Sensor Scheduling Design for Complex Networks under a Distributed State Estimation Framework
This paper investigates sensor scheduling for state estimation of complex networks over shared transmission channels. For a complex network of dynamical systems, referred to as nodes, a sensor network is adopted to measure and estimate the system states in a distributed way, where a sensor is used to measure a node. The estimates are transmitted from sensors to the associated nodes, in the presence of one-step time delay and subject to packet loss. Due to limited transmission capability, only a portion of sensors are allowed to send information at each time step. The goal of this paper is to seek an optimal sensor scheduling policy minimizing the overall estimation errors. Under a distributed state estimation framework, this problem is reformulated as a Markov decision process, where the one-stage reward for each node is strongly coupled. The feasibility of the problem reformulation is ensured. In addition, an easy-to-check condition is established to guarantee the existence of an optimal deterministic and stationary policy. Moreover, it is found that the optimal policies have a threshold, which can be used to reduce the computational complexity in obtaining these policies. Finally, the effectiveness of the theoretical results is illustrated by several simulation examples.
Code (0)
등록된 구현이 없습니다.
Tasks
SchedulingState EstimationSimilar Papers 제목 키워드 기반
Sensor Scheduling for Linear Systems: A Covariance Tracking Approach
We consider the classical sensor scheduling problem for linear systems where only one sensor is activated at each time. We show that the sensor scheduling problem has a close relation to the sensor design problem and the…
SchedulingOIDM: An Observability-based Intelligent Distributed Edge Sensing Method for Industrial Cyber-Physical Systems
Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule la…
Deep Reinforcement LearningEdge-computingSchedulingEnergy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space
Millions of battery-powered sensors deployed for monitoring purposes in a multitude of scenarios, e.g., agriculture, smart cities, industry, etc., require energy-efficient solutions to prolong their lifetime. When these …
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)SchedulingOptimal scheduling strategy for networked estimation with energy harvesting
Joint optimization of scheduling and estimation policies is considered for a system with two sensors and two non-collocated estimators. Each sensor produces an independent and identically distributed sequence of random v…
Decision MakingSchedulingPreble: Efficient Distributed Prompt Scheduling for LLM Serving
Prompts to large language models (LLMs) have evolved beyond simple user questions. For LLMs to solve complex problems, today's practices are to include domain-specific instructions, illustration of tool usages, and/or lo…
GPUScheduling