Unmanned aerial vehicles (UAVs), a key enabler for the Internet of Things' (IoT) evolution to 3D spatial dimensions, play a critical role in data collection across fields. However, path planning in obstacle-rich and threat-prone environments remains a core bottleneck for their safe and efficient operation. Traditional meta-heuristic algorithms suffer from insufficient exploration, slow convergence, and local optima issues. To address this, we propose an enhanced multi-mechanism DBO algorithm (MMDBO), integrating SPM chaotic mapping, dynamic global exploration, adaptive T-distribution, and dynamic weight mechanisms. Comparative experiments against five classical algorithms on 12 benchmarks test functions and three complex terrains show MMDBO achieves superior performance across the majority of key path-planning metrics-including flight trajectory length, altitude profile fidelity, and path smoothness-while incurring only a modest increase in computational time. The results of the statistical test further indicate that the MMDBO algorithm significantly outperforms the comparison algorithms in both convergence speed and accuracy. These advances deliver actionable, highly reliable guidance for UAV flight path optimization.

The extensive deployment of wireless infrastructure provides the possibility of locating mobile users in indoor environments using received signal strength (RSS). One approach to localization in this context is the use of Wi-Fi RSS fingerprinting and this formulation has been found to work reasonably well for location recognition of mobile phone users. For such, machine learning techniques such as hidden Markov models (HMMs) and hidden semi-Markov models (HsMM) have been extensively used to study human mobility and movements which permits the inclusion of prior knowledge about the geography of the environment alongside RSS measurements in the estimation process. Conventional HMMs, with their assumption of the Markov property, whose memory length is 1, i.e., dependency only on the last state, offer a simpler and more computationally manageable framework. Movements of typical mobile users through the quantized cells of the building, however, are usually not Markovian. HMM estimates suffer from ambiguity recognition on the movement of a Markov chain between subsets of state spaces. HsMMs outperform HMMs by allowing a semi-Markov chain with a variable sojourn time for each state, however still fail to capture the longer dependency beyond just the previous state. Combinatory Categorial Grammar (CCG) was designed to deal with the long-range dependencies in computational linguistics. In this article, we investigate the feasibility of implementing CCG as an alternative to HMM to formulate the building layout to a category of semantics and construct a walking path by CCG parsing from the RSS observations. The CCG parser allows the construction of the estimated path by recursively combining different path segments, thus building up a longer dependency between locations. The authors believe this is the first application of computational linguistics in the field of localization. Field test results demonstrate the effectiveness and reliability of the grammar-based approach which can achieve a resolution of 87.5% room-level matching accuracy based on crowdsourced fingerprints under a real large-scale university public wireless sensor network. Comparison between HMM, HsMM, and the grammar approach has been made to reveal the fact that both methods show promising performance, while the grammar approach is more reliable as HMM/HsMM can occasionally fail due to ambiguity recognition while the grammar approach consistently maintains good localization accuracy.

Extensive deployment of wireless infrastructure provides an alrernative ability to locate smart phones in indoor environments by the use of received signal strength (RSS). This low-cost technology is, however, susceptible to environmental distortions, requiring sophisticated signal processing. In this paper, we propose to design a Viterbi algorithm under a double-layer hidden Markov model (DHMM) for reliable smartphone user tracking. Use of a batch processing scheme enables the DHMM Viterbi algorithm to effectively make use of auxiliary information such as room topology and user orientation for estimating the most likely user location sequence (route). Comparisons between the proposed Viterbi algorithm and an existing filtering algorithm from a theoretical perspective are also conducted. Experimental results show that use of the Viterbi algorithm is able to provide more reliable indoor positioning results.

The extensive deployment of wireless infrastructure provides alternative low-cost methods for location awareness of mobile phone users (MPUs) in indoor environments by processing the received signal strength (RSS) of the mobile phone. In such a signal-processing framework, hidden Markov models (HMMs) are often used to model the uncertainties of RSS data and incorporate environmental information into localization. Since hidden semi-Markov models (HsMMs) outperform HMMs in their ability to model state duration more flexibly, employing HsMMs for indoor user positioning is a promising research direction. In this aspect, a user’s personal preference for staying in a particular area, and the functionality of certain areas, such as a dining room, as well as navigation landmarks, can be utilized in the HsMM to assist localization. This article proposes an online HsMM forward recursion (HsMM-FR) algorithm to incorporate this information for real-time smartphone user tracking. We apply the proposed HsMM-FR algorithm to simulated, synthesized, and real RSS datasets in typical indoor environments for validation.

