As 6G networks must support diverse applications with heterogeneous quality-of-service requirements, efficient allocation of limited network resources becomes important. This paper addresses the critical challenge of user admission control in 6G networks enhanced by Reconfigurable Intelligent Surfaces (RIS) and Mobile Edge Computing (MEC). We propose an optimization framework that leverages RIS technology to enhance user admission based on spatial characteristics, priority levels, and resource constraints. Our approach first filters users based on angular alignment with RIS reflection directions, then constructs priority queues considering service requirements and arrival times, and finally performs user grouping to maximize RIS resource utilization. The proposed algorithm incorporates a utility function that balances Quality of Service (QoS) performance, RIS utilization, and MEC efficiency in admission decisions. Simulation results demonstrate that our approach significantly improves system performance with RIS-enhanced configurations. For high-priority eURLLC services, our method maintains over 90% admission rates even at maximum load, ensuring mission-critical applications receive guaranteed service quality.

Named Data Networking (NDN) is an information-centric internet architecture that delivers packets based on the name of the content in the packet. A key component of NDN is the caching strategy designed to reduce total network latency and load on content producers. To improve the speed and reliability of web content delivery, existing caching strategies typically cache content on a large number of intermediate nodes, which incur significant energy consumption and memory overhead. However, in Internet of Things (IoT) scenarios, memory and energy of nodes are scarce resources. Therefore, in NDN-based IoT applications, traditional caching strategies can cause node failures due to energy depletion, which can significantly reduce the network operational lifetime as well as create problems that caching is supposed to solve. In this paper, a caching strategy based on node centrality and energy availability, called Energy-aware Approximate Betweenness Centrality (EABC) is proposed for NDN-based IoT. EABC uses a topology-based heuristic to cache data content on nodes with high centrality and makes caching decisions based on the remaining energy of the nodes. We evaluate EABC using simulations based on ndnSIM in different topologies and compare it with several existing NDN caching strategies. The results show that EABC performs better in different types of network topologies, reduces the average transmission delay of data and balances the energy consumption of highly central nodes, thus extending the network lifetime.

Heralded entanglement generation between nodes of a future quantum internet is a fundamental operation that unlocks the potential for quantum communication. In this paper, we propose a decentralized synchronization protocol that operates at the classical control-plane of the link layer, to navigate the coordination challenges of generating heralded entanglement across few-qubit quantum network nodes. Additionally, with quantum network simulations using NetSquid, we show that our protocol achieves lower entanglement request latencies than a naive distributed queue approach. We observe a sixfold reduction in average request latency growth as the number of quantum network links increases. The Eventual Synchronization Protocol (ESP) allows nodes to coordinate on heralded entanglement generation in a scalable manner within multi-peer quantum networks. To the best of our knowledge, this is the first decentralized synchronization protocol for managing heralded entanglement requests.

The conventional approach of choosing the best route to carry network traffic in wireless multi-hop networks does not maximize the overall network throughput and can lead to short-term instabilities in network state with dire consequences. To date, wireless network route selection considers mainly network or link metrics, always picking the best links, thus channeling all packets through a subset of all available links. This leaves weaker links under-utilized although such links can in fact be used to carry smaller packets or packets with less stringent requirements and free up bandwidth on the better links for larger packets or traffic with higher service requirements. As network traffic volume and heterogeneity increase in future networks, we need to maximize the usage of available network bandwidth and distribute the network traffic load. We combine network link metrics and packet attributes to determine the successful packet transmission probability, and then use this outcome to pick suitable links to forward the packet, which is not necessarily the link with the best metric. To validate the efficacy of our proposed approach in routing performance and energy efficiency, we applied it in routing for wireless multi-hop networks. More importantly, we are able to spread the traffic across nodes in the network, thus achieving better network load-balancing and higher network resource utilization.

