Modern society relies heavily on communication networks that in turn rely on both wired and wireless infrastructure. This work pertains to scenarios where a group of people or robots need to communicate in an environment where there is no preexisting communications infrastructure. These include sites of emergencies and disasters (e.g., inside burning buildings, search and rescue operations) and unexplored areas on Earth and other planets. Wireless ad hoc or mesh networks offer the ability to keep such entities connected, but they falter when any single entity wishes to leave the developed coverage area. Utilizing mobile repeater nodes can help, but is costly and complicated. By eliminating the need for repeater nodes to traverse the environment, their size and cost can be vastly reduced. This work explores the use of static "breadcrumb" repeater nodes to increase the reach of such a network. Determining when and where to place a static repeater node can be difficult in an environment where radio propagation characteristics are unknown. In this work, several algorithms for node placement are compared under the constraint that placement of a static repeater node should not dictate the entity's movement. The algorithms investigated range from calculating rolling averages to modeling channel parameters on-the-fly. The placement algorithms were configured to run in real-time on TP-Link MR-3040 portable WiFi routers and the approach is demonstrated in an outdoor uncharacterized environment.

"In a wireless network environment, all the users are able to access the wireless channel. Thus, if malicious users exploit this feature by mimicking the characteristics of a normal user or even the central wireless access point (AP), they can intercept almost all the information through the network. This scenario is referred as a Man-in-the-middle (MITM) attack. In the MITM attack, the attackers usually set up a rogue AP to spoof the clients. In this thesis, we focus on the detection of MITM attacks in Wi-Fi networks. The thesis introduces the entire process of performing and detecting the MITM attack in two separate sections. The first section starts from creating a rogue AP by imitating the characteristics of the legitimate AP. Then a multi-point jamming attack is conducted to kidnap the clients and force them to connect to the rogue AP. Furthermore, the sniffer software is used to intercept the private information passing through the rogue AP. The second section focuses on the detection of MITM attacks from two aspects: jamming attacks detection and rogue AP detection. In order to enable the network to perform defensive strategies more effectively, distinguishing different types of jamming attacks is necessary. We begin by using signal strength consistency mechanism in order to detect jamming attacks. Then, based on the statistical data of packets send ratio (PSR) and packets delivery ratio (PDR) in different jamming situations, a model is built to further differentiate the jamming attacks. At the same time, we gather the received signal strength indication (RSSI) values from three monitor nodes which process the random RSSI values employing a sliding window algorithm. According to the mean and standard deviation curve of RSSI, we can detect if a rogue AP is present within the vicinity. All these proposed approaches, either attack or detection, have been validated via computer simulations and experimental hardware implementations including Backtrack 5 Tools and MATLAB software suite. "

Localization of individual nodes in a wireless network is useful in many applications, e.g for tracking patients in hospitals. Using the Received Signal Strength Indicator, RSSI, for this purpose has been explored in numerous studies. It is energy efficient and rarely requires customised hardware of configuration. The possibility to use pre-configured, off-the-shelf products is especially important in large scale sensor network deployments. Using RSSI has, however, many drawbacks, since the radio signal is heavily affected by the surrounding envi- ronment. Most studies in this area discuss the impact of multipath effects. Our study on range based distance estimations, using the Telos hardware, shows that individual profiling requirements and antenna quality are equally challenging. Still, RSSI based indoor localization systems remains an active field of research. A multitude of approaches and algorithms have been proposed to gain accuracy in position estimations. The most common of these techniques are explored in this report. Based on previous work at The Polytechnic University of Catalonia, the Telos hardware has been integrated successfully with existing software to form local wireless sensor networks for indoor localization. We present applications developed on top of TinyOS, an operating system for embedded systems. These applications serve as a platform for related future work at The Polytechnic University of Catalonia and elsewhere.

Wireless sensor network, which is constitute of autonomous sensors, is used for monitoring physical or environmental conditions like temperature, sound, pressure, and so on. The dispersed sensors or nodes will respectively pass their data through the network to the main location. Currently, several standards are ratified or in developing for wireless sensor network, like Wireless Hart, ISA, 100.11a, WIA-PAA, IEEE 802.15.4, etc. Among the standards, Zigbee is often used in industrial applications that require short-range and low-rate wireless transfer. In the research, all the data is collected under industrial environment using IEEE 802.15.4 compliant physical layer, some packets are interfered only by multi-path fading while others are also interfered by Wi-Fi interference. The goal of the thesis is to find out the dependence between the received power (RSS), correlation value (CORR) and bit error rate (BER) of the received message, and their distribution in situations both when the packet is lost or not. Besides, the performance of bit error rate such as the distribution and the features of burst error length under Wi-Fi interference or not will also be tested. All of them are based on a precise statistical processing.

