Wireless Sensor Networking is envisioned as an economically viable paradigm and a promising technology because of its ability to provide a variety of services, such as intrusion detection, weather monitoring, security, tactical surveillance, and disaster management. The services provided by wireless senor networks (WSNs) are based on collaboration among small energy-constrained sensor nodes. The large deployment of WSNs and the need for energy efficient strategy necessitate efficient organization of the network topology for the purpose of balancing the load and prolonging the network lifetime. Clustering has been proven to provide the required scalability and prolong the network lifetime. Due to the bottle neck phenomena in WSNs, a sensor network loses its connectivity with the base station and the remaining energy resources of the functioning nodes are wasted. This thesis highlights some of the research done to prolong the network lifetime of wireless sensor networks and proposes a solution to overcome the bottle neck phenomena in cluster-based sensor networks. Transmission tuning algorithm for a cluster-based WSNs is proposed based on our modeling of the extra burden of the sensor nodes that have direct communication with the base station. Under this solution, a wireless sensor network continues to operate with minimum live nodes, hence increase the longevity of the system. An information theoretic metric is proposed as a cluster head selection criteria for breaking ties among competing clusters, hence as means to decrease node reaffiliation and hence increasing the stability of the clusters, and prolonging the network lifetime. This proposed metric attempts to predict undesired mobility caused by erosion.

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.

<p>Advances in the miniaturization of microelectromechanical components have led to battery powered and inexpensive sensor nodes, which can be networked in an ad hoc manner to perform distributed sensing and information processing. While sensor networks can be deployed in inhospitable terrain to provide continuous monitoring and processing capabilities for a wide range of applications, sensor nodes are severely resource-constrained; they typically run on batteries and have a small amount of memory. Therefore, energy-efficient and lightweight protocols are necessary for distributed information processing in these networks. </p><p>The data provided by a sensor node is often useful only in the context of the location of the data source. Thus, sensor networks rely on localization schemes to provide location information to sensor nodes. The premise of this thesis is that location-aware protocols, which are based on the assumption that sensor nodes can estimate their location, improve the efficiency of data gathering and resource utilization of wireless sensor networks. Location-awareness improves the energy-efficiency of the protocols needed for routing, transport, data dissemination and self-organization of sensor networks. Existing sensor network protocols typically do not use location information effectively, hence they are not energy-efficient. In this thesis, we show how location information can be leveraged in novel ways in sensor network protocols to achieve energy efficiency. The contributions of this thesis are in four important areas related to network protocol design for wireless sensor networks: 1) self-organization; 2) data dissemination or node reprogramming; 3) service differentiation; and 4) data collection. Work on self-organization (SCARE) and data dissemination (LAF) was carried out from 2002 to 2004 and the work on service differentiation (SensiQoS) and data collection (HTDC) was carried out from 2004 to 2009.</p><p>This thesis first presents a new approach for self-configuration of ad hoc sensor networks. The self-configuration of a large number of sensor nodes requires a distributed solution. We propose a scalable self-configuration and adaptive reconfiguration (SCARE) algorithm that exploits the redundancy in sensor networks to extend the lifetime of the network. SCARE distributes the set of nodes in the sensor network into subsets of coordinator nodes and non-coordinator nodes. While coordinator nodes stay awake, provide coverage, and perform multi-hop routing in the network, non-coordinator nodes go to sleep. When nodes fail, SCARE adaptively re-configures the network by selecting appropriate non-coordinator nodes to become coordinators and take over the role of failed coordinators. This scheme only needs local topology information and uses simple data structures in its implementation. SCARE organizes nodes into coordinator and non-coordinator nodes. A recent approach, termed Ripples, has improved upon the selforganization and reconfiguration mechanism proposed in SCARE. It uses a lightweight clustering algorithm to elect cluster heads instead of coordinator nodes based on location information as proposed by SCARE. Ripples selects fewer cluster-head nodes compared to the number of coordinator nodes elected by SCARE by varying the cluster radius and consequently realizes more energy savings while providing comparable sensing coverage.</p><p>This thesis next presents an energy-efficient protocol for data dissemination in sensor networks. Sensor networks also enable distributed collection and processing of sensed data. These networks are usually connected to the outside world with base stations or access points through which a user can retrieve the sensed data for further inference and action. Dissemination of information is a challenging problem in sensor networks because of resource constraints. Conventional methods use classical flooding for disseminating data in a sensor network. However, classical flooding suffers from disadvantages such as the broadcast storm problem. We have proposed an energy-efficient scheme that uses the concept of virtual grids to partition (self-configure) the set of nodes into groups of gateway nodes and internal nodes. While gateway nodes forward the packets across virtual grids, internal nodes forward the packets within a virtual grid. The proposed location-aided flooding protocol (LAF) reduces the number of redundant transmissions and receptions by storing a small amount of state information in a packet and inferring the information about nodes that already have the packet from the modified packet header. More recent approach, termed ALAF, has extended the virtual grid concept proposed by LAF to non-uniform sensor network deployments. In ALAF, non-uniform virtual grids are used to improve upon the energy savings provided by LAF and achieve higher energy savings for non-uniform sensor network topologies.</p><p>This thesis also addressees the challenging problem of timely data delivery in sensor networks. We propose SensiQos, which leverages the inherent properties of the data generated by events in a sensor network such as spatial and temporal correlation, and realizes energy savings through application-specific in-network aggregation of the data. This data delivery scheme is based on distributed packet scheduling, where nodes make localized decisions on when to schedule a packet for transmission to save energy and to which neighbor they should forward the packet to meet its end-to-end real-time deadline. </p><p>Finally, this thesis presents an energy-efficient data collection protocol for sensor networks. It is based on a combination of geographic hash table and mobile sinks that leverage mobile sinks to achieve energy-efficiency in event-driven sensor networks. Next, an analysis of the energy savings realized by the proposed protocol is presented. Simulation results demonstrate significant gains in energy savings for data collection with change in various parameter values.</p><p>In summary, this thesis represents an important step towards the design of location-aware energy-efficient protocols for self-configuration, data dissemination, data delivery, and data collection in wireless sensor networks. It is expected to lead to even more efficient protocols for data dissemination, routing, and transport-layer protocols for energy-constrained and failure-prone sensor networks.</p> / Dissertation

