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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.

Recent advancements in sensory devices are presenting various opportunities for widespread applications of wireless sensor networks (WSNs). The most distinguishing characteristic of a WSN is the fact that its sensors have nite and non-renewable energy resources. Many research e orts aim at developing energy e cient network topology and routing schemes for prolonging the network lifetime. However, we notice that, in the majority of the literature, topology control and routing problems are handled separately, thus overlooking the interrelationships among them. In this dissertation, we consider an integrated topology control and routing problem in WSNs which are unique type of data gathering networks characterized by limited energy resources at the sensor nodes distributed over the network. We suggest an underlying hierarchical topology and routing structure that aims to achieve the most prolonged network lifetime via e cient use of limited energy resources and addressing operational speci cities of WSNs such as communication-computation trade-o , data aggregation, and multi-hop data transfer for better energy e ciency. We develop and examine three di erent objectives and their associated mathematical models that de- ne alternative policies to be employed in each period of a deployment cycle for the purpose of maximizing the number of periods so that the network lifetime is prolonged. On the methodology side, we develop e ective solution approaches that are based on decomposition techniques, heuristics and parallel heuristic algorithms. Furthermore, we devise visualization tools to support our optimization e orts and demonstrate that visualization can be very helpful in solving larger and realistic problems with dynamic nature. This dissertation research provides novel analytical models and solution methodologies for important practical problems in WSNs. The solution algorithms developed herein will also contribute to the generalized mixed-discrete optimization problem, especially for the problems with similar characteristics.

With the constant expansion of the industrial monitoring system, there is an urgent requirement to reduce investment and operating costs for the development of industrial communication technology. For industrial real-time monitoring systems, wireless technology can be used in a practical industrial production to take advantages of its flexibility and robustness. As wireless sensor networks have many advantages such as low investment costs, flexible structure and ease of transformation, it has become the focus with regards to industrial areas. THVRG is a routing algorithm that selects the routing path based on two-hop information. Since different information sensed by the sensors may have different requirements in order to reach the sink, a priority-based routing algorithm is required in order to adapt to this kind of situation. This thesis has proposed a priority routing algorithm based on the THVRG (Priority-based THVRG). In addition, a simulation of this algorithm was performed in OPNET. Finally, the report provides an evaluation of the proposed algorithm in industrial wireless sensor networks.

Wireless Sensor Network (WSN), a collection of “sensor nodes” promises to change the scientist’s approach of gathering the environmental data in various fields. Sensor nodes can be used for non-stop sensing, event detection, location sensing and local control of actuators, this concept gives surety to many latest application areas like agriculture, military, home or factory automation, logistics and so on. Remote surveillance and measurement missions can be performed by using WSNs. The hot research topic now-a-days is to make such networks remotely controllable and adaptive to the environment and mission. The work carried out in this thesis is the development of a surveillance application using TinyOS/nesC. The purpose of this application is to perform event-detection mission by using any one of the built-in sensor on Mica2 motes as well as a setup protocol is designed to make the WSN remotely controllable and adaptive to the mission. In this thesis, an experimental work is also performed using TinyDB to build up a surveillance system whose purpose is to detect and count the total number of person present at any time in a given room and to view the results at a remote place. Besides these two system applications, a comparative study between TinyDB and nesC is described which concludes that more hardware control can be achieved through nesC which is a more power efficient platform for long-term applications.

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. 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.

A significant challenge in the Wireless Sensor Networks (WSN) research field is to find flexible and energy efficient strategies to perform the network setup and configuration to accomplish specified sensing missions. This thesis presents an approach which uses mobile agents to disseminate and allocate sensing missions to the sensor nodes. The addressed problem refers to the selection of appropriate nodes to perform the sensing mission, by using a decentralized approach supported by mobile software agents. Traditional approaches to deal with WSN setup use the pre-planned strategies, which are deliberately modelled, designed and tuned before the network deployment, and thus are not flexible. This thesis presents an alternative approach based on Belief Desire Intention-model agents using JASON, instead of traditional approaches. Simulation results provides evidences that this approach can achieve the goals of a sensing mission setup by decisions autonomously taken by the sensor node, diminishing then the need for communication among the sensor nodes, hence saving energy resources.