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The proliferation of the Internet of Things (IoT) is occurring swiftly and is all-encompassing. The cyber attack on Dyn in 2016 brought to light the notable susceptibilities of intelligent networks. The issue of security in the realm of the Internet of Things (IoT) has emerged as a significant concern. The security of the Internet of Things (IoT) is compromised by the potential danger posed by exploiting devices connected to the Internet. The susceptibility of Things to botnets poses a significant threat to the entire Internet ecosystem (smart devices). In recent years, there has been a simultaneous evolution in the complexity and variety of security attack vectors. Therefore, it is imperative to analyse IoT methodologies to detect and alleviate emerging security breaches. The present study analyses network datasets, distinguishing between those of the Internet of Things (IoT) and those that do not, and provides a thorough overview of the findings. Our primary focus is on IoT Network Intrusion Detection (NID) studies, wherein we examine the available datasets, tools, and machine learning (ML) techniques employed in the implementation of network intrusion detection (NID). Subsequently, an evaluation, assessment, and summary of the current state-of-the-art research on IoT-related Network Intrusion Detection (NID) conducted between 2018 and 2024 is presented. This includes an analysis of the publication year, dataset, attack types, experiment results, and the advantages, disadvantages, and classifiers employed in the studies. This review emphasises research related to IoT NID that employs Supervised Machine Learning classifiers, owing to the high success rate of such classifiers in security and privacy domains. Furthermore, this survey incorporates a comprehensive analysis of research endeavours on IoT NID. Furthermore, we have identified publicly available IoT datasets that can be utilised for NID experiments, which would benefit academic and industrial research purposes. Moreover, we analyse potential prospects and future advancements. The review’s findings indicate that the Internet of Things (IoT) has been substantiated by its swift proliferation in recent times, leading to even broader network coverage. This study presented conventional datasets gathered over a decade ago and current datasets published within the past decade and utilised in recent research. The survey provides a succinct overview of prevailing research trends in IoT NID for security professionals.

Open Radio Access Network (RAN) introduces a groundbreaking industry standard for Radio Access Networks, fostering vendor interoperability and network flexibility through open interfaces while leveraging network softwarization, Artificial, and Machine Learning Intelligence; however, it also poses significant security challenges due to its unique configuration, prompting stakeholders to cautiously approach its deployment and necessitating thorough analysis and implementation of security measures and standards. This paper systematically examines existing literature and case studies to underscore the indispensable role of Intrusion Detection Systems (IDS) in identifying and mitigating security breaches within Open RAN environments. We elucidate the distinct challenges that Open RAN’s disaggregated architecture introduced and classify them into technical and non-technical threats. Finally, we discussed a series of new advancements gaining momentum in the Open RAN security domain and provided insights for future research directions.

In this paper, we propose Triangular Code (TC), a new class of fountain code with near-zero redundancy and linear encoding and decoding computational complexities of O(Lklogk), where k is the packet batch size and L is the packet data length. Different from previous works where the optimal performance of codes has been shown under asymptotic assumption, TC enjoys near-zero redundancy even under non-asymptotic settings for small-moderate number of packets. These features make TC suitable for practical implementation in battery-constrained devices in IoT, D2D and M2M network paradigms to achieve scalable reliability, and minimize latency due to its low decoding delay. TC is a non-linear code, which is encoded using the simple shift and XOR addition operations, and decoded using the simple back-substitution algorithm. Although it is nonlinear code at the packet level, it remains linear code when atomized at the bit level. We use this property to show that the back-substitution decoder of TC is equivalent to the Belief Propagation (BP) decoder of LT code. Therefore, TC can benefit from rich prolific literature published on LT code, to design efficient code for various applications. Despite the equivalency between the decoders of TC and LT code, we show that compared to state-of-the-art optimized LT code, TC reduces the redundancy of LT code by 68%–99% for k reaching 1024.© 2022 Published by Elsevier Ltd.

