Wireless sensor networks are becoming important in several monitoring and sensing applications. Ultra low power consumption in the sensor nodes is important for extending the battery life of the nodes. In this dissertation, two low power BFSK receiver architectures are proposed and verified with prototype implementations in silicion. A 2.4 GHz 1 Mb/s polyphase filter (PPF) BFSK receiver demonstrates ±180 ppm frequency offset tolerance (FOT) and 40 dB adjacent channel rejection (ACR) at a modulation index (MI) of 2, with a power consumption of 1.9 mW. High FOT at low MI is achieved by a frequency-to-energy conversion architecture using PPFs without any frequency correction. The proposed hybrid topology of the PPF provides an improved ACR at reduced power. To further improve the energy efficiency, a low energy 900 MHz mixer-less BFSK receiver is designed. High gain frequency-to-amplitude conversion and better sensitivity is achieved by a linear amplifier with Q-enhanced LC tank, eliminating the need for local oscillators and mixers. With a power consumption of 500 μW, the receiver achieves sensitivities of -90 dBm and -76 dBm for data rates of 0.5 Mb/s and 6 Mb/s, respectively. The energy efficiency is 80 pJ/b when operating at 6 Mb/s. / Graduation date: 2013 / Access restricted to the OSU Community at author's request from March 25, 2013 - March 25, 2014

In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.

IEEE 802.15.4 is a standard designed for low data rate wireless personal area networks (WPANs) intended to provide connectivity to mobile devices. Such devices present considerable storage, energy, and communication constraints. However, they can be used in a variety of applications like home/office automation, environmental control and more. In order to extend the lifetime of the WPAN, we propose some changes to the standard including modifications to the Superframe Guaranteed Time Slot (GTS) distribution which can be optimized to reduce energy consumption. We implemented the proposed improvements to the IEEE 802.15.4 protocol using real sensor nodes. Specifically, we conducted an energy study of the proposed acknowledgment-based GTS descriptor distribution scheme and compared the results with the standard implementation. Experiments show that the proposed changes reduce energy consumption up to nearly 50% when 7 devices allocate guaranteed time slots descriptors during normal communication.

This dissertation develops optimal algorithms for distributed detection and estimation in static and mobile sensor networks. In distributed detection or estimation scenarios in clustered wireless sensor networks, sensor motes observe their local environment, make decisions or quantize these observations into local estimates of finite length, and send/relay them to a Cluster-Head (CH). For event detection tasks that are subject to both measurement errors and communication errors, we develop an algorithm that combines a Maximum a Posteriori (MAP) approach for local and global decisions with low-complexity channel codes and processing algorithms. For event estimation tasks that are subject to measurement errors, quantization errors and communication errors, we develop an algorithm that uses dithered quantization and channel compensation to ensure that each mote's local estimate received by the CH is unbiased and then lets the CH fuse these estimates into a global one using a Best Linear Unbiased Estimator (BLUE). We then determine both the minimum energy required for the network to produce an estimate with a prescribed error variance and show how this energy must be allocated amongst the motes in the network. In mobile wireless sensor networks, the mobility model governing each node will affect the detection accuracy at the CH and the energy consumption to achieve this level of accuracy. Correlated Random Walks (CRWs) have been proposed as mobility models that accounts for time dependency, geographical restrictions and nonzero drift. Hence, the solution to the continuous-time, 1-D, finite state space CRW is provided and its statistical behavior is studied both analytically and numerically. The impact of the motion of sensor on the network's performance is also studied.

In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.

In this dissertation, we study data collection and its achievable network capacity in Wireless Sensor Networks (WSNs). Firstly, we investigate the data collection issue in dual-radio multi-channel WSNs under the protocol interference model. We propose a multi-path scheduling algorithm for snapshot data collection, which has a tighter capacity bound than the existing best result, and a novel continuous data collection algorithm with comprehensive capacity analysis. Secondly, considering most existing works for the capacity issue are based on the ideal deterministic network model, we study the data collection problem for practical probabilistic WSNs. We design a cell-based path scheduling algorithm and a zone-based pipeline scheduling algorithm for snapshot and continuous data collection in probabilistic WSNs, respectively. By analysis, we show that the proposed algorithms have competitive capacity performance compared with existing works. Thirdly, most of the existing works studying the data collection capacity issue are for centralized synchronous WSNs. However, wireless networks are more likely to be distributed asynchronous systems. Therefore, we investigate the achievable data collection capacity of realistic distributed asynchronous WSNs and propose a data collection algorithm with fairness consideration. Theoretical analysis of the proposed algorithm shows that its achievable network capacity is order-optimal as centralized and synchronized algorithms do and independent of network size. Finally, for completeness, we study the data aggregation issue for realistic probabilistic WSNs. We propose order-optimal scheduling algorithms for snapshot and continuous data aggregation under the physical interference model.

