Neuro-fuzzy Architecture for Handover Decision Making in Emerging Heterogeneous Networks

In emerging heterogeneous networks, seamless vertical handover is a critical issue. There must be a trade-off between the handover decision delay and accuracy. This paper’s concern is to contribute to reliable vertical handover decision making that makes a trade-off between complexity and effectiveness. So, the paper proposes a neuro-fuzzy architecture that joints the capacity of learning of the artificial neural networks with the power of linguistic interpretation of the fuzzy logic. The architecture can learn from experience how executing a handover to a particular access network affects the quality of service. Simulation results reveal that this architecture is fast, enhances the overall performance and reliability better than the fuzzy logic-based approach.

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

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.

New Network Selection Algorithm Based on Cosine Similarity Distance and PSO in Heterogeneous Wireless Networks

Future wireless communication networks will be composed of different technologies with complementary characteristics. Thus, vertical handover (VHO) must support seamless mobility in such heterogeneous environments. The network selection is an important phase in the VHO process and it can be formulated as a multiattribute decision-making problem. So, the mobile terminal equipped with multiple interfaces will be able to choose the most suitable network. This work proposes an access network selection algorithm, based on cosine similarity distance, subjective weights using Fuzzy ANP, and objective weights using particle swarm optimization. The comprehensive weights are based on the cosine similarity distance between the networks and the ideal network. Finally, the candidate network with the minimum cosine distance to the ideal network will be selected in the VHO network selection stage. The performance analysis shows that our proposed method, based on cosine similarity distance and combination weights, reduces the ranking abnormality and number of handoffs in comparison with other MADM methods in the literature.

Secure Vertical Handover to NEMO Using Hybrid Cryptosystem

To provide security to all pairs of nodes in network mobility (NEMO) while executing the handoff between different technologies, a hybrid cryptosystem with a suitable network selection mechanism is proposed. All pairs of nodes, i.e., Mobile Node (MN), Mobile Router (MR), Correspondent Node (CN) and MN, and Home Agent (HA), respectively, are considered. A proper security mechanism is proposed to provide confidentiality to Bound Update (BU) during handoff and conversation between MN, MR, and HA using the elliptic curve cryptography (ECC). In this solution, a network selection mechanism is proposed based on user preference and Received Signal Strength (RSS) in a heterogeneous network. The proposed model can protect the communication using security analysis from all NEMO standard attacks. Whenever NEMO moves, MR intimates to HA about the address change using (BU) and MR receives Binding Acknowledgement (BA) as a reply. During data (frame) exchange and registration between MN, CN, and HA, various security threats arise. In the earlier work, only the security solution is given, and the best network selection algorithm is not provided in a heterogeneous environment. Therefore, in this paper, the best network selection is contributed based on Received Signal Strength (RSS) and user preferences. A comparison of the proposed model is drawn with Return Routability Procedure (RRP). Authentication is provided for communication between MN and CN. The proof is derived using BAN logic. Many standard security attacks have been successfully avoided on all pairs of communications. It has been observed that the proposed model achieves 2.4854% better throughput than the existing models. Also, the proposed model reduces the handoff latency and packet loss by 2.7482% and 3.8274%, respectively.