Akıllı ve Güvenli Ev İçin Şeylerin İnterneti Tabanlı Zigbee Sniffer

Yıl 2022, Cilt: 6 Sayı: 1, 45 - 65, 30.06.2022

Farah Shakir Galip Cansever

https://doi.org/10.53600/ajesa.983849

Öz

Bu araştırmanın amacı, IoT’de kümeleme yardımı ile ZigBee Protokolünde yüksek spektrum tahsisli enerji veya güç kullanımını belirlemektir. Bu araştırma, geniş bant şebeke enerji sektörüne ve karşı karşıya olduğu zorluklara genel bir bakış sunmaya başlar. Enerji verimliliğini teşvik eden, tüketicinin aktif rolünü teşvik eden, tüketici davranışlarının önemini anlatan ve tüketici haklarını koruyan enerji politikalarında bir değişiklik gözlemlenmektedir. Elektrik, enerji kaynağı olarak yer kazanmakta olup, önümüzdeki on yıllarda da payı sürekli artmaya devam edecektir. Bu enerji tüketimi segmentasyonunun arkasındaki amaç, enerji tüketimini ve ilgili maliyetlerini azaltmak, enerji verimliliği önlemlerini teşvik etmek ve tüketici katılımını geliştirmek için her gruba kişiselleştirilmiş öneriler sunabilmektir. İstenen segmentasyon, hesaplamalı kümeler hesaplamasına (python programlama dili kullanılarak) dayanan yinelemeli bir süreçle elde edilir ve enerji tüketimini tespit etmek ve bunları daha uygun bir gruba yeniden tahsis etmek için görselleştirme ve istatistiksel veri madenciliği tekniğini uygulayan bir kümeleme sonrası analizi ile sonuçlandırılır. K-Means kümeleme tekniği test edildi ve karşılaştırıldı, 100GHz yüksek spektrumlu tüm enerji yükü profilleri için en iyi doğruluk tahminini %98,46 verdi. K-Means kümelemesinden elde edilen çözüm, nihai enerji tüketimi segmentasyonunu elde etmek için kümeleme sonrası aşamanın temeli olarak kullanılan, aranan segmentasyona daha iyi uyum sağlayan çözümdür. Bu metodolojilerin çoğu, daha yüksek enerji tasarrufu potansiyeline sahip kullanıcıları belirlemeye odaklandıkları için 100 kWh cinsinden mutlak değerleri kullanır. Bu durumda, enerji tasarrufu tavsiyelerinin ZigBee protokolünün belirli özelliklerine göre kişiselleştirilmesine, uygun zamanda yeterli tavsiyeyi sunarak tüketici deneyiminin iyileştirilmesine, enerji verimliliğinin etkinliğini artıran gerçeklere, geleceğe yönelik tavsiyelerin hizmetine izin verir.

Anahtar Kelimeler

enerji, Nesnelerin İnterneti (IoT), K-Means, ZigBee Protokolü, Kümeleme, Tüketim, Akıllı, İzleme

Internet Of Things Based Zigbee Sniffer For Smart And Secure Home

Öz

The aim of this research is to determine the usage of energy or power with high spectrum allocation in ZigBee Protocol with the help of clustering in IoT. This research starts presenting an overview of the broadband network energy sector and the challenges that are facing. It is observed a change on the energy policies promoting the energy efficiency, encouraging an active role of the consumer, instructing them about the importance of the consumer behavior and protecting consumer rights. Electricity is gaining room as energy source; its share will keep increasing constantly in the following decades. The objective behind this energy consumption segmentation is to be able to provide personalized recommendations to each group in order to reduce their energy consumption and the associated costs, fostering energy efficiency measures and improving the consumer engagement. The desired segmentation is obtained by an iterative process, based on computational clusters calculation (using python programming language) and finalized by a post-clustering analysis applying visualization and statistical data mining technique to detect the energy consumption and reallocate them to a more appropriate group. The K-Means clustering technique was tested and compared, giving the best prediction of accuracy 98.46% for all energy load profiles with high spectrum of 100GHz. The solution from the K-Means clustering is the one that better adapts to the segmentation sought, which is used as the base of the post-clustering stage to obtain the final energy consumption segmentation. Most of these methodologies use the absolute values in 100 kWh, as they were more focused on identify the users with higher energy savings potential. In this case, it allows personalizing energy savings recommendations according to the specific characteristics of ZigBee protocol, improving the consumer experience by being able to provide the adequate advice at the appropriate time, facts that increase the effectiveness of the energy efficiency advises’ service for future ZigBee protocol.

