With the advances of wireless communications and micro-electronic device technologies, wireless sensor networks have been applied in a wide spectrum of applications, including one of the killer applications--object tracking. Among numerous challenges in object tracking, one of the important issues is the energy management. One solution to the above issue is the mobile agent-based paradigm. Using the mobile agent in wireless sensor networks has many advantages over the client/server paradigm in terms of energy consumptions, networks band-width, etc. In this thesis, we adopt the mobile agent-based paradigm to support object track-ing in wireless sensor networks. Although using the mobile agents for object tracking can improve the overall perfor-mance, the hurdle is the determination of the mobile agent itinerary. The past studies on ob-ject tracking considered the object¡¦s movement behavior as randomness or the direction and the speed of the object remain constant for a certain period of time. However, in most real-world cases, the object movement behavior is often based on certain underlying events rather than randomness complete. With this assumption, the movements of objects are some-times predictable. Through the prediction, the mobile agent can determine which node to mi-grate in order to reduce energy consumption and increase the performance of object tracking. In this thesis, we develop a mining-based approach to discover the useful patterns from the object¡¦s movement behavior. This approach utilizes the discovered rules to choose the sensor node the mobile agent needs to migrate in order to reduce the number of wrong migration, to reduce total energy consumed by sensor nodes, and to prolong the lifetime of the wireless sensor network. Experimental results show the efficiency of the proposed approach.

In this thesis, we present analytical analysis of key distribution schemes on wireless sensor networks. Since wireless sensor network is under unreliable environment, many random key pre-distribution based schemes have been developed to enhance security. Most of these schemes need to guarantee the existence of specific properties, such as disjoint secure paths or disjoint secure cliques, to achieve a secure cooperation among nodes. Two of the basic questions are as follows: 1. Under what conditions does a large-scale sensor network contain a certain structure? 2. How can one give a quantitative analysis behave as n grows to the infinity? However, analyzing such a structure or combinatorial problem is complicated in classical wireless network models such as percolation theories or random geometric graphs. Particularly, proofs in geometric setting models often blend stochastic geometric and combinatorial techniques and are more technically challenging. To overcome this problem, an approximative quasi-random graph is employed to eliminate some properties that are difficult to tackle. The most well-known solutions of this kind problems are probably Szemeredi's regularity lemma for embedding. The main difficulty from the fact that the above questions involve extremely small probabilities. These probabilities are too small to estimate by means of classical tools from probability theory, and thus a specific counting methods is inevitable.

In the literature, most distributed detection developed so far mainly focuses on the test rule based on fixed sample size. However, in the real situations, sequential tests are more suitable to be utilized since it might achieve the same detection performance by using fewer number of samples as compared with the fixed-sample-size test. Thus, this theses will propose a new distributed sequential detection approach for the applications in wireless sensor networks(WSNs) and cognitive radios(CRs). First we refer to the sequential detection, and it has been developed by Wald in 1994, which is well known as the sequential probability ratio test (SPRT). The SPRT is proved to be able to decrease the required average sample numbers or reducing the average detection time. Indeed, the SPRT is the optimal sequential detection in terms of the minimizing the required number of samples given the constraint of false alarm and miss probabilities when the observation samples are independent and identical distributed (i.i.d.). However, if the observation samples are not dentically distributed, by simulation results show that the SPRT is not the optimal test. Based on a heuristic approach, this thesis then developed a new distributed detection scheme based on the sorted samples. Finally , the simulation results obtained by this thesis shows that the proposed scheme can further reduce the number of samples required for making the final decision as compared with SPRT.

