In this paper, we consider multi-agent stochastic optimization and control problems, with partial information. The agent can operate in a distributed and asynchronous fashion. We investigate new problems that arise ou...
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In this paper, we consider multi-agent stochastic optimization and control problems, with partial information. The agent can operate in a distributed and asynchronous fashion. We investigate new problems that arise out of the interaction between observations and control actions by the agent. We show that new non-classical and non-commutative probability models are needed in order to properly formulate such problems. The models we develop here are inspired by models developed for dynamical physics problems. We establish a series of fundamental results for the trade-off between information and control patterns in distributed stochastic control, detection and estimation.
The livestock industry can benefit tremendously from systems that continuously monitor cattle state-of-health, allowing the industry to maintain high meat quality, react to the presence of disease, and predict its spr...
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The livestock industry can benefit tremendously from systems that continuously monitor cattle state-of-health, allowing the industry to maintain high meat quality, react to the presence of disease, and predict its spread. Requirements for these monitoring systems are similar to requirements that drive human ambulatory monitoring systems based on wearable sensors and wireless data communication. This paper presents early results from an effort to develop a veterinary telemedicine infrastructure based upon wearable monitoring technology originally developed for home health care. The functional layout of the infrastructure is described, and initial hardware and physiological measurements are presented.
The next generation of computingsystems will be embedded, in a virtually unbounded number, and dynamically connected. The current software, network architectures, and their associated programming models are not suita...
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ISBN:
(纸本)0769515851
The next generation of computingsystems will be embedded, in a virtually unbounded number, and dynamically connected. The current software, network architectures, and their associated programming models are not suitable for this scenario. This paper presents a distributedcomputing model, Cooperative computing, and the Smart Messages architecture for programming large networks of embedded systems. In Cooperative computing, distributed applications are dynamic collections of migratory execution units, called Smart Messages, working to achieve a common goal. Virtually any user-defined distributed application can be implemented using our model. We present preliminary results for our prototype implementation as well as simulation results for two previously proposed applications for sensor networks, Directed Diffusion and SPIN, implemented using Smart Messages.
In-network data aggregation is essential for wireless sensor networks where energy resources are limited. In a previously proposed data dissemination scheme (directed diffusion with opportunistic aggregation), data is...
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ISBN:
(纸本)0769515851
In-network data aggregation is essential for wireless sensor networks where energy resources are limited. In a previously proposed data dissemination scheme (directed diffusion with opportunistic aggregation), data is opportunistically aggregated at intermediate nodes on a low-latency tree. In this paper, we explore and evaluate greedy aggregation, a novel approach that adjusts aggregation points to increase the amount of path sharing, reducing energy consumption. Our preliminary results suggest that, under investigated scenarios, greed), aggregation can achieve tip to 45% energy savings over opportunistic aggregation in high-density networks without adversely impacting latency, or robustness.
The emergence of mobile ad hoc sensor networks has brought new challenges to traditional network design. This paper compares the performance of two distributedcomputing paradigms, the client/server-based paradigm and...
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ISBN:
(纸本)0769517609
The emergence of mobile ad hoc sensor networks has brought new challenges to traditional network design. This paper compares the performance of two distributedcomputing paradigms, the client/server-based paradigm and the mobile-agent-based paradigm, through mathematical modeling and simulation. Previous works have shown that the mobile-agent-based paradigm is more appropriate to handle computations in ad hoc sensor networks. However no simulation;work has been done to quantitatively measure the performance. This paper first describes how computing is accomplished in the mobile-agent-based paradigm. It then presents a modified mathematical model and uses the execution time as a metric to measure the Performance. Eight experiments are designed to show the effect of different parameters to the performance of the paradigms. Experimental results show that in the context of mobile ad hoc sensor networks with hundreds or Eden thousands of nodes, unreliable communication links, and reduced bandwidth, the mobile-agent-based computing provides solutions to low network latency and reliable data processing.
sensor networks are distributed event-based systems that differ from traditional communication networks in several ways: sensor networks have severe energy constraints, redundant low-rate data, and many-to-one flows. ...
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ISBN:
(纸本)0769515886
sensor networks are distributed event-based systems that differ from traditional communication networks in several ways: sensor networks have severe energy constraints, redundant low-rate data, and many-to-one flows. Data-centric mechanisms that perform in-network aggregation of data are needed in this setting for energy-efficient information flow. In this paper we model data-centric routing and compare its performance with traditional end-to-end routing schemes. We examine the impact of source-destination placement and communication network density on the energy costs and delay associated with data aggregation. We show that data-centric routing offers significant performance gains across a wide range of operational scenarios. We also examine the complexity of optimal data aggregation, showing that although it is an NP-hard problem in general, there exist useful polynomial-time special cases.
We have designed and built a set of miniature robots called Scouts and have developed a distributed software system to control them. This paper addresses the fundamental choices we made in the design of the control so...
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We have designed and built a set of miniature robots called Scouts and have developed a distributed software system to control them. This paper addresses the fundamental choices we made in the design of the control software, describes experimental results in a surveillance task, and analyzes the factors that affect robot performance. Space and power limitations on the Scouts severely restrict the computational power of their on-board computers, requiring a proxy-processing scheme in which the robots depend on remote computers for their computing needs. While this allows the robots to be autonomous, the fact that robots' behaviors are executed remotely introduces an additional complication-sensor data and motion commands have to be exchanged using wireless communications channels. Communications channels cannot always be shared, thus requiring the robots to obtain exclusive access to them. We present experimental results on a surveillance task in which multiple robots patrol an area and watch for motion. We discuss how the limited communications bandwidth affects robot performance in accomplishing the task, and analyze how performance depends on the number of robots that share the bandwidth.
In the robot navigation problem, noisy sensor data must be filtered to obtain the best estimate of the robot position. The discrete Kalman filter, which usually is used for prediction and detection of signal in commun...
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ISBN:
(纸本)0769517307;0769517315
In the robot navigation problem, noisy sensor data must be filtered to obtain the best estimate of the robot position. The discrete Kalman filter, which usually is used for prediction and detection of signal in communication and control problems has become a commonly used method to reduce the effect of uncertainty from the sensor data. However, due to the special domain of robot navigation, the Kalman approach is very limited. The use of Total Least Squares Filter has been proposed 13] which is capable of converging with many fewer readings and achieving greater accuracy than the classical Kalman filter. The main disadvantage of those approaches is that they can not deal with the case where the noise subspace of dimension higher than one. Here a parallel Krylov subspace method on parallel distributed memory computers which uses the Lanezos bidiagonalization process with updating techniques is proposed which is more computationally attractive to solve the total least squares problems. The parallel algorithm is derived such that all inner products of a single iteration step are independent. Therefore, the cost of global communication which represents the bottleneck of the parallel performance on parallel distributed memory computers can be significantly reduced. This filter is very promising for very large data information and from our very preliminary experiments we can obtain more precise accuracy and better speedup.
We present a new energy-efficient directed diffusion protocol by using the proposed diagonal-based hexagonal-mesh scheme for a wireless sensor network. The wireless sensor network is more reasonable to build a fixed-t...
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