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.

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 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 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.

scholarly journals Improving Learning-from-Crowds through Expert Validation

2017 ◽  
Author(s):  
Mengchen Liu ◽  
Liu Jiang ◽  
Junlin Liu ◽  
Xiting Wang ◽  
Jun Zhu ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Supervised Learning ◽  
Real World ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Uncertainty Assessment ◽  
Effective Learning ◽  
Complete Uncertainty ◽  
Expert Validation ◽  
Selection Of

Although several effective learning-from-crowd methods have been developed to infer correct labels from noisy crowdsourced labels, a method for post-processed expert validation is still needed. This paper introduces a semi-supervised learning algorithm that is capable of selecting the most informative instances and maximizing the influence of expert labels. Specifically, we have developed a complete uncertainty assessment to facilitate the selection of the most informative instances. The expert labels are then propagated to similar instances via regularized Bayesian inference. Experiments on both real-world and simulated datasets indicate that given a specific accuracy goal (e.g., 95%) our method reduces expert effort from 39% to 60% compared with the state-of-the-art method.

A real-time push system on road traffic information

2014 ◽  
Author(s):  
Yang Xu ◽  
Zhang Zhenjiang ◽  
Liu Yun
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Traffic Information

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.

Selection of Materials and Sensors for Health Monitoring of Composite Structures

2003 ◽  
Vol 785 ◽  
Author(s):  
Seth S. Kessler ◽  
S. Mark Spearing
Keyword(s):  
Health Monitoring ◽  
Lamb Wave ◽  
Composite Structures ◽  
Structural Damage ◽  
Performance Metrics ◽  
Full Range ◽  
Transportation Systems ◽  
Health Monitoring Systems ◽  
The Relationship ◽  
Selection Of

ABSTRACTEmbedded structural health monitoring systems are envisioned to be an important component of future transportation systems. One of the key challenges in designing an SHM system is the choice of sensors, and a sensor layout, which can detect unambiguously relevant structural damage. This paper focuses on the relationship between sensors, the materials of which they are made, and their ability to detect structural damage. Sensor selection maps have been produced which plot the capabilities of the full range of available sensor types vs. the key performance metrics (power consumption, resolution, range, sensor size, coverage). This exercise resulted in the identification of piezoceramic Lamb wave transducers as the sensor of choice. Experimental results are presented for the detailed selection of piezoceramic materials to be used as Lamb wave transducers.

scholarly journals Monotonic Functions Method and Its Application to Staging of Patients with Prostate Cancer According to Pretreatment Data

2021 ◽  
Vol 11 (9) ◽  
pp. 3836
Author(s):  
Valeri Gitis ◽  
Alexander Derendyaev ◽  
Konstantin Petrov ◽  
Eugene Yurkov ◽  
Sergey Pirogov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Preoperative Staging ◽  
Threshold Value ◽  
Logical Function ◽  
Adequate Treatment ◽  
Monotonic Functions ◽  
Selection Of

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.

Interpreting testing and assessment: A state-of-the-art review

2021 ◽  
pp. 026553222110361
Author(s):  
Chao Han
Keyword(s):  
State Of The Art ◽  
Professional Certification ◽  
Automatic Assessment ◽  
Prospective Students ◽  
Assessment Practice ◽  
High Stakes ◽  
Future Directions ◽  
Assessment Tasks ◽  
Interpreter Education ◽  
Selection Of

Over the past decade, testing and assessing spoken-language interpreting has garnered an increasing amount of attention from stakeholders in interpreter education, professional certification, and interpreting research. This is because in these fields assessment results provide a critical evidential basis for high-stakes decisions, such as the selection of prospective students, the certification of interpreters, and the confirmation/refutation of research hypotheses. However, few reviews exist providing a comprehensive mapping of relevant practice and research. The present article therefore aims to offer a state-of-the-art review, summarizing the existing literature and discovering potential lacunae. In particular, the article first provides an overview of interpreting ability/competence and relevant research, followed by main testing and assessment practice (e.g., assessment tasks, assessment criteria, scoring methods, specificities of scoring operationalization), with a focus on operational diversity and psychometric properties. Second, the review describes a limited yet steadily growing body of empirical research that examines rater-mediated interpreting assessment, and casts light on automatic assessment as an emerging research topic. Third, the review discusses epistemological, psychometric, and practical challenges facing interpreting testers. Finally, it identifies future directions that could address the challenges arising from fast-changing pedagogical, educational, and professional landscapes.

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