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Wireless sensor networks have been used in different applications due to the advancement of sensor technology. These uses also have raised different optimization issues. Most of the algorithms proposed as solutions to the various optimization problems are either centralized or distributed which are not ideal for these real life applications. Very few strictly local algorithms for wireless sensor networks exist in the literature. In this thesis, we consider some of these optimization problems of sensor networks, for example, sleep-wake scheduling, mobile dispersion, mobile object monitoring, and gathering problems. We also consider the depth adjustment problem of underwater sensor networks. We design cellular automaton based local algorithms for these problems. The cellular automaton is a bioinspired model used to model different physical systems including wireless sensor networks. One of the main advantages of using cellular automaton based algorithms is that they need very little local information to compute a solution. We perform different simulations and analysis and find that our algorithms are efficient in practice. / Thesis (Ph.D, Computing) -- Queen's University, 2012-11-25 13:37:36.854

Over the past decade, wireless sensor networks have gained more research attention for their potential applications in healthcare, defense, environmental monitoring, etc. Due to the strict energy limitation in the sensor node, techniques used for energy saving are necessary for this kind of network. MIMO technology is proven to be an effective method of increasing the channel capacity and supporting higher data rate under a fixed power budget and bit-error-rate requirement. So, wireless sensor networks and MIMO technology are combined and investigated in this thesis. The key contributions of this thesis are detailed below. Firstly, the extended total energy consumption equations for different transmission modes in cluster-based wireless sensor networks are derived. The transmitting energy consumption and the circuit energy consumption are taken into account in both intra-cluster and inter-cluster phases respectively. Secondly, a resource allocation framework is proposed for cluster-based cooperative MIMO on consideration of circuit energy. By introducing two adjusting parameters for the transmitting energy and the time slot allocation between intra-cluster and inter-cluster phases, this framework is designed to achieve the maximum data throughput of the whole system whilst maintaining the capacity and outage probability requirement in these two phases respectively. Thirdly, on comparison of various transmission modes in wireless sensor networks, a relatively energy-efficient mode switching framework is proposed for both single-hop and multi-hop transmissions. Based on the destination and the neighboring nodes’ path-loss, the source node can decide which transmission mode, SISO or cooperative MISO, single-hop or multi-hop, should be chosen. Conditions for each mode switching are investigated. The possible existing area of the cooperative nodes and the relaying nodes can be obtained from this framework.

This thesis investigated the suitability of wireless, unattended ground sensor networks for military applications. The unattended aspect requires the network to self-organize and adapt to dynamic changes. A wireless, unattended ground sensor network was prototyped using commercial off-the-shelf technology and three to four networked nodes. Device and network performance were measured under indoor and outdoor scenarios. The measured communication range of a node varied between three and nineteen meters depending on the scenario. The sensors evaluated were an acoustic sensor, a magnetic sensor, and an acceleration sensor. The measured sensing range varied by the type of sensor. Node discovery durations observed were between forty seconds and over five minutes. Node density calculations indicated that the prototype was scalable to five hundred nodes. This thesis substantiated the feasibility of interconnecting, self-organizing sensor nodes in military applications. Tests and evaluations demonstrated that the network was capable of dynamic adaptation to failure and degradation.

Wireless Sensor Networks (WSNs) is an important field of study as more and more applications are enhancing daily life. The technology trend is to achieve small-sized, cheap, and power efficient sensor nodes, which will make the system reliable and efficient. The Crossbow Technologies MICAz mote is an example used in this thesis. Measurements for its propagation characteristics in a realistic environment will help the deployment and installation of these motes to form a WSN. The CST Microwave Studio is used to build a simulation for the MICAz. The Rhino software is used to build Spanagel Hall, which is the location of the simulation. All of these elements are integrated in Urbana. Urbana is a simulation tool used to simulate the propagation decay around the mote and investigate the irregularity of the electromagnetic field for the indoor environment of the motes. The results and comparisons between empirical and simulated data are intended for assisting in the design and future studies and deployment of WSNs in the real world. / Jordanian Army author.

This thesis examines the use of modelling approaches in Wireless Sensor Networks (WSNs) at node and sink to: reduce the amount of data that needs to be transmitted by each node and estimate sensor readings for locations where no data is available. First, to contextualise the contributions in this thesis, a framework for WSN monitoring applications (FieldMAP) is proposed. FieldMAP provides a structure for developing monitoring applications that advocates the use of modelling to improve the informational output of WSNs and goes beyond the sense- and-send approach commonly found in current, elded WSN applications. Rather than report raw sensor readings, FieldMAP advocates the use of a state vector to encapsulate the state of the phenomena sensed by the node. Second, the Spanish Inquisition Protocol (SIP) is presented. SIP reduces the amount of data that a sensor node must transmit by combining model-based ltering with Dual-Prediction approaches. SIP makes use of the state vector component of FieldMAP to form a simple predictive model that allows the sink to estimate sensor readings without requiring regular updates from the node. Transmissions are only made when the node detects that the predictive model no longer matches the evolving data stream. SIP is shown to produce up to a 99% reduction in the number of samples that require transmission on certain data sets using a simple linear approach and consistently outperforms comparable algorithms when used to compress the same data streams. Furthermore, the relationship between the user-specied error threshold and number of transmissions required to reconstruct a data set is explored, and a method to estimate the number of transmissions required to reconstruct the data stream at a given error threshold is proposed. When multiple parameters are sensed by a node, SIP allows them to be combined into a single state vector. This is demonstrated to further reduce the number of model updates required compared to processing each sensor stream individually. iii Third, a sink-based, on-line mechanism to impute missing sensor values and predict future readings from sensor nodes is developed and evaluated in the context of an on-line monitoring system for a Water Distribution System (WDS). The mechanism is based on a machine learning approach called Gaussian Process Regression (GPR), and is implemented such that it can exploit correlations between nodes in the network to improve predictions. An on-line windowing algorithm deals with data arriving out of order and provides a feedback mechanism to predict values when data is not received in a timely manner. A novel approach to create virtual sensors that allows a data stream to be predicted where no physical sensor is permanently deployed is developed from the on-line GPR mechanism. The use of correlation in prediction is shown to improve the accuracy of predicted data from 1.55 Pounds per Square Inch (PSI) Root Mean Squared Error (RMSE) to 0.01 PSI RMSE. In-situ evaluation of the Virtual Sensors approach over 36 days showed that an accuracy of 0:75 PSI was maintained. The protocols developed in this thesis present an opportunity to improve the output of environmental monitoring applications. By improving energy consumption, long-lived networks that collect detailed data are made possible. Furthermore, the utility of the data collected by these networks is increased by using it to improve coverage over areas where measurements are not taken or available.

