This thesis investigates the RF characteristics of Mica-Z wireless unattended sensor networks for military and commercial applications. Several experimental configurations were designed and experiments carried out to observe and analyze the behavior of the Mica-Z sensor network. The Mica-Z moteâ s propagation characteristics and network performance were measured under near free-space, indoor and outdoor environments to provide a comprehensive perspective of typical sensor network characteristics. Link-break and re-association distances with their corresponding RF power measurements were recorded to determine the Mica-Zâ s range characteristics under these different operating environments. Power loss exponents were also estimated to provide Mica-Z users a faster and more convenient way to estimate operating ranges in the different environments. A graphical numeric electromagnetic code (GNEC) simulation was also used to investigate some of the possible improvements that could be made to the existing Mica-Z antenna design to enhance the performance of the sensor network. This thesis substantiates the difficulties of operating such sensor networks in the most hostile environments. Although the measurements and analyses demonstrated that controlled deployment was possible to some extent, the effectiveness of deployment remains challenging especially for random ad-hoc deployment.

Wireless sensor networks have been a key driver of innovation and societal progressover the last three decades. They allow for simplicity because they eliminate ca-bling complexity while increasing the flexibility of extending or adjusting networksto changing demands. Wireless sensor networks are a powerful means of fillingthe technological gap for ever-larger industrial sites of growing interconnection andbroader integration. Nonetheless, the management of wireless networks is difficultin situations wherein communication requires application-specific, network-widequality of service guarantees. A minimum end-to-end reliability for packet arrivalclose to 100% in combination with latency bounds in the millisecond range must befulfilled in many mission-critical applications.The problem addressed in this thesis is the demand for algorithmic support forend-to-end quality of service guarantees in mission-critical wireless sensor networks.Wireless sensors have traditionally been used to collect non-critical periodic read-ings; however, the intriguing advantages of wireless technologies in terms of theirflexibility and cost effectiveness justify the exploration of their potential for controland mission-critical applications, subject to the requirements of ultra-reliable com-munication, in harsh and dynamically changing environments such as manufactur-ing factories, oil rigs, and power plants.This thesis provides three main contributions in the scope of wireless sensor net-works. First, it presents a scalable algorithm that guarantees end-to-end reliabilitythrough scheduling. Second, it presents a cross-layer optimization/configurationframework that can be customized to meet multiple end-to-end quality of servicecriteria simultaneously. Third, it proposes an extension of the framework used toenable service differentiation and priority handling. Adaptive, scalable, and fast al-gorithms are proposed. The cross-layer framework is based on a genetic algorithmthat assesses the quality of service of the network as a whole and integrates the phys-ical layer, medium access control layer, network layer, and transport layer.Algorithm performance and scalability are verified through numerous simula-tions on hundreds of convergecast topologies by comparing the proposed algorithmswith other recently proposed algorithms for ensuring reliable packet delivery. Theresults show that the proposed SchedEx scheduling algorithm is both significantlymore scalable and better performing than are the competing slot-based schedulingalgorithms. The integrated solving of routing and scheduling using a genetic al-vvigorithm further improves on the original results by more than 30% in terms of la-tency. The proposed framework provides live graphical feedback about potentialbottlenecks and may be used for analysis and debugging as well as the planning ofgreen-field networks.SchedEx is found to be an adaptive, scalable, and fast algorithm that is capa-ble of ensuring the end-to-end reliability of packet arrival throughout the network.SchedEx-GA successfully identifies network configurations, thus integrating the rout-ing and scheduling decisions for networks with diverse traffic priority levels. Fur-ther, directions for future research are presented, including the extension of simula-tions to experimental work and the consideration of alternative network topologies. / <p>Vid tidpunkten för disputationen var följande delarbeten opublicerade: delarbete 4 (manuskript inskickat för granskning), delarbete 5 (manuskript inskickat för granskning)</p><p>At the time of the doctoral defence the following papers were unpublished: paper 4 (manuscript under review), paper 5 (manuscript under review)</p>

