Real-time sea state estimation is vital for applications like shipbuilding and maritime safety. Traditional methods rely on accurate wave-vessel transfer functions to estimate wave spectra from onboard sensors. In contrast, our approach jointly estimates sea state and vessel parameters without needing prior transfer function knowledge, which may be unavailable or variable. We model the wave-vessel system using pseudo mass-spring-dampers and develop a dynamic model for the system. This method allows for recursive modeling of wave excitation as a time-varying input, relaxing prior works’ assumption of a constant input. We derive statistically consistent process noise covariance and implement a square root cubature Kalman filter for sensor data fusion. Further, we derive the Posterior Cramer-Rao lower bound to evaluate estimator performance. Extensive Monte Carlo simulations and data from a high-fidelity validated simulator confirm that the estimated wave spectrum matches methods assuming complete transfer function knowledge.

Data Grids provide services and infrastructure for distributed data-intensive applications accessing massive geographically distributed datasets. An important technique to speed access in Data Grids is replication, which provides nearby data access. Much of the work on the replica placement problem has focused on average system performance and ignored quality assurance issues. In a data grid environment, resource availability, network latency, and users' requests may change. Moreover, different sites may have different service quality requirements. In this paper, we introduce a new highly distributed and decentralized replica placement algorithm for hierarchical Data Grids that determines the positions of a minimum number of replicas expected to satisfy certain quality requirements. Our placement algorithm exploits the data access history for popular data files and computes replica locations by minimizing overall replication cost (read and update) while maximizing QoS satisfaction for a given traffic pattern. The problem is formulated using dynamic programming. We assess our algorithm using OptorSim. A comparison between our algorithm and its QoS-unconstrained counterpart shows that our algorithm can shorten job execution time greatly while consuming moderate bandwidth for data transfer.

Data grids support distributed data-intensive applications that need to access massive datasets stored around the world. Ensuring efficient access to such datasets is hindered by the high latencies of wide-area networks. To speed up access, files can be replicated so a user can access a nearby replica. Replication also provides improved availability, decreased bandwidth use, increased fault tolerance, and improved scalability. Since a grid environment is dynamic, resource availability, network latency, and user requests may change. To address these issues a dynamic replica placement strategy that adapts to changing behaviour is needed. In this paper, we introduce a highly distributed replica placement algorithm for hierarchical data grids. Our algorithm exploits data access histories to identify popular files and determines optimal replication locations to improve access performance by minimizing replication overhead (access and update) assuming a given traffic pattern. The problem is formulated using dynamic programming. We evaluate our algorithm using the OptorSim simulator and find that it offers shorter execution time and reduced bandwidth consumption compared to other dynamic replica placement methods.

Data grids provide geographically distributed storage for large-scale data-intensive applications. Ensuring efficient access to such large and widely distributed datasets is hindered by high latencies. To speed up data access, data grid systems replicate data in multiple locations so a user can access the data from a nearby site. In addition to reducing data access time, replication also aims to use network and storage resources efficiently. While replication is a well-known technique, the problem of replica placement has not been widely studied for data grid environments. To obtain the best possible gains from replication, strategic placement of the replicas is critical. In a grid environment resource availability, network latency, and userspsila requests can vary. To address these issues a placement strategy is needed that adapts to dynamic behavior. This paper proposes a new dynamic replica placement algorithm for hierarchical data grids based on file ldquopopularityrdquo. Our goal is to place replicas close to the clients to reduce access time while using the network and storage efficiently thereby effectively balancing storage cost and access latency. We evaluate our algorithm using OptorSim which shows that our approach outperforms other techniques in terms of access time and bandwidth used.

Heterogeneity of networks and end systems poses challenges for multicast based collaborative applications. In traditional multicasting, the sender transmits video at the same rate to all receivers independent of their network connection, end system equipment, and users' preferences. This wastes resources and may also result in some receivers having their quality expectations unsatisfied. This problem can be addressed, near the network edge, by applying dynamic, in-network transcoding of video streams. In this paper, we design, implement, and assess a network processor (NP) based video transcoding system using the Intel IXP1200. Experiments suggest that our system can adapt the video rate of MPEG-1 streams to a desired level on a per packet basis for moderate traffic levels.

Location determination is a fundamental component enabling context aware home applications. Ultrasound, floor sensors and computer vision, among others, have been proposed for location determination, each with its own benefits and limitations. In this paper we discuss the design, implementation and evaluation of a location determination system based on sensor networks. Our implementation includes three components: the location determination system which adapts and extends Motetrack for in-home use, a location storage system, and a middleware interface to allow home applications to access both current and historical location information. Our system uses Crossbow Mica2 and Mica2Dot sensors to provide 28th, 50th, 85th and 97th percentile location errors of under 1,1.5,2 and 3 meters, respectively. This will support most home services which typically require only "room level" accuracy. Using sensor nodes in our location determination scheme provides a low cost solution and eliminates dependence on the availability of existing, in-home equipment to perform the location determination computations

Active networks allow computations to be performed in-network at routers as messages pass through them. Active networks offer unique opportunities to optimize network-centric applications in ways that are not possible using conventional networks. Unfortunately, the need to route packets at full network speed means that very little computation can be done per packet, per router; this seriously restricts the range of in-network applications that can be developed. Computationally intensive applications are restricted to executing outside the network and thus many potential in-network optimizations are precluded. We propose a scalable cluster architecture using software distributed shared memory (DSM) that can be used as an "attached processor" at routers for executing active code. This novel application of DSM enables the construction of aggressive active network protocols by providing significant compute capacity outside the router's critical packet routing path. The use of DSM simplifies the implementation, and extends the capabilities of the active packet execution engine in ways that a message passing cluster cannot. Further, the characteristics of active processing enable specific optimizations to consistency maintenance.

In this paper, we describe an efficient software-only Distributed Shared Memory (DSM) consistency protocol for an unconventional but important application domain -object transactional processing.Designers of transactional applications, while sensitive to performance issues, already accept significant overhead for the added functionality provided by transactions and, we speculate, would be willing to accept some small additional overhead for the benefit of "easy" distributed parallel execution.This is in contrast to the performance critical needs of the high-end scientific applications typically targetted by DSM system designers.While, after 10 years of performance enhancement, software DSMs are still considered inadequate by a large part of the parallel computing community, we believe they are now more than sufficiently mature to support distributed transactional environments.The key to making DSM attractive to the developers of transactional applications is ease of use.The much touted simplification offered by the use of shared memory instead of message passing is not enough.It must be possible to hide the needed synchronization operations from the programmer.To achieve this goal, we have developed a system for nested object transactions that supports the automatic insertion of synchronization primitives.These primitives are then used to drive the operation of an improved form of Bershad's Entry Consistency (EC) protocol to maintain memory consistency between the processors/nodes in a distributed computing system.The protocol described is also compatible with a number of performance enhancements for DSM systems that have been described in the literature.