Depression is one of the most common mood disorders and the number of patients increases significantly in recent years. Due to the lack of biomarkers, conversation between patients and psychiatrists is still the main clinical diagnostic method which is easily influenced by subjectivity of both patients and psychiatrists. Synthetic House-tree-person test (S-HTP), a convenient and efficient mental assessment tool, minimizes subjective influences from patients, while its effectiveness is limited by the professional ability of analyst. Here we introduce a deep learning model DeHTP, a flexible and convenient depression detection method based on S-HTP without interaction between people. Experimental results demonstrate that DeHTP achieves 0.963 AUC and 0.9 accuracy, and outperforms the conventional manual analysis of S-HTP, which is conducted on the guideline of 50 conclusions from previous study related to depression. In addition, it reveals 22 depression-correlated drawing features aligned with conclusions above from the perspective of our proposed model. Leveraging the advantages of deep learning and S-HTP, this approach has the potential for widespread promotion and adoption as the available tool for daily self-mental monitoring, as well as the promising auxiliary diagnostic method in clinical.

Traditional crowd sensing based indoor localization methods rely on large scale pre-collected fingerprint data to construct a radio map with cumbersome prior preparation. However, when they lack floor plan information or only have a little of data is willing to share, the tracking accuracy degrades significantly. In this paper, we propose a singular value decomposition (SVD) track matching scheme to obtain an effective radio map based on small scale crowd sensing data, which is a non-learning based system (SVD-CSP). SVD-CSP fuses received signal strength indicator (RSSI), inertial measurement unit (IMU), and magnetic field strength to label surrounding WiFi access points as marker points. The proposed scheme uses SVD method to directly compute the rotation matrix and displacement vector among the crowd sensing trajectories and attain the reliable tracks. The radio map is constructed and users are tracked according to our developed bidirectional Bayesian filter, which contains forward filter and reverse filter. The density-based spatial clustering of applications with noise (DBSCAN) is embedded within the forward filter to improve the radio map quality. Meanwhile, the reverse filter fuses pedestrian dead reckoning (PDR) and radio map-based localization to track users. Experimental results demonstrate that SVD-CSP can achieve robust localization using extremely sparse crowd trajectories (e.g., 4 trajectories in a 648 m2scenario, 30 trajectories in a 2856 m2scenario) without deep learning training or infrastructure knowledge.

Hybrid reconfigurable intelligent surfaces (HRIS) can enable 6 G vehicle-to-everything (V2X) system to attain promising localization and communication performance due to its flexible beamforming feature. However, without jointly designing the HRIS control and system resource allocation scheme, the HRIS-V2X system can not adapt to the dynamic environment efficiently. In addition, the optimization of HRIS reflectivity and communication time slice are both non convex and nonlinear problems. In this paper, we propose an asynchronous time division multiplexing (ATDM) protocol for the HRIS-V2X system to meet integrated localization and communications requirements. We analyze the role of HRIS in signal transmission according to squared position error bound (SPEB) and achievable rate (AR). Then, we propose an adaptive block coordinate descent (ABCD) algorithm to optimize the localization accuracy and channel transmission capability, which includes two parts: the time optimization and the reflectivity optimization. Time optimization employs the iterative projection method to find the optimal time slice scheme satisfying AR constraints. Reflectivity optimization uses the Adagrad method with an adaptive learning rate to gradually achieve the optimal reflectivity scheme. The simulation results indicate that our proposed ABCD algorithm has achieved a maximum 94.1% reduction in SPEB compared to greedy algorithm, genetic algorithm (GA), artificial rabbits optimization (ARO) and particle swarm optimization (PSO).

Hybrid reconfigurable intelligent surface (HRIS) as one of the key technologies has the potential to improve the sensing and communication performance in the upcoming sixth-generation systems by creating virtual links among the entities. This paper proposes a HRIS-assisted integrated sensing and communication (ISAC) system to simultaneously perform the sensing and communication by co-designing transmit beamforming at base station and the reflection coefficients at the HRIS. Specifically, we derive the Fisher information matrix on the estimations of transmission delay and angle of departure, and derive the corresponding closedform expression of the Cramér-Rao bound (CRB) to describe the sensing performance. Then, a CRB minimization problem is formulated by taking into account the communication requirements and power constraints, which is non-convex and generally difficult to solve. To address this problem, we propose an algorithm that combines the Schur complement technique with sequential parametric convex approximation to approximate the original problem into a convex version. Finally, numerical results indicate that the proposed method effectively enhances the performance of the ISAC system by appropriately increasing the number of active reflecting elements. It also demonstrates that our proposed HRIS outperforms the conventional passive RIS and active RIS for the ISAC system under limited power budget.