A key barrier in the adoption of Wireless Sensor Networks (WSNs) is achieving long-lived and robust real-life deployments. Issues include: reducing the impact of transmission loss, node failure detection, accommodating multiple sensor modalities, and the energy requirement of the WSN network stack. In systems where radio transmissions are the largest energy consumer on a node, it follows that reducing the number of transmissions will, in turn, extend node lifetime. Research in this area has led to the development of the Dual Prediction Scheme (DPS). However, the design of specific DPS algorithms in the literature have not typically considered issues arising in real world deployments. Therefore, this thesis proposes solutions to enable DPSs to function in robust and long-lived real-world WSN deployments. To exemplify the proposed solutions, Cogent-House, an end-to-end open-source home environmental and energy monitoring system, is considered as a case study. Cogent-House was deployed in 37 homes generating 235 evaluation data traces, each spanning periods of two weeks to a year. DPSs presented within the literature are often lacking in the ability to handle several aspects of real world deployments. To address issues in real-life deployments this thesis proposes a novel generalised framework, named Generalised Dual Prediction Scheme (G-DPS). G-DPS provides: i) a multi-modal approach, ii) an acknowledgement scheme, iii) heartbeat messages, and iv) a method to calculate reconstructed data yield. G-DPS’s multi-modal approach allows multiple sensor’s readings to be combined into a single model, compared to single-modal which uses multiple instances of a DPS. Considering a node sensing temperature, humidity and CO2, the multi-modal approach transmissions are reduced by up to 27%, signal reconstruction accuracy is improved by up to 65%, and the energy requirement of nodes is reduced by 15% compared to single-modal DPS. In a lossy network use of acknowledgements improves signal reconstruction accuracy by up to 2x and increases the data yield of the system up to 7x, when compared to an acknowledgement-less scheme, with only up to a 1.13x increase in energy consumption. Heartbeat messages allow the detection of faulty nodes, and yet do not significantly impact the energy requirement of functioning nodes. Implementing DPS algorithms within the G-DPS framework enables robust deployments, as well as easier comparison of performance between differing approaches. DPSs focus on modelling sensed signals, allowing accurate reconstruction of the signal from fewer transmissions. Although transmission scan be reduced in this way, considerable savings are also possible at the application level. Given the information needs of a specific application, raw sensor measurement data is often highly compressible. This thesis proposes the Bare Necessities (BN) algorithm, which exploits on-node analytics by transforming data to information closer to the data source (the sensing device). This approach is evaluated in the context of a household monitoring application that reports the percentage of time a room of the home spends in various environmental conditions. BN can reduce the number of packets transmitted to the sink by 7000x compared to a sense-and-send approach. To support the implementation of the above solutions in achieving long lifetimes, this thesis explores the impact of the network stack on the energy consumption of low transmission sensor nodes. Considering a DPS achieving a 20x transmission reduction, the energy reduction of anode is only 1.3x when using the TinyOS network stack. This thesis proposes the Backbone Collection Tree Protocol (B-CTP), a networking approach utilising a persistent backbone network of powered nodes. B-CTP coupled with Linear Spanish Inquisition Protocol (L-SIP) decreases the energy requirement for sensing nodes by 13.4x compared to sense-and-send nodes using the TinyOS network stack. When B-CTP is coupled with BN an energy reduction of 14.1x is achieved. Finally, this thesis proposes a quadratic spline reconstruction method which improves signal reconstruction accuracy by 1.3x compared to commonly used linear interpolation or model prediction based reconstruction approaches. Incorporating sequence numbers into the quadratic spline method allows up to 5 hours of accurate signal imputation during transmission failure. In summary, the techniques presented in this thesis enable WSNs to be long-lived and robust in real-life deployments. Furthermore, the underlying approaches can be applied to existing techniques and implemented for a wide variety of applications.

Wireless sensor networks are widely applied to many fields such as animal habitat monitoring, air traffic control, and health monitoring. One of the current problems with wireless sensor networks is the ability to overcome communication failures due to hardware failure, distributing sensors in an uneven geographic area, or unexpected obstacles between sensors. One common solution to overcome this problem is to place a minimum number of relay nodes among sensors so that the communication among sensors is guaranteed. This is called Relay Node Placement Problem (RNP). This problem has been proved as NP-hard for a simple connected graph. Therefore, many algorithms have been developed based on Steiner graphs. Since RNP for a connected graph is NP-hard, the RNP for a survivable network has been conjectured as NP-hard and the algorithms for a survivable network have also been developed based on Steiner graphs. In this study, we show the new approximation bound for the survivable wireless sensor networks using the Steiner graphs based algorithm. We prove that the approximation bound is guaranteed in an environment where some obstacles are laid, and also propose the newly developed algorithm which places fewer relay nodes than the existing algorithms. Consequently, the main purpose of this study is to find the minimum number of relay nodes in order to meet the survivability requirements of wireless sensor networks.

Creating a secure wireless sensor network involves authenticating and encrypting messages that are sent throughout the network. The communicating nodes must agree on secret keys in order to be able to encrypt packets. Sensor networks do not have many resources and so, achieving such key agreements is a difficult matter. Many key agreement schemes like Diffie-Hellman and public-key based schemes are not suitable for wireless sensor networks. Pre-distribution of secret keys for all pairs of nodes is not viable due to the large amount of memory used when the network size is large. We propose a novel key management system that works with the random key pre-distribution scheme where deployment knowledge is unknown. We show that our system saves users from spending substantial resources when deploying networks. We also test the new system’s memory usage, and security issues. The system and its performance evaluation are presented in this thesis.

