In the recent past, wireless sensor networks have been introduced to use in many applications. To design the networks, the factors needed to be considered are the coverage area, mobility, power consumption, communication capabilities etc. The challenging goal of our project is to create a simulator to support the wireless sensor network simulation. The network simulator (NS-2) which supports both wire and wireless networks is implemented to be used with the wireless sensor network. This implementation adds the sensor network classes which are environment, sensor agent and sensor application classes and modifies the existing classes of wireless network in NS- 2. This NS-2 based simulator is used to test routing protocols – Destination-Sequenced Distance Vector (DSDV), and Ad-Hoc On-Demand Distance Vector (AODV) as one part of simulations. Finally, the sensor network application models and the extension methods of this NS-2 based simulator for simulations in specific wireless sensor network applications are proposed.

Gas sensors are devices that can convert the concentration of an analyte gas into an electronic signal. Zinc oxide (ZnO) is an important n-type metal oxide semiconductor which has been utilized as sensor for several decades. In recent years, there have been extensive investigations of nanoscale semiconductor gas sensors. The size reduction of ZnO sensors to nanometer scale provides a good opportunity to dramatically increase their sensing properties in comparison with their macroscale counterparts.<p> In this work, two kinds of ZnO nanostructures (nanowires and nanorods) were studied. ZnO nanowires were synthesized by electrodeposition while porous anodic aluminum oxide served as a growth template. Three types of ZnO nanowires with different diameters were obtained. Meanwhile, ZnO nanorods were prepared by a hydrothermal route from ZnO nanoparticle seeds. However, the aspect ratio (length/width) of nanorods was significantly smaller than that of nanowires. Both nanowires and nanorods were characterized by optical microscopy, scanning electron microscopy, powder X-ray diffraction, energy dispersive X-ray spectroscopy and energy dispersive spectroscopy.<p> The sensing performance of the synthetic ZnO nanostructures were investigated by three gases: saturated water vapour in air, saturated ethanol vapour in air, and carbon monoxide in air. Both ZnO nanostructures showed good sensitivity and selectivity to ethanol vapour. At high temperature, the ZnO nanosensors were up to seven times more responsive to ethanol vapour than water vapour and over 200 times more responsive to ethanol vapour than CO. Due to the size dependence, ZnO nanowires with the smallest diameter is considered the best sensor candidate among ZnO nanowires.<p> On the basis of previous work, Au/ZnO/Au multimetallic nanobarcodes were also synthesized by electrodeposition, and their sensing characteristics are to be investigated in the future.

A wireless sensor network (WSN) is comprised of a few hundred or thousand au-tonomous sensor nodes spatially distributed over a particular region. Each sensornode is equipped with a wireless communication device, a small microprocessor, anda battery-powered energy source. Typically, the applications of WSNs such as habitatmonitoring, re detection, and military surveillance, require data collection, process-ing, and transmission among the sensor nodes. Due to their energy constraints andhostile environments, the main challenge in the research of WSN lies in prolongingthe lifetime of WSNs.In this dissertation, we present four dierent topology management protocols forK-coverage and load balancing to prolong the lifetime of WSNs.First, we present a Randomly Ordered Activation and Layering (ROAL) protocolfor K-coverage in a stationary WSN. The ROAL suggests a new model of layer cov-erage that can construct a K-covered WSN using the layer information received fromits previously activated nodes in the sensing distance. Second, we enhance the faulttolerance of layer coverage through a Circulation-ROAL (C-ROAL) protocol. Us-ing the layer number, the C-ROAL can activate each node in a round-robin fashionduring a predened period while conserving reconguration energy. Next, MobilityResilient Coverage Control (MRCC) is presented to assure K-coverage in the presence of mobility, in which a more practical and reliable model for K-coverage with nodalmobility is introduced. Finally, we present a Multiple-Connected Dominating Set(MCDS) protocol that can balance the network trac using an on-demand routingprotocol. The MCDS protocol constructs and manages multiple backbone networks,each of which is constructed with a connected dominating set (CDS) to ensure a con-nected backbone network. We describe each protocol, and compare the performanceof our protocols with Dynamic Source Routing (DSR) and/or existing K-coveragealgorithms through extensive simulations.The simulation results obtained by the ROAL protocol show that K-coverage canbe guaranteed with more than 95% coverage ratio, and signicantly extend networklifetime against a given WSN. We also observe that the C-ROAL protocol provides abetter reconguration method, which consumes only less than 1% of the recongura-tion energy in the ROAL protocol, with a greatly reduced packet latency. The MRCCprotocol, considering the mobility, achieves better coverage by 1.4% with 22% feweractive sensors than that of an existing coverage protocol for the mobility. The resultson the MCDS protocol show that the energy depletion ratio of nodes is decreasedconsequently, while the network throughput is improved by 35%.

