In recent years, advances in signal processing have led to small, low power, inexpensive Wireless Sensor Network (WSN). The signal processing in WSN is different from the traditional wireless networks in two critical aspects: firstly, the signal processing in WSN is performed in a fully distributed manner, unlike in traditional wireless networks; secondly, due to the limited computation capabilities of sensor networks, it is essential to develop an energy and bandwidth efficient signal processing algorithms. Target localisation and tracking problems in WSNs have received considerable attention recently, driven by the necessity to achieve higher localisation accuracy, lower cost, and the smallest form factor. Received Signal Strength (RSS) based localisation techniques are at the forefront of tracking research applications. Since tracking algorithms have been attracting research and development attention recently, prolific literature and a wide range of proposed approaches regarding the topic have emerged. This thesis is devoted to discussing the existing WSN-based localisation and tracking approaches. This thesis includes five studies. The first study leads to the design and implementation of a triangulation-based localisation approach using RSS technique for indoor tracking applications. The presented work achieves low localisation error in complex environments by predicting the environmental characteristics among beacon nodes. The second study concentrates on investigating a fingerprinting localisation method for indoor tracking applications. The proposed approach offers reasonable localisation accuracy while requiring a short period of offline computation time. The third study focuses on designing and implementing a decentralised tracking approach for tracking multiple mobile targets with low resource requirements. Despite the interest in target tracking and localisation issues, there are few systems deployed using ZigBee network standard, and no tracking system has used the full features of the ZigBee network standard. Tracking through the ZigBee is a challenging task when the density of router and end-device nodes is low, due to the limited communication capabilities of end-device nodes. The fourth study focuses on developing and designing a practical ZigBee-based tracking approach. To save energy, different strategies were adopted. The fifth study outlines designing and implementing an energy-efficient approach for tracking applications. This study consists of two main approaches: a data aggregation approach, proposed and implemented in order to reduce the total number of messages transmitted over the network; and a prediction approach, deployed to increase the lifetime of the WSN. For evaluation purposes, two environmental models were used in this thesis: firstly, real experiments, in which the proposed approaches were implemented on real sensor nodes, to test the validity for the proposed approaches; secondly, simulation experiments, in which NS-2 was used to evaluate the power-consumption issues of the two approaches proposed in this thesis.

This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders' requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9).

Wireless sensor networks has been one of the major research topics in recent years because of its great potential for a wide range of applications. In some application scenarios, sensor nodes intend to report the sensing data to a far-field destination, which cannot be realized by traditional transmission techniques. Due to the energy limitations and the hardware constraints of sensor nodes, distributed transmit beamforming is considered as an attractive candidate for long-range communications in such scenarios as it can reduce energy requirement of each sensor node and extend the communication range. However, unlike conventional beamforming, which is performed by a centralized antenna array, distributed beamforming is performed by a virtual antenna array composed of randomly located sensor nodes, each of which has an independent oscillator. Sensor nodes have to coordinate with each other and adjust their transmitting signals to collaboratively act as a distributed beamformer. The most crucial problem of realizing distributed beamforming is to achieve carrier phase alignment at the destination. This thesis will investigate distributed beamforming from both theoretical and practical aspects. First, the bit error ratio performance of distributed beamforming with phase errors is analyzed, which is a key metric to measure the system performance in practice. We derive two distinct expressions to approximate the error probability over Rayleigh fading channels corresponding to small numbers of nodes and large numbers of nodes respectively. The accuracy of both expressions is demonstrated by simulation results. The impact of phase errors on the system performance is examined for various numbers of nodes and different levels of transmit power. Second, a novel iterative algorithm is proposed to achieve carrier phase alignment at the destination in static channels, which only requires one-bit feedback from the destination. This algorithm is obtained by combining two novel schemes, both of which can greatly improve the convergence speed of phase alignment. The advantages in the convergence speed are obtained by exploiting the feedback information more efficiently compared to existing solutions. Third, the proposed phase alignment algorithm is modified to track time-varying channels. The modified algorithm has the ability to detect channel amplitude and phase changes that arise over time due to motion of the sensors or the destination. The algorithm can adjust key parameters adaptively according to the changes, which makes it more robust in practical implementation.

