volume-15-Issue 1 (2021)
Latest Articles
Performance Analysis and Functionality Comparison of First Hop Redundancy Protocols
JUSPN, volume-15, Issue 1 (2021) , PP 49 - 58
Published: 18 Aug 2021
DOI: 10.5383/JUSPN.15.01.007
by M. Mansour, A. Ghneimat, R. Alasem, F. Jarray from Department of Network,University of Tripoli, Tripoli, Libya - Prince Sattam Ibn Abdulaziz University. Saudi Arabia - Al albayt University- Jordan - Higher institute of computer science, Medenine, Tunisia
Abstract: High levels of availability can be expensive to maintain, but a lack of availability may also increase costs as it may damage the reputation of the business. This has led to the development of techniques that reduce downtime until it became transparent to the user. First hop redundancy protocols are an essential tool for improving the availability of IP networks. First hop redundancy protocols are protocols used to manage and maintain network default gateway router by using one or more redundant routers that will take over in case of default router failure. In this paper, we evaluate the three particular protocols of FHRPs, namely the Hot Standby Router Protocol (HSRP), Virtual Router Redundancy Protocol (VRRP), and Gateway Load Balancing (GLBP) using GNS3 tools. The First Hop Redundancy Protocols have been implemented, tested, optimized, and compared to one another in terms of convergence time, packet loss and CPU utilization. The comparison indicates which protocol is best in which scenario and which is best among the three protocols. read more... read less...
Keywords: FHRP (First Hop Redundancy Protocol), HSRP (Hot Standby Router Protocol), VRRP (Virtual Router Redundancy Protocol), GLBP (Gateway Load Balancing Protocol
Internet of Things (IoT) based Network Integrated with Sensor Nodes for Intruder Detection and Low Energy Consumption
JUSPN, volume-15, Issue 1 (2021) , PP 43 - 48
Published: 18 Aug 2021
DOI: 10.5383/JUSPN.15.01.006
by Gauri Kalnoor, GowriShankar S from Research Scholar, BMS college of Engineering, Karnataka, India - Professor Computer science BMS college of Engineering, Karnataka, India
Abstract: The applications in Internet of Things (IoT) for a large-scale Network which necessitates the storage resources and computing tasks, are gradually deployed in most of the wireless network environments. The computing model of traditional techniques when compared with features of cloud such as unlimited expansion, dynamic acquisition and payas-you-go are represented in different IoT architectures based on the conveniences of applications. Thus, one of the key challenges is to consider the service requirements when sensors are assigned to the tasks and the network performance is improved. In the presented work, the two-phase service system using Enhanced Bernoulli Vacation (EBV) scheduling algorithm and Intrusion Detection framework is proposed to minimize the energy consumed by the sensors while the service is provided. The performance variation of Virtual Machine (VM) and its achieved delay is considered, while first the tasks are divided into different tasks at different levels. The proposed work deals with a queuing system ‘M/G/1’ for Bernoulli Vacation scheduling model at one phase and intrusion detection technique at second phase. The sensing distance is also calculated with its density of network. The tasking scheduling algorithm is considered for execution cost and residual energy where the deadlines or threshold are proposed. The delay time, accuracy, detection rate and False Alarm Positive rate are evaluated during simulation time. Based on the work flows, experiments conducted are simulated for controlled tasks of IoT which demonstrates the algorithm achieving high success rate and that the network performs better when compared with the existing algorithms. read more... read less...
Keywords: Security; Energy efficiency; Smart home automation; Internet of Things (IoT); safety; Sensor Network, Enhanced Bernoulli Vacation.
An Enhanced Deep Learning Model to Network Attack Detection, by using Parameter Tuning, Hidden Markov Model and Neural Network
JUSPN, volume-15, Issue 1 (2021) , PP 35 - 41
Published: 21 Aug 2021
DOI: 10.5383/JUSPN.15.01.005
by Choukri Djellali, Mehdi adda from Department of Mathematics, Computer Science and Engineering University of Quebec At Rimouski 300 Allée des Ursulines, Rimouski, QC G5L 3A1 Rimouski, Canada
Abstract: In recent years, Deep Learning has become a critical success factor for Machine Learning. In the present study, we introduced a Deep Learning model to network attack detection, by using Hidden Markov Model and Artificial Neural Networks. We used a model aggregation technique to find a single consolidated Deep Learning model for better data fitting. The model selection technique is applied to optimize the bias-variance trade-off of the expected prediction. We demonstrate its ability to reduce the convergence, reach the optimal solution and obtain more cluttered decision boundaries. Experimental studies conducted on attack detection indicate that our proposed model outperformed existing Deep Learning models and gives an enhanced generalization. read more... read less...
Keywords: Deep Learning, Data Mining, HMM, Neural Network, Pattern Recognition, Model aggregation, Model selection, Network Security.
