TY - JOUR T1 - Online Planning for Data Collection in Multi-Robot Systems TT - Çok-Robotlu Sistemlerde Veri Toplama için Çevrimiçi Planlama AU - Özsoyeller, Deniz PY - 2025 DA - May Y2 - 2024 DO - 10.21205/deufmd.2025278016 JF - Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi JO - DEUFMD PB - Dokuz Eylul University WT - DergiPark SN - 1302-9304 SP - 290 EP - 295 VL - 27 IS - 80 LA - en AB - Wireless sensors networks have been used for data collection in various civil and military applications. We consider a system where a group of mobile robots and a set of stationary wireless sensor nodes are sparsely deployed in a large unbounded area. In such scenarios, all sensor nodes may not be connected via a communication network. Furthermore, no pair of sensor nodes may be within the transmission range of each other. Therefore, many relay nodes are needed to guarantee the connectivity of the network. However, this approach will affect the lifetime of the system due to the energy consumption by data transmission. In this paper, we study the problem of data collection from the deployed sensors utilizing the robots. The robots do not know the locations of each other and the sensor nodes. Moreover, the sensor nodes do not know the locations of each other and the robots. We propose an online algorithm in which the robot explores the area to find the sensor nodes and collect their stored data. Depending on whether the number of robots is known by the robots in advance or not, we investigate and compare two cases of the problem. In simulations, we empirically evaluate the performance of our algorithm and show that it quantifies as a function of the environment size, the number of robots, and the communication range when both the number of robots and the number of sensors are not known in advance. KW - Data Collection KW - Online Planning KW - Multi-Robot Systems N2 - Kablosuz sensör ağları, çeşitli sivil ve askeri uygulamalarda veri toplamak için kullanılmıştır. Bir grup hareketli robotun ve bir küme hareketsiz kablosuz sensör düğümünün sınırsız geniş bir alanda aralıklı olarak konuşlandırıldığını düşünüyoruz. Bu gibi senaryolarda, tüm sensör düğümleri bir iletişim ağı ile bağlı olmayabilir. Buna ek olarak, birbirinin iletişim ağı içinde olan herhangi bir sensör düğümü çifti bulunmayabilir. Bu nedenle, ağ bağlantısının sağlanması için birçok aktarma düğümüne ihtiyaç vardır. Fakat, bu yaklaşım, veri iletiminden kaynaklanan enerji tüketiminden dolayı sistemin ömrünü etkiler. Bu makalede, robotlardan faydalanarak konuşlandırılmış olan sensör düğümlerinden veri toplama problemini çalışıyoruz. Robotlar, birbirlerinin ve sensör düğümlerin konumunu bilmezler. Dahası, sensör düğümleri de birbirlerinin ve robotların konumunu bilmezler. Robotların sensör düğümleri bulmak için alanda keşif yaptığı ve düğümlerdeki veriyi topladığı çevrimiçi bir algoritma öneriyoruz. Robotların, robot sayısını önceden bilip bilmediğine bağlı olarak problemin iki durumunu inceliyor ve karşılaştırıyoruz. Simülasyonlarla, algoritmamızın performansını deneysel olarak değerlendiriyor ve robotun, sensör düğümü ve robot sayısını önceden bilmediği durumda performansın alan boyutu, robot sayısı ve iletişim alanının bir fonksiyonu olarak ölçeklendiğini gösteriyoruz. CR - [1] Yick, J., Mukherjee, B., Ghosal, D., 2008. Wireless sensor network survey. Computer Networks, Vol.52(12), pp.2292–2330. DOI: 10.1016/j.comnet.2008.04.002. CR - [2] Hu, Y., Zhang, F., Tian, T., Ma, D., Shi, Z., 2022. Shortest path planning of a data mule in wireless sensor networks. Wireless Networks, Vol.28(3), pp.1129–1145. DOI: 10.1007/s11276-022-02891-4. CR - [3] Bhadauria, D., Tekdas, O., Isler, V., 2011. Robotic data mules for collecting data over sparse sensor fields. Journal of Field Robotics, Vol.28(3), pp.388–404. DOI: 10.1002/rob.20384. CR - [4] Yedidsion, H., Ashur, S., Banik, A., Carmi, P., Katz, M.J., Segal, M., 2020. Sensor network topology design and analysis for efficient data gathering by a mobile mule. Algorithmica, Vol.82, pp.2784–2808. DOI: 10.1007/s00453-020-00704-8. CR - [5] Ma, M., Yang, Y., Zhao, M., 2013. Tour planning for mobile data gathering mechanisms in wireless sensor networks. IEEE Transactions on Vehicular Technology, Vol.62(4), pp.1472–1483. DOI: 10.1109/TVT.2012.2229309. CR - [6] Bhadauria, D., Isler, V., 2009. Data gathering tours for mobile robots. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.3868–3873. DOI: 10.1109/IROS.2009.5354343. CR - [7] Chang, J.-Y., Jeng, J.-T., Sheu, Y.-H., Jian, Z.-J., Chang, W.-Y., 2020. An efficient data collection path planning scheme for wireless sensor networks with mobile sinks. EURASIP Journal on Wireless Communications and Networking, Vol.2020(1), p.257. DOI: 10.1186/s13638-020-01873-4. CR - [8] Chen, T.-C., Chen, T.-S., Wu, P.