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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.

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.

Permanent magnet synchronous motor and power electronics-based three-phase inverter are the major components in the modern industrial electric drive system, such as electrical actuators in an all-electric subsea Christmas tree. Inverters are the weakest components in the drive system, and power switches are the most vulnerable components in inverters. Fault detection and diagnosis of inverters are extremely necessary for improving drive system reliability. Motivated by solving the uncertainty problem in fault diagnosis of inverters, which is caused by various reasons, such as bias and noise of sensors, this paper proposes a Bayesian network-based data-driven fault diagnosis methodology of three-phase inverters. Two output line-to-line voltages for different fault modes are measured, the signal features are extracted using fast Fourier transform, the dimensions of samples are reduced using principal component analysis, and the faults are detected and diagnosed using Bayesian networks. Simulated and experimental data are used to train the fault diagnosis model, as well as validate the proposed fault diagnosis methodology.

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. The ER-CRLB fully models the complicated indoor environment, e.g., the sequential position state propagation, the target-anchor geometry effect, the non-line-of-sight (NLOS) identification, and the related prior information. First, we use an abstract function to represent the entire wireless localization system model. Then, the unknown vector of the ER-CRLB consists of two parts: The first part is the estimated vector, and the second part is the auxiliary vector that helps improve the estimation accuracy. Accordingly, the Fisher information matrix (FIM) of the ER-CRLB is divided into two parts, namely, the state matrix and the auxiliary matrix. Based on this idea, ER-CRLB can be a practical fundamental limit to denote the system which fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and NLOS identification and mitigation methods. When only a small set of unknown vectors is estimated in the system, the ER-CRLB is equivalent to the other CRLBs of the wireless indoor localization system as well. However, the ER-CRLB is more adaptable than other CRLBs when considering more unknown important factors. We employ the ER-CRLB to analyze the time-of-arrival (TOA) range-based indoor localization system. The influence of the hybrid line-of-sight (LOS)/NLOS channels, the building layout information, and the relative height differences between the target and anchors are analyzed. It is demonstrated that the ER-CRLB exploits all the available information for the indoor localization systems and serves as a fundamental limit of the unbiased estimation accuracy.

It is hard to obtain a general error model for the range-based wireless indoor positioning system due to the complicated hybrid line-of-sight/non-line-of-sight (LOS/NLOS) environment. The performance of the conventional Gaussian-based nonlinear filters is degraded in the indoor scenario. In this paper, we employ a dynamic Gaussian model (DGM) to describe the indoor ranging error. A general Gaussian approximated model is constructed first to fit the potential distribution. The instantaneous LOS or NLOS error at a typical time is considered as the drift from this general distribution dynamically. The relationship between the instantaneous error of the DGM and the estimation accuracy of nonlinear filters is analyzed. Based on our analysis, we propose a measurement adaptation method to further reduce the error according to the DGM. Then, the nonlinear filters based on the Gaussian model, which are simple and accurate, can be applied. A biased extended Kalman filter (EKF) and an adaptive Gaussian particle filter (PF) integrated with the measurement adaptation method are designed. The real indoor experiment demonstrates that the estimation accuracy of our algorithms is greatly improved without imposing complexity and that our algorithms are suitable for the dynamic indoor environment.

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.