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Many competing designs for Distributed Hash Tables exist exploring multiple models of addressing, routing and network maintenance. Designing a general theoretical model and implementation of a Distributed Hash Table allows exploration of the possible properties of Distributed Hash Tables. We will propose a generalized model of DHT behavior, centered on utilizing Delaunay triangulation in a given metric space to maintain the networks topology. We will show that utilizing this model we can produce network topologies that approximate existing DHT methods and provide a starting point for further exploration. We will use our generalized model of DHT construction to design and implement more efficient Distributed Hash Table protocols, and discuss the qualities of potential successors to existing DHT technologies.

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We investigate the stabilization of unstable multidimensional partially observed single-station, multi-sensor (single-controller) and multi-controller (single-sensor) linear systems controlled over discrete noiseless channels under fixed-rate information constraints. Stability is achieved under communication requirements that are asymptotically tight in the limit of large sampling periods. Through the use of similarity transforms, sampling and random-time drift conditions we obtain a coding and control policy leading to the existence of a unique invariant distribution and finite second moment for the sampled state. We use a vector stabilization scheme in which all modes of the linear system visit a compact set together infinitely often. / Thesis (Master, Mathematics & Statistics) -- Queen's University, 2012-12-06 15:06:37.449

The goal of the first part of this thesis is to obtain a high-level theoretical understanding of how an alliance can be managed such that its resources are used in an optimal manner. We propose a pricing mechanism to manage the interactions of carriers, through the allocation of alliance resources and profits, in a manner that encourages individual carriers to make decisions that are optimal for the alliance. Our methodology is based on modeling carrier behavior as linear programs, which are incorporated into a mechanism that manages carrier interactions by appropriately setting resource prices. After introducing two distinct behavioral models, the performance of the mechanism using each model is analyzed for its ability to ensure alliance optimal behavior is attained. We find that the behavioral model selected can significantly impact the characteristics of allocations obtained using the mechanism. In the second part of the thesis, we seek to establish practical insights regarding how the characteristics of potential partners impact the benefit that can be gained by collaborating with these partners. Computational experiments are conducted to evaluate the impact of network size, fleet capacity, demand distribution, and network compatibility on the benefit associated with collaborating. A comprehensive study for simulated two and three-carrier alliances establishes general insights regarding the compatibility of carriers with varying network sizes and fleet capacities. The impact of increasing hub-to-hub connectivity between partnering carriers is then investigated, followed by a study of the effect of market overlap on alliance success. Finally, a real-world cargo alliance is analyzed. In the third and final part of this thesis, we develop new approaches for determining and inducing fair profit allocations in alliances, providing alternatives to traditional approaches which equate minimum acceptance requirements and satisfaction. The mechanism established in the first part of the thesis is adapted to more precisely control the profit allocations obtained, in particular so that an allocation as close to some predetermined fair" allocation is obtained. Several measures of fairness are proposed and implemented, and their performance analyzed for each of the behavioral models discussed in the first part of the thesis.

In order for corporations to survive today¡¦s ever changing business climate, enterprises must tailor overall business operational infrastructure; HR is something have to be focused on and fine-tuned. Centralization HR works best when dealing with small to medium size firms; when organizations gradually grow larger, the centralization HR must be shift to service and proficiency oriented. Companies can be benefited, only by mastering and orchestrating centralization and localization HR functions. Corporation tends to revolutionize its HR structure, headquarter HR has been transformed to Shared Services Center, SSC which carries some innovational and creative concepts. Localization HR has been transformed to Account Services Team, AST. However, company politic still influences HR infrastructure greatly. Pros for passive and localization HR structure. 1. Recruiting. Company can find right employee when it sees fit. 2. Cultivation. Company can tailor orientation and classes for novice workers. 3. Utilization. Company can make use of each employee promptly. 4. Sustain. A career path can be mapped out to curb turnover rate. Cons for passive and localization HR structure. 1. Hard to orchestrate headquarter and local HR functions 2. Local HR has very overall limited authority and ability to manage even daily routine and recruiting. 3. HR itself has the propensity to have high turnover rate . 4. Supervisors tend to practice nepotism to the teeth.