The increasing demand for real-time, adaptive monitoring across various domains, such as environmental surveillance and disaster management, necessitates a shift from static to dynamic sensor deployments within Mobile Wireless Sensor Networks (MWSNs). One prominent application scenario involves dynamic changes in the Region of Interest (RoI), requiring sensor redeployment. However, defining dynamic RoI scenarios lacks specificity. To address this gap, this study introduces two distinct categories defining a change in RoI: transformed RoI, where the initial RoI undergoes affine transformations, and evolved RoI, representing entirely new polygonal configurations. Additionally, the study explores limitations within current state-of-the-art distributed deployment algorithms, which hinder performance in dynamic RoI settings. These limitations are investigated through experiments involving two main types of distributed algorithms: geometric-based and virtual force-based deployment algorithms. The findings underscore significant performance challenges faced by existing algorithms in dynamic RoI scenarios, emphasizing the need for the development of more resilient algorithms capable of adapting to dynamic RoIs while ensuring network connectivity and minimizing node movement.

Bufferbloat, or excessive queuing delay under load, is a noticeable quality of service degradation that occurs when latency-sensitive traffic experiences the effects of increased packet buffering delays in network devices. This phenomenon leads to increased latency and reduced network performance, particularly affecting real-time voice and video traffic, online gaming, and other interactive applications that demand low-latency at all times. Bufferbloat also poses serious risks to future ambitions of latency-critical applications such as telesurgery, autonomous vehicles, and virtual reality, as it undermines network consistency and creates significant operational issues for service providers to manage. Bufferbloat is especially detrimental to Wireless Internet Service Providers (WISPs), due to their often ad-hoc and dynamic nature. To better characterise the prevalence of bufferbloat, we analyse a real-world WISP network and propose "Polus", a framework for detecting and characterising adverse network conditions caused by the phenomenon.

We consider the problem of scheduling audio data transmissions in data-intensive audio sensor networks for animal tracking where the sensor nodes must use WiFi duty cycling to reduce power consumption. WiFi duty cycling entails a startup cost due to probing, authentication, association, and host configuration which are significant and can reach more than 10 seconds. As such, transmission scheduling is not trivial because a naïve approach of switching on WiFi whenever there is a data to send would result in excessive energy consumption overhead. We model duty cycling after an M/G/1 queue with removable server, formulate the optimization problem considering both energy and latency overheads, and obtain the optimal$N^{\ast}$by which the interface should be switched on to schedule data transmission. We propose Optimal Threshold-based Transmission Scheduling (OTTS), a low-complexity algorithm for determining the optimal threshold and commencing transmission. Experiments and trace-based simulations show that OTTS can yield substantial reduction in power consumption that is controllable through an energy-latency trade-off parameter. Compared with interval-based scheduling, OTTS provides lower delay which is more significant at tighter power consumption constraints.

In many Internet of Things (IoT) applications, clients are “content-centric” in the sense that they are interested in obtaining content or data regardless of their physical source. This pattern is different from “host-centric” communications wherein clients connect to a particular server to access its resources. The content-centric nature of IoT require a new kind of message delivery support whereby a client can query for data, and obtain the data from any node that has a copy of that data. Publish/subscribe, often called pub/sub for short, is a messaging paradigm that can facilitate this new communication pattern. In this paper, we propose Framework for Lightweight Publish-Subscribe (FLIPS), a lightweight topic-based pub/sub for IoT that uses the Hybrid Information-Centric Network (hICN) stack for distributed, broker-free operation. We implemented Flips using the open-source hICN transport library and tested its operation through network emulation. Compared with Message Queuing Telemetry Transport (MQTT), Flips can provide similar reli-ability and lower latency especially in scenarios where caching can opportunistically be used.

Low latency is critical to applications such as control of unmanned aerial vehicles. Such latency-sensitive services can be hosted closer to the user at the fog layer which can reduce overall latency through the reduction of transmission time and network congestion. To keep the latency low for mobile users connected to services deployed at the fog, these services need to be constantly migrated to follow the users. Unlike the cloud nodes, fog nodes are less reliable and are therefore subject to higher failure rate. In this paper, we propose an enhancement to the post-copy live migration algorithm to make it robust against failure. Simulation results show that robust migration reduces total migration time between 10-26% and downtime between 2-23% compared to non-robust migration. Furthermore, when the bandwidth to the backup node is lower, robust migration provides further improvement in both metrics.

