دانلود مقاله ISI انگلیسی شماره 79101
عنوان فارسی مقاله

ارزیابی عملکرد الگوریتم خوشه بندی سران انتخابی و تطبیقی بیش از شبکه های حسگر بی سیم

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79101 2012 13 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Performance evaluation of selective and adaptive heads clustering algorithms over wireless sensor networks
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Network and Computer Applications, Volume 35, Issue 6, November 2012, Pages 2068–2080

کلمات کلیدی
شبکه های حسگر بی سیم؛ الگوریتم های خوشه بندی جدید؛ ردیابی شیء؛ مصرف انرژی؛ ردیابی خطا؛ سرعت هدف؛ تراکم سنسور
پیش نمایش مقاله
پیش نمایش مقاله ارزیابی عملکرد الگوریتم خوشه بندی سران انتخابی و تطبیقی بیش از شبکه های حسگر بی سیم

چکیده انگلیسی

Target tracking in wireless sensor networks can be considered as a milestone of a wide range of applications to permanently report, through network sensors, the positions of a mobile target to the base station during its move across a certain path. While tracking a mobile target, a lot of open challenges arise and need to be investigated and maintained which mainly include energy efficiency and tracking accuracy. In this paper, we propose three algorithms for tracking a mobile target in wireless sensor network utilizing cluster-based architecture, namely adaptive head, static head, and selective static head. Our goal is to achieve a promising tracking accuracy and energy efficiency by choosing the candidate sensor nodes nearby the target to participate in the tracking process while preserving the others in sleep state. Through Matlab simulation, we investigate the performance of the proposed algorithms in terms of energy consumption, tracking error, sensor density, as well as target speed. The results show that the adaptive head is the most efficient algorithm in terms of energy consumption while static and selective static heads algorithms are preferred as far as the tracking error is concerned especially when the target moves rapidly. Furthermore, the effectiveness of our proposed algorithms is verified through comparing their results with those obtained from previous algorithms.

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