A genetic approach for the maximum network lifetime problem with additional operating time slot constraints

The implementation of the rural revitalization strategy is an important strategic plan to solve the social “three rural” problems on the new road of building a socialist modernized country in an all-round way. It is also the current society to promote the comprehensive rule of law and to create and improve a modern rural social governance system under the leadership of the party committee, the government is responsible, the society is coordinated, and the country is governed by law. However, due to the wide distribution of rural areas and the large population base of farmers, there are many risks in the implementation of the rural revitalization strategy. In the comprehensive use of economic, administrative, and legal means, legal means has become the key. For this reason, this article conducted in-depth research on the legal path of rural revitalization under the risk prevention of Internet of Things algorithm decision-making. The research results showed that the risk prevention of Internet of Things algorithm decision-making was introduced into the research on the legal path of rural revitalization, and a sound rural governance system that combined autonomy, rule of law, and morality is an important part of the revitalization of seven villages. It can improve the 2.67% effectiveness of risk prevention in decision-making and can also play a key role in ruling the country according to law, showing the correct direction of agricultural legal system construction and at the same time high-level rural construction and development under the framework of the rule of law. It will vigorously promote the comprehensive modernization of agriculture, the comprehensive progress of rural areas, and the revitalization and development of rural areas.

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“…Each information flow is allocated exactly one-time slot [21], and its calculation formula is as follows:…”

Section: Time Slot Constraintsmentioning

confidence: 99%

Legal Path of Rural Revitalization for Decision-Making Risk Prevention of Internet of Things Algorithm

Wen-qiang

Ding²

2022

International Transactions on Electrical Energy Systems

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“…Due to the NP-hardness of the problem, a range of approximate algorithms have been proposed in the literature in order to solve it. Examples, which also include algorithms for closely related problem formulations, are a greedy heuristic [13], some memetic algorithms [14][15][16], a cuckoo search approach [17], and finally, a genetic algorithm [18].…”

Section: Graph Problems Used To Model Wsn Lifetime Maximizationmentioning

confidence: 99%

A Population-Based Iterated Greedy Algorithm for Maximizing Sensor Network Lifetime

Bouamama

Blum

Pinacho-Davidson

2022

Sensors

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Finding dominating sets in graphs is very important in the context of numerous real-world applications, especially in the area of wireless sensor networks. This is because network lifetime in wireless sensor networks can be prolonged by assigning sensors to disjoint dominating node sets. The nodes of these sets are then used by a sleep–wake cycling mechanism in a sequential way; that is, at any moment in time, only the nodes from exactly one of these sets are switched on while the others are switched off. This paper presents a population-based iterated greedy algorithm for solving a weighted version of the maximum disjoint dominating sets problem for energy conservation purposes in wireless sensor networks. Our approach is compared to the ILP solver, CPLEX, which is an existing local search technique, and to our earlier greedy algorithm. This is performed through its application to 640 random graphs from the literature and to 300 newly generated random geometric graphs. The results show that our algorithm significantly outperforms the competitors.

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“…Due to the hardness of the problem, a variety of approximate algorithms have been devised, also for different variants of the problem. Examples include a greedy heuristic [8], memetic algorithms [9][10][11], cuckoo search [12] and a genetic algorithm [13].…”

mentioning

confidence: 99%

A Greedy Heuristic for Maximizing the Lifetime of Wireless Sensor Networks Based on Disjoint Weighted Dominating Sets

2021

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Dominating sets are among the most well-studied concepts in graph theory, with many real-world applications especially in the area of wireless sensor networks. One way to increase network lifetime in wireless sensor networks consists of assigning sensors to disjoint dominating node sets, which are then sequentially used by a sleep–wake cycling mechanism. This paper presents a greedy heuristic for solving a weighted version of the maximum disjoint dominating sets problem for energy conservation purposes in wireless sensor networks. Moreover, an integer linear programming model is presented. Experimental results based on a large set of 640 problem instances show, first, that the integer linear programming model is only useful for small problem instances. Moreover, they show that our algorithm outperforms recent local search algorithms from the literature with respect to both solution quality and computation time.

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A genetic approach for the maximum network lifetime problem with additional operating time slot constraints

Cited by 8 publications

References 21 publications

Legal Path of Rural Revitalization for Decision-Making Risk Prevention of Internet of Things Algorithm

Legal Path of Rural Revitalization for Decision-Making Risk Prevention of Internet of Things Algorithm

A Population-Based Iterated Greedy Algorithm for Maximizing Sensor Network Lifetime

A Greedy Heuristic for Maximizing the Lifetime of Wireless Sensor Networks Based on Disjoint Weighted Dominating Sets

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