Wi-Fi deployed inside a building can be used for positioning indoor users. A commonly used technology is Weighted K-Nearest Neighbor (WKNN) fingerprint which positions a user based on K nearest reference points measured beforehand. The challenge lies in how to configure the value of K to obtain the best positioning accuracy. In this paper, we propose a self-adaptive Weighted K-Nearest Neighbor (SAWKNN) algorithm with a dynamic K. By adjusting the value of K based on the signal strength, SAWKNN can obtain a better positioning accuracy than traditional WKNN. In particular, a significant percentage of the SAWKNN positioning makes use of a value K=1. The performance of the proposed algorithm has been evaluated in real-world experiments.

Storage is a promising application for permission-less blockchains. Before blockchain, cloud storage was hosted by a trusted service provider. The centralized system controls the permission of the data access. In web3, users own their data. Data must be encrypted in a permission-less decentralized storage network, and the permission control should be pure cryptographic. Proxy re-encryption (PRE) is ideal for cryptographic access control, which allows a proxy to transfer Alice’s ciphertext to Bob with Alice’s authorization. The encrypted data are stored in several copies for redundancy in a permission-less decentralized storage network. The redundancy suffers from the outsourcing attack. The malicious resource provider may fetch the content from others and respond to the verifiers. This harms data integrity security. Thus, proof-of-replication (PoRep) must be applied to convince the user that the storage provider is using dedicated storage. PoRep is an expensive operation that encodes the original content into a replication. Existing PRE schemes cannot satisfy PoRep, as the cryptographic permission granting generates an extra ciphertext. A new ciphertext would result in several expensive replication operations. We searched most of the PRE schemes for the combination of the cryptographic methods to avoid transforming the ciphertext. Therefore, we propose a new PRE scheme. The proposed scheme does not require the proxy to transfer the ciphertext into a new one. It reduces the computation and operation time when allowing a new user to access a file. Furthermore, the PRE scheme is CCA (chosen-ciphertext attack) security and only needs one key pair.

Location spoofing and non-line-of-sight (NLOS) propagation are two leading reasons of serious localization errors in wireless networks. Previous studies have managed to identify these two factors separately. However, when present in the same system, these two factors can cause localization errors in a similar manner, making the identification difficult. In this paper, we address the problem of identifying location spoofing in NLOS conditions. We first carry out a geometric analysis on NLOS and derive a bound that can be used to differentiate NLOS from location spoofing. Based on the bound, we propose an identification method. We show that the proposed method is secure against different types of spoofing attacks including those from individuals and from multiple collaborative attackers. In particular, it can be used to identify the well-known "perfect location spoofing". Simulation in wireless sensor networks indicates that our method can achieve a high accuracy with 0 false positive on identifying individual attacks and perfect location spoofing in NLOS conditions.

Today’s smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e.g., human activity recognition and smartphone on-body position recognition, in order to enable more robust context-aware applications. So far, these two problems have been studied separately without considering the interactions between each other. In this study, by first applying a novel data preprocessing technique, we propose a joint recognition framework based on the multi-task learning strategy, which can reduce computational demand, better exploit complementary information between the two recognition tasks, and lead to higher recognition performance. We also extend the joint recognition framework so that additional information, such as user identification with biometric motion analysis, can be offered. We evaluate our work systematically and comprehensively on two datasets with real-world settings. Our joint recognition model achieves the promising performance of $0.9174$ in terms of $F1$ -score for user identification on the benchmark RealWorld Human Activity Recognition (HAR) dataset. On the other hand, in comparison with the conventional approach, the proposed joint model is shown to be able to improve human activity recognition and position recognition by $5.1\%$ and $9.6\%$ respectively.

Specularity poses significant challenges in computer vision (CV), often leading to performance degradation in various tasks. Despite its importance, the CV field lacks a comprehensive review of specularity detection techniques. This survey addresses this gap by synthesizing diverse definitions of specularity and providing a unified framework to enhance consistency. It also presents a systematic review of traditional and deep learning-based methods for detecting specularity. Comparative experiments on a standardized dataset enable in-depth evaluation of each method, highlighting their strengths and limitations. The survey further provides structured insights and guidance for selecting appropriate methods across diverse scenarios. Through this, it identifies key areas for future research, aiming to support the development of more advanced detection models. By integrating diverse methodologies and quantitative analyzes, this survey contributes to a deeper understanding of current advancements and potential innovations in specularity detection.

The research on the green roof is of great importance in the field of urban beautification and improving ecological effect. According to the previous research, plants have shown a significant impact on the absorption of particulate matter (PM2.5) in the air. Therefore, it is justified that the appropriate planting design or some particular combinations of plants can be considered as a solution, dealing with the urban PM2.5. This paper presented a work in progress on developing wireless sensor networks (WSN) system based on a prototype wind tunnel, which is used for the simulation of the green roof. Several data collection processes are handled by this system, where the concentration of PM2.5, wind speed, temperature, and relative humidity are obtained and stored in the database simultaneously. Additionally, users are able to real-timely define their commands in detail, controlling the sensor’s height through a GUI on the website. Experimental and simulation results and measurements have verified the validity of the wind tunnel module as well as the reliability of the sensor network. The system can be operated on thousands of devices when the packet delay maintained in a low level.