computing location for each task is chosen adaptively without requiring future system information as prior knowledge. Compared with previous offloading schemes with/without MEC and MCC cooperation, EEDTO can achieve energy-efficient offloading decisions with relatively lower computational complexity.

Magnetic MIMO is a wireless power transfer (WPT) system that employs multiple magnetic resonance coils to provide high efficient wireless power in the near field. Magnetic energy beamforming is a typical scheme to control the currents or voltages of the transmitter coils in order to achieve some objectives. Thus, the magnetic channel information is essential to magnetic beamforming (MagBF), and it needs complicated circuits and communication protocols to feedback such information. Such information may be not available due to the circuit limits or privacy concerns. In addition, the performance will be degraded with imperfect channel estimation in the noisy and mobile dynamic environment. In this case, only some limited feedback information is available, e.g., received power. In this article, we propose a random MagBF method to achieve maximum received power efficiency and simplify the system architecture. This scheme employs iterative Monte Carlo sampling and resampling to search an optimal beamforming solution based on the received power feedbacks. We design an online training protocol to implement the proposed scheme. It is computationally light and requires only limited feedback information, which avoids complex channel estimation or AC measurements. The simulation and real experimental results indicate that our algorithm can effectively increase the received power and approach the optimal performance with a fast convergent rate.

Using IoT devices to locate targets is widely applied in many scenarios. However, replacing the batteries of these devices is time and labor consuming. In this article, we propose a wireless power-driven positioning system (WP2S) that employs MIMO-based wireless power transfer access points to supply energy to batteryless anchors. In this case, the IoT localization devices will have unlimited power. We formulate the equivalent Fisher information matrix (EFIM) as a fundamental tool to analyze the system performance. Then, we propose resource allocation schemes for optimal location estimation and energy efficiency problems by relaxing the objectives as semidefinite programming problems. In addition, we also analyze the impacts of channel uncertainty, anchor uncertainty, and NLOS for the performances of location estimation and energy consumption. The robust algorithms are developed according to uncertainty models. Both the analysis and simulations demonstrate that the estimation accuracy relies heavily on the transmitted power and the uncertainty models will consume more power to meet the location requirements.

In coupled magnetic resonance (CMR) wireless energy transfer systems, the energy transfer power is low and the power transfer efficiency changes with the coil position. One reason for this reduction in power and efficiency is the impedance mismatching (IM) between the Tx and Rx coils, achieving impedance matching for multiple-input multiple-output (MIMO) CMR IM wireless power transmission (WPT) is quite complex due to the uncertainty in the number of coils and the interaction between coils. In this paper, we provide an analytical model of MIMO CMR which fully formulates the complex relationship between multiple Tx and Rx channels. Then, we design an impedance matching network (IMN) for MIMO CMR and derive an optimal IM solution. Base on this solution, we also develop an adaptive impedance matching scheme to control IMN, based on an automatic analysis of MIMO CMR system, the resulting control scheme achieves optimal values for transmission power and efficiency through IMN and coil selection. The simulation results indicate that the scheme is able to automatically adjust the impedance matching network according to the changes of the relative positions between Tx and Rx coils to achieve high energy transfer power and efficiency.

In this paper, we propose a logo-based positioning system. First, the system uses the designed DPOTSU algorithm to enable the monocular camera to be positioned at a long distance. Secondly, this system uses the camera ranging principle to estimate the distance and angle from the object to the logo. Finally, the system uses the designed logo to transmit the absolute position coordinates required for positioning. A solution for simultaneous high-precision positioning and long-distance has been developed.

Mobile phones and tablets are becoming the primary platform of choice. However, these systems still suffer from limited battery and computation resources. A popular technique in mobile edge systems is computing outsourcing that augments the capabilities of mobile systems by migrating heavy workloads to resourceful clouds located at the edges of cellular networks. In the multi-site scenario, it is possible for mobile devices to save more time and energy by offloading to several cloud service providers. One of the most important challenges is how to choose servers to offload the jobs. In this paper, we consider a multi-site decision problem. We present a scheme to determine the proper assignment probabilities in a two-site mobile-edge computing system. We propose an open queueing network model for an offloading system with two servers and put forward performance metrics used for evaluating the system. Then in the specific scenario of a mobile chess game, where the data transmission is small but the computation jobs are relatively heavy, we conduct offloading experiments to obtain the model parameters. Given the parameters as arrival rates and service rates, we calculate the optimal probability to assign jobs to offload or locally execute and the optimal probabilities to choose different cloud servers. The analysis results confirm that our multi-site offloading scheme is beneficial in terms of response time and energy usage. In addition, sensitivity analysis has been conducted with respect to the system arrival rate to investigate wider implications of the change of parameter values.