Clustering schemes can reduce energy consumption, prolong network lifetime and improve scalability in wireless sensor networks (WSNs). In a typical cluster-based WSN, sensor nodes are organized into clusters. Each cluster elects a cluster head (CH) node. The CH is responsible for collecting the sensed data from cluster members, aggregating data and transmitting data to the sink node via a multi-hop path through intermediate CHs. Thus, the use of cluster techniques not only shortens the transmission distances for sensor nodes but also reduces energy consumption; however, each cluster imposes a larger load on the CH. Under this situation, CHs closer to the sink node tend to use up their batteries faster than those farther away from the sink node due to imbalanced traffics among CHs. To overcome this problem, we contribute to the energy balancing issues in WSNs from two aspects. In the first work, we first analyze the corona model. Based on analysis results, we found that nearly balanced energy consumption of WSNs can be achieved with the additional help of arranging different initial conditions. We then propose the Energy-balanced Node Deployment with Balanced Energy (END-BE) scheme and Energy-balanced Node Deployment with Maximum Life-Time (END-MLT) scheme, which determine the cluster density for each corona according to the energy consumption of each CH. Simulation results show that energy consumption is nearly balanced by implementing END-BE, and the network lifetime is greatly improved by adopting END-MLT. In the second work, we development a novel cluster-based routing protocol for corona-structured wireless sensor networks in order to balance the energy consumption among CHs. Based on the relaying traffic of each CH conveys, adequate radius for each corona can be determined through nearly balanced energy depletion analysis, which leads to balanced energy consumption among CHs. Simulation results demonstrate that our clustering approach effectively improves the network lifetime, residual energy and reduces the number of CH rotations in comparison with the Multi-Layer Clustering Routing Algorithm (MLCRA).

A local reconfiguration algorithm (INP) for reliable routing in wireless sensor networks that consist of many static (fixed) energy-constrained nodes is introduced in the dissertation. For routing around crash fault nodes, a communication tree structure connecting sensor nodes to the base station (sink or root) is dynamically reconfigured during information dissemination. Unlike other location based routing approaches, INP does not take any support from a high costing system that gives position information such as GPS. For reconfigurations, INP uses only local relational information in the tree structure among nearby nodes by collaboration between the nodes that does not need global maintenance, so that INP is energy efficient and it scales to large sensor networks. The performance of the algorithm is compared to the single path with repair routing scheme (SWR) that uses a global metric and the modified GRAdient broadcast scheme (GRAB-F) that uses interleaving multiple paths by computation and by simulations. The comparisons demonstrate that using local relative information is mostly enough for reconfigurations, and it consumes less energy and mostly better delivery rates than other algorithms especially in dense environments. For the control observer to know the network health status, two new diagnosis algorithms (Repre and Local) that deal with crash faults for wireless sensor networks are also introduced in the dissertation. The control observer knows not only the static faults found by periodic testing but also the dynamic faults found by a path reconfiguration algorithm like INP that is invoked from evidence during information dissemination. With based on this information, the control observer properly treats the network without lateness. Local algorithm is introduced for providing scalability to reduce communication energy consumption when the network size grows. The performance of these algorithms is computationally compared with other crash faults identification algorithm (WSNDiag). The comparisons demonstrate that maintaining the communication tree with local reconfigurations in Repre and Local needs less energy than making a tree per each diagnosis procedure in WSNDiag. They also demonstrate that providing scalability in Local needs less energy than other approaches.

Pulse synchronization is the evolution of spontaneous firing action across a network of sensor nodes. In the pulse synchronization model all nodes across a network produce a pulse, or "fire", at regular intervals even without access to a shared global time. Previous researchers have proposed the Reachback Firefly algorithm for pulse synchronization, in which nodes react to the firings of other nodes by changing their period. We propose an extension to this algorithm for tolerating arbitrary or Byzantine faults of nodes. Our algorithm queues up all the firings heard in the current cycle and discards outliers at the end of the cycle. An adjustment is computed with the remaining values and used as a starting point of the next cycle. Through simulation we validate the performance of our algorithm and study the overhead in terms of convergence time and periodicity. The simulation considers two specific kinds of Byzantine faults, the No Jump model where faulty nodes follow their own firing cycle without reacting to firings heard from other nodes and the Random Jump model where faulty nodes fire at any random time in their cycle.

