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The named data networking (NDN)-based microservice-centric in-network computation poses various challenges in terms of interest aggregation and pending interest table (PIT) lifetime management. A same-named microservice-centric interest packet may have a different number of input parameters with nonidentical input values. In addition, the same-named interest packet with the same number of parameters may have different corresponding parameter values. The vanilla NDN request aggregation (based on the interest name, while ignoring the input parameters count and/or their corresponding values) may result in false aggregation. Moreover, the microservice-centric requested computations may fail to accomplish in the default 4s PIT timer due to the input size. To address these challenges, this paper presents MIA-NDN: microservice-centric interest aggregation in named data networking. We designed microservice-centric interest-naming to enable name-based communication. MIA-NDN develops a robust interest aggregation mechanism that not only performs the interest aggregation based on the interest name but also considers the input parameter counts and their corresponding values in the interest aggregation process to avoid false packet aggregations. A dynamic PIT timer mechanism based on input size was devised that avoids the PIT entry losses if the execution time exceeds the default PIT timer value to avoid computation losses and uphold the application quality of service (QoS). Extensive software-based simulations confirm that the MIA-NDN outperforms the benchmark scheme in terms of microservice-centric interest aggregation, microservice satisfaction rate, and communication overhead. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]