As redes de sensores sem fio (RSSF) são uma tecnologia que ganhou muita importância nos últimos anos. Dentro das diversas aplicações para essas redes, o rastreamento de alvos é considerado essencial. Nessa aplicação, a RSSF deve determinar, de forma colaborativa, a trajetória de um ou mais alvos que se encontrem dentro de sua área de cobertura. O presente trabalho apresenta um algoritmo colaborativo baseado na fatoração multifrontal QR para estimação de trajetórias de alvos com RSSF. A solução proposta está inserida no âmbito da estimação por lotes, na qual os dados são coletados pelos sensores durante a aplicação e só no final é realizada a estimativa da trajetória do alvo. Uma vez coletados os dados, o problema pode ser modelado como um sistema de equações sobredeterminado Ax = b cuja característica principal é ser esparso. A solução desse sistema é dada mediante o método de mínimos quadrados, no qual o sistema é transformado num sistema triangular superior, que é solucionado mediante substituição inversa. A fatoração multifrontal QR é ideal neste contexto devido à natureza esparsa da matriz principal do sistema. A fatoração multifrontal QR utiliza um grafo denominado árvore de eliminação para dividir o processo de fatoração de uma matriz esparsa em fatorações densas de pequenas submatrizes denominadas matrizes frontais. Mapeando a árvore de eliminação na RSSF consegue-se que essas fatorações densas sejam executadas pelos nós sensoriais que detectaram o alvo durante seu trajeto pela rede. Dessa maneira, o algoritmo consegue realizar a fatoração da matriz principal do problema de forma colaborativa, dividindo essa tarefa em pequenas tarefas que os nós de sensoriais da rede possam realizar. / Wireless Sensor Networks (WSN) is a technology that have gained a lot of importance in the last few years. From all the possible applications for WSN, target tracking is considered essential. In this application, the WSN has to determine, in a collaborative way, the trajectory of one or more targets that are within the sensing area of the network. The aim of this document is to present a collaborative algorithm based on multifrontal QR factorization for the solution of the target trajectory estimation problem with WSN. This algorithm uses a batch estimation approach, which assumes that all sensing data are available before the estimation of the target trajectory. If all the observations of the target trajectory is available, the problem can be modeled as an overdetermined system of equations Ax = b where A is sparse. This system of equations is solved by least squares method. The multifrontal QR factorization uses a tree graph called elimination tree to reorganize the overall factorization of a sparse matrix into a sequence of partial factorizations of dense smaller matrices named frontal matrices. By mapping the elimination tree into the WSN, the sensor nodes that observed the target can factorize the frontal matrices. In this manner, the WSN factorizes the matrix A in a collaborative way, dividing the work in small tasks that the sensor nodes could execute.

Energy e ciency is a critical constraint in wireless sensor networks. Wireless sensor networks (WSNs) consist of a large number of battery-powered sensor nodes, connected to each other and equipped with low-power transmission radios. Usually, the sensor nodes closer to the sink are more likely to become overloaded and subject to draining their battery faster than the nodes farther away, creating a funneling e ect. The use of a mobile device as a sink node to perform data gathering is a well known solution to balance the energy consumption in the entire network. To address this problem, in this work we consider the use of an UAV as a mobile sink. An unmanned aircraft vehicle (UAV) is an aircraft without a human pilot on-board, popularly known as a Drone. In this thesis, besides the use of the UAV as a mobile sink node, we propose an UAV-aided algorithm for data gathering in wireless sensor networks, called Humming- bird. Our distributed algorithm is energy-e cient. Rather than using an arbitrary path, the UAV implements an approximation algorithm to solve the well-known NP- Hard problem, the Traveling Salesman Problem (or TSP), to setup the trajectory of node points to visit for data gathering. In our approach, both the path planning and the data gathering are performed by the UAV, and this is seamlessly integrated with sensor data reporting. The results, using ns-3 network simulator show that our algorithm improves the network lifetime compared to regular (non-UAV) data gathering, especially for data intensive applications. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Wireless sensor networks used to have a main role as a monitoring tool for environmental purposes and animal tracking. This spectrum of applications, however, has dramatically grown in the past few years. Such evolution means that what used to be application-specific networks are now multi application environments, often with federation capabilities. This shift results in a challenging environment for data privacy, mainly caused by the broadening of the spectrum of data access points and involved entities. This thesis first evaluates existing privacy preserving data aggregation techniques to determine how suitable they are for providing data privacy in this more elaborate environment. Such evaluation led to the design of the set difference attack, which explores the fact that they all rely purely on data aggregation to achieve privacy, which is shown through simulation not to be suitable to the task. It also indicates that some form of uncertainty is required in order to mitigate the attack. Another relevant finding is that the attack can also be effective against standalone networks, by exploring the node availability factor. Uncertainty is achieved via the use of differential privacy, which offers a strong and formal privacy guarantee through data perturbation. In order to make it suitable to work in a wireless sensor network environment, which mainly deals with time-series data, two new approaches to address it have been proposed. These have a contrasting effect when it comes to utility and privacy levels, offering a flexible balance between privacy and data utility for sensed entities and data analysts/consumers. Lastly, this thesis proposes a framework to assist in the design of privacy preserving data aggregation protocols to suit application needs while at the same time complying with desired privacy requirements. The framework's evaluation compares and contrasts several scenarios to demonstrate the level of flexibility and effectiveness that the designed protocols can provide. Overall, this thesis demonstrates that data perturbation can be made significantly practical through the proposed framework. Although some problems remain, with further improvements to data correlation methods and better use of some intrinsic characteristics of such networks, the use of data perturbation may become a practical and efficient privacy preserving mechanism for wireless sensor networks.