Network data constantly evolves with new network applications and protocols. There is a need for robust techniques to detect anomalous behaviour. Offline models trained with static data lose validity when new variants of traffic emerge. They require retraining but the need for ground truth and lengthy training times make this task challenging. Meanwhile, online models which detect outliers in streaming data are susceptible to the curse of dimensionality and natural variability. Today’s anomalies may be tomorrow’s new traffic and existing methods do not provide a way to differentiate between them. We propose a framework that makes the most of both approaches: an offline deep learning model extracts features of normal traffic and provides a bias for an online outlier detection model to select data for training. The online model retains its previously learnt knowledge and retrains itself with new data. Online thresholds are updated in a drifting manner and the Mann-Whitney U test is incorporated to prevent inaccurate updates. We perform analysis on the scores, develop heuristics to detect new traffic and evaluate using three deep learning models and four outlier detection methods on the UNSW-NB15 and CTU-13 datasets. The framework improves upon any individual offline or online models in isolation.

The provision of rural broadband infrastructure is a challenge for network operators across the globe, irrespective of their size. Wireless Internet Service Providers (WISPs) have shown that the small scale deployment of wireless broadband infrastructure is a viable alternative to relying on cellular network providers for remote coverage. However, out of concern for excessive energy consumption WISPs must often resort to using off-grid renewable energy sources such as solar energy for powering network sites, often resulting in undesirable, low-performance backhaul radios being used between sites. To encourage the development of high-performance, sustainable WISP networks, we present ElasticWISP, a backhaul optimisation architecture. ElasticWISP dynamically controls the configuration of backhaul radios based on bandwidth demands and the network-wide energy consumption of these radios. Through simulations driven by real WISP topology and data traffic, we show that ElasticWISP can offer energy savings of approximately 65% when following our design methodology.

The dynamic and unpredictable nature of energy harvesting sources that are used in wireless sensor networks necessitates the need for adaptive duty cycling techniques. Such adaptive control allows sensor nodes to achieve energy-neutrality, whereby both energy supply and demand are balanced. This paper proposes a framework enabling an adaptive duty cycling scheme for sensor networks that takes into account the operating duty cycle of the node, and application-level QoS requirements. We model the system as a Continuous Time Markov Chain (CTMC), and derive analytical expressions for key QoS metrics - such as latency, loss probability and power consumption. We then formulate and solve the optimal operating duty cycle as a non-linear optimization problem, using latency and loss probability as the constraints. Simulation results show that a Markovian duty cycling scheme can outperform periodic duty cycling schemes.

The dynamic and unpredictable nature of energy harvesting sources available for wireless sensor networks, and the time variation in network statistics like packet transmission rates and link qualities, necessitate the use of adaptive duty cycling techniques. Such adaptive control allows sensor nodes to achieve long-run energy neutrality, where energy supply and demand are balanced in a dynamic environment such that the nodes function continuously. In this paper, we develop a new framework enabling an adaptive duty cycling scheme for sensor networks that takes into account the node battery level, ambient energy that can be harvested, and application-level QoS requirements. We model the system as a Markov decision process (MDP) that modifies its state transition policy using reinforcement learning. The MDP uses continuous time Markov chains (CTMCs) to model the network state of a node to obtain key QoS metrics like latency, loss probability, and power consumption, as well as to model the node battery level taking into account physically feasible rates of change. We show that with an appropriate choice of the reward function for the MDP, as well as a suitable learning rate, exploitation probability, and discount factor, the need to maintain minimum QoS levels for optimal network performance can be balanced with the need to promote the maintenance of a finite battery level to ensure node operability. Extensive simulation results show the benefit of our algorithm for different reward functions and parameters.

Wireless ad hoc routing protocols must employ accurate and rapid link failure detection mechanisms in order to maintain valid multihop routes and provide high data delivery rates. Hello-based failure detection is the predominant failure detection mechanism due to its ease of implementation. Despite its prevalence, no analytical work has been carried out to better understand its fundamental behavior. In this paper, we study the performance of hello-based link failure detection via analysis and experimentation. Our analytical results, which are validated by experimental results, show the existence of optimal hello beaconing parameters which depend on network conditions such as traffic load, link failure rate and hello delivery rate. These results can be applied in real-world network deployments for obtaining the optimal hello beaconing parameters that can provide the highest data delivery rate.

