مسیریابی شبکه های حسگر بی سیم با استفاده از خوشه بندی مبتنی بر الگوریتم بهینه سازی ازدحام ذرات چندهدفه
محورهای موضوعی : انرژی های تجدیدپذیر
سید رضا نبوی
1
,
نفیسه اوسطی عراقی
2
,
جواد اکبری ترکستانی
3
1 - گروه مهندسی کامپیوتر- واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران
2 - گروه مهندسی کامپیوتر- واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران
3 - گروه مهندسی کامپیوتر- واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران
کلید واژه: شبکه‌های حسگر بی‌سیم, مسیریابی آگاه از انرژی, الگوریتم بهینه‌سازی ازدحام ذرات چندهدفه,
چکیده مقاله :
در سال های اخیر، با گسترش کاربردهای شبکه های حسگر بی سیم، بهره‌برداری از این نوع شبکه ها به منظور رسیدگی بر محیط و تحلیل داده های جمع آوری شده از محیط های خاص و متنوع بسیار رواج یافته است. شبکه های حسگر بی سیم با توجه به سهولت پیکربندی و عدم نیاز به تجهیزات گران قیمت، یکی از بهترین گزینه ها برای جمع آوری داده ها از محیط هستند. انرژی گره های حسگر در شبکه های حسگر بی سیم محدود است که با توجه به عدم وجود منبع شارژ ثابت یکی از چالش های اساسی است که با آن مواجه می‌شویم. از آن جایی که بیشترین مقدار انرژی گره ها در طی انتقال داده ها اتلاف می شود، گره ای که بیشتر از بقیه به انتقال داده ها بپردازد و یا بسته های داده ای را در فواصل طولانی انتقال دهد، انرژی آن زودتر از بقیه به اتمام می رسد. با اتمام انرژی یک حسگر در شبکه ممکن است در روند کار شبکه اختلال ایجاد شود. بنابراین، با توجه به توپولوژی پویا و طبیعت توزیع‌شده شبکه‌های حسگر بی‌سیم، طراحی پروتکل‌های انرژی کارآمد برای مسیریابی یکی از چالش‌های اصلی است. ازاین‌رو در این مقاله پروتکل مسیریابی آگاه از انرژی براساس الگوریتم بهینه سازی ازدحام ذرات چندهدفه ارائه شده است. در رویکرد پیشنهادی تابع شایستگی الگوریتم بهینه سازی ازدحام ذرات برای انتخاب گره سرخوشه بهینه براساس هدف های کیفیت خدمات شامل انرژی باقیمانده، کیفیت پیوند، تأخیر انتها به انتها و نرخ تحویل استفاده شده است. نتایج شبیه سازی نشان می دهد روش پیشنهادی با توجه به ایجاد توازن در اهداف معیارهای کیفیت خدمات، نسبت به سایر روش های موجود اتلاف انرژی کمتر و طول عمر بیشتری دارد.
With the spread of applications of wireless sensor networks, in recent years, the use of this type of network in order to monitor the environment and analyze data collected from specific environments in a variety of ways has become very common. Wireless sensor networks are one of the best options for collecting data from the environment due to their easy configuration and no need for expensive equipment. The energy of sensors in wireless sensor networks is limited, which is a major challenge due to the lack of a fixed charge source. Because most of the sensors' energy is wasted during data transmission, a sensor that transmits more data than others and transmits data over long distances with packets will run out of energy sooner than others. When a sensor in the network runs out of energy, the network process may be disrupted. Therefore, due to the dynamic topology and distributed nature of wireless sensor networks, designing energy efficient routing protocols is one of the main challenges. Therefore, in this article, energy-aware routing protocol based on multi-objective particle swarm optimization algorithm is presented. In the proposed approach, the fitness function of the particle swarm optimization algorithm for selecting the optimal cluster head based on quality-of-service goals including residual energy, link quality, end-to-end delay and delivery rate. The simulation results show that the proposed approach has less energy consuming and extend network lifetime due to balancing the goals of quality-of-service criteria than other approaches.
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[1] S. Adhyapok, H.K.D. Sarma, “Review on QoS aware routing protocols for multi-channel wireless sensor network”, Proceeding of the IEEE/ICIMIA, pp. 503–509, Bangalore, India, March 2020 (doi: 10.1109/ICIMIA48430.2020.9074932).
[2] W. Lu, H. Zhao, H. Zhao, “Distributed energy balancing routing algorithm in wireless sensor networks”, Springer, Berlin, Heidelberg, pp. 227–232, Feb. 2012.
[3] N. Z. Cedeno, O.P. Asqui, E.E. Chaw, “The performance of QoS in wireless sensor networks”, Proceeding of the IEEE/CISTI, pp. 1-5, Coimbra, Portugal, June 2019 (doi: 10.23919/CISTI.2019.8760756).
