2701730
Data Gathering with Tunable Compression in Sensor Networks
We study the problem of constructing a data gathering tree over a wireless sensor network in order to minimize the total energy for compressing and transporting information from a set of source nodes to the sink. This problem is crucial for advanced computationally intensive applications, where traditional "maximum" in-network compression may result in significant computation energy. We investigate a tunable data compression technique that enables effective trade-offs between the computation and communication costs. We derive the optimal compression strategy for a given data gathering tree and then investigate the performance of different tree structures for networks deployed on a grid topology, as well as general graphs. Our analytical results pertaining to the grid topology and simulation results pertaining to the general graphs indicate that the performance of a simple greedy approximation to the Minimal Steiner Tree (MST) provides a constant-factor approximation for the grid topology and good average performance on the general graphs. Although, theoretically, a more complicated randomized algorithm offers a polylogarithmic performance bound, the simple greedy approximation of MST is attractive for practical implementation.
14893914
Energy-balanced task allocation for collaborative processing in networked embedded systems
In this paper, we study the problem of allocating a real-time application onto a set of homogeneous processing elements connected by a single-hop wireless network. A periodic application consisting of a set of communicating tasks is considered. Each element is equipped with discrete dynamic voltage scaling for exploring the energy-latency tradeoffs. The time and energy costs of both computation and communication activities are considered. The goal is to balance the energy dissipation of the elements during each period of the application with respect to the remaining energy of elements, such that the system lifetime is maximized. An Integer Linear Programming (ILP) formulation is first developed, which can be solved to obtain the optimal solution. We then propose an efficient 3-phase heuristic. Experimental results show that for small scale problems, the performance of the heuristic achieves up to 85% of the system lifetime obtained by the ILP-based approach. For large scale problems, the performance of the heuristic shows an improvement of 120-250\% in the system lifetime compared with the case where no voltage scaling is used. Further, we present two extensions of our approaches that consider multiple communication channels and techniques for exploring the energy-latency tradeoffs of the communication activities.
15985894
Communication models for algorithm design in networked sensor systems
Towards building a systematic methodology of algorithm design for applications of networked sensor systems, we formally define two link-wise communication models, the collision free model (CFM) and the collision aware model (CAM). While CFM provides ease of programming and analysis for high level application functionality, CAM enables more accurate performance analysis and hence more efficient algorithms through cross-layer optimization, at the cost of increased programming and analysis complexity. We use the example of optimizing a probability based broadcasting scheme under CAM to illustrate algorithm optimization. Our analytical results indicate that the optimal broadcast probability for either maximizing the reachability within a given latency constraint or minimizing the latency for a given reachability constraint decreases rapidly with node density.
15993806
A Workflow Model with Temporal Logic Constraints and Its Automated Verification
Traditional workflow models are weak in describing the temporal properties and constraints between activities, while some high-level extensions based on time or temporal logic have another problem that the soundness verification of the model requires high academic knowledge. In order to solve these problems, a new workflow model LTL-WF based on WF-net and linear temporal logic is proposed in this paper. Besides introducing the new model, we also bring forward a method for automated verification of LTL-WF. The method proposed here provides the verification of workflow model with a convenient environment for model checking.
17384103
RDT Properties and Evaluations
Proposed for MPCs, RDT has already been proved to support better performances than most of the direct network, such as smaller diameter based on its vector routing algorithm, smaller degree, embedded mesh/torus topology, etc. It gives a network form method to form groups of networks with recursively structured torus/torus-like networks. A RDT structure can be formed with the method and its related parameters. After analyzing its performance by the average distance, the descending vector routing reducing redundant computations and the fault-tolerant routing algorithm are proposed. A simpler structure named RDT(2,2,1)/a are proposed for the parallel systems with no more than 5000 nodes and given its performance evaluation. Considering the structure features of the practical RDTs, they are suitable for the interconnection network of an NoC design. The design of laying out RDT(2,2, 1)/a is proposed and proved to be available
33694964
Workflow scheduling in grids: an ant colony optimization approach
Grid applications in virtue of open service grid architecture (OGSA) are promising next-generation computation techniques. One of the most important and challenging problems about grid application is the workflow scheduling problem to achieve the users' QoS (quality of service) requirements as well as to minimize the cost. This paper proposes an ant colony optimization (ACO) algorithm to tackle this problem. Several new features are introduced to the algorithm. First, we define two kinds of pheromone and three kinds of heuristic information to guide the search direction of ants for this bi-criteria problem. Each ant uses either one from these heuristic types and pheromone types in each iteration based on the probabilities controlled by two parameters. These two parameters are adaptively adjusted in the process of the algorithm. Second, we use the information of partial solutions to modify the bias of ants so that inferior choices will be ignored. Moreover, the experimental results in 3 workflow applications under different deadline constraints show that the performance of our algorithm is very promising, for it outperforms the Deadline-MDP algorithm in most cases.
37422743
A Framework for Modeling Positive Class Expansion with Single Snapshot
15348979
Cocktail Ensemble for Regression
This paper is motivated to improve the performance of individual ensembles using a hybrid mechanism in the regression setting. Based on an error-ambiguity decomposition, we formally analyze the optimal linear combination of two base ensembles, which is then extended to multiple individual ensembles via pairwise combinations. The Cocktail ensemble approach is proposed based on this analysis. Experiments over a broad range of data sets show that the proposed approach outperforms the individual ensembles, two other methods of ensemble combination, and two state-of-the-art regression approaches.