Security and trust are two interdependent concepts and are often used interchangeably when defining a secure wireless sensor network (WSN) system. However, security is different from trust in that, it assumes no node is trustworthy and requires ongoing authentication using sophisticated protocols leading to high communication and computation overheads. This makes the traditional cryptographic security tools hard, if not impossible, to be used in wireless sensor networks that are severely resource constrained. Trust on the other hand is the exact opposite of security in that any node can interact with any other and requires no authentication and unwrapping of hidden keys to carry on with their business and hence carries zero overhead. However, this leads to the miss-use and abuse of networks causing loss and damage to the owners of the networks. This thesis focuses on developing novel methods for modelling and managing trust that enable WSN to be secure while significantly reducing computing and communication overheads. Although researchers have been studying the problem of trust modelling and management in wireless sensor networks for over a decade, their focus was on the trust associated with routing messages between nodes (communication trust). However, wireless sensor networks are mainly deployed to sense the world and report data, both continuous and discrete. However, there are no methods in the literature that focus on the trust associated with misreporting data (data trust). In this thesis, we model the trust associated with the integrity of the data, and propose methods to combine the data trust with the communication trust to infer the total trust. Bayesian probabilistic approach is used to model and manage trust. A new risk assessment algorithm for establishing trust in wireless sensor networks based on the quality of services characteristics of sensor nodes, using the traditional weighting approach is introduced. Then a Beta distribution is used to model communication trust (due to its binary nature) and determine the weights in terms of the Beta distribution parameters to probabilistically combine direct and indirect trust. The thesis extends the Bayesian probabilistic approach to model data trust for cases when the sensed data is continuous. It introduces the Gaussian trust and reputation system to that accounts for uncertain characteristics of sensor data. Finally we introduce a Bayesian fusion algorithm to combine the data trust and communication trust to infer the overall trust between nodes. Simulation results are presented to demonstrate how the models accurately classify different nodes as being trustworthy or not based on their reliability in sensor reporting and routing functions.

Doctor of Philosophy(PhD) / Recent advances in miniaturization and low power design have led to a flurry of activity in wireless sensor networks. Sensor networks have different constraints than traditional wired networks. A wireless sensor network is a special network with large numbers of nodes equipped with embedded processors, sensors, and radios. These nodes collaborate to accomplish a common task such as environment monitoring or asset tracking. In many applications, sensor nodes will be deployed in an ad-hoc fashion without careful planning. They must organize themselves to form a multihop, wireless communication network. In sensor network environments, much research has been conducted in areas such as power consumption, self-organisation techniques, routing between the sensors, and the communication between the sensor and the sink. On the other hand, real-time communication with the Quality of Service (QoS) concept in wireless sensor networks is still an open research field. Most protocols either ignore real time or simply attempt to process as fast as possible and hope that this speed is sufficient to meet the deadline. However, the introduction of real-time communication has created additional challenges in this area. The sensor node spends most of its life routing packets from one node to another until the packet reaches the sink; therefore, the node functions as a small router most of the time. Since sensor networks deal with time-critical applications, it is often necessary for communication to meet real time constraints. However, research that deals with providing QoS guarantees for real-time traffic in sensor networks is still in its infancy.This thesis presents a real-time communication framework to provide quality of service in sensor networks environments. The proposed framework consists of four components: First, present an analytical model for implementing Priority Queuing (PQ) in a sensor node to calculate the queuing delay. The exact packet delay for corresponding classes is calculated. Further, the analytical results are validated through an extensive simulation study. Second, report on a novel analytical model based on a limited service polling discipline. The model is based on an M/D/1 queuing system (a special class of M/G/1 queuing systems), which takes into account two different classes of traffic in a sensor node. The proposed model implements two queues in a sensor node that are served in a round robin fashion. The exact queuing delay in a sensor node for corresponding classes is calculated. Then, the analytical results are validated through an extensive simulation study. Third, exhibit a novel packet delivery mechanism, namely the Multiple Level Stateless Protocol (MLSP), as a real-time protocol for sensor networks to guarantee the traffic in wireless sensor networks. MLSP improves the packet loss rate and the handling of holes in sensor network much better than its counterpart, MMSPEED. It also introduces the k-limited polling model for the first time. In addition, the whole sending packets dropped significantly compared to MMSPEED, which it leads to decrease the consumption power. Fourth, explain a new framework for moving data from the sink to the user, at a low cost and low power, using the Universal Mobile Telecommunication System (UMTS), which is standard for the Third Generation Mobile System (3G). The integration of sensor networks with the 3G mobile network infrastructure will reduce the cost of building new infrastructures and enable the large-scale deployment of sensor networks