Magnetic MIMO is a new paradigm for near field and high efficiency wireless power transfer (WPT) system, and mutual inductive among coils is an essential factor to determine the power transfer efficiency which is difficult to attain in a mobile scenario. In this letter, we propose a neural network method using radial based function (RBF) to model the wireless power propagation of magnetic resonance MIMO. We use this model to detect the receiver and predict the received voltage. Experimental results indicate that the RBF model is highly accurate and it is an efficient tool for magnetic channel estimation.

Crowd sensing is an efficient way to collect heterogeneous information in the complicated infrastructures for fingerprinting based indoor localization. However, the information related to the dynamic trajectory are difficult to fuse due to the reliability issues from different devices and user moving habits. In this paper, we proposed a crowd sensing based indoor localization system with heterogeneous information fusion, which is called Wimage. Wimage can efficiently fuse multiple information sources related to location information, e.g., visual image, WiFi and geomagnetic data, even if the targets are moving with different and variable speeds. Then we design image-base subregion matching algorithm to locate the initial position and segmented weighted K-nearest neighbor algorithm to attain the matched trajectories in the database. A dynamic temporal warping algorithm is proposed for further calibrating the estimations. The experimental results indicate that with the helps from different kinds of information, the root mean square error is only below 0. 4m, which is highly accurate for locating a target in a large scale of indoor environment.

In this paper, we investigate a hybrid architecture of magnetic resonance multi-input multi-output (MIMO) and relay for wireless power transfer (WPT) network which both extend the power transfer distance and increases received power efficiency. We formulate the system model and derive the closed-form optimal beamforming solution. Then, we propose a hybrid transmitter (Tx) and relay coil placement algorithm, which is a linear searching scheme to find the optimal positions to provide the maximum uniform power distribution. The simulation results demonstrate that using the placement and beamforming algorithms can attain 2 to 5 times received power efficiency improvement, which is compared with equally assigned power on each Tx coil.

The range-based localization method is widely used in wireless sensor localization systems. Many existing localization algorithms are unbiased estimators. However, the estimation performance presents biased features in the real localization systems. On the other hand, many biased location estimators show some essential advantages over unbiased estimators, e.g., robust to the noise, more accurate estimation, and low complexity. In this paper, we deeply investigate the performance of biased estimator, min-max, to achieve a new accuracy limit, and propose a hybrid Kalman filtering algorithm, which recursively locates the target based on biased feature. The first contribution is that we formulate the biased Cramer-Rao lower bound of the min-max algorithm to indicate that the biased localization algorithm can outperform the unbiased algorithms, e.g., maximum likelihood, as if the estimation bias were attained. The second contribution is that we propose a hybrid Kalman filtering algorithm while employing the min-max to construct a constrain region and using the dynamic Gaussian model for calculation in non-Gaussian environments. Our algorithm is robust to complicated environments with high accuracy. And, we implement it in an IoT target tracking platform. Both theoretical analysis and experimental evaluation indicate that the proposed algorithm outperform the unbiased optimal estimation methods. And our algorithm can control the estimation error in only 1 m.

As radio-frequency (RF) based wireless energy harvesting technology can provide remote and continuous power to low-power devices, e.g., wireless sensors, it may be a substitute for batteries and extend the lifetime of the wireless sensor networks. In this paper, we propose a wireless energy harvesting localization system (WEHLoc), which contains batteryless wireless sensors as anchors and an energy access point (E-AP) to transfer power to the anchors. We consider a passive target localization scenario, in which the anchors monitor the target and send the sensed ranging data back to the E-AP. Additionally, we formulate the optimal estimation accuracy problem which is a 0–1 mixed-integer programming problem and relates to the energy beam, target transmitted power, and deployed anchor density. Then, we develop the power allocation scheme of the E-AP to solve the objective. In order to reduce the complexity, we propose a heuristic method that converts the maximum estimation accuracy problem into the energy efficiency problem and use linear programming to solve them. The simulations demonstrate that WEHLoc can be massively deployed in a wide area, and the estimation error and the power consumption are relatively low.

