Identification and Mitigation of NLOS based on Channel Information Rules for Indoor UWB Localization

Indoor localization is an emerging technology that can be utilized for developing products and services for commercial usage, public safety, military applications and so forth. Commercially it can be applied to track children, people with special needs, help navigate blind people, locate equipment, mobile robots, etc. The objective of this thesis is to enable an indoor mobile vehicle to determine its location and thereby making it capable of autonomous localization under Non-light of sight (NLOS) conditions. The solution developed is based on Ultra Wideband (UWB) based Indoor Positioning System (IPS) in the building. The proposed method increases robustness, scalability, and accuracy of location. The out of the box system of DecaWave TREK1000 provides tag tracking features but has no method to detect and mitigate location inaccuracies due to the multipath effect from physical obstacles found in an indoor environment. This NLOS condition causes ranges to be positively biased, hence the wrong location is reported. Our approach to deal with the NLOS problem is based on the use of Rules Classifier, which is based on channel information. Once better range readings are achieved, approximate location is calculated based on Time of Flight (TOF). Moreover, the proposed rule based IPS can be easily implemented on hardware due to the low complexity. The measurement results, which was obtained using the proposed mitigation algorithm, show considerable improvements in the accuracy of the location estimation which can be used in different IPS applications requiring centimeter level precision. The performance of the proposed algorithm is evaluated experimentally using an indoor positioning platform in a laboratory environment, and is shown to be significantly better than conventional approaches. The maximum positioning error is reduced to 15 cm for NLOS using both an offline and real time tracking algorithm extended from the proposed approach

Benchmark Performance of Homomorphic Polynomial Public Key Cryptography for Key Encapsulation and Digital Signature Schemes

This paper conducts a comprehensive benchmarking analysis of the performance of two innovative cryptographic schemes: Homomorphic Polynomial Public Key (HPPK)-Key Encapsulation Mechanism (KEM) and Digital Signature (DS), recently proposed by Kuang et al. These schemes represent a departure from traditional cryptographic paradigms, with HPPK leveraging the security of homomorphic symmetric encryption across two hidden rings without reliance on NP-hard problems. HPPK can be viewed as a specialized variant of Multivariate Public Key Cryptography (MPKC), intricately associated with two vector spaces: the polynomial vector space for the secret exchange and the multivariate vector space for randomized encapsulation. The unique integration of asymmetric, symmetric, and homomorphic cryptography within HPPK necessitates a careful examination of its performance metrics. This study focuses on the thorough benchmarking of HPPK KEM and DS across key cryptographic operations, encompassing key generation, encapsulation, decapsulation, signing, and verification. The results highlight the exceptional efficiency of HPPK, characterized by compact key sizes, cipher sizes, and signature sizes. The use of symmetric encryption in HPPK enhances its overall performance. Key findings underscore the outstanding performance of HPPK KEM and DS across various security levels, emphasizing their superiority in crucial cryptographic operations. This research positions HPPK as a promising and competitive solution for post-quantum cryptographic applications in a wide range of applications, including blockchain, digital currency, and Internet of Things (IoT) devices