Wireless ad hoc networks are expected to play an important role in civilian and military settings where wireless access to wired backbone is either ineffective or impossible. Wireless sensor networks are effective in remote data acquisition. Congestion control and power consumption in wireless ad hoc networks have received a lot of attention in recent research. Several algorithms have been proposed to reduce congestion and power consumption in wireless ad hoc and sensor networks. In this thesis, we focus upon two schemes, which deal with congestion control and power consumption issues. This thesis consists of two parts. In the first part, we describe a randomization scheme for congestion control in dynamic source routing protocol, which we refer to as RDSR. We also study a randomization scheme for GDSR protocol, a GPS optimized variant of DSR. We discuss RDSR and RGDSR implementations and present extensive simulation experiments to study their performance. Our results indicate that both RGDSR and RDSR protocols outperform their non-randomized counterparts by decreasing the number of route query packets. Furthermore, a probabilistic congestion control scheme based on local tuning of routing protocol parameters is shown to be feasible. In the second part we present a simulation based performance study of energy aware data centric routing protocol, EAD, proposed by X. Cheng and A. Boukerche. EAD reduces power consumption by requiring only a small percentage of the network to stay awake. Our experiments show that EAD outperforms the well-known LEACH scheme.

<p>U doktorskoj disertaciji obrađen je problem upravljanja montažnim<br />sistemima pomoću bežične senzorske tehnologije u cilju poboljšanja<br />efikasnosti proizvodnje i poboljšanja kvaliteta proizvoda. U okviru<br />ove disertacije je razvijen model bežičnog upravljačkog sistema za<br />upravljanje i nadzor industrijskih procesa. Glavni cilj istraživanja<br />je razvoj integrisanog sistema za praćenje pozicije radnog predmeta i<br />praćenje okolnosti u kojima se radni predmet nalazi u montažnim<br />sistemima. Rezultati istraživanja su potvrđeni eksperimentalnim<br />istraživanjem u laboratorijskoj i u realnoj industrijskoj sredini.</p> / <p>The dissertation deals with the problem of monitoring and controlling<br />industrial assembly lines by wireless sensor technology with the aim of<br />improving the efficiency of production and the quality of the product. A model<br />of a wireless controlling system has been developed for monitoring and<br />controlling industrial processes. The main focus of the study is the<br />development of an integrated system for monitoring the position of the<br />product and the influences on the product in the assembly lines. The results<br />are confirmed by experiments in a laboratory and real industrial environment.</p>

<p>U doktorskoj disertaciji razmatrana je mogućnost lokalizacije proizvoda primenom tehnologija Interneta stvari. Postavljen je model za lokalizaciju proizvoda koji primenjuje RFID tehnologiju i bežične senzorske mreže. U okviru modela, predložen je i realizovan hibridni metod za lokalizaciju proizvoda koji kombinuje podatke dobijene metodom najbližih suseda i metodom optimizacije rojem čestica, a zatim i hibridni metod za lokalizaciju proizvoda koji kombinuje podatke dobijene u RFID sistemu i u bežičnoj senzorskoj mreži. Mogućnosti primene predloženog modela su eksperimentalno ispitane u simuliranim sistemima i u laboratorijskoj okolini sa industrijskim elementima..</p> / <p>This dissertation discusses the possibilities of product localization based on Internet of things technologies. A model for product localization has been proposed based on RFID technology and wireless sensor networks. Within the model, a hybrid localization method which combines outputs from nearest neighbours method and particle swarm optimization for product localization has been proposed and developed, followed by a hybrid localization method which combines data from RFID system and wireless sensor network. The application possibilities for the proposed model have been experimentally tested in simulated systems and in laboratory conditions with industrial elements.</p>

Simulations are a good method for the verification of the correct operation of solar-powered sensor nodes over the desired lifetime. They do, however, require accurate models to capture the influences of the loads and solar energy harvesting system. Artificial neural networks promise a simplification and acceleration of the modeling process in comparison to state-of-the-art modeling methods. This work focuses on the influence of the modeling process's different configurations on the accuracy of the model. It was found that certain parameters, such as the network's number of neurons and layers, heavily influence the outcome, and that these factors need to be determined individually for each modeled harvesting system. But having found a good configuration for the neural network, the model can predict the supercapacitor's charge depending on the solar current fairly accurately. This is also true in comparison to the reference models in this work. Nonetheless, the results also show a crucial need for improvements regarding the acquisition and composition of the neural network's training set.

