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Nowadays, with attention to soar in the number of network users, it is necessary to find new approaches to revolutionize network operation. Vehicular ad-hoc networks are bound to play a pivotal role in communication, therefore raising the traffic in the network, using only WiFi is unlikely to address this problem. Vehicles could use SDN and other networks such as 4G as well as 5G to distribute traffic to different networks. Moreover, many approaches for handling different data types are inappropriate due to the lack of attention to the data separation idea. In this paper, we proposed a control scheme called Improve Quality of Service in DTN and Non-DTN (IQDN) which works based on vehicle communication infrastructure using SDN idea. IQDN separates data to Delay-Tolerant Data (DTD), and Delay-Intolerant Data (DID) where the former buffers in a vehicle till the vehicle enters an RSU range and sends DTD using IEEE 802.11p. DID packets are sent by cellular networks and LTE. To transmit DTD via IEEE 802.11p, the network capacity is evaluated by SDN. If that network has room to transmit the data, SDN sends a control message to inform the vehicle. Simulations show that sending data over RSU and LTE increases the throughput and decreases the congestion, so the quality of service improves.

Performance of multi-hop ad hoc networks is highly affected by the hidden and exposed node problems. The RTS/CTS handshaking protocol used in the IEEE 802.11 standard was designed to solve the hidden node problem; however it was not successful in thoroughly eradicating the problem. We claim that it is sometimes possible to earn more throughput by controlling or even wisely creating this phenomenon. We suggest a solution to improve the throughput in IEEE 802.11based multi-hop ad hoc networks by modifying the NAV mechanism. In our approach every node controls the packet transmission decision-making through a set of fuzzy-like rules which are based on the node queue level and environment variables. Simulation results show that our approach increases the overall throughput of the network in multi-hop scenarios.

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Recently, IEEE 802.11 wireless ad-hoc networks become popular due to their flexibility and lack of infrastructure. Also they can easily be set up and almost available anywhere and anytime. Most researches which evaluate performance of these type of networks suppose saturation condition for the sake of analytical simplicity. However, most recent applications have O N/O F F property that causes unsaturation condition. In order to have that condition, each node is modeled by a M/G/1 Markov model and has Poisson distribution as its packet arrival model. Based on these assumptions, average and variance of service time are analyzed accurately. Delay as an important QoS parameter in time critical applications, is analyzed precisely in this paper. Computing service time variance ables us to evaluate average packet delay by Pollaczek-Khinchin equation. It also helps us to estimate probability density function of its service time. To prove our analytic results, extensive simulations have been done, which show that the analytical and simulation results match perfectly.

Due to the expansion of IoT applications which causes the generation of a massive amount of data, data routing is one of the most important challenges in these networks. The Routing Protocol for Lowpower and Lossy Networks (RPL) was developed to cope with the Low-power and lossy network constraints, which play a significant role in IoT networks. Although most IoT applications involve mobility and topology change that makes mobility support a substantial need to prevent disconnection of nodes and data loss, the RPL is designed for static networks. This article proposes a mobility support method called MSE as an extension of RPL. The MSE supports mobility of all nodes except the root node, and it provides a seamless connection during the mobility. It also manages a situation when a physical obstacle settles between two paired nodes in a dynamic environment. To this end, it uses a dynamic trickle timer with two different ranges, a neighbor link quality table, a function to select the best parent in case of mobility, confidence, critical zones, and a blacklist. Simulations in multiple scenarios indicate that MSE, despite causing a slight increase in signaling cost and power consumption, significantly reduces hand-off delay, increases Packet Delivery Ratio, reduces the number of lost data packets, and outperforms both RPL as a reactive and mRPL as a proactive protocol regarding mobility.