Model Predictive Control for Stochastic Resource Allocation

Castañón, David A.; Wohletz, Jerry M.

doi:10.1109/tac.2009.2024562

Cited by 31 publications

(15 citation statements)

References 17 publications

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“…There is no chance for mobility in cloud environment. Castanon and Wohletz [1] considered a class of stochastic resource allocation problems. The SDP is used to solve this problem.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years there is a sharp increase of vehicles on roads. This attracts increasing attention of leading car manufacturers who are willing to jointly work with national government agencies and wireless network research groups to develop intelligent transportation systems [1]. This scenario causes VANET as an emerging research area in wireless network community.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic dynamic programming model for optimal resource allocation in vehicular ad hoc networks

Bhuvaneswari¹,

Paramasivan²,

Kandasamy

2018

Sādhanā

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Vehicular ad hoc network (VANET) is an emerging trend where vehicles communicate with each other and possibly with a roadside unit to assist various applications like monitoring, managing and optimizing the transportation system. Collaboration among vehicles is significant in VANET. Resource constraint is one of the great challenges of VANETs. Because of the absence of centralized management, there is pitfall in optimal resource allocation, which leads to ineffective routing. Effective reliable routing is quite essential to achieve intelligent transportation. Stochastic dynamic programming is currently employed as a tool to analyse, develop and solve network resource constraint and allocation issues of resources in VANET. We have considered this work as a geographical-angular-zone-based two-phase dynamic resource allocation problem with a homogeneous resource class. This work uses a stochastic dynamic programming algorithm based on relaxed approximation to generate optimal resource allocation strategies over time in response to past task completion status history. The second phase resource allocation uses the observed outcome of the first phase task completion to provide optimal viability in resulting decisions. The proposed work will be further extended for the scenario that deals with heterogeneous resource class. Simulation results show that the proposed scheme works significantly well for the problems with identical resources.

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“…There is no chance for mobility in cloud environment. Castanon and Wohletz [1] considered a class of stochastic resource allocation problems. The SDP is used to solve this problem.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic dynamic programming model for optimal resource allocation in vehicular ad hoc networks

Bhuvaneswari¹,

Paramasivan²,

Kandasamy

2018

Sādhanā

View full text Add to dashboard Cite

show abstract

“…A more generic method is to measure the probability that a task is successfully executed (e.g. [52,53]). In the end, above proposed measures describe how well a…”

Section: Sensor Management In the Open Literaturementioning

confidence: 99%

Mission-Driven Resource Management for Reconfigurable Sensing Systems

Groot

Krasnov

Yarovoy

2018

IEEE Systems Journal

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Importance of this researchAlmost any process that undertakes actions relies on situational awareness. Such awareness can be supported by using sensors. In dynamic situations the observation task can get complicated and sensors may need to be reconfigured in real time. Choosing the right configuration and allocating the available resources, such as time, of the sensors is very challenging. This thesis proposes the concept of mission-driven resource management to automatically and optimally decide on such choices during the various sensor life cycle phases.

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“…In the controls community, many researchers have developed algorithms for task assignment among teams of vehicles under the name of cooperative plan-ning [Alighanbari, 2004, Alighanbari et al, 2003, Beard et al, 2002, Berman et al, 2009, Cassandras and Li, 2002, Castanon and Wohletz, 2009, Choi et al, 2009, Ryan et al, 2004, Saligrama and Castañón, 2006, Wang et al, 2007. As the name suggests, these methods use an existing world model for planning vehicles task assignment.…”

Section: Related Workmentioning

confidence: 99%

“…In our UAV mission planning scenarios both these assumptions are violated: the penalty function is step-shaped and the noise involved in the reward function has a multinomial distribution. Castanon and Wohletz [2009] focused on stochastic resource allocation problem. While their method scales well to domains with 20 resources and 20 tasks, it cannot be applied to cases where multiple assignments of simultaneous resources are required for the task completion.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Cooperative Control Architecture: A Framework for Performance Improvement Using Safe Learning

Geramifard

Redding

How

2013

J Intell Robot Syst

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Planning for multi-agent systems such as task assignment for teams of limited-fuel unmanned aerial vehicles (UAVs) is challenging due to uncertainties in the assumed models and the very large size of the planning space. Researchers have developed fast cooperative planners based on simple models (e.g., linear and deterministic dynamics), yet inaccuracies in assumed models will impact the resulting performance. Learning techniques are capable of adapting the model and providing better policies asymptotically compared to cooperative planners, yet they often violate the safety conditions of the system due to their exploratory nature. Moreover they frequently require an impractically large number of interactions to perform well. This paper introduces the intelligent Cooperative Control Architecture (iCCA) as a framework for combining cooperative planners and reinforcement learning techniques. iCCA improves the policy of the cooperative planner, while reduces the risk and sample complexity of the learner. Empirical results in gridworld and task assignment for fuel-limited UAV domains with problem sizes up to 9 billion state-action pairs verify the advantage of iCCA over pure learning and planning strategies.

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Model Predictive Control for Stochastic Resource Allocation

Cited by 31 publications

References 17 publications

Stochastic dynamic programming model for optimal resource allocation in vehicular ad hoc networks

Stochastic dynamic programming model for optimal resource allocation in vehicular ad hoc networks

Mission-Driven Resource Management for Reconfigurable Sensing Systems

Intelligent Cooperative Control Architecture: A Framework for Performance Improvement Using Safe Learning

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