Location-based services (LBS) such as LoRa geolocation are important aspects of IoT applications. In this article, we propose a hierarchical clustering-based technique for urban vehicle localization using received signal strength indicator (RSSI) measurements in a public LoRaWan network. The solution relies on a two-layer hierarchy: the first layer consists of a $K$ -Means clustering to partition a large urban area into several regions based on geographical coordinates of the datapoints. A coarse localizer utilizes kernel density estimation to model the received signal distribution of each gateway and determine in which the most probable regions of interests the vehicle is located, followed by a finer localization step at the second layer. For each region, reference points are grouped based on the similarity between the gateway coverage vectors. A spatial kernel-based fingerprint method that adopts spatial co-location patterns between neighbors is introduced to provide support for further fine granularity positioning within each region. The Kullback–Leibler divergence is used to measure similarities between observations and fingerprints, and the final position estimation is based on a weighted kernel regression model. The system is evaluated using a publicly available LoRaWan data set collected in large urban areas in the city of Antwerp, Belgium. We are able to achieve a median error of 158.41 m and a mean error of 346.03 m based on the raw LoRa RSSI data, and it is reported as the best accuracy based on the same data set in the literature.

By combining driver's ability with the machine intelligence, we are trying to augment the system with the combination of both roles during driving. The human-in-the-loop hybrid-augmented system is expected to monitor and model drivers' behavior; while the current datasets for driving mainly focus on the data of scene. The lack of the driver's data restricts the capability to understand driver's inner state. In the paper, we supply an effective way to monitor the driver and construct a simulation system for the human-in-the-loop autonomous driving, which realizes the modeling of driver's behavior. Based on it, we provide a data acquisition scheme for both driver's behavior and scene content and subsequently construct the corresponding dataset. For the simulated driving dataset, we provide a method for evaluating the human-in-the-loop-system, which can help the community to develop such human-in-the-loop systems.

We consider the problem of localizing a smartphone user using received signal strength (RSS) measured by a set of known network nodes in a harsh indoor environment. While the RSS of a wireless signal can be conveniently accessed, using it to estimate location is non-trivial in the presence of multipath propagation, shadowing and radio interference. Auxiliary information, such as the indoor building map and user's orientation information, potentially can help to improve localization performance. As the indoor layout is usually known as a priori, a user's moving direction or orientation in a given indoor map may contain valuable information to assist for reducing location ambiguities at estimation, typically when the radio signal channel is corrupted with noise. In this paper, we propose a double-layer hidden Markov model (DHMM) within a Bayesian learning framework for combining user orientation information and processing RSS data in the localization process to deal with RSS fluctuations induced by human body shadowing and multipath interference. Simulation and experimental results show that incorporating user orientation can potentially provide promising indoor positioning results.

The advent of sensor-rich smart devices (e.g., smartphones) has enabled a lot of applications and services. One of these applications and services is smartphone-based vehicle indoor positioning, which is a key technology for smart car parking and driverless cars. So far, most vehicle indoor positioning solutions either use infrastructures (e.g., WiFi access points) or inertial sensors, which suffer from low positioning accuracy, limited coverage, or high cost to deploy new equipment. To tackle these challenges, in this work we propose a novel Deep Learning-based Vehicle Indoor Positioning (DeepVIP) approach using smartphone built-in sensors, including accelerometer, gyroscope, magnetometer, and gravity sensor. Experiments are conducted in indoor parking areas. Experimental results show that the proposed method outperforms the state-of-the-art methods.

In this chapter, memetic strategies are analyzed for the Steiner tree problem in graphs as a classic network design problem. Steiner tree problems can model a wide range of real-life problems from fault recovery in wireless sensor networks through Web API recommendation systems. The Steiner tree problem is considered as a generalized minimum spanning tree problem. Whilst the objective function of the minimum spanning tree problems is to find the minimum-total-weight subset of edges that connects all the nodes, the Steiner tree problem does not include all the nodes. However, it still has the same objective function. It should be noted that this problem requires a subset of nodes, called terminals, to be connected and the rest of the nodes are optional for being included. The problem, unlike the minimum spanning tree, is NP-Complete, and hence necessitates the design of a hybrid metaheuristic as an appropriate solution strategy. We analyze memetic strategies, based on effective integration of different local search procedures into a genetic algorithm for tackling this very interesting problem. Computational experiments have been reported on evaluating the impact of individual components of the procedure and it is demonstrated that the proposed strategy is both effective and robust.

Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. As part of the joint IAG/FIG Working Groups 4.1.1 and 5.5 on Multi-sensor Systems, a benchmarking measurement campaign was conducted at The Ohio State University. Initial experiments have demonstrated that Cooperative Localization (CL) is extremely useful for positioning and navigation of platforms navigating in swarms or networks. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were equipped with combinations of GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), Raspberry Pi units, cameras, Light Detection and Ranging (LiDAR) and inertial sensors for CL. Pedestrians wore a specially designed helmet equipped with some of these sensors. An overview of the experimental configurations, test scenarios, characteristics and sensor specifications is given. It has been demonstrated that all involved sensor platforms in the different test scenarios have gained a significant increase in positioning accuracy by using ubiquitous user localization. For example, in the indoor environment, success rates of approximately 97

Hidden Markov Chains (HMCs) and, more recently, Hidden semi-Markov Chains (HsMCs) have been used by several groups of researchers to provide a model for indoor localization. A homogeneous HMC is completely determined by the state initial probability vector and the state transition probability matrix. This is also true for the HsMC provided the state duration probability is given. These parameters are often chosen heuristically but when sufficient measurement training data are available, they can be learned using the well-known Baum-Welch algorithm. Given the model parameters, approaches such as the forward-only algorithm, the forward-backwards algorithm and the Viterbi algorithm can be applied for state sequence inference under the HMC/HsMC framework. In indoor localization applications, there is often insufficient prior information to specify such parameters in advance of the application and they have to be learned from limited amounts of training data. In this paper, we endeavour to evaluate the parameter learning accuracy of the Baum-Welch algorithm using varying amounts of training data, and evaluate the influence of applying inaccurate model parameters on these typical state estimation algorithms under both the HMC and HsMC frameworks. All of the evaluations are based on received signal strength (RSS) for application to indoor localization.

Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. This paper reports about a sequence of extensive experiments, conducted at The Ohio State University (OSU) as part of the joint effort of the FIG/IAG WG on Multi-sensor Systems. Their overall aim is to assess the feasibility of achieving GNSS-like performance for ubiquitous positioning in terms of autonomous, global, preferably infrastructure-free positioning of portable platforms at affordable cost efficiency. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were used whereby cooperative positioning (CP) is the major focus to achieve a joint navigation solution. The GPSVan of The Ohio State University was used as the main reference vehicle and for pedestrians, a specially designed helmet was developed. The employed/tested positioning techniques are based on using sensor data from GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), vison-based positioning with cameras and Light Detection and Ranging (LiDAR) as well as inertial sensors. The experimental and initial results include the preliminary data processing, UWB sensor calibration and Wi-Fi indoor positioning with room-level granularity and platform trajectory determination. The results demonstrate that CP techniques are extremely useful for positioning of platforms navigating in swarms or networks. A significant performance improvement in terms of positioning accuracy and reliability is achieved. Using UWB, decimeter-level positioning accuracy is achievable under typical conditions, such as normal walls, average complexity buildings, etc. Using Wi-Fi fingerprinting, success rates of approximately 97 % were obtained for correctly detecting the room-level location of the user.

This paper discusses the main source of background in detecting radioactivity of low-level radiation and the methods to reduce the background in practical application.

Mobile devices regularly broadcast WiFi probe requests in order to discover available proximal WiFi access points for connection. A probe request, sent automatically in the active scanning mode, consisting of the MAC address of the device expresses an advertisement of its presence. A real-time wireless sniffing system is able to sense WiFi packets and analyse wireless traffic. This provides an opportunity to obtain insights into the interaction between the humans carrying the mobile devices and the environment. Susceptibility to loss of the wireless data transmission is an important limitation on this idea, and this is complicated by the lack of a standard specification for real deployment of WiFi sniffers. In this paper, we present an experimental analysis of sniffing performance under different wireless environments using off-the-shelf products. Our objective is to identify the possible factors including channel settings and access point configurations that affect sniffing behaviours and performances, thereby enabling the design of a protocol for a WiFi sniffing system under the optimal monitoring strategy in a real deployment. Our preliminary results show that four main factors affect the sniffing performance: the number of access points and their corresponding operating channels, the signal strength of the access point and the number of devices in the vicinity. In terms of a real field deployment, we propose assignment of one sniffing device to each specific sub-region based on the local access point signal strength and coverage area and fixing the monitoring channel belongs to the local strongest access point.

