A review of relay network on UAVS for enhanced connectivity

One of the best evolution in technology breakthroughs is the Unmanned Aerial Vehicle (UAV). This aerial system is able to perform the mission in an agile environment and can reach the hard areas to perform the tasks autonomously. UAVs can be used in post-disaster situations to estimate damages, to monitor and to respond to the victims. The Ground Control Station can also provide emergency messages and ad-hoc communication to the Mobile Users of the disaster-stricken community using this network. A wireless network can also extend its communication range using UAV as a relay. Major requirements from such networks are robustness, scalability, energy efficiency and reliability. In general, UAVs are easy to deploy, have Line of Sight options and are flexible in nature. However, their 3D mobility, energy constraints, and deployment environment introduce many challenges. This paper provides a discussion of basic UAV based multi-hop relay network architecture and analyses their benefits, applications, and tradeoffs. Key design considerations and challenges are investigated finding fundamental issues and potential research directions to exploit them. Finally, analytical tools and frameworks for performance optimizations are presented

Dynamic Q-learning and fuzzy CNN based vertical handover decision for integration of DSRC, mmWave 5G and LTE in internet of vehicles (IoV)

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

Performance evaluation of vertical handover in internet of vehicles

Internet of Vehicles (IoV) is developed by integrating the intelligent transportation system (ITS) and the Internet of Things (IoT). The goal of IoV is to allow vehicles to communicate with other vehicles, humans, pedestrians, roadside units, and other infrastructures. Two potential technologies of V2X communication are dedicated short-range communication (DSRC) and cellular network technologies. Each of these has its benefits and limitations. DSRC has low latency but it limits coverage area and lacks spectrum availability. Whereas 4G LTE offers high bandwidth, wider cell coverage range, but the drawback is its high transmission time intervals. 5G offers enormous benefits to the present wireless communication technology by providing higher data rates and very low latencies for transmissions but is prone to blockages because of its inability to penetrate through the objects. Hence, considering the above issues, single technology will not fully accommodate the V2X requirements which subsequently jeopardize the effectiveness of safety applications. Therefore, for efficient V2X communication, it is required to interwork with DSRC and cellular network technologies. One open research challenge that has gained the attention of the research community over the past few years is the appropriate selection of networks for handover in a heterogeneous IoV environment. Existing solutions have addressed the issues related to handover and network selection but they have failed to address the need for handover while selecting the network. Previous studies have only mentioned that the network is being selected directly for handover or it was connected to the available radio access. Due to this, the occurrence of handover had to take place frequently. Hence, in this research, the integration of DSRC, LTE, and mmWave 5G is incorporated with handover decision, network selection, and routing algorithms. The handover decision is to ensure whether there is a need for vertical handover by using a dynamic Q-learning algorithm. Then, the network selection is based on a fuzzy-convolution neural network 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 a jellyfish optimization algorithm that takes into account the channel, vehicle characteristics, and transmission metrics. This system is developed in an OMNeT++ simulator and the performances are evaluated in terms of mean handover, handover failure, mean throughput, delay, and packet loss

Prinsip kejuruteraan telekomunikasi

Telekomunikasi ialah proses penyampaian maklumat untuk jarak yang jauh, melebihi jarak yang boleh dicapai oleh suara manusia. Sejarah membuktikan manusia asalnya menyalurkan maklumat untuk jarak jauh melalui pelbagai kaedah primitif seperti menggunakan asap, bunyi atau surat sebagai perantara. Era sistem telekomunikasi moden tercetus apabila terciptanya telefon dan berkembang sehingga kepada komunikasi melalui internet. Sehubungan itu, buku ini meneroka prinsip asas sistem telekomunikasi yang merangkumi pemodulatan analog, digit dan kekunci. Konsep asas pemodulatan analog meliputi pemodulatan amplitud dan sudut, analisis isyarat dalam domain masa dan frekuensi, operasi litar penjana dan penerima, dan kesan hingar ke atas sistem pemodulatan. Perbincangan mengenai pemodulatan digit pula mencakupi teknik penukaran isyarat analog kepada digit seperti pemodulatan delta dan pemodulatan kod denyut yang melibatkan proses pensampelan, pengkuantuman dan pengekodan talian. Kaedah pemultipleksan dan pemodulatan kekunci isyarat digit turut disertakan untuk melengkapkan pengetahuan asas kejuruteraan telekomunikasi. Buku ini juga mengandungi contoh dan latihan di akhir setiap bab yang dapat membantu mengukuhkan kefahaman pembaca di samping mampu menjadi bahan rujukan bidang berkaitan

