A secured storage and privacy-preserving model using CRT for providing security on cloud and IoT-based applications

Due to the localization accuracy of global positioning system (GPS) cannot meet the requirements in indoor environment, the wireless sensor network (WSN) techniques are efficient methods to cope with this problem. The WSN-based indoor localization techniques have become effective methods to solve the problem of indoor localization. Since the nonline-of-sight (NLOS) effect could severely induce the localization accuracy, the primary challenge in indoor localization is the handling of NLOS errors. Due to multipath effect, near-far effect, obstacle occlusion, etc., the NLOS errors become very sophisticated. Aiming at this problem, a modified joint probabilistic data association localization (MJPDA) algorithm is proposed in this article. First, MJPDA obtains a series of preprocessing virtual points by grouping the measurements. Then, the measurements are divided into two categories, that is, line-of-sight (LOS) and NLOS, by virtual points’ density. In the case of LOS, extended Kalman filter (EKF) is used for processing. For the NLOS case, a series of particles are first generated around the prediction point, and then modified JPDA is used to data association of the virtual points and the particles. Simulations results illustrate that MJPDA is superior to MPDA algorithm and the traditional EKF algorithm in localization accuracy and robustness. Finally, we perform the real experiment to verify the performance of MJPDA. The experimental results demonstrate that MJPDA has prominent performance in mitigating large NLOS errors.

An Indoor Localization Algorithm Based on Modified Joint Probabilistic Data Association for Wireless Sensor Network

In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.

https://www.mdpi.com/2076-3417/10/3/956/pdf

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

With the development of quantum computer, making the traditional cloud storage program data integrity verification protocol is no longer safe anymore, so how to establish a safe and efficient, cost-effective cloud storage system becomes the industry's research hotspot. This paper makes improvements on the basis of the previous schemes. On the cloud storage model, we focus on the protection of user data privacy, and send the file and user signature to CSP and TPA respectively, so these methods will improve the privacy of signature information. In the cloud storage data calculation, using of lattice and Bloom filter methods, can not only resist the quantum computer attacks, but also based on the realization of dynamic integrity, improving the utilization of cloud storage space ultimately.

A dynamic integrity verification scheme of cloud storage data based on lattice and Bloom filter

Aimed at the shortcomings of low localization accuracy of the fixed multianchor method, a three-dimensional localization algorithm for wireless sensor network nodes is proposed in this paper, which combines received signal strength indicator (RSSI) and time of arrival (TOA) ranging information and single mobile anchor node. A mobile anchor node was introduced in the proposed three-dimensional localization algorithm for wireless sensor networks firstly, and the mobile anchor node moves according to the Gauss–Markov three-dimensional mobility model. Then, based on the idea of using RSSI ranging in the near end and TOA ranging in the far end, a ranging method combining RSSI and TOA ranging information is proposed to obtain the precise distance between the anchor node and the unknown node. Finally, the maximum-likelihood estimation method is used to estimate the position of unknown nodes based on the obtained ranging values. The MATLAB simulation results show that the proposed algorithm had a higher localization accuracy and lower localization energy consumption compared with the traditional RSSI localization method or TOA localization method.

/pdf/three-dimensional-localization-algorithm-of-wsn-nodes-based-3u7v3jimeg.pdf

Three-Dimensional Localization Algorithm of WSN Nodes Based on RSSI-TOA and Single Mobile Anchor Node

In recent years, location-based services have been receiving increasing attention because of their great development prospects. Researchers from all over the world have proposed many solutions for indoor positioning over the past several years. However, owing to the dynamic and complex nature of indoor environments, accurately and efficiently localising targets in indoor environments remains a challenging problem. In this paper, we propose a novel indoor positioning algorithm based on the received signal strength indication and pedestrian dead reckoning. In order to enhance the accuracy and reliability of our proposed probabilistic position selection algorithm in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a low-complexity identification approach is proposed to identify the change in the channel situation between NLOS and LOS. Numerical experiment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional methods in mixed LOS and NLOS indoor environments.

https://www.mdpi.com/2220-9964/7/6/232/pdf

A RSSI/PDR-Based Probabilistic Position Selection Algorithm with NLOS Identification for Indoor Localisation

With the adoption and diffusion of data sharing paradigm in cloud storage, there have been increasing demands and concerns for shared data security. Ciphertext Policy Attribute-Based Encryption (CP-ABE) is becoming a promising cryptographic solution to the security problem of shared data in cloud storage. However due to key escrow, backward security and inefficiency problems, existing CP-ABE schemes cannot be directly applied to cloud storage system. In this paper, an effective and secure access control scheme for shared data is proposed to solve those problems. The proposed scheme refines the security of existing CP-ABE based schemes. Specifically, key escrow and conclusion problem are addressed by dividing key generation center into several distributed semi-trusted parts. Moreover, secrecy revocation algorithm is proposed to address not only back secrecy but efficient problem in existing CP-ABE based scheme. Furthermore, security and performance analyses indicate that the proposed scheme is both secure and efficient for cloud storage.

Ke Han

Papers

A RSSI/PDR-Based Probabilistic Position Selection Algorithm with NLOS Identification for Indoor Localisation

Security and efficiency data sharing scheme for cloud storage

The optimal geometry parameters and impact of parasitic capacitance and resistance of sub-14nm SOI multi-fin FinFETs