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We consider the topic of target tracking in this thesis. Target tracking is one of the applications in wireless sensor networks (WSNs). Clustering approach prolongs sensor¡¦s lifetime and provides better data aggregation for WSNs. Most previous researches assumed that cluster regions are disjointed, while others assigned overlapping cluster regions, and utilized them in some applications, including inter-cluster routing and time synchronization. However, in overlapping clustering, processing of redundant sensing data may impair system performance. We present a regular distributed overlapping WSN in this thesis. The network is based on two kinds of sensors: (1) high-capability sensors, which are assigned as cluster heads (CHs), responsible for data processing and inter-cluster communication, (2) normal sensors, which are in a larger number when comparing with the high-capability sensors, the function of normal sensors are to provide data to the CHs. We define several operating modes of CHs and sensors. WSN works more efficient under the settings. Since a target may be located in the overlapping region, redundant data processing problem exists. To solve the problem, we utilize Cholesky decomposition to decorrelate the measurement noise covariance matrices. The correlation will be eliminated during the process. In addition, we modify extended information filter (EIF) and adapt to the decorrelated data. The CHs track the target, fuse the information from other CHs, and implement distributed positioning. The simulations are based on ultra-wideband (UWB) environment, we have verified that the proposed scheme works more efficient under the setting of different modes. The performance with decorrelated measurement is better than that with correlated ones.

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.

