scholarly journals Dynamic Q-learning and Fuzzy CNN Based Vertical Handover Decision for Integration of DSRC, mmWave 5G and LTE in Internet of Vehicles (IoV)

2021 ◽  
pp. 155-166
Author(s):  
Shaik Mazhar Hussain ◽  
◽  
Kamaludin Mohamad Yusof
Keyword(s):  
Performance Metrics ◽  
Selection Process ◽  
Success Probability ◽  
Access Network ◽  
Vertical Handover ◽  
Network Selection ◽  
Internet Of Vehicles ◽  
Handover Decision ◽  
Q Learning ◽  
Radio Access

Internet of vehicles commonly known as IOV is a newly emerged area which with the help of internet assisted communication provides the support to the vehicles. Due to the access of more than one radio access network, 5G makes the connectivity ubiquitous. Vehicle mobility demands for handover in such heterogeneous networks. Instead of using better technology for long ranges and other types of traffic, the vehicles are using devoted short range communications at short ranges. Commonly, networks for handovers were used to be selected directly or with the available radio access it used to connect automatically. With the help of this, the hand over occurrence now takes places frequently. This paper is based on the incorporation of DSRC, LTE as well as mm Wave on Internet of vehicles which is integrated with the Handover decision making algorithm, Network Selection and Routing. The decision of the handovers is to ensure that if there is any requirement of the vertical handovers using dynamic Q-learning algorithms in which entropy function is used to predict the threshold according to the characteristics of the environment. The network selection process is done using Fuzzy Convolution Neural Network commonly known as FCNN which makes the fuzzy rules by considering the parameters such as strength of its signal, its distance, the density of the vehicle, the type of its data as well the Line of Sight (LoS). V2V chain routing is presented in such a manner that V2V pairs are also selected with the help of jellyfish optimization algorithm considering three metrics – Vehicle metrics, Channel metrics and Vehicle performance metrics. OMNET++ simulator is the software in which system is developed. The performance evaluation is done according to its Handover Success Probability, Handover Failure, Redundant Handover, Mean Throughput, delay and Packet Loss.

scholarly journals Artificial intelligence based handover decision and network selection in heterogeneous internet of vehicles

2021 ◽  
Vol 22 (2) ◽  
pp. 1124
Author(s):  
Shaik Mazhar Hussain ◽  
Kamaludin Mohamad Yusof ◽  
Rolito Asuncion ◽  
Shaik Ashfaq Hussain
Keyword(s):  
Short Range ◽  
Learning Algorithm ◽  
Success Probability ◽  
Access Network ◽  
Vertical Handover ◽  
Network Selection ◽  
Internet Of Vehicles ◽  
Handover Decision ◽  
Radio Access ◽  
Current Characteristics

Internet of vehicles (IoV) is an emerging area that gives support for vehicles via internet assisted communication. IoV with 5G provides ubiquitous connectivity due to the participation of more than one radio access network. The mobility of vehicles demands to make handover in such heterogeneous network. The vehicles at short range uses dedicated short range communication (DSRC), while it has to use better technology for long range and any type of traffic. Usually, the previous work will directly select the network for handover or it connects with available radio access. Due to this, the occurrence of handover takes place frequently.  In this paper, the integration of DSRC, LTE and mmWave 5G on IoV is incorporated with novel handover decision making, network selection and routing. The handover decision is to ensure whether there is a need for vertical handover by using Dynamic Q-learning algorithm that uses entropy function for threshold prediction as per the current characteristics of the environment. Then the network selection is based on fuzzy-convolution neural network (F-CNN) that creates fuzzy rules from signal strength, distance, vehicle density, data type and line of sight. V2V chain routing is proposed to select V2V pairs using jellyfish optimization algorithm (JOA) that takes in account of channel, vehicle and transmission metrics. This system is developed in OMNeT++ simulator and the performances are evaluated in terms of success probability, handover failure, unnecessary handover, mean throughput, delay and packet loss.

scholarly journals Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles

2021 ◽  
Vol 22 (4) ◽  
pp. 392-406
Author(s):  
Shaik Mazhar Hussain ◽  
Kamaludin Mohamad Yusof ◽  
Shaik Ashfaq Hussain
Keyword(s):  
Performance Metrics ◽  
Road Traffic ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Vertical Handover ◽  
Traffic Information ◽  
Internet Of Vehicles ◽  
Radio Access ◽  
4G Lte ◽  
Selection Of