Anahtar Kelimeler

Energy, Internet of Things (IoT), K-Means, ZigBee Protocol, Clustering, Consumption, Smart, Monitoring

Kaynakça

• Ahmed, M.A., Y.C. Kang, and Y.-C. Kim. 2015. Communication Network Architectures for Smart-House with Renewable Energy Resources. Energies, 8, 8716–8735.
• Aslan, J., K. Mayers, J.G. Koomey, and C. France. 2017. Electricity Intensity of Internet Data Transmission: Untangling the Estimates. J. Ind. Ecole, p 1–14
• Baliyan, A., K. Gaurav, and S.K. Mishra. 2015. A Review of Short-Term Load Forecasting using Artificial Neural Network Models. Procedia Computer. Sci., 48, 121–125.
• Becirovic, S., and S. Mrdovic. 2019. Manual IoT Forensics of a Samsung Gear S3 Frontier Smartwatch. In Proceedings of the 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, pp. 1–5.
• Björnson, E., L. Sanguinetti, J. Hoydis, and M. Debbah. 2015. Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer? IEEE Trans. Wire. Commun., 14, 3059–3075.
• Bouktif, S., A. Fiaz, A. Ouni, and M. Serhani. 2018. Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches. Energies, 11, 1636
• Bradac, Z., V. Kaczmarczyk, and P. Fiedler. 2015. Optimal Scheduling of Domestic Appliances via MILP. Energies, 8, 217–232.
• Collotta, M., and G.A. Pau. 2015. Novel Energy Management Approach for Smart Homes Using Bluetooth Low Energy. IEEE J. Sel. Areas Commun. 33, 2988–2996.
• Ge, X., J. Yang, H. Gharavi, and Y. Sun. 2017. Energy Efficiency Challenges of 5G Small Cell Networks. IEEE Commun. Mag, 55, 184–191.
• Jaihar, J, L. Neehal, S.V. Patel, V. Gautam, and K.P. Upla. 2020. Smart Home Automation Using Machine Learning Algorithms. 1-4. 10.1109/INCET49848.2020.9154007.
• Kruger, J.-L., and H. Venter. 2019. Requirements for IoT Forensics. In Proceedings of the 2019 Conference on Next Generation Computing Applications (Next Comp), Mauritius, pp. 1–7.
• Marino, D., K. Amarasinghe, and M. Manic. 2016. Building Energy Load Forecasting using Deep Neural Networks. In Proceedings of the IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy.
• Niaz, M.T., F. Imdad, and H.S. Kim. 2017. Power Consumption Efficiency Evaluation of Multi-User FullDuplex Visible Light Communication Systems for Smart Home Technologies. Energies 10, 254.
• Pascual, J., P. Sanchis, and L. Marroyo. 2014. Implementation and Control of a Residential Electrothermal Microgrid Based on Renewable Energies, a Hybrid Storage System and Demand Side Management. Energies, 210–237.
• Peng, Y., J. Peng, J. Li, and L. Yu. 2019. Smart Home System Based on Deep Learning Algorithm. Journal of Physics: Conference Series. 1187. 032086. 10.1088/1742-6596/1187/3/032086.
• Stoyanova, M., Y. Nikoloudakis, S. Panagiotakis, E. Pallis, and E.K. Markakis. 2020. A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches and Open Issues. IEEE Commun. Surv. Tutor, 22, 1191–1221.
• Servida, F., and E. Casey. 2019. IoT forensic challenges and opportunities for digital traces. Digit. Investig, 28, 22–29.
• Wu, G., C. Yang, S. Li, G.Y. Li. 2015. Recent advances in “energy-efficient networks and their application in 5G systems. IEEE Wire. Commun., 22, 145–151.
• Yaqoob, I., I.A.T. Hashem, A. Ahmed, S.A. Kazmi, and C.S. Hong. 2019. Internet of things forensics: Recent advances, taxonomy, requirements, and open challenges. Future Gener. Computer. Syst., 92, 265–275.
• Zulkipli, N.H., A. Alenezi, and G.B. Wills. 2017. IoT Forensic: Bridging the Challenges in Digital Forensic and the Internet of Things. In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security (IoTBDS 2017), Porto, Portugal; pp. 315–324