Time synchronization in wireless sensor networks (WSNs) is essential and significant for maintaining data consistency, coordination, and performing other fundamental operations, such as power management, security, and localization. Energy efficiency is the main concern in designing time synchronization protocols for WSNs because of the limited and generally nonrechargeable power resources. In this dissertation, the problem of time synchronization is studied in three different aspects to achieve energy efficient time synchronization in WSNs. First, a family of novel joint clock offset and skew estimators, based on the classical two-way message exchange model, is developed for time synchronization in WSNs. The proposed joint clock offset and skew correction mechanisms significantly increase the period of time synchronization, which is a critical factor in the over-all energy consumption required for global network synchronization. Moreover, the Cramer-Rao bounds for the maximum likelihood estimators are derived under two different delay assumptions. These analytical metrics serve as good benchmarks for the experimental results thus far reported. Second, this dissertation proposes a new time synchronization protocol, called the Pairwise Broadcast Synchronization (PBS), which aims at minimizing the number of message transmissions and implicitly the energy consumption necessary for global synchronization of WSNs. A novel approach for time synchronization is adopted in PBS, where a group of sensor nodes are synchronized by only overhearing the timing messages of a pair of sensor nodes. PBS requires a far smaller number of timing messages than other well-known protocols and incurs no loss in synchronization accuracy. Moreover, for densely deployed WSNs, PBS presents significant energy saving. Finally, this dissertation introduces a novel adaptive time synchronization protocol, named the Adaptive Multi-hop Timing Synchronization (AMTS). According to the current network status, AMTS optimizes crucial network parameters considering the energy efficiency of time synchronization. AMTS exhibits significant benefits in terms of energy-efficiency, and can be applied to various types of sensor network applications having different requirements.

Time-synchronization in wireless ad-hoc sensor networks is a crucial piece of infrastructure. Thus, it is a fundamental design problem to have a good clock syn- chronization amongst the nodes of wireless ad-hoc sensor networks. Motivated by this fact, in this thesis, the joint maximum likelihood (JML) estimator for relative clock phase offset and skew under the exponential noise model for the reference broadcast synchronization protocol is formulated and found via a direct algorithm. The Gibbs Sampler is also proposed for joint estimation of relative clock phase offset and skew, and shown to provide superior performance compared to the JML-estimator. Lower and upper bounds for the mean-square errors (MSE) of the JML-estimator and the Gibbs Sampler are introduced in terms of the MSE of the uniform minimum variance unbiased estimator and the conventional best linear unbiased estimator, respectively. The suitability of the Gibbs Sampler for estimating additional unknown parameters is shown by applying it to the problem in which synchronization of clock drift is also needed.

Recently, wireless sensor networks have attracted a lot of attention. Such environments may consist of many inexpensive nodes, each capable of collecting, storing, and processing environmental information, and communicating with base station nodes through wireless links. In this paper, we survey a fundamental problem in wireless sensor networks, the energy consumption problem, which reflects how well a sensor field is deployed. Therefore, a critical aspect of applications with wireless sensor networks is network lifetime. Furthermore, one of the fundamental issues in sensor networks is the coverage problem, which reflects how well a sensor network is monitored or tracked by sensors. We formulate this problem as a decision problem, whose goal is to determine whether every point in the service area of the sensor network is covered by at least k sensors, where k is a given parameter. In this paper, we propose an energy-efficient method based on Genetic Algorithms to deal with the deployment problem of wireless sensor networks such that it provides target-location and surveillance services.

The advances of communication and computer techniques have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and capable of communicating in short distances. A sensor network is composed of a large number of sensor nodes that are densely deployed either inside the phenomenon to be observed or very close to it. Sensor networks open up new opportunities to observe and interact with the physical world around us. Despite the recent advances in sensor network applications and technology, sensor networks still suffer from the major problems of limited energy. It is because most sensor nodes use battery as their energy srouce and are inconvenient and sometimes difficult to be replaced when the battery run out. Understanding the events, measures, and tasks required by certain applications has the potential to provide efficient communication techniques for the sensor network. Our focus in this work is on the efficient processing of continuous queries, by which query results have to be generated according to the sampling rate specified by the user for an extended period of time. In this thesis, we will deal with two types of continuous queries. The first type of queries requires data from all sensor nodes; while the other is only interested in the data returned by some selected nodes. To answer these queries, data have to be sent to the base station at some designated rate, which may consume much energy. Previous works have developed two methods to reduce the energy consumption. They both base on the error range which the user can tolerate to determine whether current sensing data should be transmitted. While the first uses simple cache method, the second uses complex multi-dimensional model. However, the proposed methods required the user to specify the error range, which may not be easy to specify. In addition, the sensed data reported by the sensors were assumed to be accurate, which is by no means true in the real world. This thesis is based on Kalman filter to correct and predict sensing data. As a result, the sampling frequency of each sensor is dynamically adjusted, referred to as resampling which systematically determine the data sensing/transferring rate of sensors. We evaluate our proposed methods using empirical data collected from a real sensor network.