The Internet of things (IoT) has opened new dimensions of novel services and computing power for modern living standards by introducing innovative and smart solutions. Due to the extensive usage of these services, IoT has spanned numerous devices and communication entities, which makes the management of the network a complex challenge. Hence it is urgently needed to redefine the management of the IoT network. Software-defined networking (SDN) intrinsic programmability and centralization features simplify network management, facilitate network abstraction, ease network evolution, has the potential to manage the IoT network. SDN’s centralized control plane promotes efficient network resource management by separating the control and data plane and providing a global picture of the underlying network topology. Apart from the inherent benefits, the centralized SDN architecture also brings serious security threats such as spoofing, sniffing, brute force, API exploitation, and denial of service, and requires significant attention to guarantee a secured network. Among these security threats, Distributed Denial of Service (DDoS) and its variant Low-Rate DDoS (LR-DDoS), is one of the most challenging as the fraudulent user generates malicious traffic at a low rate which is extremely difficult to detect and defend. Machine Learning (ML), especially Federated Learning (FL), has shown remarkable success in detecting and defending against such attacks. In this paper, we adopted Weighted Federated Learning (WFL) to detect Low-Rate DDoS (LR-DDoS) attacks. The extensive MATLAB experimentation and evaluation revealed that the proposed work ignites the LR-DDoS detection accuracy compared with the individual Neural Networks (ANN) training algorithms, existing packet analysis-based, and machine learning approaches.

The participating nodes in Wireless Sensor Networks (WSNs) are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. Energy is one of the major constraints which requires an efficient mechanism that takes into account the energy consumption of nodes to prolong the network lifetime. Particularly in the large scale heterogeneous WSNs, this challenge becomes more critical due to high data collection rate and increased number of transmissions. To this end, clustering is one of the most popular mechanisms which is being used to minimize the energy consumption of nodes and prolong the lifetime of the network. In this paper, therefore, we propose a robust clustering mechanism for energy optimization in heterogeneous WSNs. In the proposed scheme, nodes declare themselves as cluster head (CH) based on available resources such as residual energy, available storage and computational capability. The proposed scheme employs the multi criteria decision making technique named as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) which allows the child nodes to select the optimal CH among several potential CH candidates. Moreover, we also propose mechanisms such as CH-acquaintanceship and CH-friendship in order to prolong the network lifetime. Simulation results show that our proposed scheme minimizes the control overhead, reduces the power consumption and enhances overall lifetime of the network by comparing with the most recent and relevant proposed protocol for WSNs.

Recent years have witnessed the huge popularity of Information-Centric Networking (ICN) and its realization as Named Data Networking (NDN) in the context of wireless sensor networks (WSNs). The participating nodes in WSNs are usually equipped with a single radio interface. The existing solutions lack in providing the efficient next forwarder selection in NDN-based single radio WSNs. In this work, we propose a collaborative Interest and Data Forwarding (CIDF-WSN) Strategy for Named Data Wireless Sensor Networks. CIDF-WSN develop a Neighbor Information Base (NFIB) which enables the node to select the optimal next-hop relay in Interest packet forwarding. An efficient Interest packet processing mechanism assisted by the Interest Cache Table (ICT) is provided to avoid Interest packets loss and frequent re-transmissions. In addition, CIDF-WSN also provides a robust Data packet transfer mechanism accompanied by the Temp Cache Table (TCT) to avoid Data packet losses and to ensure well-timed content delivery. Simulation results reveal that CIDF-WSN outperforms the recently published works in terms of Interest satisfaction rate, total energy consumption, Data retrieval delays, and communication overhead.

Information-centric networking (ICN) and its fruition, the named data networking (NDN) is a paradigm shift from host-centric address-based communication architecture to the content-centric name-based one. ICN intends to resolve various major issues faced by today’s internet architecture such as privacy, security, consistent routing, and mobility, to name a few. With the massive increase of mobile data traffic in today’s era, mobility is one of the major concerns in networking. On the one hand, ICN realization i.e., the NDN follows a pull-based communication model and natively supports the consumer (end-user) mobility in wired networks by maintaining the forwarding states on intermediate nodes. Nevertheless, the mobile consumer nodes confront issues in wireless networking environments such as excessive energy consumption as a result of request flooding, content retrieval delays due to intermittent connectivity, and bandwidth consumption due to the broadcasting nature of the wireless medium, among others. The producer (content-generator) mobility, on the other hand, was not initially supported in the original architectural design of NDN for both wired and wireless networks. Therefore, to efficiently address the degradation issues incurred by mobile consumer/producer nodes, a plethora of mobility management schemes have been proposed over the recent few years. In this paper, we provided a detailed survey on the existing research efforts—in the context of producer as well as consumer mobility, that have been proposed in the literature. Moreover, we outlined various research directions considering the role of mobility in futuristic technologies such as artificial intelligence-enabled smart networks, edge computing, software-defined networking, vehicular-fog computing, autonomous driving, and resource-constrained Internet of Things.

To efficiently allocate communication and computation resources among enhanced mobile broadband (eMBB) and ultrareliable low-latency communication (uRLLC) applications, this article proposes a joint communication and computation resource allocation scheme in sixth generation (6G) information-centric networks (ICNs) named: CCR-ICN. To achieve efficient communication and computation resources sharing, the CCR-ICN scheme designs a microservice-centric interest naming structure to enable named-based communication. Furthermore, the CCR-ICN scheme designs a robust forwarding strategy specifically tailored to efficiently allocate communication resources, such as bandwidth among eMBB and uRLLC application requests. In addition, for the allocation of computation resources, the proposed scheme designs a priority-driven computation resources allocation mechanism ensuring effective allocation of multiaccess edge computing computation resources. To evaluate the proposed scheme, we performed extensive simulations in a widely used NS-3-based ndnSIM simulator and the results reveal the CCR-ICN achieves 66.82% higher communication resource allocation, and a 17.51% lower communication overhead compared to the TS-6G scheme. Moreover, the CCR-ICN performs computation request satisfaction at 42.03% and 12.72% higher than the TS-6G and extended weighted fair queueing with latency constraint (EWFQ/LC) schemes, respectively. Furthermore, the CCR-ICN achieves computation resource utilization of 35.54% and 21.63% lower than the EWFQ/LC and TS-6G schemes, respectively.