This paper has covered some security aspects to consider when deploying 6LoWPAN in wireless sensor networks. The issues mentioned in this paper revolve around encryption and authentication methods as well as key distribution mechanism. Security features at the link and network layer have been evaluated and the conclusion is that both do provide with good security but it is not always sufficient. For example, hop-by-hop security at the link layer could be really strong if you are in control of all links between source and destination, however, when transmitting data over the Internet this is rarely the case. As an alternative the network layer provide with an end-to-end solution with a compressed version of IPsec, but due to insufficient models for key distribution the keys are required to be distributed manually. This means that there is no support for asymmetric keys in a wireless sensor network at the time, but further research might solve these issues.

Chan Chi Pan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves xiv-xv). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Related Works --- p.1 / Chapter 1.3 --- Our Contributions --- p.2 / Chapter 1.4 --- Organization of the Thesis --- p.3 / Chapter Chapter 2 --- Definitions and Assumptions --- p.5 / Chapter 2.1 --- Data-collection Networks --- p.5 / Chapter 2.2 --- Assumptions --- p.9 / Chapter Chapter 3 --- Canonical Networks --- p.13 / Chapter 3.1 --- Theoretical Analysis --- p.13 / Chapter 3.1.1 --- Fixed Link Distance --- p.13 / Chapter 3.1.2 --- Variable Link Distance --- p.17 / Chapter 3.2 --- Simulation --- p.20 / Chapter Chapter 4 --- Beyond the Assumptions --- p.24 / Chapter 4.1 --- Multiple Interference --- p.24 / Chapter 4.2 --- HFD versus non-HFD performance --- p.27 / Chapter Chapter 5 --- Perfect Scheduling and IEEE 802.11 Scheduling --- p.30 / Chapter 5.1 --- Relationship between Perfect Scheduling and IEEE 802.11 Scheduling --- p.30 / Chapter 5.2 --- Throughput Analysis under IEEE 802.11 scheduling --- p.33 / Chapter Chapter 6 --- General Networks --- p.37 / Chapter 6.1 --- Discussion of HFP --- p.37 / Chapter 6.2 --- HFP Formulation --- p.39 / Chapter 6.3 --- Optimization in Finding Best HFP --- p.43 / Chapter 6.4 --- Experiment --- p.44 / Chapter 6.5 --- NS-2 Simulation --- p.47 / Chapter Chapter 7 --- Applying Canonical Network to General Networks --- p.49 / Chapter 7.1 --- Direct Application --- p.49 / Chapter 7.2 --- Manifold Canonical Network with Shorter Link Distance --- p.51 / Chapter 7.3 --- Robustness on Node Positions in Manifold Canonical Network --- p.54 / Chapter Chapter 8 --- Conclusion --- p.56 / Appendix A RTS/CTS and Power Control --- p.x / References --- p.xiv

An active sensor network is a wireless network comprising a large number of mobile sensor nodes. This dissertation deals with a general model of active sensor network, heterogeneous sensor network, where sensor nodes may have different sensing ranges. The deployment problem aims at relocating a large set of sensor nodes from arbitrary locations to give a connected, hole-free and locally maximized sensing coverage of the entire network. / The major contribution of this dissertation lies in four aspects. First, it is the first work that solves the deployment problem for optimal deterministic coverage of heterogeneous sensor networks, while most existing works limit their problems on stochastic coverage and homogeneous sensing model. Second, this work envisages the use of a generalized Voronoi diagram, the power diagram, as a novel solution to geometrically analyse and visualise the coverage of a heterogeneous sensor network. Third, it an original work that applies circle packing on sensor network deployment, and analyses and proves a number of geometrical properties of circle packing. Fourth, all methods provided in this dissertation are based on localized and distributed computation; no centralized processor or common data fusion platform is assumed to exist. / This dissertation gives an algorithm to solve the self-deployment problem. It is composed three parts. In the first part, the logical topology of the sensor network is constructed as triangulation by three distributed protocols: localized Delaunay triangulation, redundant boundary edge pruning and local edge swapping. Second, the sensor nodes self-deploy to new locations that are calculated using a circle packing algorithm. The dissertation shows that the homomorphism between Voronoi and power diagrams is necessary and sufficient for the equivalences of power Delaunay triangles to Delaunay triangles. This result allows the network to preserve a unit Delaunay triangulation by localized re-triangulations among a small number of nodes. Third, the sensor nodes further relocate themselves based on a virtual force approach to eliminate all existing coverage holes and redundant overlaps. / This dissertation studies the problem of active sensor network deployment. It focuses on self-deployment, localized and distributed computation and coverage maximization of heterogeneous sensor networks. / Lam, Miu Ling. / "February 2008." / Adviser: Yun-hui Liu. / Source: Dissertation Abstracts International, Volume: 70-03, Section: B, page: 1757. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 152-158). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

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.