Routing plays an important role in energy constrained Wireless Sensor Networks (WSNs). To conserve energy in WSN, energy-efficiency of the routing protocol is an important design consideration. These protocols should maximize network lifetime and minimize energy consumption. In this thesis, a novel multipath routing protocol is proposed for WSNs, which constructs multiple paths based on residual energy of the nodes. The protocol allows the source node to select a path for data transmission from the set of discovered multiple paths based on cumulative residual energy or variance. Choosing a next-hop node based on energy, and using an alternative path for routing achieves load balancing. The results show that the proposed algorithm M-VAR has lower residual energy variance (96%, 90%, 72%, 12% less) and longer network lifetime (404%, 205%, 115%, 10%) than basic Directed Diffusion, load-balanced Directed Diffusion (LBDD-ED-RD), multipath Directed Diffusion (MDD-CRE), and the proposed algorithm M-CRE, respectively.

A microfluidic-based inductance sensor operating at 1.5 GHz is presented that can detect single 4.5 μm superparamagnetic particles flowing in a microfluidic channel. The particles are detected as they pass over a micron-sized planar gold loop electrode, with a maximum signal-to-noise ratio of 26.3 dB for an 80 μm/s flow rate; the magnetic beads are simultaneously observed with microscope images. The sensor consists of a coupled-line resonator and microwave interferometric system coupled to the loop electrode that is integrated within a polydimethylsiloxane-on-glass microfluidic chip assembly. A time-averaged inductance change caused by a single particle is related to the real part of its magnetic Clausius-Mossotti factor. The effective real part of the magnetic permeability for a particular particle is estimated to be 1.13 at 1.5 GHz. The sensor detects magnetic particles in flow and does not require an external biasing magnetic field, which distinguishes it from other magnetic microparticle sensors.

Wireless Sensor Networks (WSNs), consisting of many small sensing devices working in concert, have the potential to revolutionise every aspect of our lives. Although the technology is still in its infancy offers an unlimited number of possible applications, ranging from military surveillance to environmental monitoring. These WSNs are prone to physical sensor failures due to environmental conditions such bio fouling and an adverse ambient environment, as well as threats that arise from their operation in an open environment. Consequently, reliability and fault-tolerance techniques become a critical aspect of the research associated with WSNs. In mission critical applications, such as the monitoring of enemy troops, unreliable or faulty information produced by WSNs could potentially lead to fatal outcomes. In such applications, it necessary to receive both a correct notification of event occurrences and uncorrupted data. Developing a fault-tolerance system for WSNs is a challenging task. New self-configuration, self-recognition and self-organisation techniques are needed due to unique aspects of the operation of WSNs. Our current understanding of WSNs leads to an immunologically inspired solution to the design of a fault-tolerant network. One of the main roles of the Natural Immune System(NIS) is the recognition of self and the elimination of non-self proteins. Hence, in order to have an immune system equivalent for a sensor network, we must have a clear and stable definition of what constitutes the Self and the Non-Self Sets in a sensor network. This thesis explores two different approaches to modelling, collection and representation of the Self-Set in distributed sensor networks. We approach this problem, of identifying what constitutes the Self-Set in terms of sensor readings, using pattern recognition techniques from the machine learning field that leverages a small number of past observations of sensor nodes. We have chosen Competitive Learning Neural Network (CLNN) for the construction of the Self-Set. We define and evaluate two approaches for the aggregation of the Self-Set across multiple sensors in a WSN. The first approach is the Graph Theory Based Aggregation (GTBA) which consists of two main parts, namely: classification of the sensor readings by means of CLNN, which provides the multimodal view data and GTBA of the CLNN output, which takes intersections of intervals produced by CLNN. In this thesis we define and evaluate two different interpretations of GTBA, namely: Midpoint Intersection (MPI): one that considers the midpoint of intervals. Midpoint Free Intersection (MFI): one that does not take the midpoints into account but assigns the confidence levels to each of the resulted intersections. We evaluated both interpretations on three different types of phenomena and have shown that the second interpretation, MFI, consistently produced more precise representations of the environment under observation. However, MFI produced a very strict representation of the phenomenon, which consequently led to a large number of systems' retraining. Hence, we defined and evaluated a technique which produced a more relaxed representation of the Self-Set and at the same time preserved the finer variation in the phenomenon. The second approach is based on unsupervised learning. We define and evaluate three related unsupervised learning procedures ?? Divergence and Merging (DMP), Suboptimal Clustering (SOC), and Simple Clustering (SC) for the collection of the Self-Set. We explore the design tradeoffs in unsupervised learning schemes with respect to the clustering quality. We implement and evaluate these related unsupervised learning procedures on a realworld data set. The outcome of these experiments show that, out of the three unsupervised learning procedures studied in this thesis, the Suboptimal Clustering procedure appears to be the most suitable for the classification of sensor readings, provided that the amount of free memory is large enough to store and recluster an entire training set. We evaluate aggregation of the Self-Set produced by means of the distributed implementation of the unsupervised learning procedures. The aggregation is based on extended unsupervised learning and we evaluate the possibilities of the autonomous retraining of the system. Our experiments show that, in a naturally slowly changing environment, 40% of nodes reporting deviations is a large enough number to reinitialise the retraining of the system. The final conclusion is that it is possible to have a distributed implementation of the unsupervised procedure that produces an almost identical representation of the environment, which makes unsupervised learning suitable for a large number of sensor network architectures.

