This diploma thesis is focused on research of localization algorithms. Each developed localization algorithms are generally application specific. Because of application requirements diversity, many variants of localization algorithms exist. In case of finding appropriate localization algorithm for concrete application usability, existence of real condition simulation environment is useful. Development of this simulation environment was made simultaneously with both localization algorithms. At first, survey of available localization technic was performed and their summary was listed. Further work was aimed on research of two localization algorithms. Both of them use triangulation mechanism to determine unknown node position. This mechanism is based on measurement of distance to three reference points with known position. It would seem that both algorithms are similar, but simulation shows their difference and presents their suitability for diverse conditions.

The diploma thesis is focused on two localization algorithms, iterative algorithm, and a linked algorithm simulated in MATLAB. Further, the investigation of the influence of input errors on the errors in localization of sensor nodes examined algorithms and explore possible relationships between the input errors and localization errors. Subsequently are submitted possible ways to optimize and their results.

Greenhouse gas emissions in indoor or outdoor areas are dangerous and can have short- or long-term effects on people’s health. There are several methods to monitor the air quality in such environments. This thesis project attempts to design and evaluate a wireless sensor network with two main characteristics such as long range and low power consumption. The sensor network is built upon Long Range Wide Area Network (LoRaWAN) protocol and is composed of sensor nodes and gateways. The sensor nodes are built upon a Raspberry Pi model 3B, a LoRa SX1276 transceiver and gas sensors. The sensors are intended to measure CO2, CH4, temperature, pressure and relative humidity. The collected data is then logged and sent to The Things Network (TTN) via a backhaul connection.

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A wireless sensor node (WSN) system, capable of sensing animal motion and transmitting motion data wirelessly, is an effective and efficient way to monitor pigs' activity. However, the raw sensor data sampling and transmission consumes lots of power such that WSNs' battery have to be frequently charged or replaced. The proposed work solves this issue through WSN edge computing solution, in which a Random Forest Classifier (RFC) is trained and implemented into WSNs. The implementation of RFC on WSNs does not save power, but the RFC predicts animal behavior such that WSNs can adaptively adjust the data sampling frequency to reduce power consumption. In addition, WSNs can transmit less data by sending RFC predictions instead of raw sensor data to save power. The proposed RFC classifies common animal activities: eating, drinking, laying, standing, and walking with a F-1 score of 93%. The WSN power consumption is reduced by 25% with edge computing intelligence, compare to WSN power that samples and transmits raw sensor data periodically at 10 Hz. / Master of Science / A wireless sensor node (WSN) system that detects animal movement and wirelessly transmits this data is a valuable tool for monitoring pigs' activity. However, the process of sampling and transmitting raw sensor data consumes a significant amount of power, leading to frequent recharging or replacement of WSN batteries. To address this issue, our proposed solution integrates edge computing into WSNs, utilizing a Random Forest Classifier (RFC). The RFC is trained and deployed within the WSNs to predict animal behavior, allowing for adaptive adjustment of data sampling frequency to reduce power consumption. Additionally, by transmitting RFC predictions instead of raw sensor data, WSNs can conserve power by transmitting less data. Our RFC can accurately classify common animal activities, such as eating, drinking, laying, standing, and walking, achieving an F-1 score of 93%. With the integration of edge computing intelligence, WSN power consumption is reduced by 25% compared to traditional WSNs that periodically sample and transmit raw sensor data at 10 Hz.

