Pemilihan Prioritas Layanan Qos dengan Pendekatan Metode Fuzzy Analytical Hierarchy Process (Fahp) dan Topsis

Internet of Things dikenal juga dengan singkatan IoT merupakan sebuah konsep yang bertujuan untuk memperluas manfaat dari konektivitas internet yang tersambung secara terus-menerus. Untuk mendukung iot diperlukan analisa QoS yang baik. Quality of Service (QoS) didefinisikan sebagai suatu pengukuran tentang seberapa baik jaringan komputer memberikan layanan. QoS didesain untuk membantu end user menjadi lebih produktif dengan memastikan bahwa end user mendapatkan performansi yang handal dari aplikasi berbasis jaringan. Metode FAHP (Fuzzy Analytical Hierarchy Process) digunakan untuk mendapatkan bobot dari beberapa kriteria yang telah ditentukan. Setelah menentukan bobot, selanjutnya data diolah menggunakan model keputusan yaitu TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Metode ini digunakan untuk memberikan penilaian alternatif yang akan dipilih berdasarkan bobot dengan peringkat. Kedua metode dikombinasikan untuk menghasilkan suatu proses sesuai dengan kriteria yang diinginkan. Integrasi pendekatan FAHP dan TOPSIS dapat memberikan bobot yang sesuai kriteria dan memberikan hasil seleksi yang baik. Adapun hasilnya dari beberapa access point di LAB yang dipantau menunjukkan nilai rata rata QoS hasil uji verifikasi berkisar pada 86% setelah dilakukan perbaikan instalasi

Mobile edge computing-based data-driven deep learning framework for anomaly detection

5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the network. High false alarms and inadequate accuracy are the major limitations of modern approaches for the anomaly—outage and sudden hype in traffic activity that may result in congestion—detection in mobile cellular networks. This indicates wasting limited resources that ultimately leads to an elevated operational expenditure (OPEX) and also interrupting quality of service (QoS) and quality of experience (QoE). Motivated by the outstanding success of deep learning (DL) technology, our study applies it for detection of the above-mentioned anomalies and also supports mobile edge computing (MEC) paradigm in which core network (CN)’s computations are divided across the cellular infrastructure among different MEC servers (co-located with base stations), to relief the CN. Each server monitors user activities of multiple cells and utilizes $L$ -layer feedforward deep neural network (DNN) fueled by real call detail record (CDR) dataset for anomaly detection. Our framework achieved 98.8% accuracy with 0.44% false positive rate (FPR)—notable improvements that surmount the deficiencies of the old studies. The numerical results explicate the usefulness and dominance of our proposed detector

Artificial intelligence-powered mobile edge computing-based anomaly detection in cellular networks

Escalating cell outages and congestion-treated as anomalies-cost a substantial revenue loss to the cellular operators and severely affect subscriber quality of experience. Stateof-the-art literature applies feed-forward deep neural network at core network (CN) for the detection of above problems in a single cell; however, the solution is impractical as it will overload the CN that monitors thousands of cells at a time. Inspired from mobile edge computing and breakthroughs of deep convolutional neural networks (CNNs) in computer vision research, we split the network into several 100-cell regions each monitored by an edge server; and propose a framework that pre-processes raw call detail records having user activities to create an image-like volume, fed to a CNN model. The framework outputs a multilabeled vector identifying anomalous cell(s). Our results suggest that our solution can detect anomalies with up to 96% accuracy, and is scalable and expandable for industrial Internet of things environment