Human activity recognition (HAR) utilizing WiFi channel state information (CSI) holds profound implications owing to the pervasive WiFi coverage in daily life. Deep learning has enabled the development of many high-precision HAR systems, but also brings the challenges of degrading performance in the new environments and high complexity issues for implementing on a single chip. In this paper, we introduce a lightweight HAR scheme for rapid adaption to the new environments, which can be implemented in a WiFi based artificial intelligence internet of things (AIoT) chip. The proposed scheme consists of two modules, which are change pattern extraction (CPE) and self-attention based adaptive model (SAAM). In CPE, the intricate multi-subcarrier CSI are transformed into unique change patterns closely related to each human activity with low complexity using deep non-negative matrix factorization (DNMF). Then, SAAM facilitates the correlation of change patterns across different environments through learned temporal features, enabling rapid generalization to new environments with only a few new samples, boasting advantages of low training costs, parameter memory usage, and computational time. Experimental results demonstrate that our system achieves not only 94% accuracy in the original environment, but also exhibits promising performance in new environments, requiring only three new training samples for each activity.

In 6G vehicle to everything (V2X) communications, accurate localization is the foundation of high quality intelligent services to users. However, accurate localization requires a significant amount of information and should overcome the Nonline of Sight (NLoS). In this article, we propose the reconfigurable-intelligent-surface (RIS)-assisted cooperative channel state information localization (RCSIL) system to analyze and optimize the localization accuracy based on effective channel state information (CSI). RIS improves the channel quality by adjusting the phase of the reflection unit, thereby affecting the localization performance. Using RIS as an anchor saves a lot of hardware resources. However, the optimization problem of the RIS phase modulation scheme is a nonconvex nonlinear integer programming which is complex and hard to solve. Thus, we propose an Adagrad-gradient-descent-based phase optimization (AG-PO) algorithm, which computes the optimal phase of each reflecting unit in parallel. AG-PO has low complexity, effectively improves positioning accuracy, and performs fast computing. The simulation results indicate that AG-PO outperforms alternating optimization (AO) and genetic algorithm (GA) with a 44.17% and 44.02% reduction in squared position error bound (SPEB) respectively. Thus, RCSIL can be widely applied in 6G V2X communications systems.

A hybrid active-passive wireless sensor network (HWSN) is a cost-effective and energy-efficient way for localization systems. The active sensor nodes, which are targets, can locate themselves by sending wireless power signals to power up the passive sensors, or via cooperative localization among targets. The common used power allocation approaches e.g., semi-definite programming (SDP), are aiming to minimize the overall localization error with energy constraints. However, the global optimum may lead to issues such as decreased individual performance and unbalanced network resources. To address the above challenges, we propose a game theory power allocation framework for the cooperative localization of HWSNs in this paper. We present the Fisher information matrix (FIM) and the corresponding squared position error bound (SPEB) for two types of HWSNs, which are the general cooperation network and tree topology network. For the general cooperation network, we derive the closed-form solution of optimal power allocation using the general bargaining equilibrium. In the tree topology network, we propose a Stackelberg equilibrium approach that effectively adapts the power allocation scheme based on the obtained hierarchical information structure. Extensive simulation results demonstrate that the proposed method outperforms semidefinite programming (SDP) and equal power allocation scheme (EQ) in position estimation error. Specifically, compared to other schemes, our proposed scheme has improved the positioning accuracy by 75% in the general cooperation network and by 70% in the tree topology network.

Human activity recognition (HAR) based on WiFi channel state information (CSI) has received a lot of attentions recently due to its nonintrusive nature. Most CSI-based HAR systems use a WiFi router and a computing terminal for centralized processing, which makes it difficult to achieve real-time wide-range recognition. Recently, lightweight Artificial Intelligence Internet of Things (AIoT) devices are widely deployed. The equipped WiFi chips within such devices can collect and process CSI data in a distributed way. Thus, the AIoT devices extend the detection range of collecting CSI and enrich the applications. However, the memories of the AIoT devices are constrained and lack of appropriate lightweight CSI processing strategies. To address these challenges, we propose the LiWi-HAR system which employs a comprehensive lightweight CSI processing strategy in WiFi-based AIoT devices. The proposed lightweight CSI processing strategy extracts the main related features while compressing the data size. Then, a double hidden layer BP neural network based on particle swarm optimization (PSO-BPNN) algorithm is developed for HAR. In this case, the computing memory occupation of the device is effectively reduced, and the real-time high-accurate recognition is achieved. Extensive experimental results present that the efficiency of our system significantly outperforms other centralized deep learning-based systems and the recognition accuracy achieves 91.7%.

