Integrated Sensing and Communication (ISAC) technology provides robust support for the development of the Internet of Things (IoT) by leveraging its powerful sensing and communication capabilities. Sensing-assisted beam prediction techniques effectively enhance the communication quality and efficiency of ISAC systems, which is crucial for achieving high-speed and stable communication among IoT devices. However, ensuring high accuracy in beam prediction typically relies on traditional centralized architectures, which intrinsically pose risks to privacy and security when the central server is compromised. Therefore, this inherent trade-off between high prediction accuracy and data security poses significant challenges in privacy-sensitive IoT deployments. To resolve this fundamental contradiction, we propose a federated CNN-Transformer method for distributed sensing-assisted beam prediction in ISAC systems. Specifically, We propose an improved federated learning (FL) framework where clients transmit not only gradients but also nonlinear features to the server for subsequent computations through task offloading while enforcing client data privacy security. Addressing this problem is particularly challenging due to scattering and noise issues in the propagation of radar sensing signals. To address this, we design a hybrid model that integrates CNN and Transformer for the client side, which effectively captures both local and global features of the sensing signals, thereby improving prediction accuracy. Experimental results demonstrate that, compared to traditional centralized methods, our proposed method not only achieves significant improvements in prediction accuracy but also offers unique advantages in terms of data privacy and security, mitigating the risk of data leakage.

Wi-Fi-based positioning technology has been recognized as an effective technology for indoor positioning along with the rapid development and application of smartphones. One of its typical applications is localizing people in large public areas such as shopping malls, schools, and airports. A common and critical task from such applications is localizing people in long narrow spaces such as a long corridor which is considered as the most frequent place where people activities take place. Generally, the geographical distribution of Wi-Fi access points (APs) in long spaces is poor for localizing people since normally less than 3 APs are connected to a smartphone. In addition, all these APs are normally mounted along a straight line; hence, it is difficult to track people using traditional positioning algorithms such as trilateration and fingerprinting. To address this issue, a new approach called same-line-dual-connection (SLDC) was developed to estimate user locations with a good positioning accuracy, particularly for long narrow spaces where only limited Wi-Fi connections are available. The SLDC approach integrates geometry principle with positioning theories and machine learning ideas. The test outcome has shown that the SLDC approach produced a promising result, and a mean positioning accuracy of 1.60 m was achieved.

Currently, large amounts of Wi-Fi access logs are collected in diverse indoor environments, but cannot be widely used for fine-grained spatio-temporal analysis due to coarse positioning. We present a Log-based Differential (D-Log) scheme for post-hoc localization based on differentiated location estimates obtained from large-scale Access Point (AP) logs of WiFi connectivity traces, which can be used for data analysis and knowledge discovery of visitor behaviours. Specifically, the location estimates are calculated by utilizing a combination of Received Signal Strength Indicator (RSSI) records from two neighbouring APs. D-Log exploits real-world industry WiFi logs where RSSI data sampled at low rates from single AP sources are recorded in each connectivity trace. The approach is independent of device and network infrastructure type. D-Log is evaluated using WiFi logs collected from controlled environment as well as real-world uncontrolled public indoor spaces, which includes discrete single-AP RSSI traces of around 100,000 mobile devices over a one-year period. The experiment results indicate that, despite of the challenges with the infrequent sampling rate and the limitations of the data that only records RSSI from single AP sources in each instance, D-Log performs comparatively well to the state-of-the-art RSSI-based localization methods and presents a viable alternative for many application areas where high-accuracy positioning infrastructure may not be cost effective or where positioning applications are considered on legacy information infrastructure.

Smartphone based personal tracking is very important for people to find their destination in large complex buildings (e.g. shopping malls, airports and museums). Such applications are highly demanded in both industries and research organizations. One critical issue for these applications is lack of mature technologies for highly accurate indoor location tracking. In this paper, a new Wi-Fi and magnetic field based smartphone tracking system, named WMLoc, was introduced. The system is a part of a collaborative project between the RMIT University and a famous Australian software company. A number of tracking algorithms such as K nearest neighbor (KNN), artificial neural network (ANN) and back tracing (BT) have been developed or adopted for the system in order to obtain a real-time precise location of the smartphone user. The integration of Wi-Fi and magnetic field includes physical floor analysis, label pattern creation using ANN and BT for enhancing the tracking reliability and improving positioning accuracy. The WMLoc system was tested in two buildings at the RMIT University, Australia. The preliminary results showed that the average root-mean-square (RMS) error of the WMLoc system was less than 2.6 m.

Fingerprinting is the prevailing positioning method for location based service (LBS) and indoor positioning applications when compared with other methods such as cell of origin (CoO) and trilateration. It is especially more suitable for complicated indoor environments. However, higher positioning accuracy is still expected for it to match the capabilities of other mature techniques such as GPS. This paper presents a new algorithm for improving the positioning accuracy of the Nearest Neighbour (NN) algorithm from a Wi-Fi-based fingerprinting method. The new algorithm initially used the NN algorithm to identify the initial position estimate of the user being tracked. Then two distance correction values in two roughly perpendicular directions were calculated by the path loss model based on the two signal strength indicator (RSSI) values observed. The errors from the path loss model were eliminated through differencing two calculated distances which were derived from a similar environment. The new algorithm was tested and the results evaluated against that of the NN algorithm. The preliminary results from 24 test points showed that the positioning accuracy of the new approach has improved consistently and the root mean square accuracy improved to 3.4 m from 3.8 m with the NN algorithm.

Wi-Fi and smartphone based location tracking technologies have been widely applied in location based services (LBS). This research is a part of an Australian Research Council (ARC) project required by a large global shopping mall company located in Australia. It aims to develop an effective customer tracking approach for acquiring shopping behavior of customers and providing them better services. Currently, the log data provided by the company only recorded one Wi-Fi connection at a time for one smartphone user. This makes most of the conventional tracking and positioning methods inapplicable. In order to solve this problem, a new approach was developed, which takes advantage of both the cell of origin (CoO) method and the fingerprinting method. Its implementation mainly includes two phases: 1) the cell determination phase, including partitioning of the floors of the shopping mall using a Voronoi diagram and determining a unique cell for each wireless access point (AP), adjusting the cells manually according to the real floor layout, obstructions as well as an on-site observing and calibrating process. An AP-shop/room matching table and other relevant information are stored in a database; and 2) the user location tracking phase, including determination of the cell that the customer locates in according to the detected AP linked with the customer's mobile phone. A back-tracing method is also used to improve the tracking performance. This new approach was tested in a large shopping-mall-like space covering an area of 1000 square meters, containing 8 simulated shops and 11 APs. The results showed a 96% correct detection rate whilst the 4% erroneous detections occurred when a user was near the cell boundaries. This new approach is also easy to implement and integrate with other location tracking systems.