The main challenges in routing protocols are node mobility, resource constraints, error-prone channel states, and problems with hidden and unprotected terminals. This article provides an overview of the main protocols, their problems, and ways to improve them. The cell phone allows communication between two moving units via mobile stations (MS), mobile units, and landing units. The term "mobile ad hoc network" (MANET) refers to a group of devices connecting and communicating. The military, law enforcement, and emergency services have quickly become interested in MANETS because they provide a high quality of service. Path loss (PL), one of the biggest problems in wireless communications, can be caused by multiple reflections from an obstacle or by the source of the signal being far from the destination on the network. Multipath propagation, path loss, and interference reduce the network's quality of service (QoS). The best route is added to a router's routing table using a dynamic routing protocol, and an alternate path is chosen when the primary route is unavailable.

/pdf/a-critical-review-of-optimization-manet-routing-protocols-3civ2mx6.pdf

A critical review of Optimization MANET routing protocols

Advances in artificial intelligence and data acquisition technology are growing, the research on deep learning algorithm has gone deep into various fields. At this stage, the demand supply matching model under the comprehensive passenger transport hub travel system is obtained by analyzing the travel mode data. This paper takes traffic time as the research direction, uses machine learning and complex network theory to conduct in-depth learning algorithm research, respectively, discusses, explores and forecasts the traffic supply and demand mode data, and explores the traffic mode supply and demand model under the comprehensive passenger transport hub travel service system. It is shown in traffic data that using the prediction form of deep learning to predict traffic conditions and travel pressure can enable traffic managers to master traffic dynamics and lead the direction for future traffic development. Finally, from the perspective of MaaS system, the paper uses big data and information processing methods to explore the supply and demand matching model in terms of transportation modes. The research shows that MaaS system can make overall planning in terms of traffic resources, provide reasonable travel modes for traffic travelers and match corresponding travel services. It not only makes overall planning and guarantee for travel services, but also promotes the sustainable development of the transportation supply and demand system. 

https://www.researchsquare.com/article/rs-2550080/latest.pdf

Research on supply and demand matching model of transportation modes in MaaS system of integrated passenger transport hub based on deep learning

A Delay-Tolerant Low-Duty Cycle Scheme in Wireless Sensor Networks for Iot Applications

In modern communication systems there are heterogeneous service request from the applications like mobile devices, virtual reality, automatic driving cars, IoT devices. These devices have different QoS requirements in which network slicing enabler plays a vital role in 5G. Network Slicing unfolds a new paradigm for the providers as well as for the users. In this context the resource management has gained importance in the field of networking. Since a huge data is been generated by these devices, it is very difficult to deliver high performance with resource utilization. In such situation these traditional monitoring techniques will not be able to handle such a huge data. Towards this, the researchers have started applying with Deep learning techniques with the network monitoring system. This paper focuses on the work done towards one of the key components of network analysis (i.e.) traffic prediction. This study has reviewed the articles, which have proposed the deep learning techniques for traffic prediction towards resource management in network slicing.*CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstract. (Abstract)

Traffic Prediction with Network Slicing in 5G: A Survey

Energy efficiency is an existing research challenge in wireless sensor networks (WSNs); to make WSNs energy efficient, many researchers moved to low-duty cycle WSNs with different data forwarding schemes. Low-duty cycle is considered to be a promising approach to design routing protocols for resource-constrained WSNs. Many applications have real-time constraints, which requires an event must be reported to the sink before deadline. Furthermore, wireless links between low-power radios are highly unreliable, and end-to-end latency requirements are challenging due to multiple transmissions for a single packet delivery. By considering probabilistic delay (i.e., delay bounded data delivery with reliability constraints) and energy efficiency issues, little research has been done using different data forwarding schemes, but still, the problem exists. In this work, two strategies are proposed for data forwarding: an energy-optimal path and a delay-optimal path. The early-arrived packet follows an optimal energy route and a diverse path to achieve a delay guarantee with minimum transmission cost. The proposed scheme improves the performance of low-duty cycle WSNs in terms of delay, reliability, stability, and transmission cost. It is observed that under different low-duty cycles, the proposed scheme achieves up to 25% energy conservation compared with both MinEEC and MinEED. At last, undirected spanning trees are considered to balance communication costs. The simulation results of the proposed scheme are compared with the existing methods. 

