scholarly journals Secure Vertical Handover to NEMO Using Hybrid Cryptosystem

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Vikram Raju Reddicherla ◽  
Umashankar Rawat ◽  
Y. Jeevan Nagendra Kumar ◽  
Atef Zaguia
Keyword(s):  
Security Analysis ◽  
Signal Strength ◽  
Vertical Handover ◽  
Received Signal Strength ◽  
User Preferences ◽  
User Preference ◽  
Network Selection ◽  
Selection Mechanism ◽  
Proposed Model ◽  
Correspondent Node

To provide security to all pairs of nodes in network mobility (NEMO) while executing the handoff between different technologies, a hybrid cryptosystem with a suitable network selection mechanism is proposed. All pairs of nodes, i.e., Mobile Node (MN), Mobile Router (MR), Correspondent Node (CN) and MN, and Home Agent (HA), respectively, are considered. A proper security mechanism is proposed to provide confidentiality to Bound Update (BU) during handoff and conversation between MN, MR, and HA using the elliptic curve cryptography (ECC). In this solution, a network selection mechanism is proposed based on user preference and Received Signal Strength (RSS) in a heterogeneous network. The proposed model can protect the communication using security analysis from all NEMO standard attacks. Whenever NEMO moves, MR intimates to HA about the address change using (BU) and MR receives Binding Acknowledgement (BA) as a reply. During data (frame) exchange and registration between MN, CN, and HA, various security threats arise. In the earlier work, only the security solution is given, and the best network selection algorithm is not provided in a heterogeneous environment. Therefore, in this paper, the best network selection is contributed based on Received Signal Strength (RSS) and user preferences. A comparison of the proposed model is drawn with Return Routability Procedure (RRP). Authentication is provided for communication between MN and CN. The proof is derived using BAN logic. Many standard security attacks have been successfully avoided on all pairs of communications. It has been observed that the proposed model achieves 2.4854% better throughput than the existing models. Also, the proposed model reduces the handoff latency and packet loss by 2.7482% and 3.8274%, respectively.

scholarly journals A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Shidrokh Goudarzi ◽  
Wan Haslina Hassan ◽  
Mohammad Hossein Anisi ◽  
Seyed Ahmad Soleymani ◽  
Parvaneh Shabanzadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Curve Fitting ◽  
Particle Swarm ◽  
Signal Strength ◽  
Pso Algorithm ◽  
Vertical Handover ◽  
Received Signal Strength ◽  
Network Selection ◽  
Received Signal Strength Indicator ◽  
Swarm Optimization

The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO) algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO) and the RBF neural networks. The results of the proposed methodology compare the predictive capabilities in terms of coefficient determination (R2) and mean square error (MSE) based on the validation dataset. The results show that the effect of the model based on the CF-PSO is better than that of the model based on the RBF neural network in predicting the received signal strength indicator situation. In addition, we present a novel network selection algorithm to select the best candidate access point among the various access technologies based on the PSO. Simulation results indicate that using CF-PSO algorithm can decrease the number of unnecessary handovers and prevent the “Ping-Pong” effect. Moreover, it is demonstrated that the multiobjective particle swarm optimization based method finds an optimal network selection in a heterogeneous wireless environment.

scholarly journals ENHANCED NETWORK SELECTION AND HANDOVER SCHEMA FOR HETEROGENEOUS WIRELESS NETWORKS

2019 ◽  
Vol 01 (03) ◽  
pp. 160-171 ◽  
Author(s):  
Duraipandian M
Keyword(s):  
Wireless Networks ◽  
Heterogeneous Wireless Networks ◽  
Vertical Handover ◽  
User Preference ◽  
Network Selection ◽  
Order Preference ◽  
Simple Additive Weighting ◽  
Topsis Technique ◽  
The Cost ◽  
Grey Relational