Embedded systems are so ubiquitous that they account for almost 90% of all the computing devices. They range from very small scale devices with an 8-bit microcontroller and few kilobytes of RAM to large-scale devices featuring PC-like performance with full-blown 32-bit or 64-bit processors, special-purpose acceleration hardware and several gigabytes of RAM. Each of these classes of embedded systems have unique set of challenges in terms of hardware utilization, performance and power consumption. As network connectivity becomes a standard feature in these devices, security becomes an important concern. Public Key Cryptography is an indispensable tool to implement various security features necessary on these embedded platforms. In this thesis, we provide optimized PKC solutions on platforms belonging to two extreme classes of the embedded system spectrum. First, we target high-end embedded platforms Qualcomm Snapdragon and Intel Atom. Each of these platforms features a dual-core processor, a GPU and a gigabyte of RAM. We use the SIMD coprocessor built into these processors to accelerate the modular arithmetic which accounts for the majority of execution time in Elliptic Curve Cryptography. We exploit the structure of NIST primes to perform the reduction step as we perform the multiplication. Our implementation runs over two times faster than OpenSSL implementations on the respective platforms. The second platform we targeted is an energy-harvested wireless sensor node which has a 16-bit MSP430 microcontroller and a low power RF interface. The system derives its power from a solar panel and is constrained in terms of available energy and computational power. We analyze the computation and communication energy requirements for different signature schemes, each with a different trade-off between computation and communication. We investigate the Elliptic Curve Digital Signature Algorithm (ECDSA), the Lamport-Diffie one-time hash-based signature scheme (LD-OTS) and the Winternitz one-time hash-based signature scheme (W-OTS). We demonstrate that there’s a trade-off between energy needs, security level and algorithm selection. However, when we consider the energy needs for the overall system, we show that all schemes are within one order of magnitude from each another. / Master of Science

The Internet of Things will include many resource-constrained lightweight wireless sensing devices, hungry for energy, bandwidth and compute cycles. The sheer amount of devices involved will require new solutions to handle issues such as identification and power provisioning. First, to simplify identity management, device identification is moving from symmetric-key solutions to public-key solutions. Second, to avoid the endless swapping of batteries, passively-powered energy harvesting solutions are preferred. In this contribution, we analyze some of the feasible solutions from this challenging design space. We have built an autonomous, energy-harvesting sensor node which includes a micro-controller, RF-unit, and energy harvester. We use it to analyze the computation and communication energy requirements for Elliptic Curve Digital Signature Algorithm (ECDSA) with different security levels. The implementation of Elliptic Curve Cryptography (ECC) on small microcontrollers is challenging. Most of the earlier literature has considered optimizing the performance of ECC (with respect to cycle count and software footprint) on a given architecture. This thesis addresses a different aspect of the resource-constrained ECC implementation wherein the most suitable architecture parameters are identified for any given application profile. At the high level, an application profile for an ECC-based lightweight device, such as wireless sensor node or RFID tag, is defined by the required security level, signature generation latency and the available energy/power budget. The target architecture parameters of interest include core-voltage, core-frequency, and/or the need for hardware acceleration. We present a methodology to derive and optimize the architecture parameters starting from the application requirements. We demonstrate our methodology on a MSP430F5438A microcontroller, and present the energy/architecture design space for 80-bit and 128-bit security-levels, for prime field curves secp160r1 and nistp256. Our results show that energy cost per authentication is minimized if a microcontroller is operated at the maximum possible frequency. This is because the energy consumed by leakage (i.e., static power dissipation) becomes proportionally less important as the runtime of the application decreases. Hence, in a given energy harvesting method, it is always better to wait as long as possible before initiating ECC computations which are completed at the highest frequency when sufficient energy is available. / Master of Science

Nowadays, wireless sensor networks can be found in many new application areas. In these sensor networks there may exit a part of the network which are difficult to access or lie in a wide area, far apart. A change in the software (e.g., function update or bug fix) can entail reprogramming of all sensor nodes. This is very time consuming and labour intensive, if the patching has to be done manually for each individual sensor nodes. In the area of mobile phones, the over the air (OTA) update function has been established very well with good reliability. In embedded systems such as sensor nodes, where resources are severely restricted, an update cannot be stored but must be programmed directly with the transfer. For this to be possible, a lot of basic functionality is needed to be established to correct errors or to be able to resume a failed programming. Within the framework of this thesis a concept for the transmission and distribution of the firmware and programming the sensor node is established. Focus here is to optimize the use of resources and to provide basic functionality within the programming mode.

This thesis proposes an implementation of battery-powered, time-synchronized wireless nodes that can be deployed in a wireless network topology. Wireless sensor networks are used in a wide variety of scenarios where emphasis is placed on the wireless nodes’ battery life. The main area of focus in this thesis is to examine how wireless nodes can save battery power by utilizing a deep sleep mode and wake up simultaneously using time synchronization to carry out their data communication. This was achieved by deploying five time-synchronized, battery-powered nodes in a wireless network topology. The difference in battery current draw between continuously running nodes and sleep-enabled nodes were measured, as well as the time duration needed by the nodes to successfully send their payloads and route other nodes’ data. The nodes needed between 1502 ms and 3273 ms on average to carry out their data communication, depending on where they were located in the network topology. Measurements show that sleep-enabled nodes on average draw substantially less current than continuously running nodes during a complete data communication cycle. When sleep-enabled nodes were powered by two AA batteries, an increase in battery life of up to 1800% was observed.