Wireless communication today is a modern way to transport data from one location to another. One of the drawbacks of this feature is that a signal jammer can disrupt communications between the receiver and transmitter since the radio waves travel in the open air. This drawback can be exploited in both military and civilian applications. This thesis will aim on how to locate this kind of transmitting signal jammer over an open field using an Unmanned Aerial Vehicle (UAV) that searches the designated area with the assumption of line of sight between the UAV and the transmitting jammer. Two different methods will be investigated with different techniques, antennas and flight patterns. / Trådlös kommunikation idag är den moderna tidens sätt att transportera data från en plats till en annan. En nackdel med det är att en störsändare kan förhindra kommunikation mellan den sändande och mottagande antennen. Denna nackdel kan utnyttjas både inom militära och civila tillämpningar. Detta examensarbete undersöker två metoder för att lokalisera en störsändare över ett öppet fält med hjälp av en drönare, då fri sikt råder mellan drönare och störsändare. Dessa två olika metoder är baserade på olika tekniker, antenner och flygmönster.

In this work a demonstrator for phone as a key has been developed. This demonstrator estimates the position of a mobile device in and around a test vehicle. Trilateration, ROCRSSI and fingerprinting with neural networks have been investigated for the localization of the mobile device. To filter the input data Kalman filter and average mean filter have been investigated. The best performing technique was determined to be the use of neural networks with a Kalman filter. The resulting demonstrator can determine if the mobile device is within the ranges of three predefined zones for the technology and three zones defined in the thesis. The demonstrator can estimate the location of the mobile device in real-time for these zones.

The work is focused on localization in wireless networks and localization using inertial measurement units. There is also included a theoretical analysis of used localization techniques. The work also describes how to create application for data collection, application for receiving and processing data and used database. In conclusion of this work is verifying the functionality of whole system.

Locating a radio transmitter is important in a number of problems such as finding radio tags, people with radios, and devices that are collecting information in an unauthorized manner. Locating a radio transmitter is inherently difficult because the radio waves of concern are not in the visible spectrum, they reflect and distort easily, and they propagate at the speed of light. A number of methods for locating transmitters are currently used, the majority of which require expensive hardware and extensive processing. This thesis presents a method of using simpler measurements to produce similar location estimates in order to augment or replace current systems. While other systems have significant advantages, the methods proposed in this thesis are advantageous because they only require easily-obtained measurements that are based on the observed power of the transmission. The research uses simulations and experiments on real-world data collected locally to demonstrate the possibility of locating a transmitter using information of this type. The conclusion is that some methods are able to compensate for the difficulties in the problem more effectively and produce useful location estimates.

Typically a wireless LAN infrastructure is designed and installed by Networking professionals. These individuals are extremely familiar with wired networks, but are often unfamiliar with wireless networks. Thus, Wireless LAN installations are currently handicapped by the lack of an accurate, performance prediction model that is intuitive for use by non-wireless professionals. To provide a solution to this problem, this thesis presents a method of predicting the expected wireless LAN throughput using a site-specific model of an indoor environment. In order to develop this throughput prediction model, two wireless LAN throughput measurement products, LANFielder and SiteSpy, were created. These two products, which are patent pending, allow site-specific network performance measurements to be made. These two software packages were used to conduct an extensive measurement campaign to evaluate the performance of two IEEE 802.11b access points (APs) under ideal, multiuser, and interference scenarios. The data from this measurement campaign was then used to create empirically based throughput prediction models. The resulting models were first developed using RSSI measurements and then confirmed using predicted signal strength parameters. / Master of Science

O presente trabalho teve como objetivo apresentar uma metodologia que permita encontrar o posicionamento e rastrear as diferentes localizações de um suíno em uma baia, utilizando o valor do Receiver Signal Strenght Indicator (RSSI), entre o dispositivo móvel (suíno) e três dispositivos fixos, e uma Rede Neural Artificial do tipo Perceptron Multicamadas (PMC), responsável por interpretar os sinais RSSI e transformá-los em valores conhecidos, como em um plano cartesiano, com coordenadas no eixo X e eixo Y. A região de teste foi dividida em 289 pontos, sendo 286 utilizados para coleta de dados e para o treinamento da rede PMC. Para cada ponto, foram armazenados a sua posição dentro da baia e o valor RSSI entre o dispositivo móvel e os três dispositivos fixos. O processo foi repetido para 8 pontos escolhidos aleatoriamente dentro do espaço de teste e inseridos como entradas na rede PMC. Após treinamentos e operações realizadas com diversas arquiteturas foi possível concluir que àquela dotada de 10 neurônios na camada intermediária consistiu na melhor alternativa, cujos resultados de monitoramento e rastreamento das posições do dispositivo móvel foram encontradas com valores aceitáveis de exatidão. / This paper aims to present a methodology to find the positioning and tracking of the different locations of a pig in a stall, using the value of the Receiver Signal Strength Indicator (RSSI), between the mobile device (pig) and three devices fixed, and an Artificial Neural Network Multilayer Perceptron (MLP), responsible for interpreting the RSSI signals and turning them into known values, such as on a Cartesian plane, with coordinates on X axis and Y axis. The test region was divided into 289 points, with 286 points used for data collection and training of PMC network, and for each point, it was stored its position inside the stall and its RSSI value between the mobile devices and the three fixed. The process was repeated for 8 points chosen randomly within the space of test and entered as inputs into the PMC network. After training and operations with various architectures it was concluded that the architecture with 10 neurons in the hidden layer was the best alternative, whose the results of monitoring and tracking the positions of mobile device were found with acceptable accuracy.