Multicast data delivery can significantly reduce traffic in operators' networks, but has been limited in deployment due to concerns such as the scalability of state management. This paper shows how multicast can be implemented in contemporary software defined networking (SDN) switches, with less state than existing unicast switching strategies, by utilising a Bloom Filter (BF) based switching technique. Furthermore, the proposed mechanism uses only proactive rule insertion, and thus, is not limited by congestion or delay incurred by reactive controlleraided rule insertion. We compare our solution against common switching mechanisms such as layer-2 switching and MPLS in realistic network topologies by modelling the TCAM state sizes in SDN switches. The results demonstrate that our approach has significantly smaller state size compared to existing mechanisms and thus is a multicast switching solution for next generation networks.

* This paper was presented in 2020 Global Internet of Things Summit (GIoTS) (pp. 1-6). IEEE. using a Matlab based simulation framework. The simulation results demonstrate that the proposed algorithm balances the needs of the blockchain agents to maximise the overall social welfare, i.e. the sum of profits across all parties.

In recent years, the information-centric networking (ICN) concept has been attracting increasing attention of the research community. The aim is to overcome intrinsic inefficiencies of the existing host-to-host communication paradigm, as well as to provide new and enhanced services to mobile and fixed users. A key feature of ICN is the support for in-network content caching. In this paper, we present a new cache-aware routing scheme for ICN. Our scheme takes into account the information about the locations of caches in the network and constructs delivery paths for efficient content dissemination. The proposed approach does not impose additional signaling overhead in the network; while at the same time it is agnostic of the cached contents. The performance of the proposed scheme is verified by simulation studies, which show an up to 50% delay reduction compared to traditional routing approaches.

Blockchain technology has brought significant advantages for security and trustworthiness, in particular for Internet of Things (IoT) applications where there are multiple organisations that need to verify data and ensure security of shared smart contracts. Blockchain technology offers security features by means of consensus mechanisms; two key consensus mechanisms are, Proof of Work (PoW) and Practical Byzantine Fault Tolerance (PBFT). While the PoW based mechanism is computationally intensive, due to the puzzle solving, the PBFT consensus mechanism is communication intensive due to the all-to-all messages; thereby, both may result in high energy consumption and, hence, there is a trade-off between the computation and the communication energy costs. In this paper, we propose a hybrid-blockchain (H-chain) framework appropriate for scenarios where multiple organizations exist and where the framework enables private transaction verification and public transaction sharing and audit, according to application needs. In particular, we study the energy consumption of the hybrid consensus mechanisms in H-chain. Moreover, this paper proposes a reward plan to incentivize the blockchain agents so that they make contributions to the H-chain while also considering the energy consumption. While the work is generally applicable to IoT applications, the paper illustrates the framework in a scenario which secures an IoT application connected using a software defined network (SDN). The evaluation results first provide a method to balance the public and private parts of the H-chain deployment according to network conditions, computation capability, verification complexity, among other parameters. The simulation results demonstrate that the reward plan can incentivize the blockchain agents to contribute to the H-chain considering the energy consumption of the hybrid consensus mechanism, this enables the proposed H-chain to achieve optimal social welfare.

The Internet of Things is a fast emerging technology, however, there have been a significant number of security challenges that have hindered its adoption. This work explores the use of machine learning methods for anomaly detection in network traffic of an IoT network that is connected through a Software Defined Network (SDN). The use of SDN allows a hierarchical approach to machine learning with the aim of reducing the packet level processing of anomaly detection at the edge through applying additional, centralized, machine learning in the SDN controller. For the sake of evaluation, we compare several supervised classification algorithms using a publicly available dataset. The results support a decision-tree based approach and show that the proposed solution promises a considerable reduction in the per-packet processing at the network edge compared to a single stage classifier.