This student paper shows preliminary results on the possibility of using radar systems to identify small payloads carried by a commercial drone. This can be of great interest for traffic monitoring and situational awareness in near-future scenarios when drones are expected to be used for parcel delivery and many other applications. In this work, we consider a distributed and rather small payload, approximately 92g, carried by a commercial drone flying back and forth. Different combinations of features extracted from the micro-Doppler signatures and dwell time have been analyzed, showing promising results with median accuracy around 80% and maximum accuracy approaching 90% for the optimal case.

In a Magnetic Coupling Resonant Wireless Power Transfer (MCRWPT) system, adding relay coils is an effective way to increase the transmission distance which reception will enhanced differently at different placements, meanwhile, multi transmitter coils are used to improve the transmission efficiency. In this paper, we adopt a method mixed with switched rely and optional source to enhance the acceptable coverage area of the entire system, so that the receiver can effectively cover the entire space. We establish a general system model to perform mathematical analysis and simulation on the MCRWPT systems of arbitrary coils, and analyse the spatial characteristics from the frequency domain combined with the coupling coefficient. In order to better utilize the spatial characteristics of different system states, we use the feedback information of the receiver to control the relay switch and source, and compare the performance analysis of different control methods.

In the underwater scenario, the multiple inputs and single output (MISO) technology can greatly enhance the wireless power transfer (WPT) range. In this paper, we investigate the wireless power propagation model for MISO based underwater WPT (UWPT). Experiments are conducted to record the transmission characteristics of the system, using the voltage and phase to model the coupling state of the system. We measure the voltage and phase at different positions in a plane 55mm away from the transmitters, and the experimental results demonstrate that the system has a good transmission performance in most areas of the measurement range. Compared to the experiment with the same setups in the air, the UWPT system can transmit more power and longer distance.

This paper presents a low-noise single-loop third-order single-bit continuous-time (CT) Delta-Sigma modulator for sensor applications. The modulator employs the CIFF-B structure and chopping technique to reduce the power consumption and low-frequency noise. The FIR feedback DAC is adopted to reduce the sensitivity of CT ΔΣ modulators to clock jitter, and the method of moments is used to design the coefficients of loop filter. The chopping frequency is also designed carefully to avoid the noise aliasing effect, and modulator coefficients and circuit implementation have been optimized for low power. The modulator, implemented in a 150nm CMOS technology with a core area of 0.48mm 2, achieves a 107dB peak SNR and a 104dB peak SNDR over a 1kHz signal bandwidth. The power consumption is 176µW at 1.65V supply voltage.

In the existing positioning system based on bluetooth (BT), the interference of the positioning device signal, the slow processing speed of the positioning data and the large energy consumption of the positioning device affect the system positioning accuracy and service quality. In this paper, we propose an Ultra-Low power indoor localization system using mobile cloud computing. The mobile cloud server reduces the signal interference of the positioning device, improves the positioning accuracy and reduces the system energy consumption by controlling the working mode of the positioning device. A simultaneous localization and power adaptation scheme is developed. In the real experiment evaluation, our proposed system can localize the area of a terminal located within 3 m distance with \(98\%\) accuracy and average positioning error less then 1.55 m. Compare with other BLE system, \(97\%\) average energy consumption of our system is reduced.

Using magnetic beamforming, the power transmission efficiency can be highly increased for realizing the practical near-field magnetic resonant coupling (MRC) wireless power transfer (WPT). The usage of relay coils is also of great benefit for power transmission in the WPT system containing multiple transmitters (TXs) each with a coil. In this paper, we study the influence of relay coils and the magnetic beamforming for a MRC-WPT system with multiple TXs, relay coils and a single receiver (Rx), called multi-relay WPT (MWPT) system. The optimization problem is formulated to design the currents flowing through TXs in different places so as to maximize the power delivered to the RX load, subject to a given constraint of summational power consumed by all resistances in the MWPT system. In general, the problem is a non-convex quadratically constrained quadratic programming (QCQP) problem, which can be solved with relaxation of the Lagrange multiplier. Numerical results show that by comparing with equal-current and magnetic beamforming mode, relay coils significantly enhances the power transmission efficiency over the longer distances, and magnetic beamforming with passive relay coils significantly improve the transmission power, efficiency and distance.

Utilizing the received power on the spatially distributed sensors can efficaciously infer the various characteristics of the active wireless transmitter. In this paper, the joint estimation problem of the transmit power and positions of one and multiple wireless nodes were studied, which was based on the received power by the sensors without any priori knowledge of its transmit power. A particle swarm optimization algorithm was proposed to estimate the transmitter locations and power. Such a method can achieve an original estimation without sufficient measurements from sensors. The particles were generated in the constrained testing area. And a signal initial position estimation algorithm was improved in this paper. The performance was verified throuzh numerical simulations.

