دانلود مقاله در مورد محدوده انتقال سنسورها در شبکه حسگر بی سیم پراکنده
خلاصه
1. معرفی
2. کنترل قدرت انتقال
3. دورترین همسایه
4. الگوریتم پیشنهادی
5. تجزیه و تحلیل
6. ارزیابی عملکرد
7. نتیجه گیری و کارهای آینده
بیانیه نویسنده اعتبار
اعلامیه منافع رقابتی
تصدیق
در دسترس بودن داده ها
منابع
Abstract
1. Introduction
2. Transmission power control
3. Farthest neighbor
4. The proposed algorithm
5. Analysis
6. Performance evaluation
7. Conclusions and future works
Credit author statement
Declaration of competing interest
Acknowledgment
Data availability
References
چکیده
یکی از چالش های اصلی در طراحی شبکه های بی سیم ad-hoc و حسگر، کاهش مصرف انرژی و تداخل و برخورد رادیویی است که به شدت و به شدت با محدوده انتقال گره ها مرتبط است. در این مقاله، محدوده انتقال حسگرها در شبکه‌های حسگر بی‌سیم پراکنده (WSN) به صورت تئوری مورد تجزیه و تحلیل قرار گرفته و نشان داده می‌شود که می‌توان محدوده انتقال سنسورها را در این نوع WSNها بدون قطع اتصال به میزان قابل توجهی کاهش داد. در مرحله بعد، روشی برای محاسبه محدوده های انتقال کاهش یافته ارائه می شود. در این روش، یک مکانیسم تنظیم محدوده انتقال تکراری برای انتخاب یک محدوده تقریباً انتقال استفاده می‌شود در حالی که هیچ اطلاعاتی در مورد مکان گره‌ها یا فاصله بین گره‌ها مورد نیاز نیست. این روش بسیار کارآمد است و سربار ارتباط آن کم است. می توان از آن به عنوان یک پیش مرحله از هر الگوریتم مسیریابی به منظور کاهش مصرف انرژی و تداخل رادیویی در سناریوهای هدفمند استفاده کرد. نتایج شبیه سازی کارایی روش پیشنهادی را نشان می دهد. ما حداقل 50% بهبود در مصرف انرژی در مقایسه با شبکه برق کامل، در شبکه های پراکنده WSN داشتیم.
Abstract
One of the main challenges in the design of wireless ad-hoc and sensor networks is to reduce energy consumption and radio interference and collision, which are strictly and strongly correlated to the transmission range of the nodes. In this article, the transmission ranges of sensors in sparse Wireless Sensor Networks (WSN) is theoretically analyzed and is shown that the transmission ranges of sensors can be significantly reduced in this type of WSNs without losing any connection. Next, a method is presented to calculate reduced transmission ranges. In this method, an iterative transmission range adjustment mechanism is used to select an almost transmission range while no information is needed on the location of nodes or the distance between nodes. This method is very efficient and its communication overhead is low. It can be used as a pre-step of any routing algorithm in order to decrease energy consumption and radio interference in targeted scenarios. The simulation results show the efficiency of the proposed method. We got at least 50% improvement in energy consumption in comparison with full power network, in sparse WSNs.
Introduction
Wireless Sensor Networks (WSNs) have become increasingly popular in recent years, as they offer a powerful and cost-effective solution for monitoring and collecting data from a variety of environments. A Wireless Sensor Network (WSN) consists of a large number of distributed sensor nodes that cooperatively monitor the physical world [1]. Each node in these networks is typically equipped with a wireless communication device, a small microcontroller, a memory unit and a power source. The nodes in a WSN are typically battery-powered and have limited processing power, memory, and communication bandwidth. Therefore, the design of a WSN requires careful consideration of these constraints, such as the choice of communication protocol, data routing algorithms, and energy management strategies [2].
The complexity of the WSN domain as well as the presence of many sensor nodes unavoidably introduces a large amount of data in these networks that must be processed, transmitted and received. The sheer amount of data generated by WSNs can pose a significant challenge for the network design and operation. The sensors in a WSN can generate data continuously or periodically, depending on the application requirements. This data must be processed, stored, and transmitted to the base station in a timely and efficient manner. So the complexity of the WSN domain and the presence of many sensor nodes can introduce a large amount of data that must be processed, transmitted, and received [3]. Despite their profound advantages, the utilization of WSNs is often battery-powered and strictly limited due to energy constraints. In fact, most of the energy expenditure of a sensor node occurs during wireless communication, and the remaining energy is consumed during sensing and data processing. The radio transceiver of a node consumes a significant amount of energy during data transmission and reception. Therefore, the energy consumption of a node must be carefully managed to ensure the longevity of the network [4]. Transmission power adjustment for a special transmitter-receiver pair depends on several environmental conditions. The transmission power required to reach the receiver is affected by two main factors including distance and wireless connection quality. Distance affects transmission power. As the distance between the transmitter and receiver increases, the signal strength decreases, resulting in a weaker connection.
Conclusions and future works
We studied the transmission range of sensors in wireless sensor networks in this study, and proposed a simple yet effective distributed transmission power control algorithm for sparse wireless sensor networks. We showed that this algorithm has advantages in many cases. The main idea of algorithm is simple, but performance of algorithm in targeted scenarios is excellent. We analyzed the performance of this method theoretically and show that using this method is worthwhile in some scenarios, especially in sparse wireless sensor networks with high amount of communication traffic. In these scenarios, our method can be used as a pre-step of any routing algorithm and decrease energy consumption and radio interference. As a future work, we intend to design methods to overcome the collisions problem. Designing methods for resisting the algorithm against the sensor failures is another future work. Furthermore we intend to work on using learning methods like Reinforcement learning for power control. Designing a good energy efficient reward mechanism is one of the main challenges of these methods.