[4] C.W. Tsai, T.P. Hong, G.N. Shiu, “Metaheuristics for the lifetime of WSN: A review”, IEEE Sensors Journal, vol. 16, no. 9, pp. 2812–2831, May 2016 (doi: 10.1109/JSEN.2016.2523061).
[5] A. Jari, A. Avokh, “PSO-based sink placement and load-balanced anycast routing in multi-sink WSNs considering compressive sensing theory”, Engineering Applications of Artificial Intelligence, vol. 100, p. 104164, Apr. 2021 (doi: 10.1016/j.engappai.2021.104164).
[6] S. Tian, Y. Li, Y. Kang, J. Xia, “Multi-robot path planning in wireless sensor networks based on jump mechanism PSO and safety gap obstacle avoidance”, Future Generation Computer Systems, vol. 118, pp. 37–47, May 2021 (doi: 10.1016/j.future.2020.12.012).
[7] S. P. Tirani, A. Avokh, S. Azar, “WDAT-OMS: A two-level scheme for efficient data gathering in mobile-sink wireless sensor networks using compressive sensing theory”, IET Communications, vol. 14, no. 11, pp. 1826–1837, Jul. 2020 (doi: 10.1049/iet-com.2019.0433).
[8] S. Prithi, S. Sumathi, “LD2FA-PSO: A novel learning dynamic deterministic finite automata with PSO algorithm for secured energy efficient routing in wireless sensor network”, Ad Hoc Networks, vol. 97, p. 102024, Feb. 2020 (doi: 10.1016/j.adhoc.2019.102024).
[9] K. Vijayalakshmi, P. Anandan, “A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN”, Cluster Computing, vol. 22, no. 5, pp. 12275–12282, Sep. 2019 (doi: 10.1007/s10586-017-1608-7).
[10] F. Gao, W. Luo, X. Ma, “Energy constrained clustering routing method based on particle swarm optimization”, Cluster Computing, vol. 22, no. 3, pp. 7629–7635, May 2019 (doi: 10.1007/s10586-018-2339-0).
[11] E. Rezaei, S. Ghasemi, “Energy-aware data aggregation in wireless sensor networks using particle swarm optimization algorithm”, American Journal of Information Science and Computer Engineering, vol. 4, no. 1, pp. 1–6, 2018.
[12] M. Challa, M. D. Reddy, P. Venkata, S. Reddy, “Energy aware WSN transmission and network lifetime improvement using particle swarm based distance error optimization”, International Journal of Applied Engineering Research, vol. 13, no. 11, pp. 9218–9227, 2018.
[13] P. C. S. Rao, P. K. Jana, H. Banka, “A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks”, Wireless Networks, vol. 23, no. 7, pp. 2005–2020, Oct. 2017 (doi: 10.1007/s11276-016-1270-7).
[14] Y. Zhou, N. Wang, W. Xiang, “Clustering hierarchy protocol in wireless sensor networks using an improved PSO algorithm”, IEEE Access, vol. 5, pp. 2241–2253, 2017 (doi: 10.1109/ACCESS.2016.2633826).
[15] B. Singh, D. K. Lobiyal, “A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks”, Human-centric Computing and Information Sciences, vol. 2, no. 1, pp. 1–18, Dec. 2012 (doi: 10.1186/2192-1962-2-13).
[16] D. Wu, S. Geng, X. Cai, G. Zhang, and F. Xue, “A many-objective optimization WSN energy balance model”, KSII Transactions on Internet and Information Systems, vol. 14, no. 2, pp. 514–537, 2020 (doi: 10.3837/tiis.2020.02.003).
[17] I. S. Akila, R. Venkatesan, “A cognitive multi-hop clustering approach for wireless sensor networks”, Wireless Personal Communications, vol. 90, no. 2, pp. 729–747, Sep. 2016 (doi: 10.1007/s11277-016-3200-5).
[18] M. Habib, I. Aljarah, H. Faris, S. Mirjalili, “Multi-objective particle swarm optimization: Theory, Literature Review, and Application in feature selection for medical diagnosis”, Springer, Singapore, 2020, pp. 175–201.
[19] C. A. Coello Coello, M. S. Lechuga, “MOPSO: A proposal for multiple objective particle swarm optimization”, Proceedings of the 2002 Congress on Evolutionary Computation, pp. 1051–1056, Honolulu, USA, May 2002 (doi: 10.1109/CEC.2002.1004388).
[20] C. Saranya, G. Manikandan, “A study on normalization techniques for privacy preserving data mining”, vol. 5, pp. 2701–2704, Jun. 2013.
[21] “A review of spatial interpolation methods for environmental scientists - datasets - data.gov.au.” https://data.gov.au/data/dataset/a-review-of-spatial-interpolation-methods-for-environmental-scientists, (accessed Jan. 11, 2021).
[22] T. Kaur, D. Kumar, “MACO-QCR: Multi-objective ACO based QoS-aware cross-layer routing protocols in WSN”, IEEE Sensors Journal, p. 1, 2020 (doi: 10.1109/JSEN.2020.3038241).