Abstract Internet of Vehicles (IoV) is a network of vehicles communicating with each other by exchanging road traffic information via radio access technologies. Two potential technologies of V2X that have gained attention over the past years are DSRC and cellular networks such as 4G LTE and 5G. DSRC is suitable for low latency communications, however provides a shorter coverage range whereas, 4G LTE offers a wide coverage range but has high transmission time intervals. In contrast, 5G offers higher data rates, low latencies but prone to blockages. Single technology might not fully accommodate the requirements of vehicular communications. Hence, it is required to interwork with more than one radio access network to satisfy the requirements of safety vehicular applications. One issue identified when working with multiple radio access networks is the selection of the most appropriate network for vertical handover. Usually, in the previous works, the network is selected directly or will be connected to the available network due to which the handover had to take place frequently resulting in unnecessary handovers. Hence, in the existing state-of-the-art, the need for handover is not validated. In this paper, we have proposed a dynamic Q-learning algorithm to validate the need for handover, and then, appropriate selection of network would take place by using a fuzzy convolutional neural network. Besides, a modified jellyfish optimization algorithm is proposed to select the shortest paths by forming V2V pairs that take into account channel metrics, vehicle metrics, and vehicle performance metrics. The proposed algorithms are then evaluated using OMNET++ and compared with the existing state-of-the-art concerning mean handover, HO failure, throughput, delay, and packet loss as the performance metrics.

User-Driven Network Selection during Vertical Handover in 4G Networks

2017 ◽  
Vol 10 ◽  
pp. 1
Author(s):  
Abubakar Muhammad Miyim ◽  
Mahamod Ismail ◽  
Rosdiadee Nordin
Keyword(s):  
Blocking Probability ◽  
Service Selection ◽  
Vertical Handover ◽  
Network Selection ◽  
Selection Strategy ◽  
Intelligent Network ◽  
Decision Algorithm ◽  
Radio Access ◽  
Service Application ◽  
Multi Access

The importance of network selection for wireless networks, is to facilitate users with various personal wireless devices to access their desired services via a range of available radio access networks. The inability of these networks to provide broadband data service applications to users poses a serious challenge in the wireless environment. Network Optimization has therefore become necessary, so as to accommodate the increasing number of users’ service application demands while maintaining the required quality of services. To achieve that, the need to incorporate intelligent and fast mechanism as a solution to select the best value network for the user arises. This paper provides an intelligent network selection strategy based on the user- and network-valued metrics to suit their preferences when communicating in multi-access environment. A user-driven network selection strategy that employs Multi-Access Service Selection Vertical Handover Decision Algorithm (MASS-VHDA) via three interfaces; Wi-Fi, WiMAX and LTE-A is proposed, numerically evaluated and simulated. The results from the performance analysis demonstrate some improvement in the QoS and network blocking probability to satisfy user application requests for multiple simultaneous services.

scholarly journals Nelder-Mead Optimized MADM Decision Support for Vertical Handover

2019 ◽  
Vol 8 (2S3) ◽  
pp. 601-604
Keyword(s):  
Decision Process ◽  
Vertical Handover ◽  
Network Selection ◽  
Heterogeneous Environment ◽  
Challenging Problem ◽  
Wireless Technology ◽  
Decision Strategy ◽  
Handover Decision ◽  
Required Quality

The vertical handover decision strategy is a challenging problem to support seamless mobility in different wireless technology. The handover decision process is reported to facilitate the required Quality of Service, but bad network selection and overload condition of the chosen network fallout in handover failures. Traditional handoff techniques typically are based on single parameter and are inefficient due to superfluous handovers. In addition, existing fuzzy based handoff method reduces the handover failures but increase the computational complexity. Furthermore, the existing mobile controlled handover techniques are not suitable for heterogeneous environment. Therefore this paper focus the need of efficient, robust and flexible vertical handover technique to reduce the handover failures and design an MADM based network controlled handover that maximize QoS in heterogeneous environment

scholarly journals Fast Multiattribute Network Selection Technique for Vertical Handover in Heterogeneous Emergency Communication Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Igor Bisio ◽  
Andrea Sciarrone
Keyword(s):  
Communication Systems ◽  
Performance Metrics ◽  
Vertical Handover ◽  
Mobile Nodes ◽  
Radio Access ◽  
Topsis Approach ◽  
Multiple Network ◽  
Definition Of ◽  
Emergency Scenarios ◽  
Network Interfaces

The telecommunication infrastructure in emergency scenarios is necessarily composed of heterogeneous radio/mobile portions. Mobile Nodes (MNs) equipped with multiple network interfaces can assure continuous communications when different Radio Access Networks (RANs) that employ different Radio Access Technologies (RATs) are available. In this context, the paper proposes the definition of a Decision Maker (DM), within the protocol stack of the MN, in charge of performing network selections and handover decisions. The DM has been designed to optimize one or more performance metrics and it is based on Multiattribute Decision Making (MADM) methods. Among several MADM techniques considered, taken from the literature, the work is then focused on the TOPSIS approach, which allows introducing some improvements aimed at reducing the computational burden needed to select the RAT to be employed. The enhanced method is called Dynamic-TOPSIS (D-TOPSIS). Finally, the numerical results, obtained through a large simulative campaign and aimed at comparing the performance and the running time of the D-TOPSIS, the TOPSIS, and the algorithms found in the literature, are reported and discussed.

Vertical Handover Decision Schemes in Fourth Generation Heterogeneous Cellular Networks

2018 ◽  
Vol 14 (1) ◽  
pp. 1-26
Author(s):  
Adnan Mahmood ◽  
Shadi M. S. Hilles ◽  
Hushairi Zen
Keyword(s):  
Multiple Access ◽  
Heterogeneous Wireless Networks ◽  
Access Network ◽  
Decision Criterion ◽  
Vertical Handover ◽  
Fourth Generation ◽  
Handover Decision ◽  
Advantages And Disadvantages ◽  
Network Technologies

Vertical Handover Decision (VHD) algorithms are indispensable components of forthcoming 4G heterogeneous wireless networks architecture – so as to provide requisite Quality of Service to an assortment of applications anywhere at any time, while allowing seamless roaming in highly dynamic scenarios (i.e. multitude of access network technologies that vary in bandwidth, latency, monetary cost, etc.) using Mobile Terminals (MTs) enabled with multiple access interfaces. In this article, a critical review of the existing VHD algorithms has been carried out as an effort to update the previous studies. To offer a methodical contrast, recently published VHD algorithms have been classified into four major classes depending on the key handover decision criterion used, i.e. RSS based algorithms, bandwidth based algorithms, cost function based algorithms, and the combination algorithms. Moreover, operational fundamentals, advantages, and disadvantages of exemplary VHD algorithms for each class have been presented to assess the tradeoffs between their intricacy of implementation and the efficacy.

Delay-Sensitive Network Selection and Offloading in LTE-A and Wi-Fi Heterogeneous Networks

2020 ◽  
pp. 2150120
Author(s):  
Chun-Chuan Yang ◽  
Jeng-Yueng Chen ◽  
Yi-Ting Mai ◽  
Yi-Chih Wang
Keyword(s):  
Traffic Congestion ◽  
Access Network ◽  
Access Point ◽  
Base Station ◽  
Network Selection ◽  
Core Network ◽  
Delay Sensitive ◽  
Radio Access ◽  
Lte Advanced ◽  
Average System

LTE-Advanced (LTE-A) offloading is concerned about alleviating traffic congestion for the LTE-A network, which includes the core network and the radio access network (RAN). Due to the scarcity of the radio resource, offloading for the LTE-A RAN is more critical, for which an efficient way is to integrate Wi-Fi with LTE-A to form a heterogeneous RAN environment. An LTE-A UE (User Equipment) with the wi-fi interface can therefore access the Internet via an LTE-A base station of Evolved Node B (eNB) or a wi-fi Access Point (AP). In this paper, wireless network selection for UEs with delay-sensitive traffic in the heterogeneous RAN of LTE-A and wi-fi is addressed. Based on the queueing model of M/G/1, a novel network selection and offloading scheme, namely Delay-Sensitive Network Selection and Offloading (DSO), is proposed. The average system time at LTE-A eNBs and wi-fi APs calculated according to M/G/1 is used for network selection as well as offloading operations in DSO. The benefit of DSO in terms of satisfying the delay budget of UEs and load balancing is demonstrated by the simulation study.

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