A Delay-Tolerant low-duty cycle scheme in wireless sensor networks for IoT applications

Handling faults in a running cellular network can impair the performance and dissatisfy the end users. It is important to design an automatic self-healing procedure to not only detect the active faults, but also to diagnosis them automatically. Although fault detection has been well studied in the literature, fewer studies have targeted the more complicated task of diagnosing. Our presented method aims to tackle fault detection and diagnosis using two sets of data collected by the network: performance support system data and drive test data. Although performance support system data is collected automatically by the network, drive test data are collected manually in three mode call scenarios: short, long and idle. The short call can identify faults in a call setup, the long call is designed to identify handover failures and call interruption, and, finally, the idle mode is designed to understand the characteristics of the standard signal in the network. We have applied unsupervised learning, along with various classified algorithms, on performance support system data. Congestion and failures in TCH assignments are a few examples of the detected and diagnosed faults with our method. In addition, we present a framework to identify the need for handovers. The Silhouette coefficient is used to evaluate the quality of the unsupervised learning approach. We achieved an accuracy of 96.86% with the dynamic neural network method.

/pdf/automatic-fault-detection-and-diagnosis-in-cellular-networks-2s2vbhyj.pdf

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

In today’s world, interconnected Vehicular ad-hoc networks (VANET) and intelligent transportation systems have become more popular. Although IoV can bring many benefits for the smart cities and provide comforts for the passengers, however, the increasing needs for keeping the QoS and QoE at an acceptable level in time sensitive applications seems crucial and needs to be investigated deeply. Also, allocating the right number of resources to avoid congestions and fill the deficiencies in a distributed manner is a challenging issue. So, with the increase in users, attention must be given to Quality of Service (QoS) and resource allocation. As the vehicle network provides information to provide safety, comfort, and entertainment to drivers and passengers, they are one of the most compelling research topics in intelligent transportation systems. TDMA protocol is used in this study to increase the efficiency of the network and the quality of service it provides. To solve the synchronization problem, the Markov method predicts the size of slots and frames. The scenario field is used in the Markov application section to better predict TDMA gaps on solving the synchronization problem. Accordingly, the higher the quality of service, the lower the latency of the network, and the better the allocation of resources. Optimizing allocation and quality of service is further motivated by reducing collision between packets. In terms of its implementation, this method is divided into two components, the first being the database proposal for constructing the Markov matrix and the second being the simulation on VanetMobisim and implementation of the network in NS2. The proposed method performed better in different scenarios in terms of computational complexity, PDF, latency, and overhead, as shown in the results section. 

Improving Resources in Internet of Vehicles Transportation Systems Using Markov Transition and TDMA Protocol

Location prediction is essential for location-based applications such as route recommendation systems, resource allocation optimization in congested areas, congestion avoidance, traffic management, just to mention a few. Knowing approximate vehicle locations can enhance our ability to better manage and prepare the existing mobile edge computing resources in a Vehicular ad hoc network. In this paper, we focus on a graph neural network model to estimate vehicle locations in a timely manner. We first model the vehicle locations based on events that happen between different regions of an area and the car itself. Leveraging our event-based model, we introduce Temporal Location Prediction (TLP) to capture essential features from each node, edges, and neighboring nodes to achieve timely location prediction. Afterwards, instead of using GPS coordinates as input, we demonstrate a new data structure to feed Bidirectional LSTM (BiLSTM) and LSTM for vehicle location prediction in different time intervals. Thus, our main contribution is a network model that utilizes a Temporal Graph neural network for dynamic location prediction. We explain the advantages and disadvantages of each model and how we can improve them. Our experiments on a real dataset show that our model (TLP) outperforms LSTM and BiLSTM in short-term prediction, considering the same scenario and conditions.

Farimasadat Miri

Papers

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

Improving Resources in Internet of Vehicles Transportation Systems Using Markov Transition and TDMA Protocol

A Novel Short-term Vehicle Location Prediction using Temporal Graph Neural Networks