-W., 2011. On data collection using mobile robot in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, Vol.41(6), pp.1213–1224. DOI: 10.1109/TSMCA.2011.2157132. CR - [9] Das, A., Mazumder, A., Sen, A., Mitton, N., 2016. On mobile sensor data collection using data mules. In: 2016 International Conference on Computing, Networking and Communications (ICNC), pp.1–7. DOI: 10.1109/ICCNC.2016.7440562. CR - [10] Tsilomitrou, O., Tzes, A., 2022. Mobile data-mule optimal path planning for wireless sensor networks. Applied Sciences, Vol.12(1), p.247. DOI: 10.3390/app12010247. CR - [11] Papatheodorou, S., Smyrnakis, M., Hamidou, T., Tzes, A., 2018. Path planning and task assignment for data retrieval from wireless sensor nodes relying on game-theoretic learning. In: 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT), pp.1073–1078. DOI: 10.1109/CoDIT.2018.8394924. CR - [12] Nguyen, M.T., Nguyen, C.V., Do, H.T., Hua, H.T., Tran, T.A., Nguyen, A.D., Ala, G., Viola, F., 2021. UAV-assisted data collection in wireless sensor networks: A comprehensive survey. Electronics, Vol.10(21), p.2603. DOI: 10.3390/electronics10212603. CR - [13] Gul, O.M., Erkmen, A.M., Kantarci, B., 2022. UAV-driven sustainable and quality-aware data collection in robotic wireless sensor networks. IEEE Internet of Things Journal, Vol.9(24), pp.25150–25164. DOI: 10.1109/JIOT.2022.3195677. CR - [14] Wu, Q., Sun, P., Boukerche, A., 2019. Unmanned aerial vehicle-assisted energy-efficient data collection scheme for sustainable wireless sensor networks. Computer Networks, Vol.165, p.106927. DOI: 10.1016/j.comnet.2019.106927. CR - [15] Gul, O.M., Erkmen, A.M., 2023. Energy-aware UAV-driven data collection with priority in robotic wireless sensor network. IEEE Sensors Journal, Vol.23(15), pp.17667–17675. DOI: 10.1109/JSEN.2023.3286877. CR - [16] Luo, C., Chen, W., Li, D., Wang, Y., Du, H., Wu, L., Wu, W., 2021. Optimizing flight trajectory of UAV for efficient data collection in wireless sensor networks. Theoretical Computer Science, Vol.853, pp.25–42. DOI: 10.1016/j.tcs.2020.05.019. CR - [17] Wu, Q., Sun, P., Boukerche, A., 2018. An energy-efficient UAV-based data aggregation protocol in wireless sensor networks. In: Proceedings of the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, pp.34–40. DOI: 10.1145/3272036.3272047. CR - [18] Alfattani, S., Jaafar, W., Yanikomeroglu, H., Yongacoglu, A., 2019. Multi-UAV data collection framework for wireless sensor networks. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp.1–6. DOI: 10.1109/GLOBECOM38437.2019.9014306. CR - [19] Chen, M., Liang, W., Li, Y., 2020. Data collection maximization for UAV-enabled wireless sensor networks. In: 29th International Conference on Computer Communications and Networks (ICCCN), pp.1–9. DOI: 10.1109/ICCCN49398.2020.9209619. CR - [20] Bouhamed, O., Ghazzai, H., Besbes, H., Massoud, Y., 2020. A UAV-assisted data collection for wireless sensor networks: Autonomous navigation and scheduling. IEEE Access, Vol.8, pp.110446–110460. DOI: 10.1109/ACCESS.2020.3002538. CR - [21] Rezende, J.d.C.V., Silva, R.I.d., Souza, M.J.F., 2020. Gathering big data in wireless sensor networks by drone. Sensors, Vol.20(23), p.6954. DOI: 10.3390/s20236954. CR - [22] Chaudhary, M., Goyal, N., Benslimane, A., Awasthi, L.K., Alwadain, A., Singh, A., 2022. Underwater wireless sensor networks: enabling technologies for node deployment and data collection challenges. IEEE Internet of Things Journal, Vol.10(4), pp.3500–3524. DOI: 10.1109/JIOT.2022.3218766. CR - [23] Wei, X., Guo, H., Wang, X., Wang, X., Qiu, M., 2021. Reliable data collection techniques in underwater wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, Vol.24(1), pp.404–431. DOI: 10.1109/COMST.2021.3134955. CR - [24] Liu, Z., Meng, X., Liu, Y., Yang, Y., Wang, Y., 2021. AUV-aided hybrid data collection scheme based on value of information for internet of underwater things. IEEE Internet of Things Journal, Vol.9(9), pp.6944–6955. DOI: 10.1109/JIOT.2021.3115800. CR - [25] Khan, W., Hua, W., Anwar, M.S., Alharbi, A., Imran, M., Khan, J.A., 2022. An effective data-collection scheme with AUV path planning in underwater wireless sensor networks. Wireless Communications and Mobile Computing, Vol.2022(1), p.8154573. DOI: 10.1155/2022/8154573. CR - [26] Huang, M., Zhang, K., Zeng, Z., Wang, T., Liu, Y., 2020. An AUV-assisted data gathering scheme based on clustering and matrix completion for smart ocean. IEEE Internet of Things Journal, Vol.7(10), pp.9904–9918. DOI: 10.1109/JIOT.2020.2988035. CR - [27] Ozsoyeller, D., Isler, V., Beveridge, A., 2012. Symmetric rendezvous in planar environments with and without obstacles. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol.26, pp.2046–2052. DOI: 10.1609/aaai.v26i1.8385. UR - https://doi.org/10.21205/deufmd.2025278016 L1 - https://dergipark.org.tr/en/download/article-file/4189800 ER -