In the offshore engineering community, reliable deep-water anchor performance is critical for mooring floating platforms such as Mobile Offshore Drilling Units. In a typical installation, an anchor is fully embedded into the seabed (up to 100 m). This has to be done with high fidelity as anchor failure can cause floating units to go adrift, damaging oil and gas pipelines. The likelihood of this happening may be reduced with a data acquisition system comprising various sensors/measurement instruments housed in the anchor, where their data is then transmitted in some ways towards the seabed, and then onwards to the installation vessel. In this paper, we explore the feasibility of electromagnetic wave underground wireless communications as a low cost means to transmit key anchor parameters from the anchor to the seabed. We employ two semi-empirical models to obtain the path loss characteristics at different operating frequencies and volumetric water contents.

We are witnessing pervasive use of wireless sensor networks (WSN)s in a wide variety of applications such as monitoring of road infrastructure. As they are expected to be deployed in harsh environments for long durations, the research community have turned their attention to tapping on ambient energy to power such networks. However, since energy harvesting rates are still significantly lower than the power consumption in each wireless sensor node, the energy availability is sporadic, making the design of runtime policies in WSN powered by ambient energy harvesting (WSN-HEAP) to maximize performance an important but challenging task. In this paper, using extensive simulations, we evaluate the efficacy of transmission power control for 2-D WSN-HEAP deployed in a grid topology in terms of throughput, data delivery ratio and fairness. When a fixed power is assigned to all nodes, we observe a trade-off between throughput and fairness: throughput is maximized at lower powers at the expense of fairness and vice versa. When nodes are assigned powers according to their proximity from the sinks, we observe that assigning the minimum transmission power required for each node to communicate with its nearest sink maximizes all performance metrics. This indicates that minimizing interference dominates over multi-sink redundancy in a 2-D WSN-HEAP.

Wireless multihop ad hoc networks face a multitude of challenging problems including highly dynamic multihop topologies, lossy and noisy communication channels, and sporadic connectivity which contribute to frequent link failures. Rapid and accurate link failure detection is therefore important to maintain correct and optimum operation of network routing protocols. In this paper, we propose a unified link failure detection and recovery architecture (ulfra) which uses link layer feedback for rapid failure detection and packet salvaging for packet recovery. While link layer feedback and packet salvaging have been studied in simulations and simple experiments, no thorough experimental study have been undertaken to evaluate their real-world performance. This paper essentially fills this void as we implement and evaluate ulfra in an IEEE 802.11 multihop ad hoc network. Our experimental results show that link layer feedback, as modeled in current network simulators, actually performs worse than hello beaconing as it generates excessive false failure detections. To improve its performance, we implement a veto mechanism to reduce spurious detections. Experimental results show that the veto mechanism dramatically improves the performance of link layer feedback in terms of packet delivery, delay, and routing overhead as it considerably reduces the number of false detections. Compared with hello, it delivers 15–20% more packets at high node failure and 12–20% more at high network traffic.

A mobile ad hoc network is an autonomous system of infrastructure-less, multihop, wireless mobile nodes. Reactive routing protocols perform well in this environment due to their ability to cope quickly against topological changes. This paper proposes a new routing protocol named CHAMP (caching and multiple path) routing protocol. CHAMP uses cooperative packet caching and shortest multipath routing to reduce packet loss due to frequent route failures. We show through extensive simulation results that these two techniques yield significant improvement in terms of packet delivery, end-to-end delay and routing overhead. We also show that existing protocol optimizations employed to reduce packet loss due to frequent route failures, namely local repair in AODV and packet salvaging in DSR, are not effective at high mobility rates and high network traffic.

A mobile ad hoc network is an autonomous system of infrastructureless, multihop wireless mobile nodes. Reactive routing protocols perform well in such an environment due to their ability to cope quickly against topological changes. In this paper, we propose a new routing protocol called Caching and Multipath (CHAMP) Routing Protocol. CHAMP uses cooperative packet caching and shortest multipath routing to reduce packet loss due to frequent route breakdowns. Simulation results reveal that by using a five-packet data cache, CHAMP exhibits excellent improvement in packet delivery, outperforming AODV and DSR by at most 30% in stressful scenarios. Furthermore, end-to-end delay is significantly reduced while routing overhead is lower at high mobility rates.