This work is aimed to design power supply for nodes of wireless sensors networks using solar energy in indoor spaces. The proposal is made for the particular layout, but the process described can be applied generally. The result is the proposed involvement of the operating power circuit for node of wireless sensor network used indoors.

This master's thesis deals with creating an application for mobile devices with an Android operating system. The main task of this application is interpreting ALLL language commands. This allows any mobile device running this application to be a node of a wireless sensor network. First, well-known principles of wireless sensor networks, agents and ALLL language, which describes the agents in wireless sensor network, are explained in this project. The method for building the application on these bases follows. There are also some examples of agents interpreted by this application at the end.

The master thesis is concerned with problem of localization of nodes in wireless sensor network WSN implemented by ZigBee technology. The thesis provide description of ZigBee architecture involving signal description. The method of evaluating coordinates basically do not depend on used measuring methods. The method  of fingerprinting is included in separated category based on only statistical processing of RSSI data. Determination of coordinates of mobile node always needs finding out range, signal time of arrival (ToA) or angle of arrival (AoA) in respect to anchors node given dimension magnitude. The author found out three possible solutions for range measuring and one solution for angle of arrival measuring useable in context of ZigBee wireless sensor network. Finally the author discuss possibilities and constraints of ZigBee network with used measuring type.

>Magister Scientiae - MSc / The fifth-generation mobile network (5G) is a new global wireless standard that enables state-of-the-art mobile networks with enhanced cellular broadband services that support a diversity of devices. Even with the current worldwide advanced state of broadband connectivity, most rural and low-income settings lack minimum Internet connectivity because there are no economic incentives from telecommunication providers to deploy wireless communication systems in these areas. Using a team of Unmanned Aerial Vehicles (UAVs) to extend or solely supply the 5G coverage is a great opportunity for these zones to benefit from the advantages promised by this new communication technology. However, the deployment and applications of innovative technology in rural locations need extensive research.

Although sensor planning in computer vision has been a subject of research for over two decades, a vast majority of the research seems to concentrate on two particular applications in a rather limited context of laboratory and industrial workbenches, namely 3D object reconstruction and robotic arm manipulation. Recently, increasing interest is engaged in research to come up with solutions that provide wide-area autonomous surveillance systems for object characterization and situation awareness, which involves portable, wireless, and/or Internet connected radar, digital video, and/or infrared sensors. The prominent research problems associated with multisensor integration for wide-area surveillance are modality selection, sensor planning, data fusion, and data exchange (communication) among multiple sensors. Thus, the requirements and constraints to be addressed include far-field view, wide coverage, high resolution, cooperative sensors, adaptive sensing modalities, dynamic objects, and uncontrolled environments. This article summarizes a new survey and analysis conducted in light of these challenging requirements and constraints. It involves techniques and strategies from work done in the areas of sensor fusion, sensor networks, smart sensing, Geographic Information Systems (GIS), photogrammetry, and other intelligent systems where finding optimal solutions to the placement and deployment of multimodal sensors covering a wide area is important. While techniques covered in this survey are applicable to many wide-area environments such as traffic monitoring, airport terminal surveillance, parking lot surveillance, etc., our examples will be drawn mainly from such applications as harbor security and long-range face recognition.

Many wireless sensor-based applications use LEACH as a preferred routing protocol owing to its unique features such as optimal sleeping time, minimum packet collisions, dynamic channel selection, and least power consumption. The traditional LEACH protocol wastes the transmission opportunities as it processes data only in an event occurring, leading to wastage of resources. To resolve this issue, this paper proposes a more robust Energy Enhanced Threshold Routing Protocol (ETH-LEACH) for WSNs. The working of ETH-LEACH is conceptualized in two parts. In the first part, TDMA is implemented to estimate the opportunistic paths to remove network overhead. Furthermore, in the second part, a threshold value is calculated for choosing the forwarder nodes. The proposed technique minimizes the energy usage of the sensor nodes and consequently enhances the network's lifetime by extending the duration of node death. The ETH-LEACH protocol is contrasted with the different variants of LEACH to verify its effectiveness. The experimental results show that the proposed ETH-LEACH protocol outperforms the traditional routing protocols. In this paper, the ETH-LEACH protocol performs nearly 54.6% efficient than LEACH, 47.6% efficient than Q-LEACH, 41.3% efficient than NR-LEACH, 33.6% efficient than LEACH-GA, and 29.7% efficient than LEACH-POS in reducing the usage of energy in the overall simulation.

Cette thèse s’intéresse au problème d’estimation et d’optimisation distribuée dans les réseaux asynchrones, c’est à dire en n’utilisant que des communication locales et asynchrones. A partir de multiples applications allant de l’apprentissage automatique aux réseaux de capteurs sans-fils, nous concevons et analysons théoriquement de nouveaux algorithmes résolvant trois problèmes de nature très différentes : la propagation de la plus grande des valeurs initiales, l’estimation de leur moyenne et enfin l’optimisation distribuée. / This thesis addresses the distributed estimation and optimization of a global value of interest over a network using only local and asynchronous (sometimes wireless) communications. Motivated by many different applications ranging from cloud computing to wireless sensor networks via machine learning, we design new algorithms and theoretically study three problems of very different nature : the propagation of the maximal initial value, the estimation of their average and finally distributed optimization.

Wireless sensor networks (WSNs) is a virtual layer in the paradigm of the Internet of Things (IoT). It inter-relates information associated with the physical domain to the IoT drove computational systems. WSN provides an ubiquitous access to location, the status of different entities of the environment, and data acquisition for long-term IoT monitoring. Since energy is a major constraint in the design process of a WSN, recent advances have led to project various energy-efficient protocols. Routing of data involves energy expenditure in considerable amount. In recent times, various heuristic clustering protocols have been discussed to solve the purpose. This article is an improvement of the existing stable election protocol (SEP) that implements a threshold-based cluster head (CH) selection for a heterogeneous network. The threshold maintains uniform energy distribution between member and CH nodes. The sensor nodes are also categorized into three different types called normal, intermediate, and advanced depending on the initial energy supply to distribute the network load evenly. The simulation result shows that the proposed scheme outperforms SEP and DEEC protocols with an improvement of 300% in network lifetime and 56% in throughput.

Wireless sensor networks (WSNs) have been a topic of research for decades. Researchers have been exploring different uses for UAVs with their growing popularity. In this thesis I develop a wireless sensor network (WSN) and introduce the theoretical effects of an unmanned aerial vehicle (UAV) for wireless recharging of individual nodes in the WSN. My research focuses on understanding how to use wireless recharging technology to maximize the lifetime of a WSN by simulating recharging on the physical nodes. Using a three by three grid of nine sensor nodes, I proved that recharging the lowest powered node in the network at each sink iteration increased the lifetime of the WSN by 538% when compared to no recharging. I also further investigate the potential uses of a WSN and UAV for detecting and deterring animals. Using wireless sensor nodes to initially detect movement, and the UAV to find the object proved to be a viable solution for offloading the more power intensive tasks from the WSN to the UAV.

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