Multi-hop sensor network scheduling for optimal remote estimation

Automatica ◽  
2021 ◽  
Vol 127 ◽  
pp. 109498
Author(s):  
Takuya Iwaki ◽  
Junfeng Wu ◽  
Yuchi Wu ◽  
Henrik Sandberg ◽  
Karl Henrik Johansson
Keyword(s):  
Sensor Network ◽  
Network Scheduling ◽  
Remote Estimation

scholarly journals Application of Genetic Algorithm in Time-Based Wireless Sensor Network Schedule Optimization

2021 ◽  
Vol 31.2 (149) ◽  
pp. 29-34
Keyword(s):  
Genetic Algorithm ◽  
Sensor Network ◽  
Fitness Function ◽  
Optimal Solution ◽  
Complex Problem ◽  
Wireless Sensor ◽  
Network Scheduling ◽  
Schedule Optimization ◽  
Wide Range ◽  
Network Schedule

In recent years, wireless sensor networks (WSN) have been particularly interested, studied and applied very strongly. A sensor network is generally limited in resources and energy, which greatly restrict its applicability. Sensor network optimization in practice is a very diverse with a wide range of applications, whereas sensor network scheduling is important in lowering energy consumption and maximizing network lifetime. However, optimization of sensor network schedule a very complex problem with many constraints that is not trivial to solve by analytical methods. This article discusses a heuristical approach using a genetic algorithm to find an optimal solution for network scheduling. The evaluation of fitness function, as well as selection with crossover and mutation operations help to evolve individuals in the population through generations in an optimal direction.

scholarly journals On Stochastic Sensor Network Scheduling for Multiple Processes

2017 ◽  
Vol 62 (12) ◽  
pp. 6633-6640 ◽  
Author(s):  
Duo Han ◽  
Junfeng Wu ◽  
Yilin Mo ◽  
Lihua Xie
Keyword(s):  
Sensor Network ◽  
Network Scheduling ◽  
Multiple Processes

scholarly journals Sensor network scheduling for identification of spatially distributed processes

2012 ◽  
Vol 22 (1) ◽  
pp. 25-40 ◽  
Author(s):  
Dariusz Uciński
Keyword(s):  
Sensor Network ◽  
Hypothesis Test ◽  
Information Matrix ◽  
Spatial Domain ◽  
Performance Measure ◽  
Design Criterion ◽  
Network Scheduling ◽  
Cost Constraints ◽  
Distributed Processes ◽  
Spatially Distributed

Sensor network scheduling for identification of spatially distributed processesThe work treats the problem of fault detection for processes described by partial differential equations as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A simple node activation strategy is discussed for the design of a sensor network deployed in a spatial domain that is supposed to be used while detecting changes in the underlying parameters which govern the process evolution. The setting considered relates to a situation where from among a finite set of potential sensor locations only a subset of them can be selected because of the cost constraints. As a suitable performance measure, theDs-optimality criterion defined on the Fisher information matrix for the estimated parameters is applied. The problem is then formulated as the determination of the density of gauged sites so as to maximize the adopted design criterion, subject to inequality constraints incorporating a maximum allowable sensor density in a given spatial domain. The search for the optimal solution is performed using a simplicial decomposition algorithm. The use of the proposed approach is illustrated by a numerical example involving sensor selection for a two-dimensional diffusion process.

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