Wireless Sensor Networks (WSNs) overcome the difficulties of other monitoring systems, as they require no human attendance on site, provide real-time interaction with events, and maintain cost and power efficient operations. However, further efficiencies are required especially in the case of Outdoor Environment Monitoring (OEM) applications due to their harsh operational conditions, huge targeted areas, limited energy budget, and required Three-Dimensional (3D) setups. A fundamental issue in defeating these practical challenges is the deployment planning of the WSNs. The deployment plan is a key factor of many intrinsic properties of OEM networks, summarized in connectivity, lifetime, fault-tolerance, and cost-effectiveness. In this thesis, we investigate the problem of WSNs deployments that address these properties in order to overcome the unique challenges and circumstances in OEM applications. A natural solution to this problem is to have multiple relay nodes that reserve more energy for sensing, and provide vast coverage area. Furthermore, assuming a subset of these relay nodes are mobile can contribute in repairing the network connectivity problems and recovering faulty nodes, in addition to granting balanced load distributions, and hence prolonging the network lifetime. We investigate this promising research direction by proposing a 3D grid-based deployment planning for heterogeneous WSNs in which Sensor Nodes (SNs) and Relay Nodes (RNs) are efficiently deployed on grid vertices. Towards this efficiency, we analyze and characterize the grid connectivity property in the 3D space. Afterward, we design optimization schemes for the placement of SNs and RNs on the 3D grid models. Based on theoretical analysis and extensive simulations, the proposed schemes show a significant enhancement in terms of network connectivity and lifetime in OEM applications. / Thesis (Ph.D, Computing) -- Queen's University, 2011-05-02 10:29:01.785

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In recent years, wireless sensor networks (WSNs) have grown rapidly and have had a substantial impact in many applications. A WSN is a network that consists of interconnected autonomous nodes that monitor physical and environmental conditions, such as temperature, humidity, pollution, etc. If required, nodes in a WSN can perform actions to affect the environment. WSNs present an interesting and challenging field of research due to the distributed nature of the network and the limited resources of the nodes. It is necessary for a node in a WSN to be small to enable easy deployment in an environment and consume as little energy as possible to prolong its battery lifetime. There are many challenges in WSNs, such as programming a large number of nodes, designing communication protocols, achieving energy efficiency, respecting limited bandwidth, and operating with limited memory. WSNs are further constrained due to the deployment of the nodes in indoor and outdoor environments and obstacles in the environment. In this dissertation, we study some of the fundamental optimisation problems related to the programming, coverage, mobility, data collection, and data loss of WSNs, modelled as standalone optimisation problems or as optimisation problems integrated with protocol design. Our proposed solution methods come from various fields of research including constraint programming, integer linear programming, heuristic-based algorithms, and data inference techniques. / ProFuN

Low-power wireless technology enables numerous applications in areas from environmental monitoring and smart cities, to healthcare and recycling. But resource-constraints and the distributed nature of applications make low-power wireless networks difficult to develop and understand, resulting in increased development time, poor performance, software bugs, or even network failures. Network simulators offer full non-intrusive visibility and control, and are indispensible tools during development. But simulators do not always adequately represent the real world, limiting their applicability. In this thesis I argue that high simulation timing accuracy is important when developing high-performance low-power wireless protocols. Unlike in generic wireless network simulation, timing becomes important since low-power wireless networks use extremely timing-sensitive software techniques such as radio duty-cycling. I develop the simulation environment Cooja that can simulate low-power wireless networks with high timing accuracy. Using timing-accurate simulation, I design and develop a set of new low-power wireless protocols that improve on throughput, latency, and energy-efficiency. The problems that motivate these protocols were revealed by timing-accurate simulation. Timing-accurate software execution exposed performance bottlenecks that I address with a new communication primitive called Conditional Immediate Transmission (CIT). I show that CIT can improve on throughput in bulk transfer scenarios, and lower latency in many-to-one convergecast networks. Timing-accurate communication exposed that the hidden terminal problem is aggravated in duty-cycled networks that experience traffic bursts. I propose the Strawman mechanism that makes a radio duty-cycled network robust against traffic bursts by efficiently coping with hidden terminals. The Cooja simulation environment is available for use by others and is the default simulator in the Contiki operating system since 2006.