Magnetic beamforming can effectively increase the power transmission efficiency for multi-coil magnetic resonance wireless power transfer (WPT) system. However, the current beamforming schemes need complicated circuits and communication protocols. In this paper, we propose a random magnetic beamforming method to achieve maximum received power. This scheme is computationally light and requires only limited feedback information. Then, a test-bed for magnetic resonance WPT is developed. The experimental results indicate that our algorithm can effectively increase the received power with a fast convergence rate.

Magnetic resonance is a promising technique of far-field wireless power transfer for consumer electronic devices due to its high efficiency and long charging distance. In this paper, we present a smart multi-coil array magnetic resonance charging prototype which can integrate several intelligent functions and charge multiple devices simultaneously with high efficiency. In this platform, magnetic beamforming, impedance matching network and receiver position estimation are implemented on this platform. With these schemes, the received power and transfer efficiency are increased effectively. The experimental results indicate that our platform can effectively charge a device in 20cm with above 90% transmission efficiency.

The radio-frequency (RF) enabled wireless power transfer (WPT) system can be benefit from the MIMO technique. However, due to the limited resource, internet of things (IoT) devices can only feedback partial information which is received signal strength (RSS) value instead of channel state information (CSI). Thus, channel estimation based beamforming scheme from receiver side is not applicable for real applications. In this paper, we propose an adaptive random beamforming algorithm based on Monte-Carlo method to supply multiple batteryless IoT devices with high received power efficiency. Our algorithm does not require the complex channel estimation and adapts the beamforming scheme only according to the partial feedback information. We employ Gibbs sampling and re-sampling methods to generate several random beamforming weight vectors, and choose the optimal one. A simulated annealing algorithm is employed to control the convergence rate. We use the proposed algorithm to supply power in two cases: the maximum power transmission and robust power transmission. The simulation results indicate that this algorithm can fast converge to an optimal value and provide far-field power to multiple IoT devices.

The Protons beam position monitor (BPM) system is one of the most important diagnostic elements set up around the Rapid Cycling Synchrotron (RCS) for the China Spallation Neutron Source (CSNS). This paper presents the design of Data Acquisition (DAQ) System for CSNS RCS BPM. The beam position data are published to the network layer through the Experimental Physics and Industrial Control System (EPICS), which allows others to process the data. The data acquisition software has completed the preliminary test; the results show that the software has good practicality and scalability.

Nowadays, edge computing which provides low delay services has gained much attention in the research filed. However, the limited resources of the platform make it necessary to predict the usage, execution time exactly and further optimize the resource utilization during offloading. In this paper, we propose a feedback prediction model (FPM), which includes three processes: the usage prediction process, the time prediction process and the feedback process. Firstly, we use average usage instead of instantaneous usage for usage prediction and calibrate the prediction results with real data. Secondly, building the time prediction process with the predicted usage values, then project the time error to usage value and update the usage values. Meanwhile, our model re-executes the time prediction process. Thirdly, setting a judgment and feedback number to the correction process. If prediction values meet the requirement or reach the number, FPM stops error feedback and skips to the next training. We compare the testing results to other two model which are BP neural network and FPM without feedback process (NO-FP FPM). The average usage and time prediction errors of BP and NO-FP FPM are 10%, 25% and 16%, 12%. The prediction accuracy in FPM has a great improvements. The average usage prediction errors can reach less than 8% and time error reach about 6%.

According to the data acquisition requirements of China Spallation Neutron Source (CSNS) Small Angle Neutron Spectrometer (SANS), this paper adopts Linux, C++ and open source technology such as Qt and MySQL to develop the SANS DAQ software. The software realized basic DAQ functions such as electronics configuration, run control, data readout, online data processing and storage, status monitor and error alarm, as well as additional functions like Graphic User Interface (GUI), Online Database and Waveform Reconstruction. Test results show that the software is stable, reliable and efficient, meets the requirements of CSNS SANS.

The multiple magnetic resonance coils are effective at transmitting wireless power to a single receiver. However, the receiver's location is essential to the transmitter for power transmission efficiency. In this paper, we propose a position estimation scheme for magnetic multiple-input single-output (MISO) wireless power transfer (WPT) system. The MISO WPT system with the fixed frequency at 6.78MHz is applied in this work. We construct an offline training data set as the reference. Then, a KNN algorithm is employed to search the nearest instance with the current measurement. With such location information, the efficiency of wireless power transfer can be enhanced.