Continuing advances in wireless communications and MEMS (Micro-Electro Mechan- ical Systems) technologies have fostered the construction of a wide variety of sensor and ad hoc networks. These networks have broad applications spanning wide ar- eas, such as environmental monitoring, infrastructure maintenance, traffic manage- ment, energy management, disaster mitigation, personal medical monitoring, smart building, as well as military and defence. While these applications require high per- formance from the network, they suffer from resource constraints (such as limited battery power, processing capability, buffer space, etc.) that do not appear in tra- ditional wired networks. The inherent infrastructure-less characteristic of the sensor and ad hoc networks creates significant challenges. This dissertation addresses these challenges with two protocol designs. The main contributions of this dissertation are the design and evaluation of CS- MAC (stands for CDMA Sensor MAC), a novel multi-channel media access control (MAC) protocol for direct sequence code division multiple access (DS-CDMA) wire- less sensor networks. Our protocol design uses combination of DS-CDMA and fre- quency division to reduce the channel interference and consequently improves system capacity and network throughput. We provide theoretical characterisation of the mean multiple access interference (MAI) at a given node in relation to the number of frequency channels. We show that by using only a small number of frequency chan- nels, the mean MAI can be reduced significantly. Through discrete event simulation (using UC Berkerly NS-2 simulator), we provide comparison of our proposed system to a pure DS-CDMA system as well as a contention based system. Simulation results reveal that our proposed system can achieve significant improvement in system efi ciency (measured in packet/second/channel) of a contention based system. When the same number of packets are transmitted in the network, our system consumes much less communication energy compared to the contention based system. A distributed channel allocation protocol is also proposed for the network forma- tion phase. We prove that our algorithm converges with correct channel assignments. Simulation results reveal that a much smaller number of channels is required than theoretical value when nodes are uniformly randomly deployed. The second contribution of this dissertation involves the design and evaluation of two location-aware select optimal neighbour (SON) algorithms for CSMA/CA based MAC protocol for wireless ad hoc networks. Both algorithms concentrate on the improvement of energy eficiency of the whole network through the optimisation of the number of neighbours of each node. Our algorithms not only consider radio electronic energy consumption (e.g., coding, decoding) and radio transmission energy consumption (e.g., power amplifier), but also the electronic energy consumption at those irrelevant receivers (those who are not addressed by the transmission) that are located within the transmission range. Through simulations, we show that our algorithms can achieve signi??cant energy savings compared to the standard IEEE 802.11.

