Real-time monitoring of the prototype design of electric system by the ubidots platform

In this paper, a prototype DC electric system was practically designed. The idea of the proposed system was derived from the microgrid concept. The system contained two houses each have a DC generator and load that consists of four 12 V DC lamps. Each house is controlled fully by Arduino UNO microcontroller to work in Island mode or connected it with the second house or main electric network. House operating mode depends on the power generated by its source and the availability of the main network. Under all operating cases, the minimum price of electricity consumption should satisfy as possible. Information between the houses about the operating mode and the main network state was exchanging wirelessly with the help of the RF-HC12. This information uploaded to the Ubidots platform by the Wi-Fi-ESP8266 included in the node MCU microcontroller. This platform has several advantages such as capture, visualization, analysis, and management of data. The system was examined for different cases to verify its working by varying the load in each building. All tested states showed that the houses transfer from one mode to another automatically with high reliability and minimum energy cost. The information about the main grid states and the sources of the houses were monitored and stored at the Ubidots platform

Analisis Insider Threat pada Sistem Keamanan Rumah Cerdas Menggunakan Malicious Traffic Monitoring

Ancaman serangan siber semakin banyak dan kompleks, berdasarkan catatan Badan Siber dan Sandi Negara (BSSN) bahwa di Indonesia pada tahun 2022 terdapat anomali trafik atau malicious traffic ratusan juta. Berdasarkan sumber ancaman maka dapat serangan siber dapat dikategorikan serangan siber yang bersumber dari internal (insider threat) dan serangan siber yang bersumber dari luar (outsider threat). Saat ini serangan siber tidak hanya dari luar atau outsider karena serangan siber dapat bersumber dari perangkat yang digunakan atau kebiasaan pengguna dalam mengakses internet. Untuk mendeteksi ancaman serangan siber pada ekosistem rumah cerdas menggunakan penelitian ini mengadopsi metode Network Development Life Cycle (NDLC). Berdasarkan hasil analisis pada ekosistem rumah memungkinkan diterapkan teknik port mirroring pada router. Sehingga pada perancangan mengggunakan Miktorik dan MalTrail sebagai sensor deteksi malicious traffic untuk mengetahui aktivitas anomali. Hasil dari penelitian ini menunjukan bahwa ancaman serangan siber yang bersumber dari internal dapat disebabkan dari kebiasaan pengguna dalam mengakses internet. Sedangkan perangkat cerdas yang terpasang dalam penelitian ini tidak ditemukan adanya malicious traffic atau aktivitas anomali. Maka penelitian ini masih perlu dilakukan improvisasi menggunakan teknik network packet capture

Klasifikasi Citra Rontgen Covid-19 dengan menggunakan Deep Learning

Citra adalah representasi dari suatu obyek yang ditulis ulang pada suatu medium dengan nilai tertentu (intensitas) yang memiliki koordinat x dan y. Citra Rontgent merupakan salah satu jenis citra medis yang dapat digunakan untuk mendeteksi dan mempelajari suatu penyakit. Namun citra rontgen terkadang terlihat kabur sehingga sedikit sulit untuk mengintepretasi citra. Terlebih lagi adanya redaman sinar-X yang berbeda antara kelenjar pada jaringan yang normal dengan yang terpapar penyakit. Dengan mengimplementasikan deep learning dengan metode klasifikasi citra dapat memilah gambar berdasarkan ekstrasi fitur dan bobot pada jaringan syaraf tiruan. Ketika GPU yang dimiliki adalah AMD, salah satu cara agar dapat menjalankan Deep Learning menggunakan AMD adalah mnggunakan PlaidML.Tahapan yang dilakukan pada pelatihan dan pengujian adalah melakukan pre-procesessing, ektraksi fitur menggunakan lapisan JST VGG16 tanpa lapisan pengklasifikasi (konvolusi dan pooling) yang menghasilkan bottleneck.npy, kemudian membuat lapisan pengklasifikasi sendiri untuk melatih klasifikasi kelas covid dan normal menggunakan data bottleneck.npy. Tingkat akurasi yang diperoleh pada tahap pelatihan beserta validasi pada pelatihan, dan pengujian berturut-turut adalah 99%, 97%, dan 94%. Selanjutnya ketika dievaluasi dengan F1 Score mendapatkan hasil 0,939