This paper presents the design of data acquisition (DAQ) software for China Spallation Neutron Source (CSNS) General Purpose Powder Diffractometer (GPPD). GPPD is made up of 36 MA-PMT detector modules and 6192 electronics channels. Total hit rate of GPPD is about 300 kHz. DAQ software is composed of readout module, detector and electronics configuration parameter management module, distributed communication module, EPICS interface module, data analysis for electronics module, interface with offline data analysis software, etc. Raw data from front-end electronics are read out via SiTCP gigabit Ethernet. C/C++ are used as the programming language. Qt is used as a development tool for Linux system, while LabVIEW is used for DAQ prototype design and GUI on Windows platform. The DAQ software has been tested in cobalt neutron source experiment and reactor neutron beam line experiment with detector and electronics system. The results show that the software is stable and reliable, meets the requirements of engineering deployment. Some improvement, optimization and function expansion are still undergoing according to the new experimental results

Using multi-antennas can improve the received energy efficiency of the radio-frequency (RF) enabled wireless power transfer (WPT) system. However, for the resource constrained internet of things (IoT) devices, only partial information which is received signal strength (RSS) value instead of channel state information (CSI) can be fed back. In this paper, we propose an adaptive random beamforming algorithm based on Monte-Carlo method to achieve the maximum received power efficiency. The proposed algorithm does not require any complicated channel estimation and it adapts the beamforming scheme only according to the RSS values. Gibbs sampling is used to generate the random beamforming weight vectors and re-sample them according to the feedback RSS values in an iterative manner. In addition, we employ a simulated annealing algorithm to control the convergence rate. The simulation results indicate that this algorithm can fast converge to an optimal value and achieve the maximum received power.

Geomagnetic field is almost stable in the indoor environment. The interference sources which are distributed randomly throughout the whole area can form an identical pattern. In this paper, we propose a sensitive based adaptive particle filter scheme to locate the target with geomagnetic field. Firstly, we construct the feature map using interpolation method after gathering intensity and three-component of geo-magnetic field by a smartphone. Then we divide the space into some subspaces and utilize the measured values to find best match map. Finally, we integrate perceptual information into particle filter for adaptive processing. Our scheme can adjust the filter parameters to match different feature points automatically, which enhance the relationship between the target positions and the feature map using particle filter. The experimental results indicate that comparing to the traditional particle filter, our scheme is more stable. The positioning error achieves an average loss of 0.51 m through numerical experiments.

Many exist localization algorithms are unbiased estimators. However, the estimation performance presents biased feature in the real location systems. On the other hand, many biased location estimators show advantages that unbiased estimators can not achieve, e.g., robust to the noise, more accurate estimation and low complexity. In this paper, we propose a biased localization estimator and a hybrid Kalman filtering algorithm. The proposed algorithm is robust to the complicated environment with high accuracy. Both theoretical analysis and experimental evaluation indicate that the proposed algorithm outperform the unbiased optimal estimation methods.

Numerical methods for recursive Bayesian estimation are widespread in position tracking of robotics. However, challenges arise to indoor network positioning due to the limited processing power and inaccurate ranging measurements of low-end network nodes. For efficient and robust indoor position tracking, we incorporate a recursive bound to a grid-based filter namely RBGF, which approximates the posterior of the target's position by a grid of weighted cells over a bounded state-space. The state-space (the set in which the state samples can take) is recursively confined based on both the previous estimation and current measurements, therefore, the grid cells converge to the true state and the effect of non-line-of-sight (NLOS) measurements is bounded. Experimental results by an indoor sensor test-bed demonstrate RBGF achieves the average and the worst-case of positioning errors about 1 meter and 3 meters, respectively on condition that the average ranging error is about 3 meters.

The widespread use of internet provides extensive heterogeneous platforms for drug sales. The internet has greatly facilitated the development of merchandise sales, meanwhile, many fake drug sellers that has been strongly restricted in the market by law enforcement agencies build their own platforms for sales via the internet. In order to against the fake drug website, reduce time and human resource consumption, it is necessary to screen and identify the drug information on the internet automatically. In this paper, we develop an automatic drug information screening and content analytical system which online distract the information and mine the hidden relationship and find the source of the sellers. Our major contributions lie in those aspects as follows: (1) We apply focused crawler technique to transform the unstructured data on the drug website into structured data, and stored the data in the local database. (2) An integrated fake drug identification method is proposed which consists of an image recognition module and information retrieval module. Based on this method, the fake drug website is not only identified one by one, but we also extract the hidden connections of multiple platforms. Experimental results demonstrate that our system can successfully identify a large number of fake drug website.

