Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks

Bhola, Jyoti; Soni, Surender Kumar; Cheema, Gagandeep Kaur

doi:10.1007/s12652-019-01382-3

Cited by 134 publications

(81 citation statements)

References 16 publications

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“…WSN discovery applications are mainly used to collect information from remote locations, such as environmental monitoring, military, and transportation security [6]. In order to use the lifetime of effector nodes to improve energy efficiency, they proposed an energy-saving routing protocol, low-energy adaptive clustering hierarchy (LEACH), and optimization algorithm genetic algorithm (GA) [7]. Due to the diversification of the application environment of unconnected effector Internet and different design requirements and goals of each environment, the routing algorithms used in each Internet are also considered to be changed, and as the application environment changes and improves, routing algorithm also becomes diversified [8,9].…”

Section: Introductionmentioning

confidence: 99%

Parameter Detection of an On-Chip Embedded Debugging System of Wireless Sensor Internet Based on LEACH Algorithm

Zhou

2021

Mathematical Problems in Engineering

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With the rapid development and maturity of unconnected communication technology, effector technology, embedded computing technology, and distributed information processing technology, as well as the rapid advancement of digital processing and computing capabilities, unconnected effector Internet has received more and more attention. It is a new type of self-organizing unconnected multihop Internet. With the development and progress of technology and society, the development of unconnected effector Internet is also advancing by leaps and bounds, and it has a wide range of applications in many fields such as military, civil, environmental, medical, and industrial. At present, the research on unconnected effector Internet mainly focuses on the communication protocol, but there is almost no research on input review of unconnected effector Internet. Due to the limited energy of the effector and the limitation of the transmission signal bandwidth, the use of limited resources for input review research in effector Internet is of great significance to the development of unconnected effector Internet. Therefore, this paper studies the input review of unconnected effector Internet based on the L-A model on the on-chip embedded tune system. Because the classic low-power adaptive cluster layering protocol (LEACH) has the problems of unbalanced energy consumption and short node life cycle, this paper uses the embedded tune technology based on the L-A model and analyzes the remaining energy and location of the unconnected effector Internet node. Parameters are tested and studied. Through the research on the input review of unconnected effector Internet, the simulation results obtained show that the research in this paper is feasible and reasonable.

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Section: Introductionmentioning

confidence: 99%

Parameter Detection of an On-Chip Embedded Debugging System of Wireless Sensor Internet Based on LEACH Algorithm

Zhou

2021

Mathematical Problems in Engineering

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“…Mao et al [32], Du et al [33], and Zhang et al [34] have applied a reinforcement learning approach for mitigating stragglers, which reduce job completion time, but the preciseness of identification of stragglers may not be optimum. Existing approaches used in decentralizing data consists of Alternating Direction Method of Multipliers (ADMM) based algorithms like [35][36][37][38][39].…”

Section: Security and Communication Networkmentioning

confidence: 99%

Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

Deshmukh

Rao

Shabaz

2021

Security and Communication Networks

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Modern big data applications tend to prefer a cluster computing approach as they are linked to the distributed computing framework that serves users jobs as per demand. It performs rapid processing of tasks by subdividing them into tasks that execute in parallel. Because of the complex environment, hardware and software issues, tasks might run slowly leading to delayed job completion, and such phenomena are also known as stragglers. The performance improvement of distributed computing framework is a bottleneck by straggling nodes due to various factors like shared resources, heavy system load, or hardware issues leading to the prolonged job execution time. Many state-of-the-art approaches use independent models per node and workload. With increased nodes and workloads, the number of models would increase, and even with large numbers of nodes. Not every node would be able to capture the stragglers as there might not be sufficient training data available of straggler patterns, yielding suboptimal straggler prediction. To alleviate such problems, we propose a novel collaborative learning-based approach for straggler prediction, the alternate direction method of multipliers (ADMM), which is resource-efficient and learns how to efficiently deal with mitigating stragglers without moving data to a centralized location. The proposed framework shares information among the various models, allowing us to use larger training data and bring training time down by avoiding data transfer. We rigorously evaluate the proposed method on various datasets with high accuracy results.

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“…Nevertheless, some group of researchers from the literature tried to achieve the implementation of the first and second parameters on a multiheterogeneous network area to improve the lifetime of the network further. Similarly, to get the desired result for energy efficiency, some constraints of WSNs strategies were adopted for minimizing the data among the communicating parameters [23][24][25][26][27].…”

Section: Mobile Information Systemsmentioning

confidence: 99%

A Hyper-Heuristic Heterogeneous Multisensor Node Scheme for Energy Efficiency in Larger Wireless Sensor Networks Using DEEC-Gaussian Algorithm

Aroba

Naicker

Adeliyi

2021

Mobile Information Systems

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A wireless sensor network (WSN) is an intellect-sustainable network that comprises multiple spatially distributed sensor nodes and several sink nodes that collect data from sensors. WSNs remain an active research area in the literature due to challenging factors such as the selection of sensor location according to a given premise, finding optimal routing algorithm, and ensuring energy efficiency and consumption. Minimizing energy and prolonging the network lifetime in the WSNs are the focus of this research work. In the literature, a clustering approach is used in grouping sensor nodes into clusters and is seen as an effective technique used in optimizing energy consumption in WSNs. Hence, in this paper, we put forward a novel clustering-based approach by amalgamating the Gaussian elimination method with the Distributed Energy-Efficient Clustering to produce DEEC_Gaussian (DEEC_Gaus) to stabilize energy efficiency optimization in WSNs. We took the advantages of DEEC and Gaussian elimination algorithms to resolve energy efficiency problems in WSNs. DEEC presents attributes such as increased heterogeneity performance level, clustering stability in operation, and energy efficiency which helps to prolong network lifetime while the Gaussian elimination algorithm added an additional advantage to improve and optimize energy efficiency, to aggregate packets of operations performed in the network lifestyle of energy efficiency in WSNs. The simulations were carried out using MATLAB software with 1000 to 1500 nodes. The performance of the proposed work was compared with state-of-the-art algorithms such as DEEC, DDEEC, and EDEEC_E. The simulated results presented show that the proposed DEEC-Gauss outperformed the three other conventional algorithms in terms of network lifetime, first node dead, tenth node dead, alive nodes, and the overall timing of the packets received at the base station. The results showed that the proposed hyper-heuristic heterogeneous multisensor DEEC-Gauss algorithm presented an average percentage of 3.0% improvement for the tenth node dead (TND) and further improvement of 4.8% for the first node dead (FND). When the performance was compared to the state-of-the-art algorithms in larger networks, the overall delivery was greatly improved and optimized.

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Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks

Cited by 134 publications

References 16 publications

Parameter Detection of an On-Chip Embedded Debugging System of Wireless Sensor Internet Based on LEACH Algorithm

Parameter Detection of an On-Chip Embedded Debugging System of Wireless Sensor Internet Based on LEACH Algorithm

Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

A Hyper-Heuristic Heterogeneous Multisensor Node Scheme for Energy Efficiency in Larger Wireless Sensor Networks Using DEEC-Gaussian Algorithm

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