Reduction in ping-pong effect in heterogeneous networks using fuzzy logic

Yusof

International Journal on Smart Sensing and Intelligent Systems

et al. 2021

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.

Section: Related Workmentioning

confidence: 99%

Performance evaluation of vertical handover in Internet of Vehicles

Yusof

International Journal on Smart Sensing and Intelligent Systems

et al. 2021

“…Dengan Multiple Attribute Decision Making (MADM) menggunakan logika fuzzy berguna untuk mengambil sebuah keputusan yang tepat dan berjalan secara realtime. Terdapat beberapa jenis sistem inferensi pada logika fuzzy seperti Mamdani, Sugeno-Tsukamoto, tetapi dalam skema ini menerapkan metode Sugeno-Tsukamoto (Naeem, Ngah, & Hashim, 2018) untuk menghasilkan kemungkinan handover yang akan dijalankan sistem.…”

Section: Pengambilan Keputusan Handover Berbasis Fuzzy Logicunclassified

Skema Handover pada Multi-kamera dengan Logika Fuzzy untuk Sistem Pemantauan Orang

2021

ABSTRAKAdanya berbagai peristiwa yang membahayakan di tempat keramaian menyebabkan diperlukannya sebuah sistem pemantauan aktifitas manusia di sekitarnya untuk pengawasan keamanan. Sistem multi-kamera sangat cocok digunakan untuk pemantauan target pada lingkungan area yang luas. Disaat target meninggalkan jangkauan area kamera menuju lainnya, proses pemantauan target harus tetap bekerja dan diserahkan ke kamera lainnya. Protokol serah terima target dapat berjalan jika terdapat komunikasi antar kamera yang tersedia. Penelitian ini menyajikan skema handover pada sistem multi-kamera dengan menerapkan pengambilan keputusan handover berbasis logika fuzzy. Dengan begitu, target akan selalu ditangani oleh kamera meskipun target bergerak menjauhinya. Berdasarkan hasil simulasi, skema handover ini mampu mereduksi total number of handover sebesar 20% dibandingkan dengan metode AHCS (Active Handover Control Scheme). Selain itu, handover delay pada metode usulan memperoleh waktu 123.72μs dan masih lebih lama dari AHCS.Kata kunci: handover, multi-kamera, pemantauan orang, fuzzy logic ABSTRACTThe existence of various dangerous events in a crowded place causes the need of surveillance system to monitor the human activity continuously in a certain area. Multi-camera systems are used to monitor targets in large areas. When the target leaves the camera’s range for another, the target monitoring process should continue to work and be left to other cameras. The target handover protocol may work if there is communication between the available cameras. This document presents a handover scheme in a multi-camera system by applying a fuzzy logic handover decision. Thus, the target will always be processed by the camera, even if the target is moving away from it. Based on the simulation results, this handover scheme is able to reduce the total number of handovers by 20% compared to the AHCS (Active Handover Control Scheme) method. In addition, the handover delay in the proposed method obtains 123.72 μs and is still longer than the AHCS.Keywords: handover, multi-camera, human monitoring, fuzzy logic

“…Step 1, the PIS + and the NIS − are obtained. According to the calculation method of the fuzzy Euclidean distance in Formula (5) above, the distance between each candidate network and the fuzzy hesitant PIS + and NIS − is determined using Formula (13) and Formula (14) in Step 2, which are respectively written as:…”

Section: Inmentioning

confidence: 99%

“…Traditional access selection algorithms usually adopt mathematical models, such as multi-attribute decision making (MADM) [8,9], utility theory [10,11], fuzzy logic [12,13], game theory [14][15][16], optimal calculation [17,18], and neural network [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Multi-Attribute Access Selection Algorithm for Heterogeneous Wireless Networks Based On Uncertain Network Attribute Values

Guo

Omar

Zaini

et al. 2020

Preprint

A heterogeneous wireless network (HWN) environment contains many kinds of wireless networks, such as UMTS, LTE, and WLAN, where users move around within their coverage area. How to ensure mobile users select the most suitable network is a hot research topic for HWNs. While traditional access selection algorithms assume that mobile users can obtain accurate network attribute values, the network attribute values obtained by mobile users are often uncertain due to the mobility of users, the interference of wireless signals, and the fluctuation of the network state. To solve this problem, this paper designs an access selection algorithm for HWNs in the context of inaccurate network attribute values. First, the algorithm calculates the network attribute values based on the hesitant fuzzy theory, then calculates the weights of network attributes using the fuzzy analytic hierarchy process (FAHP), and finally sorts the candidate networks using the hesitant fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. The simulation results show the proposed algorithm enables users to select the most suitable network to access under the inaccurate network attribute environment and obtain higher gains.