خوشه بندی کارآمد انرژی در شبکه‌های چند گامی حسگر بی‌سیم با استفاده از الگوریتم بهینه‌سازی ازدحام ذرات چندهدفه | ||
| پژوهش های نظری و کاربردی هوش ماشینی | ||
| مقاله 5، دوره 2، شماره 2، اسفند 1403، صفحه 67-81 اصل مقاله (1015.67 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/abmir.2025.22425.1077 | ||
| نویسندگان | ||
| محمدمهدی حسینی* ؛ حامد زرگری | ||
| استادیار، دانشکده فنی و مهندسی، واحد شاهرود، دانشگاه آزاد اسلامی، شاهرود، ایران | ||
| چکیده | ||
| یکی از چالش‌های اصلی در شبکه‌های حسگر بی‌سیم (WSN)، طراحی روشی بهینه برای انتقال اطلاعات از گره‌های شبکه به ایستگاه پایه با مصرف انرژی کمتر است. به همین منظور در این مقاله یک روش خوشه‌بندی مبتنی بر بهینه‌سازی اجتماع ذرات چند هدفه (MOPSO) برای افزایش کارایی انرژی در این شبکه‌ها ارائه‌شده است. درروش پیشنهادی، از یک الگوریتم بهینه‌سازی ازدحام ذرات دودویی برای مکان‌یابی گره توزیع‌شده در شبکه‌های حسگر بی‌سیم استفاده‌شده و یک مدل مسیریابی چند قیدی با تبدیل محدودیت‌ها به تابع پنالتی، طراحی گردیده است. همچنین با الهام از ایده انتخاب طبیعی و جهش الگوریتم ژنتیک (GA)، تنوع ذرات در الگوریتم MOPSO افزایش داده‌شده است. نتایج شبیه‌سازی نشان می‌دهد که روش پیشنهادی در مقایسه با سایر روش‌ها، توانسته زمان محاسباتی را به‌اندازه 8/59 درصد کاهش دهد. همچنین، نرخ موفقیت مکان‌یابی، که به درصد گره‌هایی اشاره دارد که مکان آن‌ها به‌درستی تشخیص داده‌شده است، تا 54 درصد افزایش‌یافته است. بااین‌حال، میزان خطای مکان‌یابی، که نشان‌دهنده انحراف میان مکان تخمین‌زده‌شده و مکان واقعی گره‌ها است، به میزان 2/3 درصد بیشتر شده است. این بهبودها منجر به افزایش طول عمر شبکه و کارایی انرژی در شبکه‌های حسگر بی‌سیم، حتی با وجود افزایش جزئی در خطای مکان‌یابی شده است. | ||
| کلیدواژه‌ها | ||
| خوشه‌بندی؛ مکان‌یابی؛ بهینه‌سازی ازدحام ذرات؛ شبکه‌های حسگر بی‌سیم؛ یکپارچه‌سازی داده‌ها | ||
| عنوان مقاله [English] | ||
| An Energy Efficient Clustering in multi-hop WSN Using a Swarm Optimization Algorithm | ||
| نویسندگان [English] | ||
| Mohammad Mehdi Hosseini؛ Hamed Zargari | ||
| Assistant Professor, Department of Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran | ||
| چکیده [English] | ||
| One of the major challenges in wireless sensor networks is how to transfer information from nodes within the network to the base station and choose the best possible route to transmit this information. Choosing the best route can be affected based on various factors such as energy consumption, response speed and latency, data transfer accuracy, and so on. Our goal is to choose the best route in terms of energy consumption. in this paper, an optimal method using tools such as multi-objective particle swarm optimization algorithm, environment segmentation, and node location search is presented to increase energy efficiency. In the proposed method, a binary particle swarm optimization algorithm is used to locate the nodes distributed in the wireless sensor networks and uses the positioning error and computation time during the simulations as a performance criterion. In the proposed method to meet the different needs of the users regarding the QoS network, a multi-constrained QoS routing model is launched and the fit function is constructed by converting the QoS constraints to the penalty function. We also introduced the ideas of natural selection and GA mutation for PSO to improve the performance of the PSO algorithm, which led to a greater variety of particles. The results show that the proposed method can solve the CAB examples very effectively and efficiently and provide better computational results than the PSO. The simulation results showed that in the proposed method the computational time required for locating decreased by 59.8% and the locating error increased by 3.2%. By reducing the computational time for locating, the energy saving and lifetime of the wireless sensor network could be increased. It should be noted, however, that the success rate of the algorithm also increased by about 36% to 54% in algorithms with similar structures. | ||
| کلیدواژه‌ها [English] | ||
| Clustering, Data Integration, Localization, Particle Swarm Optimization, Wireless Sensor Networks | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 93 تعداد دریافت فایل اصل مقاله: 70 |
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