Continuing advances in wireless communications and MEMS (Micro-Electro Mechan- ical Systems) technologies have fostered the construction of a wide variety of sensor and ad hoc networks. These networks have broad applications spanning wide ar- eas, such as environmental monitoring, infrastructure maintenance, traffic manage- ment, energy management, disaster mitigation, personal medical monitoring, smart building, as well as military and defence. While these applications require high per- formance from the network, they suffer from resource constraints (such as limited battery power, processing capability, buffer space, etc.) that do not appear in tra- ditional wired networks. The inherent infrastructure-less characteristic of the sensor and ad hoc networks creates significant challenges. This dissertation addresses these challenges with two protocol designs. The main contributions of this dissertation are the design and evaluation of CS- MAC (stands for CDMA Sensor MAC), a novel multi-channel media access control (MAC) protocol for direct sequence code division multiple access (DS-CDMA) wire- less sensor networks. Our protocol design uses combination of DS-CDMA and fre- quency division to reduce the channel interference and consequently improves system capacity and network throughput. We provide theoretical characterisation of the mean multiple access interference (MAI) at a given node in relation to the number of frequency channels. We show that by using only a small number of frequency chan- nels, the mean MAI can be reduced significantly. Through discrete event simulation (using UC Berkerly NS-2 simulator), we provide comparison of our proposed system to a pure DS-CDMA system as well as a contention based system. Simulation results reveal that our proposed system can achieve significant improvement in system efi ciency (measured in packet/second/channel) of a contention based system. When the same number of packets are transmitted in the network, our system consumes much less communication energy compared to the contention based system. A distributed channel allocation protocol is also proposed for the network forma- tion phase. We prove that our algorithm converges with correct channel assignments. Simulation results reveal that a much smaller number of channels is required than theoretical value when nodes are uniformly randomly deployed. The second contribution of this dissertation involves the design and evaluation of two location-aware select optimal neighbour (SON) algorithms for CSMA/CA based MAC protocol for wireless ad hoc networks. Both algorithms concentrate on the improvement of energy eficiency of the whole network through the optimisation of the number of neighbours of each node. Our algorithms not only consider radio electronic energy consumption (e.g., coding, decoding) and radio transmission energy consumption (e.g., power amplifier), but also the electronic energy consumption at those irrelevant receivers (those who are not addressed by the transmission) that are located within the transmission range. Through simulations, we show that our algorithms can achieve signi??cant energy savings compared to the standard IEEE 802.11.

Continuing advances in wireless communications and MEMS (Micro-Electro Mechan- ical Systems) technologies have fostered the construction of a wide variety of sensor and ad hoc networks. These networks have broad applications spanning wide ar- eas, such as environmental monitoring, infrastructure maintenance, traffic manage- ment, energy management, disaster mitigation, personal medical monitoring, smart building, as well as military and defence. While these applications require high per- formance from the network, they suffer from resource constraints (such as limited battery power, processing capability, buffer space, etc.) that do not appear in tra- ditional wired networks. The inherent infrastructure-less characteristic of the sensor and ad hoc networks creates significant challenges. This dissertation addresses these challenges with two protocol designs. The main contributions of this dissertation are the design and evaluation of CS- MAC (stands for CDMA Sensor MAC), a novel multi-channel media access control (MAC) protocol for direct sequence code division multiple access (DS-CDMA) wire- less sensor networks. Our protocol design uses combination of DS-CDMA and fre- quency division to reduce the channel interference and consequently improves system capacity and network throughput. We provide theoretical characterisation of the mean multiple access interference (MAI) at a given node in relation to the number of frequency channels. We show that by using only a small number of frequency chan- nels, the mean MAI can be reduced significantly. Through discrete event simulation (using UC Berkerly NS-2 simulator), we provide comparison of our proposed system to a pure DS-CDMA system as well as a contention based system. Simulation results reveal that our proposed system can achieve significant improvement in system efi ciency (measured in packet/second/channel) of a contention based system. When the same number of packets are transmitted in the network, our system consumes much less communication energy compared to the contention based system. A distributed channel allocation protocol is also proposed for the network forma- tion phase. We prove that our algorithm converges with correct channel assignments. Simulation results reveal that a much smaller number of channels is required than theoretical value when nodes are uniformly randomly deployed. The second contribution of this dissertation involves the design and evaluation of two location-aware select optimal neighbour (SON) algorithms for CSMA/CA based MAC protocol for wireless ad hoc networks. Both algorithms concentrate on the improvement of energy eficiency of the whole network through the optimisation of the number of neighbours of each node. Our algorithms not only consider radio electronic energy consumption (e.g., coding, decoding) and radio transmission energy consumption (e.g., power amplifier), but also the electronic energy consumption at those irrelevant receivers (those who are not addressed by the transmission) that are located within the transmission range. Through simulations, we show that our algorithms can achieve signi??cant energy savings compared to the standard IEEE 802.11.