Two important applications of the fiber Fabry-Perot Interferometer (FFPI) sensor are investigated: (1) an optical binary switch for aerospace application, and (2) an FFPI weigh-in-motion sensor for measuring the weight of trucks traveling down a highway. In the fiber optical switch, the FFPI sensor is bonded to a copper cantilever to sense the elongation of cavity length induced by force applied to the end of the cantilever via a pushed button. Light from a superluminescent diode light source passes through a scanned Michelson interferometer and is reflected from a sensing FFPI and a reference FFPI to produce a fringe pattern. A secondary interferometer uses a distributed feedback laser light source to compensate for irregularities in the mechanical scanning rate of the moving stage to achieve precision measurement of the optical path difference. The system is calibrated by applying known weights to the cantilever. The elongation measured by the FFPI sensor shows excellent linearity as a function of the force applied, and little hysteresis was observed. By comparing the measured force to a threshold, the system produces a binary signal that indicates the state of the pilotactuated system; i. e., whether or not the button has been pushed. In FFPI weigh-in-motion sensors system, the FFPI sensors are installed in metal bars so that they will experience the strain induced by applied loads and are connected to the Signal Conditioning Unit (SCU). The SCU determines the induced phase shift in the FFPI and produces voltage outputs proportional to the phase shifts. Laboratory Material Testing System tests show that the fiber optic sensor response is a fairly linear function of the axial displacement. In highway tests the FFPI sensors showed strong responses and consistently reproduced the expected characteristics of truck wheel crossings. A falling weight deflectometer was used to calibrate the sensor response and predict unknown loads. All sensors in steel bars and aluminum bars showed excellent repeatability and accurate predictions, with an average relative percentage error within 2%. The study on sensor response variation with applied load positions shows a bell shaped distribution. Truck tests on the road sensors indicate that the repeatability of wheel crossings at similar position is good. The sensor can accurately measure axle spacing, speed, and truck class.

The fundamental objective of sensor networks underpinning a variety of applications is the collection of reliable information from the surrounding environment. The correctness of the collected data is especially important in applications involving societal welfare and safety, in which the acquired information may be utilized by end-users for decision-making. The distributed nature of sensor networks and their deployment in unattended and potentially hostile environments, however, renders this collection task challenging for both scalar and visual data. In this work we propose and address the twin problem of carrying out and defending against a stealthy attack on the information gathered by a sensor network at the physical sensing layer as perpetrated by a competing hostile network. A stealthy attack in this context is an intelligent attempt to disinform a sensor network in a manner that mitigates attack discovery. In comparison with previous sensor network security studies, we explicitly model the attack scenario as an active competition between two networks where difficulties arise from the pervasive nature of the attack, the possibility of tampering during data acquisition prior to encryption, and the lack of prior knowledge regarding the characteristics of the attack. We examine the problem from the perspective of both the hostile and the legitimate network. The interaction between the networks is modeled as a game where a stealth utility is derived and shown to be consistent for both players in the case of stealthy direct attacks and stealthy cross attacks. Based on the stealth utility, the optimal attack and defense strategies are obtained for each network. For the legitimate network, minimization of the attacker’s stealth results in the possibility of attack detection through established paradigms and the ability to mitigate the power of the attack. For the hostile network, maximization of the stealth utility translates into the optimal attack avoidance. This attack avoidance does not require active communication among the hostile nodes but rather relies on a level of coordination which we quantify. We demonstrate the significance and effectiveness of the solution for sensor networks acquiring scalar and multidimensional data such as surveillance sequences and relate the results to existing image sensor networks. Finally we discuss the implications of these results for achieving secure event acquisition in unattended environments.

We have investigated and implemented several general strategies in the development of microfluidics-based chemical/biochemical sensing systems. The research in this dissertation covers the immobilization of biological reagents inside microfluidic channels using polystyrene (PS) microbeads and photopolymerizable hydrogel, electrochemical sensing via electrochemiluminescence (ECL) reporting with bipolar and two-electrode configurations, and integration of these general functions to realize multiplexing and networking on-chip. Photopolymerizable hydrogel based on Poly(ethylene glycol) (PEG) and streptavidin-coated polystyrene (PS) microbeads were employed as building blocks as well as functional components in microfluidic system. PEG hydrogels can be used to define local microenvironments at different locations in the same microchannel, which enables the introduction of multiple sensing events on the same device. Monitoring of DNA hybridization and enzyme/substrate interaction were realized thereafter by using either fluorescence or electrochemistry as the detection method. Electrogenerated chemiluminescence based on Ru(bpy)32+ (bpy = 2,2??-bipyridine) and tripropylamine (TPA) was used to photonically report various redox events in microfluidic systems. By using microfluidic electrochemical cells based on either two-electrode or bipolar electrode (one-electrode), electroactive species that undergo reduction can be electrically linked to this anodic ECL process and thus be reported by the latter. This ECL sensing scheme essentially broadens the spectrum of redox compounds that can be detected by ECL since the analytes are not required to directly participate into the light-generating processes. Microfluidics offers some unique technical advantages of performing electrochemistry over conventional methods. In particular, laminar flow allows multiple analyte streams to be brought together in parallel with little mixing. Moreover, electrochemical signals can be generally utilized as a convenient means to link individual microchannels together hence to realize microfluidic networking and cross-communication. Electrochemical microfluidic devices can be used to mimic general functions of microelectronic devices such as diodes, transistors, and logic gates. These novel functions rendered by electrochemistry are believed to bring us closer to the final goals of micro total analysis systems and lab-on-a-chip.