Augmenter la durée de vie d'une pile, voire s'en passer est aujourd'hui devenu une obligation pour les microsystèmes. En effet, à cette échelle, le remplacement des piles et leur rejet dans l'environnement sont problématiques. La voie préconisée pour répondre à cet enjeu est d'utiliser des sources d'énergie renouvelables (solaire, thermique et mécanique). Pour cela, nous proposons de développer une plateforme de récupération d'énergie multi-sources/multi-charges (MANAGY) capable de s'adapter à son environnement pour en extraire le maximum d'énergie et répondre à des applications diverses. L'architecture est constituée de chemins directs et de chemins indirects où l'énergie provenant des sources est d'abord transférée dans une unité de stockage avant d'être réutilisée par les charges du microsystème. L'utilisation de cette nouvelle architecture permet d'optimiser le transfert d'énergie entre sources et charges et améliore le rendement du système de 33%. Avant de développer une architecture multi-sources, nous avons cherché à améliorer le rendement de la source photovoltaïque (PV) qui, au vu de l'état de l'art, a la densité de puissance la plus élevée. La recherche du rendement maximum de la source PV revient à la recherche du point de puissance maximum (MPPT). Il existe pour chaque condition d'irradiance, de température, et d'énergie extraites un couple tension-courant permettant à la source de fournir un maximum de puissance (MPP). Grâce à l'utilisation de deux chemins de puissance, nous arrivons simultanément à créer une boucle de régulation faible puissance agissant sur le rapport cyclique du système de gestion d'énergie (MPPT) et une boucle de régulation de la tension de sortie agissant sur le transfert de l'énergie. La modélisation du système nous a permis de spécifier ses performances. Pour atteindre les performances requises, des architectures innovantes ont été réalisées qui ont fait l'objet de trois brevets. De plus, des blocs ne sont activés qu'aux instants de changement d'état du système et sont conçus, quand cela a été possible, avec des transistors fonctionnant en mode faible inversion. Toutes ces optimisations permettent au système de fonctionner sur une large plage de variation de l'éclairement (de conditions intérieures supérieures à 500 lux à extérieures) avec un rendement proche de 90%. / Enhancing the life time of battery or being able to work without it is today mandatory for microsystems. Most of systems are nowadays limited by the capacity of the embedded battery. Moreover the replacement and waste of baterries is no more possible at this scale. One way to achieve longer life time is the use of renewable energy sources (solar, thermal, or kinetic). This work proposes to develop a new energy harvesting platform with numerous sources and loads (MANAGY) able to adapt itself to the surrounding environment in order to extract the maximum of energy while answering to various of applications. The architecture is composed of directs and indirects power paths where the extracted energy coming from renewable sources is firstly transferred to a storage unit before being used by loads. This novel architecture makes it possible to optimize the energy transfer between sources and loads and to achieve a 33% gain. Before developing this architecture with numerous sources, we have searched to enhance the efficiency of the photovoltaic source which has the best power density at the state of the art. Looking for improving the efficiency of the PV source is the same as tracking the maximum power point (MPPT). There is for each irradiance, temperature and quantity of energy extracted a couple of voltage and current enabling the PV source to deliver the maximum of power (MPP). Thanks to the two power paths used we are able to create a low power feedback loop adjusting the duty cycle from the power management unit (MPPT) while having a second feedback loop optimizing the power transfer and regulating the output voltage. Thanks to a high level model we have specified the system performances. To achieve the performances required we have realized novel architectures protected through three patents. Moreover, blocs are only activated when the system changes its state and furthermore there are designs, when achievable, with transistors working in weak inversion. All these optimizations make the system working for a large range of irradiance (from inside conditions higher than 500 lux to outdoor conditions) with an efficiency close to 90%.