Performance of movement direction distance-based vertical handover algorithm under various femtocell distributions in HetNet

Ahmad, Rami; Ismail, Mahamod; Sundararajan, Elankovan; Othman, Nor Effendy; Zain, Abdullah M.

doi:10.1109/micc.2017.8311768

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…The algorithm starts by continuous verification (period of time) of the distance between each WSN node (n) and its CH. Since the WSN nodes are moving at a low speed of about 5 m/s, the distance will be checked once per second [47]. If the distance begins to approach the distance threshold value (d T ), the WSN node begins to search for the closest CH to pair with it.…”

Section: Clustering Managementmentioning

confidence: 99%

Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

Wazirali¹,

Ahmad²

2022

Computers, Materials &Amp; Continua

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Energy and security remain the main two challenges in Wireless Sensor Networks (WSNs). Therefore, protecting these WSN networks from Denial of Service (DoS) and Distributed DoS (DDoS) is one of the WSN networks security tasks. Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks. This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time. We examined the performance metrics of different machine learning classification categories such as K-Nearest Neighbour (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Gboost, Decision Tree (DT), Naïve Bayes, Long Short Term Memory (LSTM), and Multi-Layer Perceptron (MLP) on a WSN-dataset in different sizes. The test results proved that the statistical and logical classification categories performed the best on numeric statistical datasets, and the Gboost algorithm showed the best performance compared to different algorithms on average of all performance metrics. The performance metrics used in these validations were accuracy, F1-score, False Positive Ratio (FPR), False Negative Ratio (FNR), and the training execution time. Moreover, the test results showed the Gboost algorithm got 99.6%, 98.8%, 0.4% 0.13% in accuracy, F1-score, FPR, and FNR, respectively. At training execution time, it obtained 1.41 s for the average of all training time execution datasets. In addition, this paper demonstrated that for the numeric statistical data type, the best results are in the size of the dataset ranging from 3000 to 6000 records and the percentage between categories is not less than 50% for each category with the other categories. Furthermore, this paper investigated the effect of Gboost on the WSN lifetime, which resulted in a 32% reduction compared to other Gboost-free scenarios.

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Section: Clustering Managementmentioning

confidence: 99%

Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

Wazirali¹,

Ahmad²

2022

Computers, Materials &Amp; Continua

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“…Table 3 shows the parameters that will be calibrated based on simulation requirements, part of which was taken from Darabkh et al [17]. The simulation system randomly distributes the nodes in the dimensions of 1000 by 1000 of the terrain connected to nine CHs in the initial values as illustrated in Figure 6, then they start to move randomly at a rate of 5 m per second [44]. In our scenario the UDP clients (normal nodes) send Constant Bit Rate (CBR) traffic of around 64 bytes with the periodicity of 0.01 s. The simulation system randomly distributes the nodes in the dimensions of 1000 by 1000 of the terrain connected to nine CHs in the initial values as illustrated in Figure 6, then they start to move randomly at a rate of 5 m per second [44].…”

Section: Simulation and Performance Metricsmentioning

confidence: 99%

“…The simulation system randomly distributes the nodes in the dimensions of 1000 by 1000 of the terrain connected to nine CHs in the initial values as illustrated in Figure 6, then they start to move randomly at a rate of 5 m per second [44]. In our scenario the UDP clients (normal nodes) send Constant Bit Rate (CBR) traffic of around 64 bytes with the periodicity of 0.01 s. The simulation system randomly distributes the nodes in the dimensions of 1000 by 1000 of the terrain connected to nine CHs in the initial values as illustrated in Figure 6, then they start to move randomly at a rate of 5 m per second [44]. In our scenario the UDP clients (normal nodes) send Constant Bit Rate (CBR) traffic of around 64 bytes with the periodicity of 0.01 s. Related to performance metrics that will be used in this simulation, the network lifetime, node power consumption and packet loss.…”

