In this paper, a distributed filtering scheme is presented to deal with the soft fault detection problem of nonlinear stochastic systems with wireless sensor networks (WSNs). The nonlinear stochastic systems, which are of discrete-time form, are represented by interval type-2 (IT2) Takagi--Sugeno (T--S) fuzzy models. Randomly occurred packet losses are considered between the WSN and the filter unit. A novel type of IT2 T--S fuzzy distributed fault detection filter is proposed corresponding to each sensor node. Meanwhile, a fault reference model is adopted for improving the performance of the fault detection system. Then a new distributed fault detection system is formulated in a type-2 fuzzy model framework. New techniques are utilized to analyse and synthesize of the overall fault detection system with a disturbance attenuation performance. Finally, a truck-trailer system with a four-node WSN is established to validate the effectiveness and applicability of the presented distributed fault detection scheme.

The Global Positioning System (GPS) is widely used in outdoor environmental positioning. However, GPS cannot support indoor positioning because there is no signal for positioning in an indoor environment. Nowadays, there are many situations which require indoor positioning, such as searching for a book in a library, looking for luggage in an airport, emergence navigation for fire alarms, robot location, etc. Many technologies, such as ultrasonic, sensors, Bluetooth, WiFi, magnetic field, Radio Frequency Identification (RFID), etc., are used to perform indoor positioning. Compared with other technologies, RFID used in indoor positioning is more cost and energy efficient. The Traditional RFID indoor positioning algorithm LANDMARC utilizes a Received Signal Strength (RSS) indicator to track objects. However, the RSS value is easily affected by environmental noise and other interference. In this paper, our purpose is to reduce the location fluctuation and error caused by multipath and environmental interference in LANDMARC. We propose a novel indoor positioning algorithm based on Bayesian probability and K-Nearest Neighbor (BKNN). The experimental results show that the Gaussian filter can filter some abnormal RSS values. The proposed BKNN algorithm has the smallest location error compared with the Gaussian-based algorithm, LANDMARC and an improved KNN algorithm. The average error in location estimation is about 15 cm using our method.

Accurate and sufficient location information is the prerequisite for most Wireless Sensor Networks (WSNs) applications. Existing range-based localization approaches often suffer from incomplete and corrupted range measurements. Recently, some matrix completion based localization approaches have been proposed, which only take into account Gaussian noise and outlier noise when modeling the range measurements. However, in some real-world applications the inevitable structural noise usually degrades the localization accuracy and prevents the outlier recognition drastically. To address these challenges, we propose a noise-tolerant Localization via Multi-norms Regularized Matrix Completion (LMRMC) approach in this paper. Leveraging the intrinsic low-rank property of Euclidean Distance Matrix (EDM), the reconstruction problem of true underlying EDM is formulated as a Multi-norms Regularized Matrix Completion (MRMC) model, where the outlier noise and structural noise are explicitly sifted by L1-norm and L1,2-norm respectively, while the Gaussian noise is implicitly smoothed by employing the well-known Alternating Direction Method of Multiplier (ADMM) optimization method. To the best of our knowledge, this is the first scheme being able to efficiently recover the unknown range measurements under the coexistence of Gaussian noise, outlier noise and structural noise. Extensive experiments validate the superiority of our proposed LMRMC approach, outperforming the state-of-the-art localization approaches with regard to the localization accuracy. Besides, LMRMC can also achieve an accurate detection of both outlier noise and structural noise, making it promising for further nodes fault diagnosis and topology control in WSNs.

Nowadays, location-based services, which include services to identify the location of a person or an object, have many uses in social life. Though traditional GPS positioning can provide high quality positioning services in outdoor environments, due to the shielding of buildings and the interference of indoor environments, researchers and enterprises have paid more attention to how to perform high precision indoor positioning. There are many indoor positioning technologies, such as WiFi, Bluetooth, UWB and RFID. RFID positioning technology is favored by researchers because of its lower cost and higher accuracy. One of the methods that is applied to indoor positioning is the LANDMARC algorithm, which uses RFID tags and readers to implement an Indoor Positioning System (IPS). However, the accuracy of the LANDMARC positioning algorithm relies on the density of reference tags and the performance of RFID readers. In this paper, we introduce the weighted path length and support vector regression algorithm to improve the positioning precision of LANDMARC. The results show that the proposed algorithm is effective.

With the fast development of the Internet of Things, Radio Frequency Identification (RFID) has been widely applied into many areas. Nevertheless, security problems of the RFID technology are also gradually exposed, when it provides life convenience. In particular, the appearance of a large number of fake and counterfeit goods has caused massive loss for both producers and customers, for which the clone tag is a serious security threat. If attackers acquire the complete information of a tag, they can then obtain the unique identifier of the tag by some technological means. In general, because there is no extra identifier of a tag, it is difficult to distinguish an original tag and its clone one. Once the legal tag data is obtained, attackers can be able to clone this tag. Therefore, this paper shows an efficient RFID mutual verification protocol. This protocol is based on the Physical Unclonable Function (PUF) and the lightweight cryptography to achieve efficient verification of a single tag. The protocol includes three process: tag recognition, mutual verification and update. The tag recognition is that the reader recognizes the tag; mutual verification is that the reader and tag mutually verify the authenticity of each other; update is supposed to maintain the latest secret key for the following verification. Analysis results show that this protocol has a good balance between performance and security.

Due to the small size, low cost, and autonomous nature, unmanned aerial vehicles (UAVs) have attracted growing attention in improvingtraditional networks performance. This paper considers a multi-UAV enabled large-scale wireless recharge networks, where a group of UAVs is dispatched as mobile wireless power transfer and information collection systems to serve a set of ground low-power sensor nodes. Specifically, the UAV employs radio frequency (RF) wireless power transfer (WPT) to supply energy towards the sensor nodes, and the communication services are available via wireless information transfer (WIT) systems. To achieve balanced energy consumption among UAVs, we maximize the energy utilization efficiency of UAVs and minimize the communication delay by optimizing the UAV trajectory jointly with constraints of the UAV energy capacity and the area of the target region. The formulated problem is a mixed-integer programming problem that is a variation of multiple traveling salesman problem. Thus we present a heuristic algorithm that combines the evolutionary algorithm and variable neighborhood search to achieve the optimal visited sequence of the sensor nodes. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithm. It draws new insights on the estimation of the feasibility of the given UAVs whose energy capacity is limited.