In wireless sensor networks (WSN), nodes have very limited power due to hardware constraints. Packet losses and retransmissions resulting from congestion cost precious energy and shorten the lifetime of sensor nodes. This problem motivates the need for congestion control mechanisms in WSN. In this thesis, an observation of multiple non-empty queues in sensor networks is first reported. Other aspects affected by congestion like queue length, delay and packet loss are also studied. The simulation results show that the number of occupied queues along a path can be used to detect congestion. Based on the above result, a congestion control scheme for the transport layer is proposed in this thesis. It is composed of three parts: (i) congestion detection by tracking the number of non-empty queues; (ii) On-demand midway non-binary explicit congestion notification (CN) feedback; and (iii) Adaptive rate control based on additive increase and multiplicative decrease (AIMD). This scheme has been implemented in ns2. Extensive simulations have been conducted to evaluate it. Results show that it works well in mitigating and avoiding congestion and achieves good performance in terms of energy dissipation, latency and transmission effciency.

A novel sensing methodology using two-axis Hall-effect sensors is proposed, where the absolute positioning of a device atop any magnet matrix is possible. This methodology has the capability of micrometer-order positioning resolution as well as unrestricted translational and rotational range in planar 3-DOF (degree-of-freedom) motions, with potential capability of measuring all 6-DOF motions. This research presents the methodology and preliminary experimental results of 3-DOF planar motion measurements atop a Halbach magnet matrix using two sets of two-axis Hall-effect sensors. Analysis of the Halbach magnet matrix is presented to understand the generated magnetic field. The algorithm uses the Gaussian least squares differential correction (GLSDC) algorithm to estimate the relative position and orientation from the Hall-effect sensor measurements. A recursive discrete-time Kalman filter (DKF) is used in combination with the GLSDC to obtain optimal estimates of position and orientation, as well as additional estimates of velocity and angular velocity, which we can use to design a multivariable controller. The sensor and its algorithm is implemented to a magnetic levitation (maglev) stage positioned atop a Halbach magnet matrix. Preliminary experimental results show its position resolution capability of less than 10 µm and capable of sensing large rotations. Controllers were designed to close the control loop for the three planar degrees of freedom motion using the GLSDC outputs at a sampling frequency of 800 Hz on a Pentek 4284 digital signal processor (DSP). Calibration was done by comparing the laser interferometers’ and the GLSDC’s outputs to improve the positioning accuracy.

The work described in this dissertation details the development of a biomimetic materials for use in sensors and therapeutics, based on new advances in material science. The sensors developed herein target neurodegenerative diseases. Two of the diseases, the transmissible spongiform encephalopathies (TSEs) and Alzheimer’s disease (AD), are diseases associated with the abnormal folding of a protein, thus detecting the disease is dependent upon developing structure specific sensor technologies. Both sensors developed in this work take advantage of the unique optical properties associated with nanoscale metal particles, however they use different types of spectroscopies for optical detection of the presence of the disease associated abnormal protein, and different types of recognition elements that bring the disease associated proteins close to the nanoscale metal particles. In the case of TSEs, the recognition element was a commercially available antibody. In the case of AD, the recognition element was a molecular scale self-assembled surface. A therapeutic for AD was developed based on the molecular scale materials developed for the AD biosensor. Mathematical models were developed that facilitated the rational design of the biosensors described in this work that could also be used in future biosensor development.

In this study, an efficient new hybrid approach for multiple sensors data fusion and fault detection is presented, addressing the problem with possible multiple faults, which is based on conventional fuzzy soft clustering and artificial immune system (AIS). The proposed hybrid system approach consists of three main phases. In the first phase signal separation is performed using the Fuzzy C-Means (FCM) algorithm. Subsequently a single (fused) signal based on the information provided from the sensor signals is generated by the fusion engine. The information provided from the previous two phases is used for fault detection in the third phase based on the Artificial Immune System (AIS) negative selection mechanism. The simulations and experiments for multiple sensor systems have confirmed the strength of the new approach for online fusing and fault detection. The hybrid system gives a fault tolerance by handling different problems such as noisy sensor signals and multiple faulty sensors. This makes the new hybrid approach attractive for solving such fusion problems and fault detection during real time operations. This hybrid system is extended for early fault detection in complex mechanical systems based on a set of extracted features; these features characterize the collected sensors data. The hybrid system is able to detect the onset of fault conditions which can lead to critical damage or failure. This early detection of failure signs can provide more effective information for any maintenance actions or corrective procedure decisions.