In an IMANET, mobile users access both text and media web contents on the Internet through gateway nodes, with web access patterns, i.e., the Zipf-like distribution or the Stretched Exponential distribution. To reduce data access delay from the Internet, we consider the cache placement problem in cooperative caching, which is that each mobile node selects a subset of web contents to cache cooperatively in its limited cache so that total access cost is minimized. It has been proved NP-hard. We propose a solution named Adaptive Allocation Cooperative Caching (AACC), which adaptively divides the cache space of each node into two components: altruistic and selfish, according to detected data access patterns. AACC aims to find the optimal cache allocation solution to allocate appropriate cache spaces for two components in order to minimize total access cost. Given the Zipf-like access distribution, we find a near-optimal allocation solution to the cache placement problem. Simulation results show that AACC achieves much better performance than the existing best cooperative caching strategy in IMANETs in terms of average query delay, caching overheads, and query success ratio. In particular, AACC reduces caching overheads by 40% in average.

In this paper, we propose an extended recursive Cramer-Rao lower bound (ER-CRLB) method as a fundamental tool to analyze the performance of wireless indoor localization systems. According to the non-parametric estimation method, the Fisher information matrix of the ER-CRLB is divided into two parts: the state matrix and the auxiliary matrix, which builds a general framework to consider all the possible factors that may influence the estimation performance. Based on this idea, ER-CRLB can fully model the estimation process in the complicated indoor environment, e.g., the sequential position state propagation, target-anchor geometry effect, the NLOS identification, and the related prior information, which are demonstrated in the comprehensive simulations.

Recently various indoor positioning techniques have been developed based on smartphone. However, most of them need external signals. In this paper a self-contained approach relying on built-in inertial sensors is implemented. Taking advantage of Pedestrian Dead Reckoning, it updates the current position by measuring the length and the heading of each step. Foremost the whole walking process is divided into segments, in which only straight walking is involved. After that the Feature Vectors are extracted for step detection. Specially, to cope with the instabilities caused by gait change, an equivalent Model Wave is created to substitute the original data. Finally, Particle Filter is employed for map matching. According to a group of experiments, our approach is as accurate as traditional positioning technique but shows more robustness.

Target positioning and tracking become a very challenging topic for indoor applications. To improve the accuracy of indoor position tracking, it is useful to acquire map descriptions of indoor environments, with two major paradigms: accurate metric map (Mmap) and topological map (Tmap). Research has shown the efficiency of using Tmap for positioning, but often poses difficulty to explicitly represent environments. This paper proposes a recursive Bayesian filter incorporating Tmap and TOA (Time-of-Arrival) sensor ranging measurements for meter-level localization, namely T-loc. Constraining Bayesian recursion by both Tmap and ranging measurements not only substantially reduces the number of state samples, but also bounds the estimation error against non-line-of-sight (NLOS) ranging errors. Three Bayesian filters are tested in both simulations and real-world experiments, taking different target trajectories in a large-scale indoor scenario. Results show that T-loc outperforms generic particle filters and achieves an average localization error about 1 meter, indicating significant improvements compared to approaches without Tmap.

It is hard to obtain a general error model for range-based wireless indoor target tracking system due to the complicated hybrid LOS/NLOS environment. In this paper, we employ a dynamic Gaussian model (DGM) to describe the indoor ranging error. A general Gaussian distribution is constructed firstly. The instantaneous LOS or NLOS error at a typical time is considered as the drift from this general distribution dynamically. Based on this modeling method, we propose an adaptive likelihood method of particle filter. Our method is adaptable for dynamic environment and achieves accurate estimation. The simulation and real indoor experiment demonstrate that the estimation accuracy of our algorithm is greatly improved without imposing computational complexity

Nowadays positioning and navigation technologies based on smartphone are sprouting up for numerous application scenarios. In this paper a more self-contained approach is introduced by which merely inertial units within the smartphone are utilized. By the Pedestrian Dead Reckoning technique, all kinds of indoor location information are provided at users' disposal. With the gyroscope, the attitude of smartphone is measured. So the real time accelerations in standard coordinate system without gravity component can be calculated. Here only vertical acceleration signals are made use of to extract the features for steps counting as well as step lengths estimation. A series of algorithms are employed to eliminate the noise and deviation, such as Zero Velocity Compensation, Moving Average Filter, Kalman Filter, and Successive Peaks Merging. Particularly the whole walking process is divided into small segments in each of which only straight walking, no stop, no turn is contained. So, different segments are processed respectively with distinctive parameters. The breakpoints are determined by moving variance analysis for accelerations and rotation angles, after which the heading and length of every step are acquired so that the mileage and position can be updated, closely followed by moving trajectory. In experiments, the average deviation of our approach is 0.48 m.