Wireless Sensor Networks (WSNs), consisting of many small sensing devices working in concert, have the potential to revolutionise every aspect of our lives. Although the technology is still in its infancy offers an unlimited number of possible applications, ranging from military surveillance to environmental monitoring. These WSNs are prone to physical sensor failures due to environmental conditions such bio fouling and an adverse ambient environment, as well as threats that arise from their operation in an open environment. Consequently, reliability and fault-tolerance techniques become a critical aspect of the research associated with WSNs. In mission critical applications, such as the monitoring of enemy troops, unreliable or faulty information produced by WSNs could potentially lead to fatal outcomes. In such applications, it necessary to receive both a correct notification of event occurrences and uncorrupted data. Developing a fault-tolerance system for WSNs is a challenging task. New self-configuration, self-recognition and self-organisation techniques are needed due to unique aspects of the operation of WSNs. Our current understanding of WSNs leads to an immunologically inspired solution to the design of a fault-tolerant network. One of the main roles of the Natural Immune System(NIS) is the recognition of self and the elimination of non-self proteins. Hence, in order to have an immune system equivalent for a sensor network, we must have a clear and stable definition of what constitutes the Self and the Non-Self Sets in a sensor network. This thesis explores two different approaches to modelling, collection and representation of the Self-Set in distributed sensor networks. We approach this problem, of identifying what constitutes the Self-Set in terms of sensor readings, using pattern recognition techniques from the machine learning field that leverages a small number of past observations of sensor nodes. We have chosen Competitive Learning Neural Network (CLNN) for the construction of the Self-Set. We define and evaluate two approaches for the aggregation of the Self-Set across multiple sensors in a WSN. The first approach is the Graph Theory Based Aggregation (GTBA) which consists of two main parts, namely: classification of the sensor readings by means of CLNN, which provides the multimodal view data and GTBA of the CLNN output, which takes intersections of intervals produced by CLNN. In this thesis we define and evaluate two different interpretations of GTBA, namely: Midpoint Intersection (MPI): one that considers the midpoint of intervals. Midpoint Free Intersection (MFI): one that does not take the midpoints into account but assigns the confidence levels to each of the resulted intersections. We evaluated both interpretations on three different types of phenomena and have shown that the second interpretation, MFI, consistently produced more precise representations of the environment under observation. However, MFI produced a very strict representation of the phenomenon, which consequently led to a large number of systems' retraining. Hence, we defined and evaluated a technique which produced a more relaxed representation of the Self-Set and at the same time preserved the finer variation in the phenomenon. The second approach is based on unsupervised learning. We define and evaluate three related unsupervised learning procedures ?? Divergence and Merging (DMP), Suboptimal Clustering (SOC), and Simple Clustering (SC) for the collection of the Self-Set. We explore the design tradeoffs in unsupervised learning schemes with respect to the clustering quality. We implement and evaluate these related unsupervised learning procedures on a realworld data set. The outcome of these experiments show that, out of the three unsupervised learning procedures studied in this thesis, the Suboptimal Clustering procedure appears to be the most suitable for the classification of sensor readings, provided that the amount of free memory is large enough to store and recluster an entire training set. We evaluate aggregation of the Self-Set produced by means of the distributed implementation of the unsupervised learning procedures. The aggregation is based on extended unsupervised learning and we evaluate the possibilities of the autonomous retraining of the system. Our experiments show that, in a naturally slowly changing environment, 40% of nodes reporting deviations is a large enough number to reinitialise the retraining of the system. The final conclusion is that it is possible to have a distributed implementation of the unsupervised procedure that produces an almost identical representation of the environment, which makes unsupervised learning suitable for a large number of sensor network architectures.

This dissertation presents the calibration-free image sensor modelling process applicable for localisation, such that these are robust to changes in environment and in sensor properties. The modelling process consists of two distinct parts, which are deterministic and stochastic techniques, and is achieved using mechanistic deconvolution, where the sensor???s mechanical and electrical properties are utilised. In the deterministic technique, the sensor???s effective focal length is first estimated by known lens properties, and is used to approximate the lens system by a thick lens and its properties. The aperture stop position offset???which is one of the thick lens properties???then derives a new factor, namely calibration-free distortion effects factor, to characterise distortion effects inherent in the sensor. Using this factor and the given pan and tilt angles of an arbitrary plane of view, the corrected image data is generated. The corrected data complies with the image sensor constraints modified by the pan and tilt angles. In the stochastic technique, the stochastic focal length and distortion effects factor are first approximated, using tolerances of the mechanical and electrical properties. These are then utilised to develop the observation likelihood necessary in recursive Bayesian estimation. The proposed modelling process reduces dependency on image data, and, as a result, do not require experimental setup or calibration. An experimental setup was constructed to conduct extensive analysis on accuracy of the proposed modelling process and its robustness to changes in sensor properties and in pan and tilt angles without recalibration. This was compared with a conventional modelling process using three sensors with different specifications and achieved similar accuracy with one-seventh the number of iterations. The developed model has also shown itself to be robust and, in comparison to the conventional modelling process, reduced the errors by a factor of five. Using area coverage method and one-step lookahead as control strategies, the stochastic sensor model was applied into a recursive Bayesian estimation application and was also compared with a conventional approach. The proposed model provided better target estimation state, and also achieved higher efficiency and reliability when compared with the conventional approach.

