Kohonen som with conscience function neural net based energy efficient clustering and routing wireless sensor networks

العناوين الأخرى

استخدام كوهينين مع دالي الضمير العصبونية لتوفير الطاقة و ذلك بإيجاد نموذج محسن للعنقدة و التوجيه اللاسلكي للشبكات

مقدم أطروحة جامعية

al-Azzam, Sad

مشرف أطروحة جامعية

Naum, Riyad Shakir

أعضاء اللجنة

Viktorov, Oleg
al-Hammuz, Sadiq

الجامعة

جامعة الشرق الأوسط

الكلية

كلية تكنولوجيا المعلومات

القسم الأكاديمي

قسم علم الحاسوب

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2014

الملخص الإنجليزي

The current development within communication field lead to continuous and urgent needs for new data transfer techniques that can perform the communication process with high performance .

WSN emerged recently as a common and significant type of network which can be used within the environment that cannot be continuously managed by the human being.

To enhance WSN performance in terms of several criteria, including; lifetime and energy, then several procedures can be employed, such as; clustering.

KSOM was emerged as a technique for clustering in WSN.

The aim of this thesis is to evaluate the WSN performance in terms of average lifetime and consumed energy after adding conscience function of neural network.

The system is simulated in MATLAB software environment.

The performance was evaluated in two stages, the first stage investigate and compare the performance for Kohenon and KSOM, and the second stage investigates the effect of adding conscience function of neural network to the KSOM.

The results confirmed the effectiveness of KSOM and it is improvement for both energy consumption and network lifetime in comparison with Kohenon technique.

Furthermore the conscience function of the NN will in turns enhance the performance of the WSN over KSOM algorithm, and hence over SOM algorithm.

KSOM achieved an enhancement of 30.7% and 2.22% over Kohenon algorithm in terms of Lifetime and average energy respectively at 400 nodes number.

Furthermore, enhancements of 13.33% and 3.03% were achieved due to applying conscience function in terms of average lifetime and average consumed energy respectively at 200 nodes.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

عدد الصفحات

71

قائمة المحتويات

Table of contents.

Abstract.

Abstract in Arabic.

Chapter One : Introduction.

Chapter Two : Related works.

Chapter Three : Methodology and system implementation.

Chapter Four : Results and discussion.

Chapter Five : Conclusion and future works.

References.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

al-Azzam, Sad. (2014). Kohonen som with conscience function neural net based energy efficient clustering and routing wireless sensor networks. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-699510

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

al-Azzam, Sad. Kohonen som with conscience function neural net based energy efficient clustering and routing wireless sensor networks. (Master's theses Theses and Dissertations Master). Middle East University. (2014).
https://search.emarefa.net/detail/BIM-699510

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

al-Azzam, Sad. (2014). Kohonen som with conscience function neural net based energy efficient clustering and routing wireless sensor networks. (Master's theses Theses and Dissertations Master). Middle East University, Jordan
https://search.emarefa.net/detail/BIM-699510

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-699510