Short range wireless data communication networks that are used for sport and health care are sometimes called Wireless Body Area Networks (WBANs) and they are located more or less on a person. Sole Integrated Gait Sensor (SIGS) is a research project in WBAN, where wireless pressure sensors are placed like soles in the shoes of persons with different kinds of deceases. The sensors can measure the pressure of the foot relative to the shoe i.e. the load of the two legs is measured. This information can be useful e.g. to not over or under load a leg after joint replacement or as a bio feedback system to help e.g. post stroke patients to avoid falling. The SIGS uses the ANT Protocol and radio specification. ANT uses the 2.4 GHz ISM band and TDMA is used to share a single frequency. The scheduling of time slots is adaptive isochronous co-existence i.e. the scheduling is not static and each transmitter sends periodically but checks for interference with other traffic on the radio channel. In this unidirectional system sole sensors are masters (transmitters) and the WBAN server is the slave in ANT sense. The message rate is chosen as 8 Hz which is suitable for low power consumption. Hence in the SIGS system, it is necessary to synchronize the left and the right foot sensors because of low message rate. In our thesis, we found a method and developed a prototype to receive the time synchronized data in WBAN server from ANT wireless sensor nodes in SIGS system. For this thesis work, a hardware prototype design was developed. The USB and USART communication protocols were also implemented in the hardware prototype. The suitable method for time synchronization was implemented on the hardware prototype. The implemented method receives the sensor data, checks for the correct stream of data; add timestamp to the sensor data and transmit the data to the Linux WBAN server. The time slots allocation in the ANT protocol was found. Alternative solution for the time synchronization in ANT protocol was also provided. The whole SIGS system was tested for its full functionality. The experiments and analysis which we performed were successful and the results obtained provided good time synchronization protocol for ANT low power wireless sensor network and for Wireless Bio-feedback system.

Wireless Sensor Networks have found diverse applications from health to agriculture and industry. They have a potential to profound social changes, however, there are also some challenges that have to be addressed. One of the problems is the limited power source available to energize a sensor node. Longevity of a node is tied to its low power design. One of the areas where great power savings could be made is in nodal communication. Different schemes have been proposed targeting low power communication and short network latency. One of them is the introduction of ultra-low power wake-up receiver for monitoring the channel. Although it is a recent proposal, there has been many works published. In this thesis work, the focus is study and comparison of architectures for a wake-up receiver. As part of this study, an envelope detector based wake-up receiver is designed in 130nm CMOS Technology. It has been implemented in schematic and layout levels. It operates in the 2.4GHz ISM band and consumes a power consumption of 69µA at 1.2V supply voltage. A sensitivity of -52dBm is simulated while receiving 100kb/s OOK modulated wake-up signals. / This is a master's thesis work by a communication electronics student in a German company called IMST GmbH.

This thesis investigates a graph and information theoretic approach to design and analysis of low-density parity-check (LDPC) codes and wireless networks. In this work, both LDPC codes and wireless networks are considered as random graphs. This work proposes solutions to important theoretic and practical open problems in LDPC coding, and for the first time introduces a framework for analysis of finite wireless networks. LDPC codes are considered to be one of the best classes of error-correcting codes. In this thesis, several problems in this area are studied. First, an improved decoding algorithm for LDPC codes is introduced. Compared to the standard iterative decoding, the proposed decoding algorithm can result in several orders of magnitude lower bit error rates, while having almost the same complexity. Second, this work presents a variety of bounds on the achievable performance of different LDPC coding scenarios. Third, it studies rate-compatible LDPC codes and provides fundamental properties of these codes. It also shows guidelines for optimal design of rate-compatible codes. Finally, it studies non-uniform and unequal error protection using LDPC codes and explores their applications to data storage systems and communication networks. It presents a new error-control scheme for volume holographic memory (VHM) systems and shows that the new method can increase the storage capacity by more than fifty percent compared to previous schemes. This work also investigates the application of random graphs to the design and analysis of wireless ad hoc and sensor networks. It introduces a framework for analysis of finite wireless networks. Such framework was lacking from the literature. Using the framework, different network properties such as capacity, connectivity, coverage, and routing and security algorithms are studied. Finally, connectivity properties of large-scale sensor networks are investigated. It is shown how unreliability of sensors, link failures, and non-uniform distribution of nodes affect the connectivity of sensor networks.

This thesis investigates a graph and information theoretic approach to design and analysis of low-density parity-check (LDPC) codes and wireless networks. In this work, both LDPC codes and wireless networks are considered as random graphs. This work proposes solutions to important theoretic and practical open problems in LDPC coding, and for the first time introduces a framework for analysis of finite wireless networks. LDPC codes are considered to be one of the best classes of error-correcting codes. In this thesis, several problems in this area are studied. First, an improved decoding algorithm for LDPC codes is introduced. Compared to the standard iterative decoding, the proposed decoding algorithm can result in several orders of magnitude lower bit error rates, while having almost the same complexity. Second, this work presents a variety of bounds on the achievable performance of different LDPC coding scenarios. Third, it studies rate-compatible LDPC codes and provides fundamental properties of these codes. It also shows guidelines for optimal design of rate-compatible codes. Finally, it studies non-uniform and unequal error protection using LDPC codes and explores their applications to data storage systems and communication networks. It presents a new error-control scheme for volume holographic memory (VHM) systems and shows that the new method can increase the storage capacity by more than fifty percent compared to previous schemes. This work also investigates the application of random graphs to the design and analysis of wireless ad hoc and sensor networks. It introduces a framework for analysis of finite wireless networks. Such framework was lacking from the literature. Using the framework, different network properties such as capacity, connectivity, coverage, and routing and security algorithms are studied. Finally, connectivity properties of large-scale sensor networks are investigated. It is shown how unreliability of sensors, link failures, and non-uniform distribution of nodes affect the connectivity of sensor networks.