Interest in broadband wireless access (BWA) has been growing due to increased user mobility and the need for data access at all times. IEEE 802.16e based WiMAX networks promise the best available quality of experience for mobile data service users. Unlike wireless LANs, WiMAX networks incorporate several quality of service (QoS) mechanisms at the Media Access Control (MAC) level for guaranteed services for data, voice and video. The problem of assuring QoS is basically that of how to allocate available resources among users in order to meet the QoS criteria such as delay, delay jitter and throughput requirements. IEEE standard does not include a standard scheduling mechanism and leaves it for implementer differentiation. Scheduling is, therefore, of special interest to all WiMAX equipment makers and service providers. This paper discusses the key issues and design factors to be considered for scheduler designers. In addition, we present an extensive survey of recent scheduling research. We classify the proposed mechanisms based on the use of channel conditions. The goals of scheduling are to achieve the optimal usage of resources, to assure the QoS guarantees, to maximize goodput and to minimize power consumption while ensuring feasible algorithm complexity and system scalability.

The explosive rise of intelligent devices with ubiquitous connectivity have dramatically increased Internet of Things (IoT) traffic in the cloud environment and created potential attack surfaces for cyber-attacks. Traditional security approaches are insufficient and inefficient to address security threats in cloud-based IoT networks. In this vein, software defined networking (SDN), network function virtualization (NFV), and machine learning techniques introduce numerous advantages that can effectively resolve cybersecurity matters for cloud-based IoT systems. In this paper, we propose a collaborative and intelligent network-based intrusion detection system (NIDS) architecture, namely SeArch for SDN-based cloud IoT networks. It composes a hierarchical layer of intelligent IDS nodes working in collaboration to detect anomalies and formulate policy into the SDN-based IoT gateway devices to stop malicious traffic as fast as possible. We first describe a new NIDS architecture with a comprehensive analysis in terms of the system resource and path selection optimizations. Next, the system process logic is extensively investigated through main consecutive procedures, including initialization, runtime operation, and database update. Afterward, we conduct a detailed implementation of the proposed solution in an SDN-based environment and perform a variety of experiments. Finally, evaluation results of the SeArch architecture yield outstanding performance in anomaly detection and mitigation as well as bottleneck problem handling in the SDN-based cloud IoT networks in comparison with existing solutions.

This paper proposes a novel traffic monitoring framework, namely, DeepMonitor, for SDN-based IoT networks to provide fine-grained traffic analysis capability for different IoT traffic types at the network edges. Specifically, we first develop an intelligent flow rule match-field control system, called DeepMonitor agent, for SDN-based IoT edge nodes, taking different granularity-level requirements and their maximum flow-table capacity into consideration. We then formulate the control optimization problem for each edge node employing the Markov decision process (MDP). Next, we develop a double deep ${Q}$ -network (DDQN) algorithm to quickly achieve the optimal flow rule match-field policy. Moreover, we propose a federated DDQN-based traffic monitoring mechanism to significantly improve the learning performance of the edge nodes. The results obtained through extensive emulations show that by applying the DeepMonitor, the flow-table overflow problem at the edge nodes can be completely bypassed. The average number of match-fields in a flow rule achieved by DeepMonitor is increased by approximately 37% (for medium and diverse granularity-level requirements) and 41.9% (for high granularity-level requirement) compared to that of an existing solution, i.e., FlowStat. Finally, by adopting DeepMonitor, the DDoS attack detection performance of an intrusion detection system can be enhanced by up to 22.83% compared with that of FlowStat.