In recent years, many strides have been achieved in the area of Wireless Sensor Networks (WSNs), which is leading to constant innovations in the types of applications that WSNs can support. Much advancement has also been achieved in the area of smart homes, enabling its occupants to manually and easily control their utility expenses. In this thesis, both areas of research will be colluded for a simple, yet critical application: efficient and economical comfort control in smart residential spaces. The goal is to design a central, modular energy consumption control system for residential spaces, which manages energy consumption in all aspects of a typical residence. This thesis is concerned with two facets of energy consumption in residences. The first facet is concerned with controlling when the heating, ventilating, and air conditioning unit (HVAC) operates for each room separately. This is in contrast to a typical HVAC system where comfort is provided across the floor as a whole. The second facet is concerned with controlling the lighting in each room so as to not exceed a certain input value. The communication network that supports the realization of these coveted goals is based on Zigbee interconnected sensor nodes which pour data unto a smart thermostat which does all the required calculations and activates the modules required for comfort control and energy management, if needed. A Java-based discrete event simulator is then written up to simulate a floor of a typical Canadian single-family dwelling. The simulation assumes error-less communication and proceeds to record certain room variables and the ongoing cost of operation periodically. These results from the simulator are compared to the results of the well known simulator, created by DesignBuilder, which describes typical home conditions. The conclusion from this analysis is that the Comfort Control and Energy Management Algorithms (CEMA) are feasible, and that their implementation incurs significant monetary savings.

Sensor network web services have recently emerged as promising tools to provide remote management, data collection and querying capabilities for sensor networks. They can be utilized in a large number of elds among which Demand-Side Energy Management (DSEM) is an important application area that has become possible with the smart electrical power grid. DSEM applications generally aim to reduce the cost and the amount of power consumption. In the traditional power grid, DSEM has not been implemented widely due to the large number of households and lack of ne-grained automation tools. However by employing intelligent devices and implementing communication infrastructure among these devices, the smart grid will renovate the existing power grid and it will enable a wide variety of DSEM applications. In this thesis, we analyze various DSEM scenarios that become available with sensor network web services. We assume a smart home with a Wireless Sensor Network (WSN) where the sensors are mounted on the appliances and they are able to run web services. The web server retrieves data from the appliances via the web services running on the sensor nodes. These data can be stored in a database after processing, where the database can be accessed by the utility, as well as the inhabitants of the smart home. We showthat our implementation is e cient in terms of running time. Moreover, the message sizes and the implementation code is quite small which makes it suitable for the memory-limited sensor nodes. Furthermore, we show the application scenarios introduced in the thesis provide energy saving for the smart home.

In Wireless Sensor and Robot Networks (WSRNs), static sensors report event information to one of the robots. In the k nearest neighbour query processing problem in WSRNs, the robot receives event report needs to find exact k nearest robots (KNN) to react to the event, among those connected to it. We are interested in localized solutions, which avoid message flooding to the whole network. Several existing methods restrict the search within a predetermined boundary. Some network density-based estimation algorithms were proposed but they either result in large message transmission or require the density information of the whole network in advance which is complex to implement and lacks robustness. Algorithms with tree structures lead to the excessive energy consumption and large latency caused by structural construction. Itinerary based approaches generate large latency or unsatisfactory accuracy. In this thesis, we propose a new method to estimate a search boundary, which is a circle centred at the query point. Two algorithms are presented to disseminate the message to robots of interest and aggregate their data (e.g. the distance to query point). Multiple Auction Aggregation (MAA) is an algorithm based on auction protocol, with multiple copies of query message being disseminated into the network to get the best bidding from each robot. Partial Depth First Search (PDFS) attempts to traverse all the robots of interest with a query message to gather the data by depth first search. This thesis also optimizes a traditional itinerary-based KNN query processing method called IKNN and compares this algorithm with our proposed MAA and PDFS algorithms. The experimental results followed indicate that the overall performance of MAA and PDFS outweighs IKNN in WSRNs.