Location closeness model for VANETs with integration of 5G

Nowadays. 5G is playing a significant role in the efficiency of network security and creating more and faster channels for communication. 5G is evoking industries such as healthcare, education, marketing, transportation, and V2X (Vehicle-to-everything). In addition. 5G considers a new radio access technology that is adding new applications like the Internet of Tilings (IoT). Augmented Reality. Virtual Reality, connected cars, connected people-to-people, smart city, connected homes that are considered using higher bandwidth and low latency. Mainly, this paper is focusing on security challenges faced by the Vehicular ad-hoc network (VANET). VANET faces threats in three different fields: Security, safety, and infotainment, which further have numerous attacks. More precisely, this research conducted an in-depth study and proposed a VANET trust model. Therefore the proposed model deals specifically with the "location closenessb" parameter. Moreover, the trust model integrated with 5G cloud to support greater coverage, effective network density with respect to network infrastructure and IoT as well. Therefore, in this article, an effort has been put forward to implement the model using case studies to validate the trust model based on the "location closeness parameter. The results proved the valid implementation of the model by identifying the trusted communication between the vehicles

On the latency and jitter evaluation of software defined networks

Conventional networking devices require that each is programmed with different rules to perform specific collective tasks. Next generation networks are required to be elastic, scalable and secured to connect millions of heterogeneous devices. Software defined networking (SDN) is an emerging network architecture that separates control from forwarding devices. This decoupling allows centralized network control to be done network-wide. This paper analyzes the latency and jitter of SDN against a conventional network. Through simulation, it is shown that SDN has an average three times lower jitter and latency per packet that translate to improved throughput under varying traffic conditions

Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET

A review on frequency synchronization in collaborative beamforming: a practical approach

Coherent signal reception from distributed beamforming nodes of virtual antenna array formation requires frequency synchronization of the participating nodes. Signals at the target receiver are out of phase due to unsynchronized local oscillator’s (LO) reference signal of all the nodes in the systems. Practical cases of this problem are considered. In this article, a brief overview is presented of the need for the frequency synchronization and the resulting effect of mitigation avoidance. A variant of the closed-loop feedback algorithm is used to provide LO drifts information to the beamforming transmitters. These feedbacks are used to estimate, correct, and predict the nonlinear LO offsets that will result in near (0) phase offset of the received signal. The algorithms are implemented in software defined radio (SDR) and transmitted through the RF front end of devices like the NI 2920/N210 USRP

Detection and Classification of Conflict Flows in SDN Using Machine Learning Algorithms

Software-Defined Networking (SDN) is a new type of technology that embraces high flexibility and adaptability. The applications in SDN have the ability to manage and control networks while ensuring load balancing, access control, and routing. These are considered the most significant benefits of SDN. However, SDN can be influenced by several types of conflicting flows which may lead to deterioration in network performance in terms of efficiency and optimisation. Besides, SDN conflicts occur due to the impact and adjustment of certain features such as priority and action. Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector Machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. The EFDT and hybrid DT-SVM algorithms were designed and deployed based on DT and SVM algorithms to achieve improved performance. Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. The experimental results of the detection of conflict flows show that the DT and SVM algorithms achieve accuracies of 99.27% and 98.53% respectively while the EFDT and hybrid DT-SVM algorithms achieve respective accuracies of 99.49% and 99.27%. In addition, the proposed EFDT algorithm achieves 95.73% accuracy on the task of classification between conflict flow types. The proposed EFDT and hybrid DT-SVM algorithms show a high capability of SDN applications to offer fast detection and classification of conflict flows