Section: Simulation and Performance Metricsmentioning

confidence: 99%

Adaptive Trust-Based Framework for Securing and Reducing Cost in Low-Cost 6LoWPAN Wireless Sensor Networks

et al. 2022

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Wireless Sensor Networks (WSNs) are the core of the Internet of Things (IoT) technology, as they will be used in various applications in the near future. The issue of security and power consumption is still one of the most important challenges facing this type of network. 6LoWPAN protocol was developed to meet these challenges in networks with limited power and resources. The 6LoWPAN uses a hierarchical topology and the traditional method of encryption and key management, keeping power consumption levels high. Therefore, in this paper, a technique has been developed that helps in balancing security and energy consumption by exploiting the Trust technique between low-cost WSN nodes called Trust-Cluster Head (Trust-CH). Trust between nodes is built by monitoring the behavior of packet transmission, the number of repetitions and the level of security. The Trust-CH model provides a dynamic multi-level encryption system that depends on the level of Trust between WSN nodes. It also proposes a dynamic clustering system based on the absolute-trust level in the mobile node environment to minimize power consumption. Along with a set of performance metrics (i.e., power consumption and network lifetime), the Cooja simulator was used to evaluate the Trust-CH model. The results were compared to a static symmetric encryption model together with various models from previous studies. It has been proven that the proposed model increases the network lifetime by 40% compared to previous studies, as well as saves as much as 28% power consumption in the case of using a static encryption model. While maintaining the proposed model’s resistance to many malicious attacks on the network.

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“…The mobile user's mobility and energy efficiency are the two important challenges in the heterogeneous wireless network (Ahmad et al, 2018;Ahmad et al, 2017a). Thus, to address these challenges, the handover techniques (Ahmad et al, 2020;Ahmad et al, 2017b) considers the metrics such as cost efficiency (Desogus and Venturi, 2019;Desogus and Casu, 2019), spectrum utilization and energy consumption (Agliata et al, 2020) in the next-…”

Section: Introductionmentioning

confidence: 99%

“…The mobile user’s mobility and energy efficiency are the two important challenges in the heterogeneous wireless network (Ahmad et al , 2018; Ahmad et al , 2017a). Thus, to address these challenges, the handover techniques (Ahmad et al , 2020; Ahmad et al , 2017b) considers the metrics such as cost efficiency (Desogus and Venturi, 2019; Desogus and Casu, 2019), spectrum utilization and energy consumption (Agliata et al , 2020) in the next-generation (Marino and Pariso, 2020) fifth-generation (5G) heterogeneous communication networks (Addali et al , 2019). Furthermore, to upgrade the reliability and wireless transmission capacity in the 5G small cell systems, cooperative transmission is a powerful approach for vehicular communications (Jain et al , 2018) and handover location management (LM) is also considered to improve performance in 5G networks (Semenova et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

A survey on various handover technologies in 5G network using the modular handover modules

Kulkarni

Bavarva

2021

IJPCC

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Purpose Fifth-generation (5G) networks play a significant role in handover methods. 5G wireless network is open, flexible and highly heterogeneous along with the overlay coverage and small cell deployments. Handover management is one of the main problems in the heterogeneous network. Also, handover satisfies the needs of ultra-reliable communications along with very high reliability and availability in 5G networks. Handover management deals with every active connection of a user’s device, which moves the connection between the user’s device and the counterparty from one network point to another. Thus, the handover decision determines the best access network and also decides whether the handover is performed or not. Design/methodology/approach The main intention of this survey is to review several existing handover technologies in 5G. Using the categories of analysis, the existing techniques are divided into different techniques such as authentication-based techniques, blockchain-based techniques, software-defined-based techniques and radio access-based techniques. The survey is made by considering the methods such as used software, categorization of methods and used in the research works. Furthermore, the handover rate is considered for performance evaluation for the handover techniques in 5G. The drawbacks present in the existing review papers are elaborated in research gaps and issues division. Findings Through the detailed analysis and discussion, it can be summarized that the widely concerned evaluation metric for the performance evaluation is the handover rate. It is exploited that the handover rate within the range of 91%–99% is achieved by three research papers. Originality/value A survey on the various handover mechanisms in 5G networks is expected in this study. The research papers used in this survey are gathered from different sources such as Google Scholar and IEEE. Also, this survey suggests a further extension for the handover mechanism in 5G networks by considering various research gaps and issues.

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Performance of movement direction distance-based vertical handover algorithm under various femtocell distributions in HetNet

Cited by 9 publications

References 12 publications

Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

Adaptive Trust-Based Framework for Securing and Reducing Cost in Low-Cost 6LoWPAN Wireless Sensor Networks

A survey on various handover technologies in 5G network using the modular handover modules

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