A large number of indoor positioning systems are based on sensor networks or WLAN ranging techniques with a filter to remove the positioning uncertainty coming from the ranging errors. Bayesian filter has emerged as a useful approach for sequential position estimation, which generally resorts to a numerical solution due to the nonlinearity and the non-Gaussian nature of mobile positioning. The accuracy of numerical Bayesian approaches depends mostly on two factors: the sample density of the state approximation and how closely the state transition model mimics the true motion of each iteration. However, dense samples typically cause high computation and memory complexity; worse, an improper transition model can lead to the problem of filter divergence. We hold that in the presence of at least one line-of-sight (LOS) range, the state space can be effectively confined by the geometries of ranging measurements. Therefore, this paper proposes a geometric filter (GeoF) learning the transition model by the geometry of the most recent TOA ranges. The key idea of GeoF is to adaptively generate the sample set of the state based on the intersections of every pair-wise range circles. Therefore, our approach employs a very small number of samples, causing much smaller implementation and computation overhead compared to general numerical Bayesian approaches. The experiment results of mobile robot localization in typical LOS/NLOS mixed scenarios show that GeoF yields better performance over extended Kalman filter, generic particle filter and grid-based filter.

Bayesian estimation methods are widely used for wireless localization systems. They employ priori information and current measurement error distribution models to derive the state of a mobile target. Cramer-Rao lower bound (CRLB) is a fundamental tool to analyze the performance of Bayesian estimators. Although CRLB is derived based on the measurement error distribution, only a few works have investigated the performance using priori information. In this paper, we derive the CRLB formulation in three cases by using the priori information: (1) fundamental Bayesian process; (2) recursive process; (3) adaptive process. These three processes represent the common Bayesian tracking algorithms for wireless system. Simulations are constructed to compare the localization performance according to the different processes. The results indicate how the priori information influences the location estimation and how to improve the performance according to different scenarios.

For indoor network positioning, the ranging error modeling plays an important role in positioning algorithm optimization, simulation setup, system parameters calibration, performance evaluation and test-bed configuration, etc. However, the error model is commonly assumed as a Normal distribution or other distributions which cannot capture the negative value, the positive bias and the right-side tail phenomenon of the indoor ranging error. We use a non-parametric modeling for the ranging error, as the indoor wireless propagation is non-analytical. Seven error models are configured by distribution fitting with measurements from both stationary and mobile Time-of-flight (TOA) experiments implemented in typical indoor scenarios. Then the configured models are evaluated by Kolmogorov-Smirnov (KS) goodness-of-fit hypothesis test, indicating that a biased statistical model is sufficient to characterize indoor ranging and has a good fitting to real-world measurements. Further, the KS test results are verified by comparing the simulated and experimental positioning results. Our modeling method works for most indoor scenarios, and the modeling results can significantly improve the effective of simulations to reality.

The accuracy of particle filtering estimation for the tracking system is prone to be influenced by the high measurement noise (or errors) in wireless sensor networks (WSNs). We first analyze the impact of instantaneous measurement noise which is introduced into the likelihood function and biases the particle filtering estimation. Based on our analysis, we propose a likelihood adaptation method considering the prior information of measurement and introduce a belief factor θ, which is a tuning parameter for adaptation. The optimal θ is attained by deriving the minimum Kullback-Leibler divergence. We integrate our adaptation method with bootstrap particle filter for time-of-arrival based target tracking. The simulation and experiment results of demonstrate that our likelihood adaptation method has greatly improved the estimation performance of particle filter in a high noise environment.