The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

It is envisaged that 5G can enable many vehicular use cases that require high capacity, ultra-low latency and high reliability. To support this, 5G proposes the use of dense small cells technology as well as and highly directional mmWave systems deployment, among many other new advanced communication technologies, to boost the network capacity, reduce latency and provide high reliability. In such systems, enabling vehicular communication, where the nodes are highly mobile, requires robust mobility management techniques to minimise signalling cost and interruptions during frequent handovers. This presents a major challenge that communication system engineers need to address to realise the promise of 5G systems for V2X and similar applications. In this paper, we provide an overview of recent progresses in the development of handover and beam management techniques in 5G communication systems. We conduct a critical appraisal of current research on beam level and cell level mobility management in 5G mmWave networks considering the ultra-reliable and low-latency communication requirements within the context of V2X applications. We also provide an insight into the open challenges and the emerging trends as well as the possible evolution beyond the horizon of 5G.

This paper surveys the literature relating to the application of machine learning to fault management in cellular networks from an operational perspective. We summarise the main issues as 5G networks evolve, and their implications for fault management. We describe the relevant machine learning techniques through to deep learning, and survey the progress which has been made in their application, based on the building blocks of a typical fault management system. We review recent work to develop the abilities of deep learning systems to explain and justify their recommendations to network operators. We discuss forthcoming changes in network architecture which are likely to impact fault management and offer a vision of how fault management systems can exploit deep learning in the future. We identify a series of research topics for further study in order to achieve this.

Recently, the Internet of Things (IoT) is attracting significant attention from both academia and industry. To connect the huge amount of IoT devices effectively, software-defined networking (SDN) is considered as a promising way because of its centralized network management and programmable routing logic. However, due to the limited resources in both the data plane and the control plane, SDN is vulnerable to the new-flow attack, which can disable the SDN-based IoT by exhausting the switches or the controller. Therefore, in this paper, we propose a smart security mechanism (SSM) to defend against the new-flow attack. The SSM uses the standard southbound and northbound interfaces of SDN, and it includes a low-cost method that monitors the new-flow attack by reusing the asynchronous messages on the control link. The monitor method can differentiate the new-flow attack from the normal flow burst by checking the hit rate of the flow entries. Based on the monitoring result, the SSM uses a dynamic access control method to mitigate the new-flow attack by perceiving the behavior of the security middleware in the IoT. The dynamic access control method can intercept the attack flows at their access switch. Extensive simulations and testbed-based experiments are conducted and the corresponding results verify the feasibility of our claims.

The in-network caching strategy in named data networking can not only reduce the unnecessary fetching of content from the original content server deep in the core network and improve the user response time, but also ease the traffic in the core network. However, challenges exist in in-network caching, such as the distributed locations of storage and relatively small cache space which limit the hit rate, and the cache management introduces further overhead. In this paper, we propose a two-layer hierarchical cluster-based caching solution to improve in-network caching efficiency. A network is grouped into several clusters, then, a cluster-head is nominated for each cluster to make caching decision. The clustering approach offers scalability and permits multiple aspects of inputs to be used for decision making. Our solution jointly considers the location and content popularity for caching. We implement our strategy in ndnSIM and test it on GEANT-based network and AS3967 network. Our simulation results show significant improvement over its peers.

Information centric networking (ICN) has been recently proposed as a prominent solution for content delivery in vehicular ad hoc networks. By caching the data packets in vehicular unused storage space, vehicles can obtain the replicate of contents from other vehicles instead of original content provider, which reduces the access pressure of content provider and increases the response speed of content request. In this paper, we propose a community similarity and population-based cache policy in an ICN vehicle-to-vehicle scenario. First, a dynamic probability caching scheme is designed by evaluating the community similarity and privacy rating of vehicles. Then, a caching vehicle selection method with hop numbers based on content popularity is proposed to reduce the cache redundancy. Moreover, to lower the cache replacement overhead, we put forward a popularity prediction-based cooperative cache replacement mechanism, which predicts and ranks popular content during a period of time. Simulation results show that the performance of our proposed mechanisms is greatly outstanding in reducing the average time delay and increasing the cache hit ratio and the cache hit distance.