Collecting data from sensor nodes to designated sinks is a common and challenging task in a wide variety of wireless sensor network (WSN) applications, ranging from animal monitoring to security surveillance. A number of approaches exploiting sink mobility have been proposed in recent years: some are proactive, in that sensor nodes push their read- ings to storage nodes from where they are collected by roaming mobile sinks, whereas others are reactive, in that mobile sinks pull readings from nearby sensor nodes as they traverse the sensor network. In this thesis, we point out that deciding which data collection approach is more energy-efficient depends on application characteristics, includ- ing the mobility patterns of sinks and the desired latency of collected data. We introduce novel adaptive data collection schemes that are able to automatically adjust to changing sink visiting patterns or data requirements, thereby significantly easing the deployment of a WSN. We illustrate cases where combining proactive and reactive modes of data collection is particularly beneficial. This motivates the design of TwinRoute, a novel hybrid algorithm that can flexibly mix the two col- lection modes at appropriate levels depending on the application sce- nario. Our extensive experimental evaluation, which uses synthetic and real-world sink traces, allows us to identify scenario characteristics that suit proactive, reactive or hybrid data collection schemes. It shows that TwinRoute outperforms the pure approaches in most scenarios, achiev- ing desirable tradeoffs between communication cost and timely delivery of sensor data.

This dissertation investigates data reduction strategies from a signal processing perspective in centralized detection and estimation applications. First, it considers a deterministic source observed by a network of sensors and develops an analytical strategy for ranking sensor transmissions based on the magnitude of their test statistics. The benefit of the proposed strategy is that the decision to transmit or not to transmit observations to the fusion center can be made at the sensor level resulting in significant savings in transmission costs. A sensor network based on target tracking application is simulated to demonstrate the benefits of the proposed strategy over the unconstrained energy approach. Second, it considers the detection of random signals in noisy measurements and evaluates the performance of eigenvalue-based signal detectors. Due to their computational simplicity, robustness and performance, these detectors have recently received a lot of attention. When the observed random signal is correlated, several researchers claim that the performance of eigenvalue-based detectors exceeds that of the classical energy detector. However, such claims fail to consider the fact that when the signal is correlated, the optimal detector is the estimator-correlator and not the energy detector. In this dissertation, through theoretical analyses and Monte Carlo simulations, eigenvalue-based detectors are shown to be suboptimal when compared to the energy detector and the estimator-correlator.

Swimming performance is primarily judged on the overall time taken for a swimmer to complete a specified distance performing a stroke that complies with current regulations defined by the Fédération Internationale de Natation (FINA), the International governing body of swimming. There are three contributing factors to this overall time; the start, free swimming and turns. The contribution of each of these factors is event dependent; for example, in a 50m event there are no turns, however, the start can be a significant contributor. To improve overall performance each of these components should be optimised in terms of skill and execution. This thesis details the research undertaken towards improving performance-related feedback in swimming. The research included collaboration with British Swimming, the national governing body for swimming in the U.K., to drive the requirements and direction of research. An evaluation of current methods of swimming analysis identified a capability gap in real-time, quantitative feedback. A number of components were developed to produce an integrated system for comprehensive swim performance analysis in all phases of the swim, i.e. starts, free swimming and turns. These components were developed to satisfy two types of stakeholder requirements. Firstly, the measurement requirements, i.e. what does the end user want to measure? Secondly, the process requirements, i.e. how would these measurements be achieved? The components developed in this research worked towards new technologies to facilitate a wider range of measurement parameters using automated methods as well as the application of technologies to facilitate the automation of current techniques. The development of the system is presented in detail and the application of these technologies is presented in case studies for starts, free swimming and turns. It was found that developed components were able to provide useful data indicating levels of performance in all aspects of swimming, i.e. starts, free swimming and turns. For the starts, an integrated solution of vision, force plate technology and a wireless iii node enabled greater insight into overall performance and quantitative measurements of performance to be captured. Force profiles could easily identify differences in swimmer ability or changes in technique. The analysis of free swimming was predominantly supported by the wireless sensor technology, whereby signal analysis was capable of automatically determining factors such as lap times variations within strokes. The turning phase was also characterised in acceleration space, allowing the phases of the turn to be individually assessed and their contribution to total turn time established. Each of the component technologies were not used in isolation but were supported by other synchronous data capture. In all cases a vision component was used to increase understanding of data outputs and provide a medium that coaches and athletes were comfortable with interpreting. The integrated, component based system has been developed and tested to prove its ability to produce useful, quantitative feedback information for swimmers. The individual components were found to be capable of providing greater insight into swimming performance, that has not been previously possible using the current state of the art techniques. Future work should look towards the fine-tuning of the prototype system into a useable solution for end users. This relies on the refinement of components and the development of an appropriate user interface to enable ease of data collection, analysis, presentation and interpretation.