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

Abstract

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.

Power Generation Using Waste Materials Based Iot Approach

“…It intends to create electrical energy power from Municipal Solid Waste Materials, transform that low electrical energy into higher electrical energy power, and charge mobile phones with the generated electricity. Given the tremendous improvements and growth in the field of Ai/machine Learning Technologies [41][42][43][44][45][46] and its reach in the field of predictive technologies, this is the most outstanding solution for the forecasting applications required. This article and its production will be beneficial in terms of renewable energy and a secondary source of electrical energy.…”

Evaluating Latency in Fifth-Generation Vehicle-To-Tverything Communications Using Adaptive Neuro-Fuzzy Inference System Model

“…Mathematical analysis of handover can improve the delay in 5G V2X communications [3]. Hussain et al 2021 [11] enhanced the throughput, delay, and packet loss during the handover process using a Q-learning algorithm and fuzzy logic. Liu et al [15] presented a fuzzy model that improves the handover in 5G communications.…”

“…(1) 5G performance evaluations of data delivery using a V2V network [4][5]35] (2) Evaluations of delay, throughput, and packet loss in 5G using V2V and V2I with multi-access edge computing (MEC) [26,30] (3) 5G performance evaluations of V2V and V2I without MEC or a core network [12] [27-28-29] (4) Evaluations of 5G scheduling schemes using V2V and V2I [2,9] (5) Measurements of theoretical routing performance in 5G using V2V and V2I [14] (6) Network-slicing enhancement in 5G using V2V and V2I [40] To enhance the NSA mode in 5G, researchers have recently improved the beamforming [18][19]22], handover [3,11,15], or beamforming with handover [34]. However, to our knowledge, no 5G V2X evaluations in NSA mode in the V2V, V2I, and V2N environments of a highway scenario with VoIP applications have employed adaptive neural fuzzy neural models, which can simultaneously consider both beamforming and handover.…”

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

“…The authors in [20,21] have discussed Energy-Efficient system modeling for Ad-Hoc Networks and in [22] multipath routing protocols for MANET. Hussain et al,in [23], have proposed a new method for network selection called the Fuzzy Convolution Neural Network. The handoff decision, based on performance metrics like vehicle speed and signal strength, is made by utilizing the Shannon entropy-based Q-learning algorithm.…”