Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node

Yusof

Transport and Telecommunication Journal

2021

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.

Section: Prior Work On Handovermentioning

confidence: 99%

Section: Whenmentioning

confidence: 99%

Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles

Yusof

Transport and Telecommunication Journal

2021

“…C H checks its C MT , if no more cluster members exist, C H resigns from the position of the cluster head and transfers its status to U R . We borrow the concept of soft handoff [43] in cluster maintenance to minimize packet loss rate due to link disconnection between cluster members and C H . As illustrated in Algorithm 4, when a C M listens to beacon from more than one cluster heads, the C M joins a C H with minimum MM value before disconnecting from its respective C H .…”

Section: Cluster Maintenance Phasementioning

confidence: 99%

RBO-EM: Reduced Broadcast Overhead Scheme for Emergency Message Dissemination in VANETs

Ullah

Abbas

et al. 2020

IEEE Access

Effective information dissemination constitutes the cornerstone of communication in Vehicular Ad hoc Networks (VANETs). Avoiding broadcast storm is one of the considerable challenges in the development of an effective dissemination scheme for VANETs, which leads to extensive rebroadcasting. Reliable emergency messages delivery is another substantial measure of performance, especially in dense traffic and high mobility scenarios. A generic solution to deal with these problems is to cluster vehicles, so that emergency messages can be re-broadcasted to adjacent clusters only by a smaller number of vehicles. Thus, reliable selections of cluster head and relay nodes are important to reduce the number of retransmissions. In this regard, we propose a clustering technique for Reduced Broadcast Overhead Scheme for Emergency Message Dissemination (RBO-EM). RBO-EM is based on the mobility metrics to strengthen the cluster formation, avoid communication overhead and maintain the message reliability in a high mobility scenario. Moreover, we introduce relay nodes based on estimated link stability to limit the number of retransmissions. RBO-EM is evaluated against eminent schemes by varying traffic densities and speed. Simulation results indicate that RBO-EM enables a reasonable performance gain in terms of coverage, end-to-end delay and message reliability.

“…In IoV, the vehicles move at high speed and hence there is frequent change in connectivity as well as topology. Due to the change in topology, the data transmission using DSRC depends on routing [14]. The routes are selected using Q-learning, sweep algorithm and deep reinforcement learning algorithm [15]- [18].…”

Section: Introductionmentioning

confidence: 99%

“…The routes are selected using Q-learning, sweep algorithm and deep reinforcement learning algorithm [15]- [18]. In [14] the authors have proposed a cluster based handoff and proposed dynamic edge-backup node (DEBCK). The drawback in this work is the failure of backup mobile edge-node or cluster head will lead to poor handoff or it may not allow to perform handoff whenever there is a need.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence based handover decision and network selection in heterogeneous internet of vehicles

Yusof

Asuncion

et al. 2021

IJEECS

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.