Indoor range-based localization always suffers from imprecise range measurements, especially non-line-of-sight (N-LOS) errors. Probabilistic approaches like maximum likelihood estimation (ML) are efficient under measurement errors; however, to get the optimal solution poses a non-convex problem stagnating to local optima instead of the global optimum. More important, the convergence of the objective function can be greatly misled by the inappropriately modeled NLOS errors. We propose a grid-scan approach to approximate the global convergence of ML estimate and a bias function against the positive indoor ranging error and the NLOS effect, namely GMLb. GMLb combines the robustness of Bounding-box algorithm and the reality of an exponentially bias function to indoor ranging characteristics: the Bounding-box intersection ensures that the NLOS ranges cannot deviate the estimate far away; further, the bias function tunings the model to be specific for positive ranging errors. Monte Carlo simulations and real-world indoor experiments are implemented to investigate the efficiency of the strategies against the non-convex and NLOS problems. The Monte Carlo simulations demonstrate that GMLb achieves comparable accuracy to ML in an ideal case and higher robustness than ML series algorithms in the NLOS case. The experiment reveals that GMLb is superior in scenarios rich of positive ranging errors, in particular, GMLb performs the best in worst case. And GMLb works well with a small number of grids, causing a low computational requirement on the ability-limited network devices.

Distributed particle filter using gossiping algorithm is a robust and efficient tool for decentralized state estimation in wireless sensor networks. Reducing communication overhead without sacrificing estimation accuracy is a major challenge. In this paper, we propose a distributed particle filter by using a pairwise selective gossiping algorithm, named PSG-DPF, which updates particles according to coefficient weights calculation instead of local weights and selects the significant particles to share among the nodes. PSG- DPF guarantees that every sensor node converges to an optimal consensus estimation which can achieve to high estimation accuracy. The communication overhead is reduced by transmitting only significant particles and controlling communication iterations. The simulation results illustrate that the accuracy of our scheme approaches to the centralized SIR particle filter.

The main challenge of indoor wireless positioning is the large positive bias of non-line-of-sight (NLOS) ranging, which directly degrades the localization accuracy. It is necessary for a practical positioning algorithm to learn the specific of indoor ranging from statistics. Thus, we analyze the ranging characteristics based on real-world indoor experiments with both static and mobile cases, which finds that bounding-box algorithm is robust to the NLOS bias. Then, we propose a twostep statistics-based least squares (SLS) method consisting of a NLOS bias elimination and a linear least squares (LLS) process. SLS first removes the NLOS bias by an intermediate estimation obtained from bounding-box algorithm, then uses a weighted LLS estimator to handle the remained ranging error. The difference between SLS and other NLOS mitigation approaches is that SLS aims to remove the bias away from the NLOS range while the others try to less emphasize or discard the NLOS range. SLS is compared with three NLOS mitigation algorithms on a sensor test-bed in a typical hallway. Results demonstrate that an effective NLOS mitigation of SLS.

This paper introduces a light-weight packet delivery approach called Packet-based Cooperative Diversity to improve the reliability of Wireless Sensor Networks (WSNs). Our approach is based on the principle of cooperative diversity and fail-over concepts of Redundant Array of Independent Disks (RAID), by grouping, gathering, and rejecting redundant packets with the unique packet ID per message. It is suitable for nodes which require high reliability in message delivery but have enhanced energy capability. Our approach imitates two RAID levels to investigate their effect in reliability by redundancy. Based on an analytical model and ns-2 simulations, we compare the performance of our packet-based diversity schemes with direct transmission. Our schemes improve the reliability significantly in terms of the Improved Message Reconstruction Ratio per Redundancy (MRR/R) about 25% in the analytical model and 15% in simulation. The results indicate that our approach is especially effective in situations with strong interferences.

Optical Network has given great contribution to the growth of telecommunication technologies. In the future, high capacity, multi service support and high quality of service or experience are the needy requirements of end users for the optical network. In this article, we reviewed promising optical network architectures such as Service-Oriented-Architecture (SOA) based Optical Distribution Network and Hybrid Wireless-Optical Broadband Access Network (WOBAN). Based on these networks, we propose several user-oriented service guarantee schemes. We deeply investigate the network performance, high bandwidth access, network protection, services mobility support, and resource allocation. All the proposed schemes are implemented on the test-beds. Experimental results indicate that these schemes could improve the service performance, and also verify their validity.

According to disadvantages of wired transmission system in grain depot temperature test equipment, we design a distributed grain depot temperature monitor and control system based on wireless communication technology and single chip microprocessor technology, etc. Field slave computer realizes each grain depot condition temperature signal collection through 1-wire bus digital temperature sensor DS18B20. Single chip microprocessor MSP430F149 processes monitor signal and drives temperature control equipment to realize constant temperature control. Based on wireless transceiver chip nRF905, system can complete collection signal wireless communicate from grain depot monitor equipment to central computer, and central computer controller connects with upper PC by serial interface RS-232, which the distributed grain depot monitor and control system is carried out. This paper mainly introduces system’s structure, hardware and software design in detail.