Multiuser Coding and Signal Processing in a Low Power Sensor Network

Backscatter communication system is a wireless communication system that is used by both academic community and industry circles in recent years. This communication system only requires ultra-low power usage and simple design of the sensors. This project is using backscatter communication system to transmit data with backscatter tags. The method we used is semi-passive backscatter communication. This project focuses on transmitting signals with multiple sensors so there is a problem about distinguishing the signal reflected by different nodes. We modulated the transmitting digital signal with Walsh function to solve the problem of separating the signals between different nodes. By using spread sequences we have interferences between different signals from each node and also from the bouncing and direct path signals. We want to estimate the channel between the sensors to suppress the effect of the interferences. In addition, to make the system more practical with multiple usages and applications, we made the receiver and the illuminator on a moving platform. With this dynamic system it is important to deal with the interference of bouncing signals by analyzing the Doppler shift of received signal. With these approaches the purpose of this project is having the reader of the sensor network to communicate with multiple nodes with backscatter communication. This system can be used on variety of applications such as environmental sensing, signal recording and data communicating with less power usage compared with traditional communication systems. Advisor: Andrew Harm

DC Power Line Communication (PLC) on 868 MHz and 2.4 GHz Wired RF Transceivers

Efficient management through monitoring of Li-ion batteries is critical to the progress of electro-mobility and energy storage globally, since the technology can be hazardous if pushed beyond its safety boundaries. Battery management systems (BMSs) are being actively improved to reduce size, weight, and cost while increasing their capabilities. Using power line communication, wireless monitoring, or hybrid data links are one of the most advanced research directions today. In this work, we propose the use of radio frequency (RF) transceivers as a communication unit that can deliver both wired and wireless services, through their superior analog and digital signal processing capability compared to PLC technology. To validate our approach computational simulation and empirical evaluation was conducted to examine the possibility of using RF transceivers on a direct current (DC) bus for wired BMS. A key advantage of this study is that it proposes a flexible and tested system for communication across a variety of network scenarios, where wireless data links over disrupted connections may be enabled by using this technology in short-range wired modes. This investigation demonstrates that the IEEE 802.15.4-compliant transceivers with operating frequencies of 868 MHz and 2.4 GHz can establish stable data links on a DC bus via capacitive coupling at high data rates

Rapid Learning Optimization Approach for Battery Recovery-Aware Embedded System Communications

To date, battery optimization for embedded systems still a crucial subject. Actually, the majority of carried out works focus on transmission controls without taking into account the specifications of the batteries themselves. Indeed, an improvementof 70\% is reported by exploiting the battery recovery effect.In this paper, the recovery phenomenon is exploited to design an algorithm that optimizes both the lifetime of the battery and the performance of the studied system. The algorithms from Dynamic programming and Reinforcement learning fields are the first to be considered. When in Dynamic programming prior detailed information are assumed to be available, in reinforcement learning those information becomes unknown and long calculation times are needed to converge toward an optimal policy solution. The paper contribution is about designing a new Rapid Learning Algorithm (RLA) that combines both Dynamic programming and Reinforcement learning features. RLA exploits a reduced model of the system instead of exploring the whole and heavy system state model as Dynamic programming do. The RLA run-time is then shortened. Based on battery stochastic model, the simulation results obtained with RLA are compared to the Dynamic programming and Reinforcement learning algorithms under the same conditions. By taking into account the recovery effect this paper illustrates that the calculation time and the system performance are greatly improved when RLA is adopted

Ultra-Low Power Wake Up Receiver For Medical Implant Communications Service Transceiver

This thesis explores the specific requirements and challenges for the design of a dedicated wake-up receiver for medical implant communication services equipped with a novel “uncertain-IF†architecture combined with a high – Q filtering MEMS resonator and a free running CMOS ring oscillator as the RF LO. The receiver prototype, implements an IBM 0.18μm mixed-signal 7ML RF CMOS technology and achieves a sensitivity of -62 dBm at 404MHz while consuming \u3c100 μW from a 1 V supply

Dynamic Recofiguration Techniques for Wireless Sensor Networks

The need to achieve extended service life by battery powered Wireless Sensor Networks (WSNs) requires new concepts and technqiues beyond the state-of-the-art low-power designs based on fixed hardware platforms or energy-efficient protocols. This thesis investigates reconfiguration techniques that enable sensor hardware to adapt its energy consumption to external dynamics, by means of Dynamic Voltage Scaling (DVS), Dynamic Modulation Scaling (DMS), and other related concepts. For sensor node-level reconfiguration, an integration of DVS and DMS techniques was proposed to minimize the total energy consumption. A dynamic time allocation algorithm was developed, demonstrating an average of 55% energy reduction. For network-level reconfiguration, a node activation technique was presented to reduce the cost of recharging energy-depleted sensor nodes. Network operation combined with node activation was modeled as a stochastic decision process, where the activation decisions directly affected the energy efficiency of the network. An experimental test bed based on the Imote2 sensor node platform was realized, which demonstrated energy reduction of up to 50%. Such energy saving can be effectively translated into prolonged service life of the sensor network