Security and trust are two interdependent concepts and are often used interchangeably when defining a secure wireless sensor network (WSN) system. However, security is different from trust in that, it assumes no node is trustworthy and requires ongoing authentication using sophisticated protocols leading to high communication and computation overheads. This makes the traditional cryptographic security tools hard, if not impossible, to be used in wireless sensor networks that are severely resource constrained. Trust on the other hand is the exact opposite of security in that any node can interact with any other and requires no authentication and unwrapping of hidden keys to carry on with their business and hence carries zero overhead. However, this leads to the miss-use and abuse of networks causing loss and damage to the owners of the networks. This thesis focuses on developing novel methods for modelling and managing trust that enable WSN to be secure while significantly reducing computing and communication overheads. Although researchers have been studying the problem of trust modelling and management in wireless sensor networks for over a decade, their focus was on the trust associated with routing messages between nodes (communication trust). However, wireless sensor networks are mainly deployed to sense the world and report data, both continuous and discrete. However, there are no methods in the literature that focus on the trust associated with misreporting data (data trust). In this thesis, we model the trust associated with the integrity of the data, and propose methods to combine the data trust with the communication trust to infer the total trust. Bayesian probabilistic approach is used to model and manage trust. A new risk assessment algorithm for establishing trust in wireless sensor networks based on the quality of services characteristics of sensor nodes, using the traditional weighting approach is introduced. Then a Beta distribution is used to model communication trust (due to its binary nature) and determine the weights in terms of the Beta distribution parameters to probabilistically combine direct and indirect trust. The thesis extends the Bayesian probabilistic approach to model data trust for cases when the sensed data is continuous. It introduces the Gaussian trust and reputation system to that accounts for uncertain characteristics of sensor data. Finally we introduce a Bayesian fusion algorithm to combine the data trust and communication trust to infer the overall trust between nodes. Simulation results are presented to demonstrate how the models accurately classify different nodes as being trustworthy or not based on their reliability in sensor reporting and routing functions.

<p>X3 C is a Swedish company which develops a world wide good tracking system by using ARFID </p><p>tags placed on every item which has to be delivered and base stations as gateway in a wireless </p><p>sensor network. The requirement of a long lifespan of their ARFID tags made it difficult to </p><p>implement security. Firstly an evaluation of possible security mechanisms and their power </p><p>consumption was done by measuring the avalanche effect and character frequency of the sym- </p><p>metric algorithms Blowfish, RC2 and XTEA. Secondly, the required CPU time which is needed </p><p>by each algorithm for encrypting a demo plaintext, was measured and analyzed. Summariz- </p><p>ing both analysis, the XTEA algorithm, run in CBC mode, is the recommendation for the XC </p><p>ARFID tags. The testing processes and the results are presented in detail in this thesis.</p>

<p>Wireless Sensor Networks have to be very flexible and self-organizing, providing an ease way to be deployed. This feature is required due to a wide variety of possible applications and deployment scenarios in which they can be used. They can support different kinds of tools and applications in distinct areas, such as in agriculture, military, health care, home or factory automation, among other. </p><p> An assisted living system supported by a WSN is presented in this work. The main feature of this system is to locate and track inhabitant’s behaviour. The system has been implemented using Mica2 Motes, which were placed in different rooms in a house. In order to make the use of the WSN easier, this project investigated and used a middleware called TinyDB, which represents a key technology to improve the usability of WSN.</p>