Wireless Sensor Network routing protocols rely on the estimation of the quality of the links between nodes to determine a suitable path from the data source nodes to a data-collecting node. Several link estimators have been proposed, but most of these use only one link property. Fuzzy logic based link quality estimators have been recently proposed which consider a number of link quality metrics. The fuzzification of crisp values to fuzzy values is done through membership functions. The shape of the fuzzy link quality estimator membership functions is primarily performed leveraging qualitative knowledge and an improper assignment of fuzzy membership functions can lead to poor route selection and hence to unacceptable packet losses. This thesis evaluated the Channel Quality membership function of, an existing fuzzy link quality estimator and it was seen that this membership function didn???t perform as well as expected. This thesis presents an experimental approach to determine a suitable Channel Quality fuzzy membership function based on varying the shape of the fuzzy set for a multipath wireless sensor network scenario and choosing an optimum shape that maximizes the Packet Delivery Ratio of the network. The computed fuzzy set membership functions were evaluated against an existing fuzzy link quality estimator under more complex scenarios and it is shown the performance of the experimental refined membership function was better in terms of packet reception ratio and end to end delay.The fuzzy link quality estimator was applied in WiseRoute (a simple converge cast based routing protocol) and shown that this SNR based fuzzy link estimator performed better than the original implemented RSSI based link quality used in WiseRoute.

Wireless sensor networks suffer from limited power resources. Therefore, managing the energy constraints and exploring new ways to minimize the power consumption during the operation of the nodes are critical issues. Conventional MAC protocols deal with this problem without considering the internal properties of the sensor nodes’ batteries. However, recent studies about battery modeling and behaviour showed that the pulsed discharge mechanism and the charge recovery effect may have a significant impact on wireless communication in terms of power saving. In this thesis we propose two battery-aware MAC protocols that take benefit of these factors to save more energy and to prolong the lifetime of the nodes/network without affecting the throughput. In both protocols we measure the remaining battery capacity of the node and use that measurement in the back-off scheme. The first protocol gives the nodes with higher remaining battery capacity more priority to access the medium, while the other one provides more medium access priority to the nodes with lower remaining battery capacity. The objective is to investigate, through simulations, which protocol reduces the power consumption of the nodes, improve the lifetime of the network, and compare the results with the CSMA-CA protocol.