In this paper, the relationship between detection limit and error probability is studied based on the statistical law of radioactivity. The conversion method between the lowest detectable radioactivity count and the activity can be obtained, which provides the basis for setting the alarm threshold reasonably.

The increasing demand for reliable indoor navigation systems is leading the research community to investigate various approaches to obtain effective solutions usable with mobile devices. Among the recently proposed strategies, Ultra-Wide Band (UWB) positioning systems are worth to be mentioned because of their good performance in a wide range of operating conditions. However, such performance can be significantly degraded by large UWB range errors; mostly, due to non-line-of-sight (NLOS) measurements. This paper considers the integration of UWB with vision to support navigation and mapping applications. In particular, this work compares positioning results obtained with a simultaneous localization and mapping (SLAM) algorithm, exploiting a standard and a Time-of-Flight (ToF) camera, with those obtained with UWB, and then with the integration of UWB and vision. For the latter, a deep learning-based recognition approach was developed to detect UWB devices in camera frames. Such information is both introduced in the navigation algorithm and used to detect NLOS UWB measurements. The integration of this information allowed a 20% positioning error reduction in this case study.

Cooperative positioning (CP) utilises information sharing among multiple nodes to enable positioning in Global Navigation Satellite System (GNSS)-denied environments. This paper reports the performance of a CP system for pedestrians using Ultra-Wide Band (UWB) technology inGNSS-denied environments. This data set was collected as part of a benchmarking measurementcampaign carried out at the Ohio State University in October 2017. Pedestrians were equippedwith a variety of sensors, including two different UWB systems, on a specially designed helmetserving as a mobile multi-sensor platform for CP. Different users were walking in stop-and-go modealong trajectories with predefined checkpoints and under various challenging environments. Inthe developed CP network, both Peer-to-Infrastructure (P2I) and Peer-to-Peer (P2P) measurementsare used for positioning of the pedestrians. It is realised that the proposed system can achievedecimetre-level accuracies (on average, around 20 cm) in the complete absence of GNSS signals,provided that the measurements from infrastructure nodes are available and the network geometryis good. In the absence of these good conditions, the results show that the average accuracydegrades to meter level. Further, it is experimentally demonstrated that inclusion of P2P cooperativerange observations further enhances the positioning accuracy and, in extreme cases when only oneinfrastructure measurement is available, P2P CP may reduce positioning errors by up to 95%. Thecomplete test setup, the methodology for development, and data collection are discussed in thispaper. In the next version of this system, additional observations such as theWi-Fi, camera, and othersignals of opportunity will be included.

Increasingly, safety and liability critical applications require GNSS-like positioning metrics in environments where GNSS cannot work. Indoor navigation for the vision impaired and other mobility restricted individuals, emergency responders and asset tracking in buildings demand levels of positioning accuracy and integrity that cannot be satisfied by current indoor positioning technologies and techniques. This paper presents the challenges facing positioning technologies for indoor positioning and presents innovative algorithms and approaches that aim to enhance performance in these difficult environments. The overall aim is to achieve GNSS-like performance in terms of autonomous, global, infrastructure free, portable and cost efficient. Preliminary results from a real-world experimental campaign conducted as part of the joint FIG Working Group 5.5 and IAG Sub-commission 4.1 on multi-sensor systems, demonstrate performance improvements based on differential Wi-Fi (DWi-Fi) and cooperative positioning techniques. The techniques, experimental schema and initial results will be fully documented in this paper.

Localisation and navigation are still two of the most important issues in mobile robotics. In certain indoor application scenarios RFID (radio frequency identification)-based absolute localisation has been found to be especially successful in supporting navigation. In this paper we evaluate the feasibility of an RFID and compass based approach to robot localisation and navigation for indoor environments that are dominated by corridors. We describe our system and evaluate its performance in a small, but full-scale, test