Performance comparison of RSS algorithms for indoor localization in large open environments

We develop and benchmark four RSS localisation algorithms where different a priori knowledge is required. The selection of the best algorithm depends on the availability of additional information on path loss exponent and/or transmit power. We compare our algorithms with centroid localization and show that the algorithms provide better results for shadowing on the values not exceeding 6dB. We perform experiments and simulations with Bluetooth Low Energy and LoRaWAN technologies and select the best technology and algorithm for localisation in large open industrial environments

Intelligent and Low Overhead Network Synchronization over Large-Scale Industrial Internet of Things Systems

With the extensive development of information and communication technologies and vertical industry applications, industrial IoT (IIoT) systems are expected to enable a wide variety of applications, including advanced manufacturing, networked control, and smart supply chain, which all exclusively hinge on the efficient cooperation and coordination among the involved IIoT machines and infrastructures. The ubiquitous connection among IIoT entities and the associated exchange of collaborative information necessitate the achievement of accurate network synchronization, which can guarantee the temporal alignment of the critical information. To enhance the temporal correlation of heterogeneous devices in large-scale IIoT systems, this thesis aims at designing industry-oriented network synchronization protocols in terms of accuracy improvement, resource-saving, and security enhancement with the assistance of learning-based methods. Initially, the real-time timestamps and historical information of each IIoT devices are collected and analyzed to explore the varying rate of the skew (VRS) at each enclosed clock. K-means clustering algorithm is adopted to organize the distributed devices into a few groups, and each of them is assigned with an optimized synchronization frequency to avoid potential resource waste while ensuring synchronization accuracy. Historical VRS values are further utilized as the identification of each clock for providing verification information so that the security against message manipulation attacks during network synchronization can be enhanced. Moreover, a digital twin-enabled clock model is established by comprehensively investigating the characteristics of each clock with diversified operating environments. A cloud-edge-collaborative system architecture is orchestrated to enhance the efficiency of data gathering and processing. With the assistance of the accurate estimation generated by the digital twin model for each clock, the situation-awareness of network synchronization is enhanced in terms of a better understanding of the clock feature and necessary synchronization frequency. Meanwhile, since temporal information generated at each local IIoT devices are efficiently gathered at the edge devices, the effect of packet delay variation is significantly reduced while the synchronization performance under various network conditions can be guaranteed. To further reduce the network resource consumption and improvement the performance under abnormal behaviors during network synchronization, a passive network synchronization protocol based on concurrent observations is proposed, where timestamps are exchanged without occupying dedicated network resources during synchronization. The proposed scheme is established based on the fact that a group of IIoT devices close to each other can observe the same physical phenomena, e.g., electromagnetic signal radiation, almost simultaneously. Moreover, multiple relay nodes are coordinated by the cloud center to disseminate the reference time information throughout the IIoT system in accomplishing global network synchronization. Additionally, a principal component analysis-assisted outlier detection mechanism is designed to tackle untrustworthy timestamps in the network according to the historical observation instants recorded in the cloud center. Simulation results indicate that accurate network synchronization can be achieved with significantly reduced explicit interactions