Wireless sensor networks (WSNs) provide rapid, untethered access to information, eliminating the barriers of distance, time, and location for many applications in national security, civilian search and rescue operations, surveillance, border monitoring, and many more. Sensor nodes are resource constraint in terms of power, bandwidth, memory, and computing capabilities. Sensor nodes are typically battery powered and depending on the application, it may be impractical or even impossible to recharge them. Thus, it is important to develop mechanisms for WSN which are energy efficient, in order to reduce the energy consumption in the network. Energy efficient algorithms result in an increased network lifetime. Data gathering is an important operation in WSNs, dealing with collecting sensed data or event reporting in a timely and efficient way. There are various scenarios that have to be carefully addressed. In this dissertation we propose energy efficient algorithms for data gathering. We propose a novel event-based clustering mechanism, and propose several efficient data gathering algorithms for mobile sink WSNs and for spatio-temporal events. Border surveillance is an important application of WSNs. Typical border surveillance applications aim to detect intruders attempting to enter or exit the border of a certain region. Deploying a set of sensor nodes on a region of interest where sensors form barriers for intruders is often referred to as the barrier coverage problem. In this dissertation we propose some novel mechanisms for increasing the percentage of events detected successfully. More specifically, we propose an adaptive sensor rotation mechanism, which allow sensors to decide their orientation angle adaptively, based on the location of the incoming events. In addition, we propose an Unmanned Aerial Vehicle UAV aided mechanism, where an UAV is used to cover gaps dynamically, resulting in an increased quality of the surveillance. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

In this thesis, we focus on topics relevant to developing and deploying large-scale wireless sensor network (WSN) applications within real dynamic urban environments. Given few reported experiences in the literature, we designed our own such network to provide a foundation for our research. The Smart Condo, a well-defined project with the goal of helping people age in place, provided the setting for our WSN that would non-intrusively monitor an occupant and environment. Although we carefully designed, developed, and deployed the network, all of our planning did not prepare us for a key challenge of that environment: significant radio-frequency interference. Most researchers tend to ignore the existence of interference along with its potentially serious implications: beyond impacting network performance, it can lead researchers to misleading or unrealistic conclusions. Interference is a particularly difficult problem to study because it varies in time, space, and intensity. Other researchers have typically approached the problem by investigating only known interferers. Instead, we approach the problem more generally and consider interference of unknown origins. We envision nodes periodically observing their environment, recognizing patterns in those observations, and responding appropriately, so we use only standard WSN nodes for our data collection. Unfortunately, collecting high-resolution data is difficult using these simple devices, and to the best of our knowledge, other researchers have only used them to collect rather coarse data. Within the Smart Condo urban environment, we recorded a transceiver's received power level at 5000 Hz, a higher rate than we encountered elsewhere in the literature, using 16 synchronized nodes. We explored traces from 256 channels and observed a number of recurring patterns; we then investigated classifying traces automatically and obtained rather promising results. We focused on the two patterns most detrimental to packet reception rates and further investigated both sampling and classification techniques tailored to them. As part of our work, we extended our simulator, making it capable of generating impulsive interference, and developed a proof-of-concept pattern-aware medium access control (MAC) protocol. Through experiments using both the simulator and WSN devices, we evaluated the classifier and proof-of-concept MAC. Our results show that impressive gains in the packet reception rates are possible when nodes can recognize and appropriately react to interference. Using our techniques, nodes can communicate more efficiently by reducing the number of failed transmissions and consequently decreasing overall network congestion.