Contribution to Multi-Energy Flow Management for Building Energy Hub,
JUSPN, volume-15, Issue 1 (2021) , PP 27 - 34
Published: 17 Aug 2021
DOI: 10.5383/JUSPN.015.01.004
by Redouane Marhoum, Chaimaa Fouhad, Mohamed El Khaili, Hassan Ouajji from SSDIA laboratory, ENSET, Hassan 2nd University of Casablanca, BP 159, Mohammedia, Morocco
Abstract: Global demand for primary fossil energy continues to increase. However, the production of energy from fossil fuels, in addition to depleting available reserves, releases millions of tons of Greenhouse Gas (GHG) into the atmosphere. Thus, it is obvious that the high concentration of GHGs in the air disrupts the natural greenhouse effect and consequently causes global warming. The implementation of action plans aimed at reducing greenhouse gas emissions has led all countries to use clean energy sources (sun, earth, wind) called renewable energies and also to rationalize the use of energies whether based on fossil fuels or renewable. Our paper presents a modeling of the demand and its management to ensure an optimization of the energy consumption and the reduction of its bill read more... read less...
Keywords: Energy efficiency, energy hub, renewable energy, smart house, Energy storage, IoT
Context-based Reasoning through Fuzzy Logic for Edge Intelligence
JUSPN, volume-15, Issue 1 (2021) , PP 17 - 25
Published: 13 Aug 2021
DOI: 10.5383/JUSPN.15.01.003
by Ramin Firouzi, Rahim Rahmani, Theo Kanter from Department of Computer and Systems Science, Stockholm University, Kista, Sweden, SE-164 07
Abstract: With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that are physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge. read more... read less...
Keywords: Internet of Things (IoT), context-awareness, edge computing, reasoning, type two fuzzy controller
Multistage Arabic and Turkish Text Compression via Characters Encoding and 7-Zip
JUSPN, volume-15, Issue 1 (2021) , PP 11 - 15
Published: 11 Aug 2021
DOI: 10.5383/JUSPN.15.01.002
by Tariq Abu Hilal, Hasan Abu Hilal, Ala’ Abu Hilal from Higher Colleges of Technology, Abu Dhabi, UAE, 41012, Zayed University, Abu Dhabi, UAE, 41012
Abstract: Turkish lossless text compression was proposed by converting the character’s from UTF-8 to ANSI system for space-preserving. Likewise, we present a decoding method that transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, some of the Turkish alphabets are being stored in 2 bytes size. All that space comes at a price. The developed sequential encoding technique will reduce the size of the text file up to 9%. Moreover, the Turkish encoded text will retain its original form after decoding. According to our proposal, it is considered as a lossless text compression, where it’s a common concern today. Thus, many parties have become interested in Unicode compression. Basically, our algorithm is mapping Unicode Turkish characters into ANSI, by using the available 8-bit legacy. For Arabic Text Compression, a sequential encoding technique was suggested that efficiently converts Arabic characters string from UTF-8 to ANSI characters coding. The encoding algorithm presented in this paper significantly reduces the file size. The decoding method transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, Arabic alphabets are currently being stored in 2 bytes size which leads to inefficient space utilization. The newly developed sequential encoding technique reduces the space required for storage up to fifty percent. In addition, the proposed technique will retain the Arabic encoded text to its original form after decoding, which is leading to a lossless text compression. Thus, addressing the common concern of the currently available Arabic characters compression techniques. In this research, a multistage compression process was implemented on Turkish and Arabic languages, by using the new encoding technique, in addition to the 7-Zip application, which has shown a significant file size reduction. read more... read less...
Keywords: Unicode; ANSI, UTF-8 Encoding, Turkish Text Compression, Arabic Text Compression, 7-Zip Application
VLC-based Data Transfer and Energy Harvesting Mobile System
JUSPN, volume-15, Issue 1 (2021) , PP 01 - 09
Published: 08 Aug 2021
DOI: 10.5383/JUSPN.15.01.001
by Bo Liu, Yingying Chen, Hongbo Liu, Yudong Yao from ECE Department, Stevens Instituted of Technology, Hoboken, NJ, USA, 07030, ECE Department, Rutgers University, New Brunswick, NJ, 08901, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610015, China
Abstract: This paper explores a low-cost portable visible light communication (VLC) system to support the increasing needs of lightweight mobile applications. VLC grows rapidly in the past decade for many applications (e.g., indoor data transmission, human sensing, and visual MIMO) due to its RF interference immunity and inherent high security. However, most existing VLC systems heavily rely on fixed infrastructures with less adaptability to emerging lightweight mobile applications. This work proposes Light Storage, a portable VLC system takes the advantage of commercial smartphone flashlights as the transmitter and a solar panel equipped with both data reception and energy harvesting modules as the receiver. Light Storage can achieve concurrent data transmission and energy harvesting from the visible light signals. It develops multi-level light intensity data modulation to increase data throughput and integrates the noise reduction functionality to allow portability under various lighting conditions. The system supports synchronization together with adaptive error correction to overcome both the linear and non-linear signal offsets caused by the low time-control ability from the commercial smartphones. Finally, the energy harvesting capability in Light Storage provides sufficient energy support for efficient short range communication. Light Storage is validated in both indoor and outdoor environments and can achieve over 98% data decoding accuracy, demonstrating the potential as an important alternative to support low-cost and portable short range communication. read more... read less...
Keywords: Visible Light Communication; Energy Harvesting; Solar Panel.