Distributed Global Function Model Finding for Wireless Sensor Network Data

Deng, Song; Yang, Lin; Yang, Dong; Xiong, Fei; Ma, Zhuo

doi:10.3390/app6020037

Cited by 4 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the proposed MS-GEP algorithm, we created MS-GEP-A, MS-GEP-I, MS-GEP, NMO-SARA [17], GEP [10], and FF-GEP [18] in the literature for comparative experiments. NMO-SARA and MS-GEP-A differ in the mutation rate settings and additional parameters.…”

Section: Competitive Experiments Of the Ms-gep Algorithmmentioning

confidence: 99%

An improved gene expression programming algorithm for function mining of map-reduce job execution in catenary monitoring systems

Ding,

Jiang,

Tan

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Gene expression programming (GEP) is one of the most prominent algorithms in function mining. In order to obtain a more accurate function model in configuration parameters-execution efficiency (CP-EE) of map-reduce job in the high-speed railway catenary monitoring system, this paper proposes a novel algorithm, called GEP based on multi-strategy (MS-GEP). Compared to traditional GEP, the proposed algorithm can escape premature convergence and jump out of local optimum. First, an adaptive mutation rate is designed according to the evolutionary generations, population diversity, and individual fitness values. A manual intervention strategy is then proposed to determine whether the algorithm enters the dilemma of local optimum based on the generations of population evolutionary stagnation. Finally, the average quality of the population is changed by randomly replacing individuals, and the ancestral population is traced to change the evolutionary direction. The experimental results on the benchmarks of function mining show that the proposed MS-GEP has better solution quality and higher population diversity than other GEP algorithms. Furthermore, the proposed MS-GEP has higher accuracy on the function model of CP-EE of high-speed railway catenary monitoring system than other commonly used algorithms in the field of function mining.

show abstract

Section: Competitive Experiments Of the Ms-gep Algorithmmentioning

confidence: 99%

An improved gene expression programming algorithm for function mining of map-reduce job execution in catenary monitoring systems

Ding,

Jiang,

Tan

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

DORS: A data overhead reduction scheme for hybrid networks in smart cities

Said

Tolba

2020

Int J Communication

View full text Add to dashboard Cite

SummaryToday, smart cities represent an effective digital platform for facilitating our lives by shifting all stakeholders toward more sustainable behavior. Consequently, the field of smart cities has become an increasingly important research area. The smart city comprises a huge number of hybrid networks, with each network containing an enormous number of nodes that transmit massive amounts of data, thus giving rise to many network problems, such as delay and loss of connectivity. Decreasing the amount of such transmitted data is a great challenge. This paper presents a data overhead reduction scheme (DORS) for heterogeneous networks in smart city environments that comprise five different methods: median, nonlinear least squares, compression, data merging, and prioritization. Each method is applied according to the current status of quality of service. To measure the performance of the proposed model, a simulation environment is constructed for a smart city using network simulation package, NS2. The obtained results indicate that DORS has the capability to decrease the size of transmitted data in the simulated smart city environment while attaining a notable performance enhancement in terms of data reduction rate, end‐to‐end delay, packet loss ratio, throughput, and energy consumption ratio.

show abstract