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One of the most promising application areas of the Industrial Internet of Things (IIoT) is Vehicular Ad hoc NETworks (VANETs). VANETs are largely used by Intelligent Transportation Systems (ITS) to provide smart and safe road transport. To reduce the network burden, Software Defined Networks (SDNs) acts as a remote controller. Motivated by the need for greener IIoT solutions, this paper proposes an energy-efficient end-to-end security solution for Software Defined Vehicular Networks (SDVN). Besides SDN’s flexible network management, network performance, and energy-efficient end-toend security scheme plays a significant role in providing green IIoT services. Thus, the proposed SDVN provides lightweight end-to-end security. The end-to-end security objective is handled in two levels: i) In RSU-based Group Authentication (RGA) scheme, each vehicle in the RSU range receives a group id-key pair for secure communication and ii) In private-Collaborative Intrusion Detection System (p-CIDS), SDVN detects the potential intrusions inside the VANET architecture using collaborative learning that guarantees privacy through a fusion of differential privacy and homomorphic encryption schemes. The SDVN is simulated in NS2 & MATLAB, and results show increased energy efficiency with lower communication and storage overhead than existing frameworks. In addition, the p-CIDS detects the intruder with an accuracy of 96.81% in the SDVN

A massive increase in the number of mobile devices and data hungry vehicular network applications creates a great challenge for Mobile Network Operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in vehicular environment plays a significant role in offloading the vehicle s data traffic from congested cellular network s licensed spectrum to the free unlicensed WiFi spectrum with the help of Road Side Units (RSUs). In this paper, an Intelligent Reward based Data Offloading in Next Generation Vehicular Networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within IR-DON architecture, an Intelligent Access Network Discovery and Selection Function (I-ANDSF) module with Q-Learning, a reinforcement learning algorithm is designed. I-ANDSF is modeled under Software-Defined Network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. Simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed QoS, reduced delay and higher throughput achieved by the I-ANDSF module.

Currently, the increasing demands of user terminals has surged drastically and pulling up the global data traffic along. According to 3GPP, offloading is one of the most beneficial and advantageous options to handle this critical traffic bottleneck, however, both Long Term Evolution (LTE) and Wireless Local Area Network (WLAN) are loosely coupled. To mitigate the User Equipment (UE) from latency issues during offloading and for tighter integration of LTE and WLAN radio networks, LTE-WLAN Aggregation (LWA) was introduced by 3GPP which is apparently suitable for Internet of Things (IoT) devices. However, LWA is not suitable for high mobility scenarios as UEs’ information need to be updated for every new environment because of the frequent aggregation triggers which are mostly non-optimal and demands for a high-level controller. To resolve the disadvantage of non-optimal aggregation triggers, in this paper, we proposed Software Defined Networking (SDN) based approach for LWA, named as LWA under SDN Assistance (LWA-SA). In this approach, SDN initiates aggregation appropriately between LTE and an optimal WLAN Access Point (AP) which avoids frequent reconnections and deprived services. As multiple parameters are required for selection of an optimal WLAN AP, so we use Genetic Algorithm (GA) that considers each parameter as fitness value for the selection of optimal WLAN AP. This maximizes the throughput of UE and reduces the traffic pressure over licensed spectrum. Further, mathematical model is formulated that uses Karush-Kuhn-Tucker (KKT) to find the maximum attainable throughput of a UE. Using NS-3, we compared our approach with offloading scenarios and LWA. The simulation results clearly depict that LWA-SA outperforms existing schemes and achieves higher throughput.

Currently, the increasing demands of user terminals has surged drastically and pulling up the global data traffic along. According to 3GPP, offloading is one of the most beneficial and advantageous options to handle this critical traffic bottleneck, however, both Long Term Evolution (LTE) and Wireless Local Area Network (WLAN) are loosely coupled. To mitigate the User Equipment (UE) from latency issues during offloading and for tighter integration of LTE and WLAN radio networks, LTE-WLAN Aggregation (LWA) was introduced by 3GPP which is apparently suitable for Internet of Things (IoT) devices. However, LWA is not suitable for high mobility scenarios as UEs’ information need to be updated for every new environment because of the frequent aggregation triggers which are mostly non-optimal and demands for a high-level controller. To resolve the disadvantage of non-optimal aggregation triggers, in this paper, we proposed Software Defined Networking (SDN) based approach for LWA, named as LWA under SDN Assistance (LWA-SA). In this approach, SDN initiates aggregation appropriately between LTE and an optimal WLAN Access Point (AP) which avoids frequent reconnections and deprived services. As multiple parameters are required for selection of an optimal WLAN AP, so we use Genetic Algorithm (GA) that considers each parameter as fitness value for the selection of optimal WLAN AP. This maximizes the throughput of UE and reduces the traffic pressure over licensed spectrum. Further, mathematical model is formulated that uses Karush-Kuhn-Tucker (KKT) to find the maximum attainable throughput of a UE. Using NS-3, we compared our approach with offloading scenarios and LWA. The simulation results clearly depict that LWA-SA outperforms existing schemes and achieves higher throughput.