Les xarxes de sensors sense fils (WSNs) han esdevingut un dels sistemes de comunicació amb més projecció d'aquesta dècada. Abasten una àmplia varietat d’aplicacions tals com la monitorització del medi ambient, la predicció de desastres naturals, en medicina, en transport, posicionament en interiors, i tasques militars. Els nodes que composen la xarxa, són típicament de baix cost, cosa que atorga una sèrie de limitacions en termes d’energia, velocitat de càlcul i d’ample de banda. Amb els avenços de les comunicacions sense fils i la creixent demanda de noves i més complexes aplicacions, les WSNs s’han d’optimitzar per tal de minimitzar aquestes limitacions. Aquesta tesi proposa un conjunt de tècniques que proporcionen a una WSN les següents característiques: 1. Implementació distribuïda sense necessitat de senyalització entre nodes sensors. 2. Comunicacions energèticament eficients. 3. Poca complexitat als nodes sensors. 4. Empra pocs recursos (temps, ample de banda, etc.) 5. Presenta un error quadràtic mig baix en reconstrucció al receptor. 6. Comunicacions secretes a capa física. Primer, s’estudia la transmissió seqüencial de mostreig reduït. Aquesta tècnica permet la disminució del nombre de transmissions i, per tant, reduir la despesa energètica associada a la comunicació a la xarxa. En particular, s’estudia el rendiment dels codificadors determinístics, probabilístics i condicionals de mostreig reduït per senyals autoregressius. S’obtenen expressions tancades de l’error quadràtic mig pel cas de mostreig reduït determinístic i probabilístic, mentre que pel cas condicional es deriven aproximacions ajustades. A continuació, s’analitza la compressió de la informació per WSNs grans. Pel cas on els paràmetres de correlació del senyal són desconeguts a priori, es proposen dos estimadors millorats: i) un per la predicció emprant el filtre de Wiener i ii) un per l’error quadràtic mig obtingut. Ambdós estimadors s’empren pels dos passos claus de l’algorisme de codificació distribuïda de canal. Aquests estimadors milloren notablement el rendiment de l’algorisme en comparació amb els estimadors de mostres clàssics, especialment quan la dimensió del vector d’observacions és comparable en magnitud amb el nombre de mostres usades a la fase d’entrenament de l’algorisme. Posteriorment, es proposa un esquema de comunicació distribuïda i energèticament eficient anomenat Amplify-and-Forward Compressed Sensing. Aquest esquema es basa en la tècnica de sensat comprimit i aprofita la correlació existent al senyal rebut per tal de reduir tant el nombre de recursos emprats com les despeses energètiques del sistema. Específicament, el sistema es dissenya seguint una funció de cost que controla el compromís existent entre error quadràtic i consum energètic de la xarxa. Per aconseguir aquest disseny, es deriva un model simple que aproxima el rendiment de l’esquema proposat en termes d’error quadràtic mig. A més, es contribueix a la teoria de sensat comprimit amb una nova i més ajustada relació entre el mínim nombre de mesures necessàries donades unes determinades propietats del senyal. Finalment, s’estudia l’esquema proposat Amplify-and-Forward Compressed Sensing des d’un punt de vista de secretisme a capa física. Es demostra que aquest esquema assoleix secretisme perfecte sota la presència d’un o d’un grup reduït d’espies, mentre que per un nombre més gran, és possible deteriorar notablement les seves capacitats d’espionatge gràcies a una tècnica proposta especialment dissenyada per introduir un extra d’incertesa solament a l’estimació dels espies. / Wireless Sensor Networks (WSNs) have emerged as one of the most promising wireless communication systems in the last decade. They can be used in a wide variety of applications such as environmental monitoring, natural disaster prediction, healthcare, transportation, indoor positioning, and military tasks. The cost and the complexity of the nodes within a WSN are typically low, which results in constraints such as energy limitation, low computational speed, and reduced communication bandwidth. With the advances in wireless communications and the growing demand of new and more complex applications, WSNs must be optimized in order to overcome their intrinsic limitations in terms of complexity and power. In this dissertation, and according to these constraints, we propose a set of techniques that provide to a WSN the following interesting features: 1. Distributed operation without the need of signaling among sensing nodes. 2. Energy-efficient communications. 3. Low complexity at the sensing nodes. 4. Low resource (i.e., bandwidth, time, etc.) utilization. 5. Low distortion level at the receiver. 6. Secret communications at the physical layer. First, we study the zero-delay downsampling transmission. This technique allows the system to reduce the number of transmissions and hence decrease the total energy spent. In particular, we study the performance of deterministic, probabilistic and conditional downsampling encoding-decoding pairs for the case of the autoregressive signal model. We obtain closed form expressions for the quadratic error of the deterministic and probabilistic encoder-decoders, while accurate approximations are derived for the quadratic error of the conditional downsampling schemes. Second, we analyze data compression applied to large WSNs. For the realistic case where the correlation parameters are not known a priori, we obtain two enhanced correlation estimators: i) one for the linear Wiener filter vector and ii) one for the achieved mean square error. Both estimators are employed in the two key steps of the distributed source coding algorithm. These estimators notably improve the performance of the algorithm in comparison to the application of classical sample estimators, specially when the dimension of the observation vector is comparable in magnitude to the number of samples used in the training phase. Then, we propose a distributed and energy-efficient communication scheme named Amplify-and-Forward Compressed Sensing. This scheme is based on compressed sensing and exploits the correlation present in the signal in order to reduce both the resource utilization and the energy consumption. More specifically, the system is designed according to a cost function that controls the trade-off between the quadratic error in the reconstruction and the energy consumption of the network. In order to aid the system design, a simple model that accurately approximates the performance of the proposed scheme in terms of the quadratic error has been derived. Furthermore, we contribute to the compressed sensing theory with a tighter relationship between the minimum number of measurements that are required for a given network dimension and the sparsity level of the transmitted signal. Finally, the proposed Amplify-and-Forward Compressed Sensing scheme is also studied in terms of secrecy and wiretap distortion at the physical layer. It is shown that the proposed scheme is perfectly secret in the presence of one or even a small group of eavesdroppers whereas for a larger eavesdropping set, it is still possible to notably deteriorate its espionage capabilities thanks to a proposed technique specifically designed to introduce extra uncertainty only in the channel estimation of the eavesdroppers.