Quantum Approximation for Wireless Scheduling

This paper proposes a quantum approximate optimization algorithm (QAOA) method for wireless scheduling problems. The QAOA is one of the promising hybrid quantum-classical algorithms for many applications and it provides highly accurate optimization solutions in NP-hard problems. QAOA maps the given problems into Hilbert spaces, and then it generates Hamiltonian for the given objectives and constraints. Then, QAOA finds proper parameters from classical optimization approaches in order to optimize the expectation value of generated Hamiltonian. Based on the parameters, the optimal solution to the given problem can be obtained from the optimum of the expectation value of Hamiltonian. Inspired by QAOA, a quantum approximate optimization for scheduling (QAOS) algorithm is proposed. First of all, this paper formulates a wireless scheduling problem using maximum weight independent set (MWIS). Then, for the given MWIS, the proposed QAOS designs the Hamiltonian of the problem. After that, the iterative QAOS sequence solves the wireless scheduling problem. This paper verifies the novelty of the proposed QAOS via simulations implemented by Cirq and TensorFlow-Quantum

Routing Protocols for Underwater Acoustic Sensor Networks: A Survey from an Application Perspective

Underwater acoustic communications are different from terrestrial radio communications; acoustic channel is asymmetric and has large and variable end‐to‐end propagation delays, distance‐dependent limited bandwidth, high bit error rates, and multi‐path fading. Besides, nodes’ mobility and limited battery power also cause problems for networking protocol design. Among them, routing in underwater acoustic networks is a challenging task, and many protocols have been proposed. In this chapter, we first classify the routing protocols according to application scenarios, which are classified according to the number of sinks that an underwater acoustic sensor network (UASN) may use, namely single‐sink, multi‐sink, and no‐sink. We review some typical routing strategies proposed for these application scenarios, such as cross‐layer and reinforcement learning as well as opportunistic routing. Finally, some remaining key issues are highlighted

A framework to maximise the communicative power of knowledge visualisations

Knowledge visualisation, in the field of information systems, is both a process and a product, informed by the closely aligned fields of information visualisation and knowledg management. Knowledge visualisation has untapped potential within the purview of knowledge communication. Even so, knowledge visualisations are infrequently deployed due to a lack of evidence-based guidance. To improve this situation, we carried out a systematic literature review to derive a number of “lenses” that can be used to reveal the essential perspectives to feed into the visualisation production process.We propose a conceptual framework which incorporates these lenses to guide producers of knowledge visualisations. This framework uses the different lenses to reveal critical perspectives that need to be considered during the design process. We conclude by demonstrating how this framework could be used to produce an effective knowledge visualisation

EVHS - Elastic Virtual Honeypot System for SDNFV-Based Networks

The SDNFV-based network has leveraged the advantages of software-defined networking (SDN) and network-function virtualization (NFV) to become the most prominent network architecture. However, with the advancement of the SDNFV-based network, more attack types have emerged. This research focuses on one of the methods (use of the honeypot system) of preventing these attacks on the SDNFV-based network. We introduce an SDNFV-based elastic virtual honeypot system (EVHS), which not only resolves problems of other current honeypot systems but also employs a new approach to efficiently manage and control honeypots. It uses a network-intrusion-detection system (NIDS) at the border of the network to detect attacks, leverages the advantages of SDN and NFV to flexibly generate honeypots, and connects attackers to these honeypots by using a moving-target defense mechanism. Furthermore, we optimize the system to efficiently reuse the available honeypots after the attacks are handled. Experimental results validate that the proposed system is a flexible and efficient approach to manage and provide virtual honeypots in the SDNFV-based network; the system can also resolve the problems encountered by current honeypot systems

Real-Time Onboard Object Detection for Augmented Reality: Enhancing Head-Mounted Display with YOLOv8

This paper introduces a software architecture for real-time object detection using machine learning (ML) in an augmented reality (AR) environment. Our approach uses the recent state-of-the-art YOLOv8 network that runs onboard on the Microsoft HoloLens 2 head-mounted display (HMD). The primary motivation behind this research is to enable the application of advanced ML models for enhanced perception and situational awareness with a wearable, hands-free AR platform. We show the image processing pipeline for the YOLOv8 model and the techniques used to make it real-time on the resource-limited edge computing platform of the headset. The experimental results demonstrate that our solution achieves real-time processing without needing offloading tasks to the cloud or any other external servers while retaining satisfactory accuracy regarding the usual mAP metric and measured qualitative performanc