Handover management in mobile WiMAX using adaptive cross-layer technique

The protocol type and the base station (BS) technology are the main communication media between the Vehicle to Infrastructure (V2I) communication in vehicular networks. During high speed vehicle movement, the best communication would be with a seamless handover (HO) delay in terms of lower packet loss and throughput. Many studies have focused on how to reduce the HO delay during lower speeds of the vehicle with data link (L2) and network (L3) layers protocol. However, this research studied the Transport Layer (L4) protocol mobile Stream Control Transmission Protocol (mSCTP) used as an optimal protocol in collaboration with the Location Manager (LM) and Domain Name Server (DNS). In addition, the BS technology that performs smooth HO employing an adaptive algorithm in L2 to perform the HO according to current vehicle speed was also included in the research. The methods derived from the combination of L4 and the BS technology methods produced an Adaptive Cross-Layer (ACL) design which is a mobility oriented handover management scheme that adapts the HO procedure among the protocol layers. The optimization has a better performance during HO as it is reduces scanning delay and diversity level as well as support transparent mobility among layers in terms of low packet loss and higher throughput. All of these metrics are capable of offering maximum flexibility and efficiency while allowing applications to refine the behaviour of the HO procedure. Besides that, evaluations were performed in various scenarios including different vehicle speeds and background traffic. The performance evaluation of the proposed ACL had approximately 30% improvement making it better than the other handover solutions

Cross-Layer Extended Persistent Timeout Policy for SCTP and DSDV

International audienceCross layer techniques applied to various protocols stacks provide fair information sharing between OSI model layers. The performance gains have been demonstrated for many studied systems within protocols interactions. The example is illustrative of the reliable transport protocols that use retransmissions to achieve that reliability function. The performance gains of the persistent timeout policy for the management of the retransmission timeout have been produce in some recent works when applying that persistent timeout policy only to reliable transport protocol. The goal was to give an appropriate behavior in response to a bad state of the wireless channel that occurs and temporally blocks the transmission of data. The channel state is given by the 802.11 link layer through cross-layer mechanism. In this paper, the persistent policy is extended to the network layer and is applied to a stack that uses a reactive routing protocol, namely the Destination Sequenced Distance-Vector (DSDV) protocol that also generates additional periodic traffic regardless to the channel state. We are measuring the influence in terms of performance gains of the extended persistent policy because of the additional periodic signalization messages deriving from the used routing protocol. After the introduction in section I; Section II of this paper presents an overview of the Stream Control Transmission Protocol (SCTP). Section III describes the behavior of the DSDV protocol. Section IV presents the extended persistent timeout policy principle and Section V presents the simulation results used to compare the using of the traditional and the extended persistent timeout policies applied to the same protocol stack using SCTP and DSDV

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

[EN] The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-tra_ c networks with multiple nodes/ sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G o_ ers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.Najm, IA.; Hamoud, AK.; Lloret, J.; Bosch Roig, I. (2019). Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment. Electronics. 8(6):1-23. https://doi.org/10.3390/electronics8060607S12386Rahem, A. A. T., Ismail, M., Najm, I. A., & Balfaqih, M. (2017). Topology sense and graph-based TSG: efficient wireless ad hoc routing protocol for WANET. 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Centralized and Localized Data Congestion Control Strategy for Vehicular Ad Hoc Networks Using a Machine Learning Clustering Algorithm. IEEE Transactions on Intelligent Transportation Systems, 17(11), 3275-3285. doi:10.1109/tits.2016.2546555Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017). State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems. IEEE Communications Surveys & Tutorials, 19(4), 2432-2455. doi:10.1109/comst.2017.2707140Gonzalez-Landero, F., Garcia-Magarino, I., Lacuesta, R., & Lloret, J. (2018). PriorityNet App: A Mobile Application for Establishing Priorities in the Context of 5G Ultra-Dense Networks. IEEE Access, 6, 14141-14150. doi:10.1109/access.2018.2811900Lloret, J., Parra, L., Taha, M., & Tomás, J. (2017). An architecture and protocol for smart continuous eHealth monitoring using 5G. 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Communication channel occupation and congestion in wireless sensor networks. Computers & Electrical Engineering, 72, 846-858. doi:10.1016/j.compeleceng.2017.12.049Najm, I. A., Ismail, M., Lloret, J., Ghafoor, K. Z., Zaidan, B. B., & Rahem, A. A. T. (2015). Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications, 58, 119-129. doi:10.1016/j.jnca.2015.09.003Najm, I. A., Ismail, M., & Abed, G. A. (2014). High-Performance Mobile Technology LTE-A using the Stream Control Transmission Protocol: A Systematic Review and Hands-on Analysis. Journal of Applied Sciences, 14(19), 2194-2218. doi:10.3923/jas.2014.2194.2218Katuwal, R., Suganthan, P. N., & Zhang, L. (2018). An ensemble of decision trees with random vector functional link networks for multi-class classification. Applied Soft Computing, 70, 1146-1153. doi:10.1016/j.asoc.2017.09.020Gómez, S. E., Martínez, B. C., Sánchez-Esguevillas, A. J., & Hernández Callejo, L. (2017). 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Recent Advances in Wireless Communications and Networks

This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters