Differential evolution optimized fuzzy controller for wireless sensor network energy management

Rodway, James; Krömer, Pavel; Karimi, Soheila; Musílek, Petr

doi:10.1109/fuzz-ieee.2016.7737708

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…A human-created reference controller was used for comparison with the generated controllers [6,54,55]. This controller consisted of two inputs, one for the energy forecast of the upcoming day and one for the state of the energy buffer.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Harvesting-Aware Energy Management for Environmental Monitoring WSN

Rodway

Musilek

2017

Energies

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Abstract:Wireless sensor networks can be used to collect data in remote locations, especially when energy harvesting is used to extend the lifetime of individual nodes. However, in order to use the collected energy most effectively, its consumption must be managed. In this work, forecasts of diurnal solar energies were made based on measurements of atmospheric pressure. These forecasts were used as part of an adaptive duty cycling scheme for node level energy management. This management was realized with a fuzzy logic controller that has been tuned using differential evolution. Controllers were created using one and two days of energy forecasts, then simulated in software. These controllers outperformed a human-created reference controller by taking more measurements while using less reserve energy during the simulated period. The energy forecasts were comparable to other available methods, while the method of tuning the fuzzy controller improved overall node performance. The combination of the two is a promising method of energy management.

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Section: Simulation Resultsmentioning

confidence: 99%

“…Our previous work on optimization of similar controllers included 5 membership functions for each input [54]. The resulting partitions for that optimization contained membership function with a large amount of overlap, leading to the selection of a lower number in the current work, i.e., 3 membership functions per input.…”

Section: Controller Optimizationmentioning

confidence: 99%

Harvesting-Aware Energy Management for Environmental Monitoring WSN

Rodway

Musilek

2017

Energies

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“…maximize the total network lifetime, in other words to minimize the total energy consumption [12]. In many WSN applications, the computational time is also a key parameter in the optimization process.…”

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confidence: 99%

Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization

et al. 2019

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Wireless Sensor Networks (WSN) have been widely adopted for years, but their role is growing significantly currently with the increase of the importance of the Internet of Things paradigm. Moreover, since the computational capability of small-sized devices is also increasing, WSN are now capable of performing relevant operations. An optimal scheduling of these in-network processes can affect both the total computational time and the energy requirements. Evolutionary optimization techniques can address this problem successfully due to their capability to manage non-linear problems with many design variables. In this paper, an evolutionary algorithm recently developed, named Social Network Optimization (SNO), has been applied to the problem of task allocation in a WSN. The optimization results on two test cases have been analyzed: in the first one, no energy constraints have been added to the optimization, while in the second one, a minimum number of life cycles is imposed.Wireless Sensor Networks (WSN) are relevant system architectures that can be applied in a wide range of applications [1], from monitoring to tracking, visual surveillance, ranging also in many fields from industrial automation to agricultural systems, and target localization, both in military and civil sectors [2].Currently, WSN are becoming much more important because the senors' computational capabilities are growing, and thus, many in-network tasks can be performed. It has been noticed that processing the information inside the network is faster and safer than sending raw data to the final user [3]. Such in-network processing can drastically reduce the total computational time required after sensing tasks with a direct impact on power consumption [4].The network complex dynamics created by these in-network operations can be suitably managed by means of evolutionary optimization techniques [5]. For instance, the problem of sensor lifetime maximization has been successfully approached in [6] by means of genetic algorithms and in [7] with genetical swarm optimization. The problem of coverage in WSN has been solved in [8] with both the genetic algorithm and ant colony optimization. Another important problem of WSN, routing, has been widely approached by several authors: in [9], a specific energy protocol has been designed to improve the network lifetime, while in [10], it has been solved with genetical swarm optimization and in [11] with particle swarm optimization.The problem of task and resource allocation is a crucial problem in many frameworks. It is aimed at finding the optimal distribution of the tasks inside the network itself with reference to a specific goal. In the case of the deployment of multiple sensor devices with batteries, a fundamental goal is to

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A Q-Learning-Based Routing Approach for Energy Efficient Information Transmission in Wireless Sensor Network

Ren

Cai

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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Differential evolution optimized fuzzy controller for wireless sensor network energy management

Cited by 4 publications

References 13 publications

Harvesting-Aware Energy Management for Environmental Monitoring WSN

Harvesting-Aware Energy Management for Environmental Monitoring WSN

Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization

A Q-Learning-Based Routing Approach for Energy Efficient Information Transmission in Wireless Sensor Network

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