Wireless Sensor Networks (WSNs), consisting of many small sensing devices working in concert, have the potential to revolutionise every aspect of our lives. Although the technology is still in its infancy offers an unlimited number of possible applications, ranging from military surveillance to environmental monitoring. These WSNs are prone to physical sensor failures due to environmental conditions such bio fouling and an adverse ambient environment, as well as threats that arise from their operation in an open environment. Consequently, reliability and fault-tolerance techniques become a critical aspect of the research associated with WSNs. In mission critical applications, such as the monitoring of enemy troops, unreliable or faulty information produced by WSNs could potentially lead to fatal outcomes. In such applications, it necessary to receive both a correct notification of event occurrences and uncorrupted data. Developing a fault-tolerance system for WSNs is a challenging task. New self-configuration, self-recognition and self-organisation techniques are needed due to unique aspects of the operation of WSNs. Our current understanding of WSNs leads to an immunologically inspired solution to the design of a fault-tolerant network. One of the main roles of the Natural Immune System(NIS) is the recognition of self and the elimination of non-self proteins. Hence, in order to have an immune system equivalent for a sensor network, we must have a clear and stable definition of what constitutes the Self and the Non-Self Sets in a sensor network. This thesis explores two different approaches to modelling, collection and representation of the Self-Set in distributed sensor networks. We approach this problem, of identifying what constitutes the Self-Set in terms of sensor readings, using pattern recognition techniques from the machine learning field that leverages a small number of past observations of sensor nodes. We have chosen Competitive Learning Neural Network (CLNN) for the construction of the Self-Set. We define and evaluate two approaches for the aggregation of the Self-Set across multiple sensors in a WSN. The first approach is the Graph Theory Based Aggregation (GTBA) which consists of two main parts, namely: classification of the sensor readings by means of CLNN, which provides the multimodal view data and GTBA of the CLNN output, which takes intersections of intervals produced by CLNN. In this thesis we define and evaluate two different interpretations of GTBA, namely: Midpoint Intersection (MPI): one that considers the midpoint of intervals. Midpoint Free Intersection (MFI): one that does not take the midpoints into account but assigns the confidence levels to each of the resulted intersections. We evaluated both interpretations on three different types of phenomena and have shown that the second interpretation, MFI, consistently produced more precise representations of the environment under observation. However, MFI produced a very strict representation of the phenomenon, which consequently led to a large number of systems' retraining. Hence, we defined and evaluated a technique which produced a more relaxed representation of the Self-Set and at the same time preserved the finer variation in the phenomenon. The second approach is based on unsupervised learning. We define and evaluate three related unsupervised learning procedures ?? Divergence and Merging (DMP), Suboptimal Clustering (SOC), and Simple Clustering (SC) for the collection of the Self-Set. We explore the design tradeoffs in unsupervised learning schemes with respect to the clustering quality. We implement and evaluate these related unsupervised learning procedures on a realworld data set. The outcome of these experiments show that, out of the three unsupervised learning procedures studied in this thesis, the Suboptimal Clustering procedure appears to be the most suitable for the classification of sensor readings, provided that the amount of free memory is large enough to store and recluster an entire training set. We evaluate aggregation of the Self-Set produced by means of the distributed implementation of the unsupervised learning procedures. The aggregation is based on extended unsupervised learning and we evaluate the possibilities of the autonomous retraining of the system. Our experiments show that, in a naturally slowly changing environment, 40% of nodes reporting deviations is a large enough number to reinitialise the retraining of the system. The final conclusion is that it is possible to have a distributed implementation of the unsupervised procedure that produces an almost identical representation of the environment, which makes unsupervised learning suitable for a large number of sensor network architectures.

Thesis (Ph. D.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 6, 2008) Includes bibliographical references.