Optimized Link State Routing Protocol (OLSR) is an efficient routing protocol used for various Ad hoc networks. OLSR employs the Multipoint Relay (MPR) technique to reduce network overhead traffic. A mobility model's main goal is to realistically simulate the movement behaviors of actual users. However, the high mobility and mobility model is the major design issues for an efficient and effective routing protocol for real Mobile Ad hoc Networks (MANETs). Therefore, this paper aims to analyze the performance of the OLSR protocol concerning various random and group mobility models. Two simulation scenarios were conducted over four mobility models, specifically the  Random Waypoint model (RWP), Random Direction model (RD), Nomadic Community model (NC), and the Reference Point Group Model (RPGM) with a low as well as high random range mobility of the nodes.  Moreover, BonnMotion Software and Network simulator NS-3 used to implement the simulation scenarios. Further, the performance of the OLSR protocol analyzed and evaluated based on latency, routing overhead, and packet loss ratio metrics. According to the results, the OLSR protocol provides the best performance over the RWP model in a low mobility environment, whereas the Nomadic mobility model is suitable for OLSR protocol in a high mobility environment.

The utilization of conventional modeling strategies in the identification and control of a nonlinear dynamical system suffers from some weaknesses. These include absence of precise, conventional knowledge about the system, a high degree of uncertainty, strongly nonlinear and time-varying behavior. In this paper, a modified training algorithm for the identification and control of a nonlinear system using a soft-computing approach is proposed. Specifically, a modified structure of the Elman neural network with spike neural networks is proposed. This modified structure includes self-feedback, which provides a dynamic trace of the training algorithm. This self-feedback has weights, which can be trained during the training process. The simulation results show that the modified structure with the modified training algorithm is capable of the identification and control of a dynamic system in a more robust manor than when solely applying the other types of neural networks by 70% in terms of minimization of the percentage of error. INDEX TERMS Identification, dynamic system, modified Elman spike neural network, spike neural network.

An Intelligent Software Defined Network (ISDN) 1 based on an intelligent controller, can manage and control the 2 network in a remarkable way. In this paper, a methodology 3 is proposed to estimate the packet flow at the sensing plane 4 in the Software Defined Network-Internet of Things (SDN-IoT) 5 based on a Partial Recurrent Spike Neural Network (PRSNN) 6 congestion controller, to predict the next step ahead of packet 7 flow and thus, reduce the congestion that may occur. That is, the 8 proposed model (Spike ISDN-IoT) is enhanced with a congestion 9 controller. This controller works as a proactive controller in 10 the proposed model. In addition, we propose another intelligent 11 clustering controller based on an artificial neural network, which 12 operates as a reactive controller, to manage the clustering in the 13 sensing area of the Spike ISDN-IoT. Hence, an intelligent queuing 14 model is introduced to manage the flow table buffer capacity in 15 the data plane of the spike ISDN-IoT network, such that the 16 Quality of Service (QoS) of the whole network is improved. A 17 modified training algorithm is introduced to train the PRSNN 18 to adjust its weight and threshold in the hidden and output 19 layers in a parallel manner. The simulation results demonstrate 20 that the QoS is improved by (14.36%) when using PRSNN as a 21 congestion controller, as compared with a convolutional neural 22 network (CNN). 23 Index Terms-Partial Recurrent Spike NN, cluster head, SDN-24 IoT, traffic load prediction, Quality of Service. 25 I. INTRODUCTION 26 T HE concept of the Internet of Things (IoT) has been 27 made a reality by the creation of Wireless Sensor Net-28 works (WSNs), which have the capability of monitoring or 29 controlling different applications across the connectivity of the 30 Internet. The basic idea of IoT is to enable real objects that are 31 inserted with sensors, actuators, and network connectivity to 32 accumulate and shuffle data among themselves in a cooperative 33 way [1]. In other words, the IoT can be described by this 34 formula Things + (Intelligence + Network = IoT) [2]. Many 35 applications in the field of networks and the Internet require 36 high speed, accuracy, security, and a high quality of services in 37 the transfer of data. Accordingly, many solutions to enhance 38 the Internet and computer networks with a high quality of 39 services have been proposed, one of which is SDN-IoT. In 40 an SDN, the data plane basically consists of a number of 41 switches, routers, and gateways, while the control plane is42

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