The parameters of physical layer radio frame for 5th generation (5G) mobile cellular systems are expected to be flexibly configured to cope with diverse requirements of different scenarios and services. This paper presents a frame structure and design, which is specifically targeting Internet of Things (IoT) provision in 5G wireless communication systems. We design a suitable radio numerology to support the typical characteristics, that is, massive connection density and small and bursty packet transmissions with the constraint of low-cost and low complexity operation of IoT devices. We also elaborate on the design of parameters for random access channel enabling massive connection requests by IoT devices to support the required connection density. The proposed design is validated by link level simulation results to show that the proposed numerology can cope with transceiver imperfections and channel impairments. Furthermore, the results are also presented to show the impact of different values of guard band on system performance using different subcarrier spacing sizes for data and random access channels, which show the effectiveness of the selected waveform and guard bandwidth. Finally, we present system-level simulation results that validate the proposed design under realistic cell deployments and inter-cell interference conditions.

Connected vehicles are seen as the next frontier for the mobile revolution. By bringing Internet connectivity to vehicles, not only infotainment of the vehicles can be enriched, but also safety and driving experience of the drivers can be enhanced. Connected vehicles can access real-time information about traffic conditions to make the journey more time efficient. They can also share local traffic information in real-time to assist other vehicles making better route planning. Consolidating various transportation data from individual vehicles and other transportation infrastructure can further enhance Intelligent Transportation System (ITS).

Transmission range plays an important role in the deployment of a practical underwater acoustic sensor network (UWSN), where sensor nodes equipping with only basic functions are deployed at random locations with no particular geometrical arrangements. The selection of the transmission range directly influences the energy efficiency and the network connectivity of such a random network. In this paper, we seek analytical modeling to investigate the tradeoff between the energy efficiency and the network connectivity through the selection of the transmission range. Our formulation offers a design guideline for energy-efficient packet transmission operation given a certain network connectivity requirement.

Accurate and low-cost autonomous self-localization is a critical requirement of various applications of a large-scale distributed wireless sensor network (WSN). Due to its massive deployment of sensors, explicit measurements based on specialized localization hardware such as the Global Positioning System (GPS) is not practical. In this paper, we propose a low-cost WSN localization solution. Our design uses received signal strength indicators for ranging, light weight distributed algorithms based on the spring-relaxation technique for location computation, and the cooperative approach to achieve certain location estimation accuracy with a low number of nodes with known locations. We provide analysis to show the suitability of the spring-relaxation technique for WSN localization with cooperative approach, and perform simulation experiments to illustrate its accuracy in localization.

Wireless communication between sensors allows the formation of flexible sensor networks, which can be deployed rapidly over wide or inaccessible areas. However, the need to gather data from all sensors in the network imposes constraints on the distances between sensors. This survey describes the state of the art in techniques for determining the minimum density and optimal locations of relay nodes and ordinary sensors to ensure connectivity, subject to various degrees of uncertainty in the locations of the nodes.

As an important infrastructure of intelligent transportation system (ITS), vehicular ad hoc networks (VANETs) provide navigation and positioning, information services, driver assistance, in-vehicle entertainment, and other functions for the owners improving the development of the future smart city. Due to the high dynamic nature of VANETs, choosing a relatively stable and reliable transmission path to reduce the link breakage is obviously very important. In this paper, we propose an efficient prediction-based data forwarding strategy to ensure the reliability and efficiency of data transmission in VANETs. Besides the link lifetime prediction, we introduce a new metric named link utility used in the process of forwarder selection, in order to improve data forwarding efficiency. Link utility reflects the impact of relative velocity and distance between the vehicles on the efficiency of intervehicle data transmissions. It can be used to optimize packet routing and minimize number of hops especially in the high-density VANETs. Finally, we evaluate the performance of the proposed forwarding strategy through simulation confirming its feasibility and effectiveness.

With the continuously increasing number of vehicles, particularly in cities, there are more and more abnormal traffic events occurring on road, which easily cause road congestion. In vehicular sensor networks (VSNs), with the intervehicle cooperative communication, vehicle nodes can detect the abnormal traffic events and disseminate the related information to the interesting vehicles or persons. This paper gives out a survey of the abnormal traffic information detection and transmission mechanisms in VSNs. Firstly, we overview the origin, characteristic, and application of VSNs. Meanwhile, the definition and features of traffic abnormal information are also introduced and presented. Then, we put more energy on elaborating the existing information detection and transmission mechanisms based on VSNs. Finally, the challenges and problems are discussed.