IET Networks最新文献

Internet-wide traffic redirection attacks have been reported for long, and are mainly caused by Border Gateway Protocol route hijacking. Such attacks can be quite harmful, impairing access to popular Internet sites for long periods. This work addresses the use of machine learning techniques (both unsupervised and supervised) leveraging from a distributed monitoring infrastructure of probes that measure the round trip time to Internet sites under surveillance. The detection process is separated into two stages: per-probe classification and a combination of individual probe decisions. Our results show that the best strategy is to classify using an unsupervised technique based on Tukey's method and to combine using Hidden Markov Models, due to its performance and adaptability to different attack types.

Social good can be defined as actions or tools that provide some sort of positive impact on people and society. In recent years, the scientific community has seen an increase in research relating to social good. In terms of technology, Blockchain, distributed ledger technologies and decentralised technologies in general such as the Interplanetary File System (IPFS), have had a positive impact towards improving social good, such as by introducing transparency, trust and security in supply chains, data sharing and other social good applications.

The aim of this Special Issue is to address the following question: ‘How does Blockchain impact the social good, and how can decentralisation improve ICT industries?’ In particular, blockchain technology can be a driver of innovation and have positive effects on our society, industry, legal systems and economic/financial systems by introducing trust among untrusted parties. Additionally, decentralised technologies can help democratise how services are delivered, therefore increasing the interoperability, transparency and security of Internet services. Lastly, the impact, future and limitations of all these technologies have different effects across specific domains, like industry, economy, society, law, etc.

A wide variety of research is being conducted to explore and discover possible challenges and opportunities to exploit blockchain and decentralised solutions for social good. This Special Issue is focused on research ideas, articles and experimental studies related to ‘Blockchain and Decentralized Solutions for Social Good’ that will advance knowledge concerning the application of the aforementioned technologies in the wide spectrum of social good.

This Special Issue is composed of four peer-reviewed papers of excellent quality.

Zichichi et al. propose a system for complex queries over Distributed Hash Tables or Distributed File Systems. The system makes use of a hypercube peer-to-peer overlay which manages the queries to be done on data. Each layer of the hypercube corresponds to specific keywords that are associated with the underlying data. Additionally, the paper proposes to introduce a governance layer to improve the decentralisation and scalability of the system. In particular, the governance layer is built using a Decentralised Autonomous Organization that manages rewards and organizational decisions by leveraging smart contracts. Lastly, the authors show the application of the system to geodata storing and retrieval. Thanks to a thorough evaluation, the authors show the benefit in terms of the performance of the hypercube overlay network, the overall time required to perform complex queries and the cost of the smart contracts.

Bapatla et al. propose PharmaChain, a supply chain network specifically designed to prevent the circulation of counterfeit pharmaceuticals. Pharmachain is divided into five logical components: IoT sensors to be installed on transport trucks

Below content is reversed to correct an error of citation in our original publication in journal IET Networks [1].

On page 147, Figure 3 title is to be replaced as ‘Fig 3. ETPB model path analyses [2] and variable definition’.

On page 146, Section 3.3, the sentence ‘As classical SEM model only represents a static system and cannot express behaviour change over time, to expand the ETPB SEM model with dynamic effects, we proposed a fluid analogy to postulate and parallel inventory management in supply chains’ has to be replaced as ‘As classical SEM model only represents a static system and cannot express behaviour change over time, to expand the ETPB SEM model with dynamic effects, we utilised a fluid analogy [2] to postulate and parallel inventory management in supply chains’.

The Internet of Things (IoT) has induced many advances in the modern world, thus always arousing great interest from the scientific community. Due to a large number of connected devices, this paradigm has put into circulation an enormous data quantity to be processed and offloaded while respecting latency and energy constraints that Central Cloud alone cannot meet. Hence, Fog Computing has come to fill these gaps by providing computing, management, and storage resources via small-distributed data centres located at the network edge. The majority of previous research on centralised clustering in an IoT environment did not consider both residual energy and the location of the IoT devices, as well as the signalling communication frequency, to solve an issue of extreme energy consumption during data exchange between IoT devices and Fog entities. Thus, the objective of this study is to find solutions taking into account these criteria to guarantee the Fog system's energy efficiency while coping with the energy consumption increase related to the handling of massive data volume. Indeed, the proposed study presents a novel approach based on Fog-IoT architecture to ensure intelligent energy management during communication and offloaded task processing in IoT applications. The simulation results show its effectiveness.

The Hard-state Protocol Independent Multicast—Dense Mode (HPIM-DM) multicast routing protocol is proposed. HPIM-DM is a hard-state version of PIM-DM that overcomes its poor convergence times and lack of resilience to replay attacks. Like PIM-DM, HPIM-DM is meant for dense networks and supports its operation on a unicast routing protocol and reverse path forwarding. However, routers maintain sense of the multicast trees at all times, allowing fast reconfiguration in the presence of network failures or unicast route changes. This is achieved by (i) keeping information on all upstream neighbours from which multicast data can be received, (ii) ensuring the reliable transmission and sequencing of control messages, and (iii) synchronizing the routing information immediately when a new router joins the network. The correctness of the protocol was extensively validated using model checking and logical reasoning. The protocol was fully implemented in Python, and the implementation is publicly available. Finally, we show both theoretically and experimentally that HPIM-DM has much better convergence times than PIM-DM.

The Deployment of new techniques to accommodate high data throughput, significant reliability, and quality of service (QoS) delivery prompts the use of 4G and 5G networks in several applications. In these networks, Vehicle-to-Everything (V2X) communications authorised by device-to-device cellular links (D2D) have gained momentum because of their potential to improve road safety, efficiency, and convenience. This improvement is to be provided by different types of V2X communications, including Vehicle-to-Pedestrian (V2P), Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Network (V2N) communications. However, considering several limiting factors such as mobility and increasing number of vehicles, efficiency of using wireless resources is a challenging task. Indeed, different research works on V2X radio resource allocation have been studied and evaluated with respect to several criteria. In this study, a survey of recent works but in a different way synthesising radio resource allocation methods is presented. Resource allocation classification into different categories is proposed. This classification is related to three main factors. The first takes into account the selection mode factor. It highlights the use of different approaches in each of the centralised, distributed, and mixed modes. The second one takes into account the considered performance criteria, including capacity, priority, delay etc. The last one focuses on the methods and scenarios used to evaluate a resource allocation approach.