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Conguring a network system to operate at optimal levels of performance re-quires a comprehensive understanding of the eects of a variety of system parameterson crucial metrics like connectivity and resilience to network attacks. Traditionally,omni-directional antennas have been used for communication in wireless sensor net-works. In this thesis, a hybrid communication model is presented where-in, nodes ina network are capable of both omni-directional and uni-directional communication.The eect of such a model on performance in randomly deployed wireless sensor net-works is studied, specically looking at the eect of a variety of network parameterson network performance.The work in this thesis demonstrates that, when the hybrid communication modelis employed, the probability of 100% connectivity improves by almost 90% and thatof k-connectivity improves by almost 80% even at low node densities when comparedto the traditional omni-directional model. In terms of network security, it was foundthat the hybrid approach improves network resilience to the collision attack by almost85% and the cost of launching a successful network partition attack was increased byas high as 600%. The gains in connectivity and resilience were found to improve withincreasing node densities and decreasing antenna beamwidths.

In the multiratecasting problem in wireless sensor networks, the source sensor is usually required to report to multiple destinations at dif- ferent rates for each of them. We present a MST-based rate-aware cost-efficient multiratecast routing protocol (MSTRC). The proposed MSTRC examines only one set partition of destinations at each for- warding step. A message split occurs when the locally-built minimum spanning tree (MST) over the current node and the set of destina- tions has multiple edges originated at the current node. Destinations spanned by each of these edges are grouped together, and for each of these subsets the best neighbor is selected as the next hop. We also suggested a novel face recovery mechanism to deal with void ar- eas, when no neighbor provides positive progress toward destinations. It constructs a MST of current node and destinations without the progress via neighbors, and for each set partition of destinations cor- responding to an edge e in MST, the face routing keeps going until a node that is closer to one of these destinations is found, allowing for greedy continuation, while the process repeats for the remaining desti- nations similarly. Our experimental results demonstrate that MSTRC is highly rate-efficient in all scenarios, and unlike existing solutions, it is adaptive to destination rate deviations.

This thesis focuses on three fundamental issues that concern data aggregation protocols for periodic data collection in sensor networks: <i>which</i> sensor nodes should report their data, <i>when</i> should they report it, and should they use <i>unicast</i> or <i>broadcast</i> based protocols for this purpose. <p> The issue of when nodes should report their data is considered in the context of real-time monitoring applications. The first part of this thesis shows that asynchronous aggregation, in which the time of each nodes transmission is determined adaptively based on its local history of past packet receptions from its children, outperforms synchronous aggregation by providing lower delay for a given end-to-end loss rate. <p> Second, new broadcast-based aggregation protocols that minimize the number of packet transmissions, relying on multipath delivery rather than automatic repeat request for reliability, are designed and evaluated. The performance of broadcast-based aggregation is compared to that of unicast-based aggregation, in the context of both real-time and delay-tolerant data collection. <p> Finally, this thesis investigates the potential benefits of dynamically, rather than semi-statically, determining the set of nodes reporting their data, in the context of applications in which coverage of some monitored region is to be maintained. Unicast and broadcast-based coverage-preserving data aggregation protocols are designed and evaluated. The performance of the proposed protocols is compared to that of data collection protocols relying on node scheduling.

Data estimation is emerging as a powerful strategy for energy conservation in sensor networks. In this thesis is reported a technique, called Data Estimation using Physical Method (DEPM), that efficiently conserves battery power in an environment that may take a variety of complex manifestations in real situations. The methodology can be ported easily with minor changes to address a multitude of tasks by altering the parameters of the algorithm and ported on any platform. The technique aims at conserving energy in the limited energy supply source that runs a sensor network by enabling a large number of sensors to go to sleep and having a minimal set of active sensors that may gather data and communicate the same to a base station. DEPM rests on solving a set of linear inhomogeneous algebraic equations which are set up using well-established physical laws. The present technique is powerful enough to yield data estimation at an arbitrary number of point-locations, and provides for easy experimental verification of the estimated data by using only a few extra sensors.