The seamless communication between the devices of the heterogeneous wireless networks still remains as a challenge due to the delay incurred, the cost difference and the bandwidth variations.so it becomes necessary for a perfect management of the hand over and the selection of the network to provide a continuous conveyance in the broadcasting or the sharing of the information’s. So the paper initiates an enhanced network selection and a vertical hand over schema that is context aware and based on the user preference utilizing the grey relational analysis integrated with the particle swarm optimization. The performance analysis of the proposed method of vertical handover evinces the perfect network selection that enables a continuous connection for the heterogeneous networks and shows an enhanced throughput and latency in handover compared to the other methods like TOPSIS (technique for order preference by similarity to ideal solution) and SAW(simple additive weighting).

Seamless Handover in Heterogeneous Wireless Network Using Cognitive Advisor Based Decision Making Algorithm

2017 ◽  
Vol 14 (1) ◽  
pp. 517-523
Author(s):  
V Sivasankaran ◽  
V Nagarajan
Keyword(s):  
Wireless Networks ◽  
Wireless Network ◽  
Heterogeneous Wireless Networks ◽  
Optimization Technique ◽  
Vertical Handover ◽  
User Preference ◽  
Network Selection ◽  
Modern Trend ◽  
Mobile Nodes ◽  
Effective Network

An important and modern trend in wireless network is managing and accessing in heterogeneous wireless networks structures effectively. Vertical handover or handoff technology is major and acting vital role in wireless communication, it has key challenging issues in wireless network, while handover gets call drop kind of latency and call quality minimization. So the communication networks needs an efficient and dynamic services and knowledge monitoring management to select network for handover of different network environments (example: WiMAX, WiFi, WLAN, etc.). In this paper we proposed the concepts of knowledgeable monitoring (KM-method), cognitive advisor (CA) and Advanced Handover Optimization (AHO) technique to improve effective network selection for seamless communication, while travels over heterogeneous wireless networks (HWN). KM-method monitoring and manage effective network selection and holding connected mobile nodes inform header files also maintaining re-initiate to next network information (network IP address, user ID, new path, etc.). CAVHO-Cognitive Advisor based Vertical Handover (VSH and V2H) and KM-methods perform and measures velocity and direction of mobility nodes through SRSS sequential Received Strength Signal. Also CA receives the nearest networks details of each another using nearest neighbouring concept with SDT ratio Algorithm. Optimization technique (AHO) performs dynamically balancing the threshold level of speed, load and quality based on Receiving signal strength and user preference. Simulation results of CAVHO Shows it secure and increases the handover probability of access also proposed system reduce delay and improve quality of services and throughput.

scholarly journals Modeling user preference dynamics with coupled tensor factorization for social media recommendation

2020 ◽  
Author(s):  
Hamidreza Tahmasbi ◽  
Mehrdad Jalali ◽  
Hassan Shakeri
Keyword(s):  
Social Media ◽  
User Satisfaction ◽  
User Preferences ◽  
User Preference ◽  
Tensor Factorization ◽  
Plausible Assumption ◽  
The Past ◽  
Proposed Model ◽  
Recommendation Accuracy ◽  
Over Time

AbstractAn essential problem in real-world recommender systems is that user preferences are not static and users are likely to change their preferences over time. Recent studies have shown that the modelling and capturing the dynamics of user preferences lead to significant improvements on recommendation accuracy and, consequently, user satisfaction. In this paper, we develop a framework to capture user preference dynamics in a personalized manner based on the fact that changes in user preferences can vary individually. We also consider the plausible assumption that older user activities should have less influence on a user’s current preferences. We introduce an individual time decay factor for each user according to the rate of his preference dynamics to weigh the past user preferences and decrease their importance gradually. We exploit users’ demographics as well as the extracted similarities among users over time, aiming to enhance the prior knowledge about user preference dynamics, in addition to the past weighted user preferences in a developed coupled tensor factorization technique to provide top-K recommendations. The experimental results on the two real social media datasets—Last.fm and Movielens—indicate that our proposed model is better and more robust than other competitive methods in terms of recommendation accuracy and is more capable of coping with problems such as cold-start and data sparsity.