The recent advancements in integrated sensing and communications (ISACs) technology have introduced new possibilities to address the quality of communication and high-resolution positioning requirements in the next-generation wireless communication network (6G) vehicle-to-everything (V2X). Simultaneously providing high-accurate positioning and high-communication capacity (CC) for the intelligent service of the vehicle target is challenging. In this article, we propose a reconfigurable intelligent surface (RIS)-assisted 6G V2X system to achieve highly accurate positioning of the vehicle target with basic communication requirements. We provide the CC and the 3-D fisher information matrix (FIM) formulations of the vehicle target. We demonstrate the direct impact of phase modulation in the reflector units on joint positioning accuracy and CC performance. Meanwhile, we design a flexible deep deterministic policy gradient (FL-DDPG) algorithm network with an$\epsilon $-greedy strategy to solve the high-dimensional nonconvex optimization problem, achieves minimal positioning error while satisfying various CC requirements. Simulation results demonstrate that the FL-DDPG algorithm enhances positioning accuracy by a minimum of 89% and improves the achievable rate of the vehicle target by nearly 3 times, which outperforms traditional mathematical methods. Compared with classical deep reinforcement learning methods, FL-DDPG achieves better positioning accuracy while satisfying the communication requirements. When confronting imperfect channel, FL-DDPG enables addressing the channel estimation errors effectively on the ISAC system.

For future 6G systems, reconfigurable intelligent surface (RIS) controls phase shift of the reflective unit to improve the channel, which affects the received signal strength (RSS) of the microwave. In this paper, we introduce the RIS as anchors into the cooperative localization wireless sensor network (RACLN) to locate the sensor nodes. The RIS in RACLN can be a base station that provides multiple passive antennas for highly accurate localization. We derive the Cramér-Rao lower bound (CRLB) and the related squared position error bound (SPEB) for RACLN. The formulations indicate that the phase shift control of the RIS can improve the localization accuracy effectively. However, determining the appropriate phase shifts poses a nonlinear, non-convex integer programming problem. Thus, we propose a semi-definite programming (SDP) based phase optimization algorithm (SDP-PO) by relaxing the objective and achieving the minimum SPEB. Further, we also develop a low-complexity phase optimization (LC-PO) algorithm to reduce the dimension of the phase shift vector of SDP-PO. The simulation results demonstrate that the SPEB of RACLN is 94:68% smaller than the WSN using sensor nodes as anchors. In addition, The proposed SDP-PO algorithms outperform the genetic algorithm (GA) and the alternate optimization (AO) with 17.12% and 37.49%.

Localization is essential for network management of the wireless backscattered sensor networks (WBSNs). In large-scale WBSN, the cooperative localization among the passive nodes effectively improves the localization accuracy. Meanwhile, reconfigurable intelligent surface (RIS) motivates nodes to gain better spatial channel using passive beamforming or phase modulation. In this article, we analyze the impact of passive beamforming for RIS on the localization accuracy of cooperative localization in the WBSN system. We derive the Fisher information matrix (FIM) and the spatial position error bound for the fully connected communication network system. We demonstrate that the phase modulation of RIS reflection units affect the localization accuracy of the cooperative localization WBSN system. However, RIS passive beamforming as a discrete and nonconvex integer programming problem is difficult to solve. Then, we propose a Monte Carlo-based random RIS passive beamforming to achieve the maximum localization accuracy. We apply Gibbs sampling and resampling methods to generate the phase shift vector samples of RIS. The sample with the highest localization accuracy is considered as the optimal solution. The simulation results demonstrate that our proposed method for RIS passive beamforming can improve 34.5% localization accuracy in the Line-of-Sight (LoS) case, while the genetic algorithm (GA) is 6.8%. In the Non-LoS (NLoS) environment, the localization accuracy improvement of our proposed method reaches 97%, and GA can only reach 85% as the comparison.