In decentralized data fusion system, if the probability model of the noise is Gaussian and the innovation informations from the sensors are uncorrlated,the information filtering technique can be the best method to fuse the information from different sensors. However, in the realistic environments, information filter cannot provide the best solution of state estimation and data integration when the noises are non-Gaussian and correlated. Since particle filter are capable of dealing with non-linear and non-Gaussian problems, it is an intuitive approach to replace the information filter by particle filter with some suitable data fusion techniques.In this thesis, we investigate a decentralized data fusion system with improved particle filters for target tracking. In order to achieve better tracking performance, the Iterated Extended Kalman Filter framework is used to incorporate the newest observations into the proposal distribution of the particle filter. In our proposed architecture, each sensor consists of one particle filter, which is used in generating the local statistics of the system state. Gaussian mixture model (GMM) is adopted to approximate the posterior distribution of the weighted particles in the filters, thereby more compact representations of the distribution for transmmision can be obtained. To achieve information sharing and integration, the GMM-Covariance Intersection algorithm is used in formulating the decentralized fusion solutions. Simulation resluts of target tracking cases in a sensor system with two sensor nodes are given to show the effectiveness and superiorty of the proposed architecture.

In this thesis, we investigate a decentralized data fusion system with improved particle filters for target tracking. In many application areas, it becomes essential to use nonlinear and non-Gaussian elements to accurately model the underlying dynamics of a physical system. Particle filters have a great potential for solving highly nonlinear and non-Gaussian estimation problems, in which the traditional Kalman filter and extended Kalman filter may generally fail. To improve the tracking performance of particle filters, initialization of the particles is studied. We construct an initial state distribution by using least square estimation. In addition, to enhance the tracking capability of particle filters, representation of target velocity by another set of particles is considered. We include another layer of particle filter inside the original particle filter for updating the velocity. In our proposed architecture, we assume that each sensor node contain a particle filter and there is no fusion center in the sensor network. Approximated a posteriori distribution at the sensor is obtained by using the local particle filters with the Gaussian mixture model (GMM), so that more compact representations of the distribution for transmission can be obtained. To achieve information sharing and integration, the GMM-covariance intersection algorithm is used in formulating the decentralized fusion solutions. Simulation results are presented to illustrate that the performance of the improved particle filter is better than standard particle filter. In addition, simulation results of target tracking in the sensor system with three sensor nodes are given to show the effectiveness and superiority of the proposed architecture.

The current air traffic concept of operations relies on a centralized process in which ground controllers are responsible for determining conflict-free trajectories. However, with new technologies such as ADS-B and GPS, aircraft could directly interact together to resolve their own conflicts in a decentralized manner. The challenge is to guarantee aircraft separation while converging to reasonably fair resolutions for all aircraft. The difficulty is that aircraft have only limited information about how the other aircraft evaluate the cost of conflict resolutions. Thus, this thesis proposes to frame decentralized conflict resolution using game theory. A collaborative decentralized conflict resolution is developed as a sequential bargaining process between the different aircraft. The goal of each aircraft is to minimize the cost associated with the conflict resolution. However, each aircraft doesn’t know the cost function and performance constraints of the other involved aircraft. In the sequential bargaining process developed, aircraft propose at each step personal trajectories to the other aircraft, corresponding to trajectories they would be ready to fly. Then they compute response trajectories, corresponding to trajectories they would have to fly to avoid the conflict if the personal trajectories were flown. If some response trajectories are cheaper than the offered personal trajectories, an agreement is reached; otherwise compromises have to be made by the aircraft by offering more expensive personal trajectories at the next step. Several pairwise conflict experiments, corresponding to different conflict geometries, were conducted to explore different ways of handling performance constraints and different ways of searching trajectories in the resolution space. Ultimately, the algorithm was demonstrated in a large scale simulation with more than a thousand aircraft flying over the Indianapolis Center, incurring more than five hundred conflicts. The traffic sets were taken from real ETMS data over five hours, to represent ‘real’ conditions. 93% of the conflicts were successfully solved by the bargaining process.