8475658
Energy Minimization for Real-Time Data Gathering in Wireless Sensor Networks
This paper studies the challenging problem of energy minimization for data gathering over a multiple-sources single-sink communication substrate in wireless sensor networks by exploring the energy-latency tradeoffs using rate adaptation techniques. We consider a real-time scenario for mission-critical applications, where the data gathering must be performed within a specified latency constraint. We first propose an offline numerical optimization algorithm with performance analysis for a special case with a complete binary data gathering tree. Then, by discretizing the transmission time, we present a simple, distributed on-line protocol that relies only on the local information available at each sensor node. Extensive simulations were conducted for both long and short-range communication scenarios using two different source placement models. We used the baseline of transmitting all packets at the highest speed and shutting down the radios afterwards. Our simulation results show that compared with this baseline, up to 90% energy savings can be achieved by our techniques (both off-line and on-line), under different settings of several key system parameters
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.
1336005
Supporting concurrent applications in wireless sensor networks
It is vital to support concurrent applications sharing a wireless sensor network in order to reduce the deployment and administrative costs, thus increasing the usability and efficiency of the network. We describe Melete 1 , a system that supports concurrent applications with efficiency, reliability, flexibility, programmability, and scalability. Our work is based on the Maté virtual machine [1] with significant modifications and enhancements. Melete enables reliable storage and execution of concurrent applications on a single sensor node. Dynamic grouping is used for flexible, on-the-fly deployment of applications based on contemporary status of the sensor nodes. The grouping procedure itself is programmed with the TinyScript language. A group-keyed code dissemination mechanism is also developed for reliable and efficient code distribution among sensor nodes. Both analytical and simulation results are presented to study the impact of several key parameters and optimization techniques on the code dissemination mechanism. Simulation results indicate satisfactory scalability of our techniques to both application code size and node density. The usefulness and effectiveness of Melete is also validated by empirical study.
6083994
On communication models for algorithm design in networked sensor systems: A case study,
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 expense of increased programming and analysis complexity. These communication models are part of an abstract network model, above which algorithm design and performance optimization is performed. We use the example of optimizing a probability based broadcasting scheme under CAM to illustrate algorithm optimization based on the defined models. Specifically, we present an analytical framework that facilitates an accurate modeling and analysis for the probability based broadcasting in CAM (PB_CAM). Our analytical results indicate that (1) 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, and (2) the optimal probability for either maximizing the reachability with a given energy constraint or minimizing the energy cost for a given reachability constraint varies slowly between 0 and 0.1 over the entire range of the variations in node density. Our analysis is also confirmed by extensive simulation results.
17009533
Utility-Driven Spatiotemporal Sampling Using Mobile Sensors
Many real-world applications for sensor networks require event sampling with sufficient resolution over both spatial and temporal dimensions. When the deployed nodes are insufficient to fully cover the sensor field to satisfy this spatiotemporal sampling requirements of all events, nodes with intelligent mobility are crucial to improve the sampling quality. We measure the sampling quality using a utility function that incorporates both the importance and spatiotemporal properties of events. We then describe a Utility Driven Mobility (UDM) scheme, which enables autonomous mobility scheduling of nodes in a distributed fashion. We evaluate the performance of UDM via extensive simulation studies. Our work provides a practical framework for spatiotemporal sampling using mobile sensor networks.
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.
62228300
SVM Based Classification Mapping for User Navigation
In order to implement active service, a kind of user navigation based on Support Vector Machine (SVM) classification was proposed which solved many traditional problems such as lack of rules and adaptability and so on. Especially, this paper completed a dual classified coordination between user attribute classification and SVM user classification using mapping matrix. In our SVM based user navigation, we first regard User interest Set as input, then apply the SVM classifier to sort out users on the basis of their interests or attributes, and what is more, combine the merit that user attributes classification promise better service being offered to users whose using frequency is not high, a dual-classified collaborating navigation between user attribute classification, and SVM user classification by using mapping matrix is being proposed. We use the mapping method to mix their advantages of common classification methods which lead them to collaborate and to provide the new and old custom with navigation. Using Classification T to statistic the latest and the most frequently used demands of each user type, then applying a mapping from user attribute class (A) to user interest class (T) with the purpose to judge what kind of user type in Classification T has the most possibility the user might belong, to and then to provide the user with the latest services according to their interests. From the experiments, the fitting degree of A and T classification and the true personality of users are derived by tracking the demand of 50 users. We can see that with the increase of frequency in use, the matching degree between Users Attribute Classification (A) and real types of user are essentially the same, and User Personalized Classification (T) is gradually reaching saturation. Regarding the certain point as a dividing line, after using classification mapping, the user's true extent of matching has a broader applicability. SVM have excellent generalization performance and minimum structural risk, which is a good machine learning method that elicits objective regulation from complex statistical dates. So we have improved the user model of active service navigation system and user navigation combined with User Attribute Classification A and SVM User Interest Classification T by mapping matrix. Classification A and T using mapping matrix to bounding them can satisfy the requirement of a different user. The records in the Classified Statistic Library can provide feedback date and research information for further study between a personalized user and user attribute.
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.
2088482
Cotton Variety Selection Model and Application Design
Choosing a  cotton variety  involves  making comparisons over many trials and across an increasingly large  number of available  varieties. To aid in this process, a computerized tool has been developed at the Texas Tech University. This tool, called COTWIZ, based on the cotton variety selection model, allows  multiple comparisons  of varieties across a number of trial locations. COTWIZ based on the cotton variety selection model and uses cotton OVT data to make  comparisons that are imported by the user.
9419066
An Improved Two-Stage Camera Calibration Method Based on Particle Swarm Optimization
According to the calibration of binocular vision, an improved two-stage camera calibration method involved with multi-distortion coefficients is introduced in this paper. At the first stage, the 3D points' coordinate are calculated by the imitated direct linear transformation (DLT) triangulation based on distortion compensation. And at the second stage, particle swarm optimization (PSO) is selected to determine two cameras' parameters. In this way the parameters of the two cameras can be tuned simultaneously. In order to assist estimating the performance of the proposed method, a new cost function is designed. Simulation and experiment are made under the same calibration data sets. The performance of PSO used to tune the parameters is also compared to that of GA. The experiment results show that the strategy of taking the 3D reconstruction errors as object function is feasible and PSO is the best choice for camera parameters' optimization.