Hybrid active and passive wireless sensor networks (HWSNs) gain advantages in extending the network lifetime and reducing the overall cost. Because the passive sensors without battery harvest the energy from distributed active sensor signal beam, and only a few active sensors can maintain a large-scale network. Thus, how to track the passive sensor’s location is essential for network management. Since the active sensors are sparsely deployed, cooperative localization which employs passive sensors to locate themselves together is a promising solution. In this article, we analyze the energy beam generated by the active sensors on the cooperative localization accuracy of the passive sensors. We consider the spatial–temporal cooperative localization based on the received signal strength (RSS) model with unknown Tx power information of each sensor due to the limited processing capabilities, circuit complexity, and energy constraints. We formulate the Fisher information matrix (FIM) and the corresponding Cramér–Rao lower bound (CRLB) for the static fully connected network and dynamic spatial–temporal recursive network. Accordingly, energy beamforming schemes are proposed to optimize localization accuracy and energy efficiency problems. For the optimal localization problem, we derive the closed-form solution of the optimal energy beamforming wave. For the optimal energy efficiency problem, we propose a semidefinite programming (SDP) solution to achieve optimal energy consumption with a self-calibration method, which can address the over-relax problem. Extensive simulation results indicate that our proposed beamforming schemes have high localization accuracy and lower power consumption compared with the existing power allocation-based schemes.

This paper presents an analog front-end (AFE) circuit with marked timing point for pulsed time-of-flight LiDAR that employs either sampling- or event-based timing approach. The proposed AFE adopts a dual-mode structure for its pre-amplifier which can be configured as a charge-sensitive amplifier (CSA) or a gated active load-assisted TIA (GALA-TIA), depending on the intensity of input current. As a result, both the sensitivity and dynamic range (DR) of the AFE have been effectively improved. In addition, the zero-crossing point of the output signal has been marked as the timing point with a built-in pulse shaper, which allows the back-end circuits of LiDAR receiver discriminate the timing conveniently and accurately. The measured input-referred RMS noise current of the proposed AFE is 37 nA. With dual-mode control, a linear output DR of 80 dB and a wide timing DR of 96 dB with walk error no more than ±160 ps have been achieved. For single-shot measurement application, only CSA mode was enabled and the pulse widths of saturated output signals have been measured to compensate the walk errors, which realized a timing DR of 78 dB with accuracy of ±150 ps. The averaged power consumption is 66 mW and the total silicon area is$0.79\times 0.42\,\,mm^{2}$in 0.18-$\mu \text{m}$CMOS process.

With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoT), we have to perform increasingly more resource-hungry and compute-intensive applications on IoT devices, where the available computing resources are insufficient. With the assistance of Mobile Edge Computing (MEC), offloading partial complex tasks from mobile devices to edge servers can achieve faster response time and lower energy consumption. However, it still suffers from finding the optimal offloading decision when the total amount of computations overflows the available computing resources in MEC systems. In this paper, we establish a multi-user and multi-task MEC model and design an offloading indicator, through which we analyze what the current environment belongs to. In the cases where the computational resources of devices are sufficient or partially sufficient, we utilize the relationship between the offloading indicator and the cost incurred by the tasks that are executed in the current workflow to find the optimal offloading decision. In the cases where the computation on local and edge are both insufficient, we propose a novel Offloading Algorithm based on K-means clustering and Genetic algorithm for solving Multiple knapsack problem (OAKGM), aiming not only to jointly optimize the time and energy incurred by the tasks that are executed in the current workflow, but also to penalize the overflowed computations so that the task pressure in the next workflow can be greatly reduced. In addition, a simplified Offloading Algorithm based on Multiple Knapsack Problem (OAMKP) is proposed to further cope with the environments with a large number of users or tasks. Experimental results demonstrate the effectiveness and superiority of the proposed algorithms when compared with several benchmark offloading algorithms, which can better exploit the computing capacities of IoT devices and the edge server, greatly avoid resource occupation in edge nodes and make sustainable MEC possible.

Large scale location management in wireless powered communication networks (WPCNs) can benefit the network performance, and it can also provide location based services for IoT applications without maintaining the batteries. However, it is difficult to attain accurate node positions only based on ranging information from anchors. Thus, cooperative localization is an effective way to increase the node positioning accuracy. In this paper, we mainly investigate the performance of cooperative localization in WPCNs, in which the nodes require energy from remote energy access point (E-AP). We firstly analyze the Cramer-Rao Lower Bound (CRLB) for the full connected´ network and the spatial recursive form for a single joint node respectively. Then we propose beamforming schemes to further optimize the cooperative localization performance, which are designed to achieve the minimum localization errors. The simulations demonstrate the highly accurate localization performance of our proposed schemes, which outperform the existing power allocation 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.