In most range-based localization methods, inferring distance from radio signal strength using mathematical modeling becomes increasingly unreliable and complicated in indoor and extreme environments, due to effects such as multipath propagation and signal interference. We propose FuzLoc, a range-based, anchor-based, fuzzy logic enabled system system for localization. Quantities like RSS and distance are transformed into linguistic variables such as Low, Medium, High etc. by binning. The location of the node is then solved for using a nonlinear system in the fuzzy domain itself, which outputs the location of the node as a pair of fuzzy numbers. An included destination prediction system activates when only one anchor is heard; it localizes the node to an area. It accomplishes this using the theoretical construct of virtual anchors, which are calculated when a single anchor is in the node’s vicinity. The fuzzy logic system is trained during deployment itself so that it learns to associate an RSS with a distance, and a set of distances to a probability vector. We implement the method in a simulator and compare it against other methods like MCL, Centroid and Amorphous. Extensive evaluation is done based on a variety of metrics like anchor density, node density etc.

Wireless sensor networks (WSNs) have received huge attention during the recent years due to their applications in a large number of areas such as environmental monitoring, health and traffic monitoring, surveillance and tracking, and monitoring and control of factories and home appliances. Also, the rapid developments in the micro electro-mechanical systems (MEMS) technology and circuit design lead to a faster spread and adoption of WSNs. Wireless sensor networks consist of a number of nodes featured in general with energy-limited sensors capable of collecting, processing and transmitting information across short distances. Clock synchronization plays an important role in designing, implementing, and operating wireless sensor networks, and it is essential in ensuring a meaningful information processing order for the data collected by the nodes. Because the timing message exchanges between different nodes are affected by unknown possibly time-varying network delay distributions, the estimation of clock offset parameters represents a challenge. This dissertation presents several robust estimation approaches of the clock offset parameters necessary for time synchronization of WSNs via the two-way message exchange mechanism. In this dissertation the main emphasis will be put on building clock phase offset estimators robust with respect to the unknown network delay distributions. Under the assumption that the delay characteristics of the uplink and the downlink are asymmetric, the clock offset estimation method using the bootstrap bias correction approach is derived. Also, the clock offset estimator using the robust Mestimation technique is presented assuming that one underlying delay distribution is mixed with another delay distribution. Next, although computationally complex, several novel, efficient, and robust estimators of clock offset based on the particle filtering technique are proposed to cope with the Gaussian or non-Gaussian delay characteristics of the underlying networks. One is the Gaussian mixture Kalman particle filter (GMKPF) method. Another is the composite particle filter (CPF) approach viewed as a composition between the Gaussian sum particle filter and the KF. Additionally, the CPF using bootstrap sampling is also presented. Finally, the iterative Gaussian mixture Kalman particle filter (IGMKPF) scheme, combining the GMKPF with a procedure for noise density estimation via an iterative mechanism, is proposed.

Network coding and duty-cycling are two popular techniques for saving energy in wireless sensor networks. To the best of our knowledge, the idea to combine these two techniques, for even more aggressive energy savings, has not been explored. One explanation is that these two techniques achieve energy efficiency through conflicting means, e.g., network coding saves energy by exploiting overhearing, whereas dutycycling saves energy by cutting idle listening and, thus, overhearing. In this thesis, we thoroughly evaluate the use of network coding in duty-cycled sensor networks. We propose a scheme called DutyCode, in which a MAC protocol implements packet streaming and allows the application to decide when a node can sleep. Additionally, a novel, efficient coding scheme decision algorithm, ECSDT, assists DutyCode to reduce further energy consumption by minimizing redundant packet transmissions, while an adaptive mode switching algorithm allows smooth and timely transition between DutyCode and the default MAC protocol, without any packet loss. We investigate our solution analytically, implement it on mote hardware, and evaluate it in a 42-node indoor testbed. Performance evaluation results show that our scheme saves 30-46% more energy than solutions that use network coding, without using duty-cycling.

Hundred and thousands of wireless sensor node comprise wireless sensor network (WSN), WSN can be applied in many situations, because a wireless sensor node have small size and wireless transmission advantages, the battery provide sensor node power, but the battery¡¦s power is limit, therefore, energy efficiency is a critical issue, routing protocol can make better energy consumption and loading balance, Clustering is useful routing protocol in WSN and provides the direction of energy efficiency. The ARC (Adaptive Re-Clustering) is based on clustering; ARC can reduce the whole WSN power consumption and protect less residue power cluster-head. The experiments prove that ARC can improve network reliability and extend network lifetime.