scholarly journals A three-dimensional pattern recognition localization system based on a Bayesian graphical model

2020 ◽  
Vol 16 (9) ◽  
pp. 155014771988489 ◽  
Author(s):  
Abdulraqeb Alhammadi ◽  
Fazirulhisyam Hashim ◽  
Mohd. Fadlee A Rasid ◽  
Saddam Alraih
Keyword(s):  
Graphical Model ◽  
Indoor Localization ◽  
Three Dimensional ◽  
Signal Strength ◽  
Local Area ◽  
Received Signal Strength ◽  
Reference Points ◽  
Indoor Environments ◽  
Localization System ◽  
Proposed Model

Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.

scholarly journals Genetic Algorithm for Vertical Handover (GAfVH)in a Heterogeneous networks

2019 ◽  
Vol 9 (4) ◽  
pp. 2534
Author(s):  
Iman Zubeiri ◽  
Yasmina El Morabit ◽  
Fatiha Mrabti
Keyword(s):  
Genetic Algorithm ◽  
Network Architecture ◽  
Signal Strength ◽  
Vertical Handover ◽  
Received Signal Strength ◽  
Wireless System ◽  
Fifth Generation ◽  
Radio Access ◽  
5G Network ◽  
Simulation Results

<p>The fifth generation (5G) wireless system will deal with the growing demand of new multimedia and broadband application. The 5G network architecture is based on heterogeneous Radio Access Technologies (RATs). In such implementation the Vertical handover is a key issue. Up till now, systems are using simple mechanisms to make handover decision, based on the evaluation of the Received Signal Strength (RSS). In some cases these mechanisms are not Efficient.This paper presents a new vertical handover algorithm based on Genetic Algorithm (GAfVH). It aims to reduce the number of unnecessary handovers, and optimizes the system performance. We compare our simulation results to the Received Signal Strength (RSS) based method. The results show that the number of handovers decreases. Moreover, we demonstrate that the network selection result can differ from an application to another.</p>

KAJIAN KEKUATAN SINYAL RADIO (RSSI) XBEE DALAM RANGKA PEMASANGAN LANDSLIDE EARLY WARNING SYSTEM (LEWS) DI KABUPATEN GARUT, TASIKMALAYA DAN MAJALENGKA

2019 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Riski Fitriani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Received Signal Strength Indicator ◽  
Landslide Early Warning ◽  
Point To Point

Salah satu inovasi untuk menanggulangi longsor adalah dengan melakukan pemasangan Landslide Early Warning System (LEWS). Media transmisi data dari LEWS yang dikembangkan menggunakan sinyal radio Xbee. Sehingga sebelum dilakukan pemasangan LEWS, perlu dilakukan kajian kekuatan sinyal tersebut di lokasi yang akan terpasang yaitu Garut, Tasikmalaya, dan Majalengka. Kajian dilakukan menggunakan 2 jenis Xbee yaitu Xbee Pro S2B 2,4 GHz dan Xbee Pro S5 868 MHz. Setelah dilakukan kajian, Xbee 2,4 GHz tidak dapat digunakan di lokasi pengujian Garut dan Majalengka karena jarak modul induk dan anak cukup jauh serta terlalu banyak obstacle. Topologi yang digunakan yaitu topologi pair/point to point, dengan mengukur nilai RSSI menggunakan software XCTU. Semakin kecil nilai Received Signal Strength Indicator (RSSI) dari nilai receive sensitivity Xbee maka kualitas sinyal semakin baik. Pengukuran dilakukan dengan meninggikan antena Xbee dengan beberapa variasi ketinggian untuk mendapatkan kualitas sinyal yang lebih baik. Hasilnya diperoleh beberapa rekomendasi tinggi minimal antena Xbee yang terpasang di tiap lokasi modul anak pada 3 kabupaten.

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