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 Cramér-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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

In Radio Frequency (RF)-based indoor localization scenarios, localization algorithms are needed to alleviate the impact of non-line-of-sight and multipath effects on the measurements and thereby estimate the true position precisely. Several resilient lateration algorithms have been proposed in the last couple of years which claim to minimize these effects. However, most of these algorithms were only evaluated using simulations or small static testbeds. We conducted an experiment using 25 anchor nodes and a mobile node installed on top a robotic reference system to collect ranging values. The robot has a localization error of 6.5cm which is an order lower than our range measurement errors. We use this robot to collect range measurements and ground truth positions along a densely grid with approx. 10 cm spacing. The experiment was carried out in a hallway of our office-like building. We collected data on approx. 300 m2. First, we examine the influence of the anchor placement and anchor density on the ranging errors we see. Then, we evaluate and analyze the robustness of localization algorithms on our measured data to decide which one works best for a constellation of anchor placement and building. Our results show, that there are significant differences between the simulations published for lateration algorithms and actual experiments in real-world indoor localization scenarios. As we show in this paper, the distance measurement error distribution has a large influence on these algorithms.

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.

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.

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. GMLbcombines 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 GMLbis superior in scenarios rich of positive ranging errors, in particular, GMLbperforms the best in worst case. And GMLbworks well with a small number of grids, causing a low computational requirement on the ability-limited network devices.

In conventional wireless positioning systems, the mobile target and anchors are not with the same height due to the infrastructure deployment. However, many localization algorithms ignore the relative differences in height and assume the distance obtained is in the 2D field (defined as 2D-ranging). In this paper, we attempt to analyze the impact of the relative height between mobile target and anchor for the estimation performance. We assume the range measurement is based on the 3D distance (defined as 3D-ranging) in a small playing field, where the relative height difference can not be ignored in the range calculation, but the position estimation is drawn on the 2D playing field. Cramér-Rao lower bounds (CRLBs) for both line-of-sight (LOS) and non-line-of-sight (NLOS) noise environment are derived. The analytical results indicate that the geometric shapes of optimal mean square error (MSE) for unbiased estimator change, if a relative height difference exists. Thus, conventional GDOP or CRLB-based location algorithm and anchor selection method are unreliable without considering the relative height difference. Besides, we evaluate spatial position error distribution (SPED) of several commonly used localization algorithms, such as linear-least-square (LLS), non-linear-least-square (NLLS), min-max algorithm, Geo-n algorithm. The estimation accuracy of the above algorithms are degraded comparing with 2D ranging case in SPED. Therefore, based on our work, the relative height should be considered in the localization algorithm design in order to obtain an accurate estimation.

The major problem of indoor localization is the imprecise ranging, which directly degrades the localization accuracy. The ordinary least-squares (OLS) estimator is able to handle unbiased and homoscedastic ranging errors, but incapable for the bias and heteroscedasticity as characterized by real-world ranging of indoor scenarios, especially the non-line-of-sight (NLOS) error. A potential improvement of LS is to weight each element related to the corresponding ranging error, known as the weighted LS (WLS). However, current weighting metrics are either impractical to get or still involve the NLOS error. We propose to weight each element in a linear LS (LLS) estimator by the difference between the measured ranges and Bounding-box results, named B-WLS. Compared with five LS type algorithms in simulations and a mobile target experiments, results demonstrate that B-WLS efficiently enables the LLS estimation to suppress to the NLOS error.

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.

Sensor devices suffer severe interference due to multi path effect, non line of sight (NLOS) and variations of the wireless propagation environment in indoor positioning systems. These interferences lead to measurement error during sensor measurement. Kalman filter (KF) and extended Kalman filter (EKF) have been widely used in tracking systems to reduce measurement noise. However, KF and EKF assume the measurement noise follows normal distribution, and the real noise distribution should be based on experimental statistical results. Besides fluctuating wireless condition makes the system unstable in indoor environments. We analyze the time of flight (TOF) measurement statistic model in experiments and design KF and EKF models for indoor positioning system according to the statistic model. We introduce our system architecture for wireless sensor networks (WSN) to overcome KF's drawbacks, which divides the positioning system into three components: measurement, pre-processing and data-processing. Measurement component measures the range based on TOF method. We developed a voting filter (VF) and an averaging filter (AF) in preprocessing to reduce measurement noise for later processing. During data-processing, Kalman filter and extended Kalman filter are used to track the positions. We also implement another scheme, low pass filter (LPF) with KF or EKF, to estimate the positions with the knowledge of geographic information. Three realistic experiments are set up using the sensor equipment nanoPAN 5375 to evaluate these methods. Comparing the experimental results, low pass filter with EKF is most suitable for indoor positioning.