On Localization Issues of Mobile Devices

Mobile devices, such as sensor nodes, smartphones and smartwatches, are now widely used in many applications. Localization is a highly important topic in wireless networks as well as in many Internet of Things applications. In this thesis, four novel localization schemes of mobile devices are introduced to improve the localization performance in three different areas, like the outdoor, indoor and underwater environments. Firstly, in the outdoor environment, many current localization algorithms are based on the Sequential Monte MCL, the accuracy of which is bounded by the radio range. High computational complexity in the sampling step is another issue of these approaches. Tri-MCL is presented, which significantly improves on the accuracy of the Monte Carlo Localization algorithm. To do this, three different distance measurement algorithms based on range-free approaches are leveraged. Using these, the distances between unknown nodes and anchor nodes are estimated to perform more fine-grained filtering of the particles as well as for weighting the particles in the final estimation step of the algorithm. Simulation results illustrate that the proposed algorithm achieves better accuracy than the MCL and SA-MCL algorithms. Furthermore, it also exhibits high efficiency in the sampling step. Then, in the GPS-denied indoor environment, Twi-Adaboost is proposed, which is a collaborative indoor localization algorithm with the fusion of internal sensors such as the accelerometer, gyroscope and magnetometer from multiple devices. Specifically, the datasets are collected firstly by one person wearing two devices simultaneously: a smartphone and a smartwatch, each collecting multivariate data represented by their internal parameters in a real environment. Then, the datasets from these two devices are evaluated for their strengths and weaknesses in recognizing the indoor position. Based on that, the Twi-AdaBoost algorithm, an interactive ensemble learning method, is proposed to improve the indoor localization accuracy by fusing the co-occurrence information. The performance of the proposed algorithm is assessed on a real-world dataset. The experiment results demonstrate that Twi-AdaBoost achieves a localization error about 0.39 m on average with a low deployment cost, which outperforms the state-of-the-art indoor localization algorithms. Lastly, the characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, and sparse deployment, pose challenging issues for successful localization of sensor nodes. In addition, sensor nodes in UWSNs are usually powered by batteries whose replacements introduces high cost and complexity. Thus, the critical problem in UWSNs is to enable each sensor node to find enough anchor nodes in order to localize itself, with minimum energy costs. An Energy-Efficient Localization Algorithm (EELA) is proposed to analyze the decentralized interactions among sensor nodes and anchor nodes. A Single-Leader-Multi-Follower Stackelberg game is utilized to formulate the topology control problem of sensor nodes and anchor nodes by exploiting their available communication opportunities. In this game, the sensor node acts as a leader taking into account factors such as `two-hop' anchor nodes and energy consumption, while anchor nodes act as multiple followers, considering their ability to localize sensor nodes and their energy consumption. I prove that both players select best responses and reach a socially optimal Stackelberg Nash Equilibrium. Simulation results demonstrate that the proposed EELA improves the performance of localization in UWSNs significantly, and in particular the energy cost of sensor nodes. Compared to the baseline schemes, the energy consumption per node is about 48% lower in EELA, while providing a desirable localization coverage, under reasonable error and delay. Based on the EELA scheme, an Adaptive Energy Efficient Localization Algorithm using the Fuzzy game theoretic method (Adaptive EELA) is proposed to solve the environment adaptation problem of EELA. The adaptive neuro-fuzzy method is used as the utility function of the Single-Leader-Multi-Follower Stackelberg game to model the dynamical changes in UWSNs. The proposed Adaptive EELA scheme is able to automatically learn in the offline phase, which is required only once. Then, in the online phase, it can adapt to the environmental changes, such as the densities of nodes or topologies of nodes. Extensive numerical evaluations are conducted under different network topologies and different network node densities. The simulation results demonstrate that the proposed Adaptive EELA scheme achieves about 35% and 66% energy reduction per node on average comparing the state-of-the-art approaches, such as EELA and OLTC, while providing a desirable localization coverage, localization error and localization delay

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

Node localization in underwater sensor networks (UWSN)

