طول عمر شبکه حسگر بی سیم
ترجمه نشده

طول عمر شبکه حسگر بی سیم

عنوان فارسی مقاله: حداکثرسازی طول عمر شبکه حسگر بی سیم متحرک با استفاده از روش های محاسباتی تکاملی
عنوان انگلیسی مقاله: Mobile wireless sensor network lifetime maximization by using evolutionary computing methods
مجله/کنفرانس: شبکه های ادهاک – Ad Hoc Networks
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات، مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: شبکه های کامپیوتری، مهندسی الگوریتم و محاسبات
کلمات کلیدی فارسی: شبکه حسگر بی سیم، حداکثرسازی طول عمر، محاسبات تکاملی، انرژی کارآمد، بهینه سازی
کلمات کلیدی انگلیسی: wireless sensor network، lifetime maximization، evolutionary computing، energy efficient، optimization
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.adhoc.2020.102094
دانشگاه: Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China
صفحات مقاله انگلیسی: 13
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 4.301 در سال 2019
شاخص H_index: 79 در سال 2020
شاخص SJR: 0.648 در سال 2019
شناسه ISSN: ۱۵۷۰-۸۷۰۵
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14644
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

۱٫ Introduction

۲٫ Lifetime model of the mobile wireless sensor network

۳٫ Evolutionary computing algorithms

۴٫ Simulation experiment

۵٫ Conclusion

Declaration of Competing Interest

Acknowledgments

Appendix. Supplementary materials

Research Data

References

بخشی از مقاله (انگلیسی)

Abstract

Due to the continuous development and progress of wireless communication technology and sensor network technology, wireless sensor networks (WSNs) have gradually become an attractive technology that facilitates people’s lives. Due to the extensive use of WSNs, maximizing the lifetime of WSNs to obtain real-time and effective information has become a critical concern. This paper studies the life of mobile wireless sensor networks (MWSNs). MWSNs are a special type of WSN in that the sensor nodes are movable within a certain area. A system model is developed to prolong the network lifetime of MWSNs. This paper uses five evolutionary computing (EC) algorithms to develop the MWSN lifetime optimization model. Numerical simulations are performed to study the advantages and disadvantages of the five algorithms for solving the model. The comparison and discussion can provide advice for using EC algorithms to solve MWSN lifetime maximization problems.

Introduction

Due to the continuous development and progress of wireless communication technology, network technology, microprocessor technology and sensor network technology, WSNs have gradually become an attractive technology that facilitates people’s lives [1]-[5]. Moreover, WSNs are a new way to acquire information through real-time monitoring of the environment. Because of their unique way of obtaining information, WSNs are widely used in various fields, such as military defense, biological medicine, smart home technology, industry and agriculture [2], [3]. As the capacity of battery of nodes is limited, the operational longevity of nodes is critical. The longevity of a WSN directly affects the overall performance of the network [4]. MWSNs are a special distributed network of many deployed sensor nodes that are movable within a monitoring area. MWSNs form a selforganizing network through wireless communication technology [5]. Unlike in static WSNs, the mobility of sensors or sink nodes in MWSNs causes network topology to change dynamically. Thus, compared to when designing static WSNs, more issues have to be addressed when designing mobile networks [4]. Recently, there have been studies on the lifetime of MWSNs. [6] studied maximizing the lifetime of MWSNs that contained mobile sink nodes. In [7], the exploration and exploitation trade-off was studied, and different methods were compared.