This dissertation focuses on node localization in underwater wireless sensor networks (UWSNs) where anchor nodes have knowledge of their own locations and communicate with sensor nodes in acoustic or magnetic induction (MI) means. The sensor nodes utilize the communication signals and the locations of anchor nodes to locate themselves and propagate their locations through the network. For UWSN using MI communications, this dissertation proposes two localization methods: rotation matrix (RM)-based method and the distance-based method. Both methods require only two anchor nodes with arbitrarily oriented tri-directional coils to locate one sensor node in the 3-D space, thus having advantages in a sparse network. Simulation studies show that the RM-based method achieves high localization accuracy, while the distance-based method exhibits less computational complexity. For UWSN using acoustic communications, this dissertation proposes a novel multi-hop node localization method in the 2-D and 3-D spaces, respectively. The proposed method estimates Euclidean distances to anchor nodes via multi-hop propagations with the help of angle of arrival (AoA) measurements. Simulation results show that the proposed method achieves better localization accuracy than existing multi-hop methods, with high localization coverage. This dissertation also investigates the hardware implementation of acoustic transmitter and receiver, and conducted field experiments with the hardware to estimate ToA using single pseudo-noise (PN) and dual PN(DPN) sequences. Both simulation and field test results show that the DPN sequences outperform the single PNs in severely dispersive channels and when the carrier frequency offset (CFO) is high --Abstract, page iv

Visible Light Communication (VLC)

Visible light communication (VLC) using light-emitting diodes (LEDs) or laser diodes (LDs) has been envisioned as one of the key enabling technologies for 6G and Internet of Things (IoT) systems, owing to its appealing advantages, including abundant and unregulated spectrum resources, no electromagnetic interference (EMI) radiation and high security. However, despite its many advantages, VLC faces several technical challenges, such as the limited bandwidth and severe nonlinearity of opto-electronic devices, link blockage and user mobility. Therefore, significant efforts are needed from the global VLC community to develop VLC technology further. This Special Issue, “Visible Light Communication (VLC)”, provides an opportunity for global researchers to share their new ideas and cutting-edge techniques to address the above-mentioned challenges. The 16 papers published in this Special Issue represent the fascinating progress of VLC in various contexts, including general indoor and underwater scenarios, and the emerging application of machine learning/artificial intelligence (ML/AI) techniques in VLC

Development of Wireless Sensor Network Technology for Soil Property Monitoring

In the first part of this thesis, we designed and developed a soil property monitoring system with two generation evolutions. Soil property parameter data including soil moisture, soil electrical conductivity and near-surface soil temperature were collected from using two kinds of sensors embedded in four different depths as 50.8, 152.4, 304.8 and 609.6mm underground. A hybrid sensor network (HSN) conceptual system module including in-field wireless sensor network, cellular network and the Internet was put forward and realized for transmitting in-field data to the lab infrastructure. Results from the first part of the thesis indicated that wireless sensor network technologies had considerable advantages in field data collection and transmission over the traditional invasive methods. System quality of service testing results as: the average packet delivery rate and in-field data error rate were 95.05% and 0% until day of test while the averaged valid data rate was above 97% in general for each node. In the second part of this thesis, outdoor as well as off-line laboratory statistical experiments were carried out for analyzing impacts of impact factors as plant height, antenna gain, transmitter & receiver height, and separation distance to in-field radio propagation and modeling radio wave path loss to make the communication performance predictable. The experiments were divided into three blocks by two plant heights as 0.05m and 0.4m. Three widely used path loss models as COST-Hata, free space and plane earth models were included for verifying their applicability for in-field scenarios. Multi-variable linear regression was applied for relating the path loss to the impact factors. Results from the second part of the thesis indicated that the blocking criteria selection was proper and the Fresnel clearance dominated the in-field radio wave attenuation while the influence of reflection patterns was non-ignorable. R2 for the four regressed path loss prediction model was 0.822, 0.810, 0.843, and 0.899, respectively. By comparing measured path loss to predicted ones using general and regressed models, COST-Hata model yielded the highest difference, free space model yielded the second highest difference, and specific regressed models generated the least difference in most cases. A threshold of 70m was obtained for reliable communication in the worst cases of our experimental scenarios.Biosystems and Agricultural Engineerin

Journal of Telecommunications and Information Technology, 2017, nr 1

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