Accurately predicting future links, known as link prediction (LP), is crucial for understanding network dynamics within temporal networks. This study focuses on the aquaculture sector, where predicting the movement patterns of live fish consignments between fish sites is essential for effective stock management. Given the bio-security risks posed by inaccurate link prediction, this study evaluates the effectiveness of various LP methods in this context. The study investigates the efficacy of LP methods using two primary threshold strategies: the fixed threshold and the threshold curve. Recognising the challenges posed by class imbalance in real-world datasets such as aquaculture networks, the study introduces and evaluates a customised class balancing technique, targeted edge removal, to assess its impact on the performance of LP methods using evaluation metrics including accuracy, precision, recall, F1 score, and the Matthews Correlation Coefficient (MCC). Among the six LP methods evaluated, the Edge Weighted Katz Index (EWKI) consistently outperforms other methods, achieving a precision of 93.59%, recall of 84.35%, F1 score of 88.73%, and an MCC of 0.89 before class balancing. After applying the class balancing technique, EWKI maintained a robust and stable performance. These findings highlight the importance of selecting appropriate threshold strategies and effective class balancing techniques to enhance link prediction accuracy in aquaculture networks. This study provides significant insights into the role of selecting the appropriate threshold strategies and class balancing techniques in link prediction for aquaculture, contributing to the theoretical understanding of link prediction.

In this paper, we present a 27-year longitudinal study of the Internet topology at Autonomous System (AS)-level, from 1998 to 2025. The Internet has been claimed to be scale-free exhibiting power-law degree distribution. This claim has been challenged from several fronts (e.g., rigor of the statistical method, assumptions made on finiteness of the network, completeness of available data). Using the latest statistical methodology, we revisit and verify whether this claim still holds. Using power-law as the reference distribution, we use maximum-likelihood fitting method with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios to identify the best fitting distribution among several common statistical distributions. In fact, being scale-free directly implies the property would remain over the passage of time and hence, we investigate how this has evolved over the past nearly three decades.We found that power-law is not the best fit for the degree distribution of the Internet. For most years, truncated power-law offers better fit while log-normal is often better than power-law. Furthermore, the simple preferential attachment model has been attributed as the underlying generative mechanism that consequently results in the topology we have today. Based on the two methods we used, we found that while there is indication of preferential attachment as previously discovered, this is only true for smaller values of degree rather than the entire distribution and that the attachment behavior hovers between both sub-linear and super-linear attachment regimes at different time points. Finally, we also found that new links do not only tend to attach to high-degree nodes. There is also a large number of new links that join low-degree nodes, contradicting the claim on new nodes prefer to join high-degree nodes. Nevertheless, when weighted by the number of nodes with specific degree ranges, the net effect of “rich-gets-richer” still stands due to imbalance node population across different degrees.

Disruptions to food supply chains can have significant impacts on food security and economic stability. This study investigates the resilience of supply networks to such disruptions, focusing on the distribution of live fish between farms in England and Wales as a case study. A decision support framework is developed to assess network vulnerability and ensure operational continuity in the face of disruptions to the supply and demand balance. The framework incorporates a novel rewiring algorithm that dynamically reconfigures network connections to maintain the flow of goods. The algorithm predicts supply-demand pairs and adjusts connections to preserve functionality during disruptions. To evaluate the performance of the framework and algorithm, a combination of topological metrics, such as connectivity and redundancy, and operational measures, including supply fulfilment and distribution efficiency, is utilised. Through simulations of random and targeted node removals, the rewiring algorithm is shown to effectively mitigate the impact of disruptions, preserve network functionality, and help ensure a consistent supply of live fish. These findings offer valuable insights for managing disruptions in aquaculture supply chains and highlight the broader applicability of the framework to enhance the resilience of other supply networks.

The effective surveillance of the distribution of live fish between aquaculture farms is crucial for maintaining food security and preventing disease outbreaks. However, existing conventional models often assume the network is static and do not incorporate other factors that contribute to movement between farms, lacking the ability to accurately predict future movements, especially given the dynamic interactions within aquaculture networks. This study addresses this gap by developing the Edge-Weighted Katz Index (EWKI), an extension of the traditional Katz index that integrates spatial information to improve the accuracy of predicting fish distribution between farms. Using a comprehensive dataset on the distribution of live fish between farms in England and Wales between 2010 and 2023, the study evaluates the performance of the EWKI model in comparison to other similarity-based link prediction methods. The results indicate that the EWKI model significantly outperforms other methods, achieving a precision of 92.89%, a recall of 81.09%, and an F1-score of 86.59%, alongside an AUPR of 93.44% and an AUROC of 99.97%. This research has practical implications, as the developed method can accurately predict the distribution of fish between farms, supporting predictions of disease spread and facilitating targeted interventions. Furthermore, the integration of spatial information into the network analysis has broader applications across various fields where understanding and predicting spatially influenced network dynamics are crucial, including transportation networks.

The emerging drone swarms are capable of carrying out sophisticated tasks in support of demanding Internet-of-Things (IoT) applications by synergistically working together. However, the target area may be out of the coverage of the ground station and it may be impractical to deploy a large number of drones in the target area due to cost, electromagnetic interference and flight-safety regulations. By exploiting the innate agility and mobility of unmanned aerial vehicles (UAVs), we conceive a mobile relaying-assisted drone swarm network architecture, which is capable of extending the coverage of the ground station and enhancing the effective end-to-end throughput. Explicitly, a swarm of drones forms a data-collecting drone swarm (DCDS) designed for sensing and collecting data with the aid of their mounted cameras and/or sensors, and a powerful relay-UAV (RUAV) acts as a mobile relay for conveying data between the DCDS and a ground station (GS). Given a time period, in order to maximize the data delivered whilst minimizing the delay imposed, we harness an -multiple objective genetic algorithm (-MOGA) assisted Pareto-optimization scheme. Our simulation results demonstrate that the proposed mobile relaying is capable of delivering more data. As specific examples investigated in our simulations, our mobile relaying-assisted drone swarm network is capable of delivering 45.38% more data than the benchmark solutions, when a stationary relay is available, and it is capable of delivering 26.86% more data than the benchmark solutions when no stationary relay is available.

Our work concerns online parking reservation platforms proposed in the last decade to cope with the parking challenge in cities worldwide. Enlisting parking resources from commercial operators (e.g., lots) and individuals (e.g., doorways) and letting drivers make online reservations through mobile apps, those platforms seek to ease transactions between the two sides and best match parking supply with parking demand. This way they maximize their value for drivers and parking space providers but also their revenue out of charged commissions. We distinguish between two types of commissions these platforms typically charge, fixed per transaction and proportional to its value, and formulate the respective optimization problems for the platform revenue maximization. We show that the two problems are NP-hard and design a novel algorithm that can treat both by combining greedy and dynamic programming principles. We study its optimality properties both analytically and experimentally, showing that the algorithm closely tracks optimal solutions for small and moderate problem sizes at run times that are several orders of size smaller than those needed by off-the-shelf ILP solvers. We then analyze real parking data we collected for the period 2018-2020 from the Bournemouth city in UK to realistically model the rich spatiotemporal dynamics of parking demand such as the location, start times and duration of parking events. These datasets drive the experimental evaluation of the proposed algorithm, which reports gains of up to 35% compared to the de facto parking resource reservation policy in such platforms. Notably, the highest gains are achieved when the platform operates under constrained supply conditions.

Smart city visions aim to offer citizens with intelligent services in various aspects of life. The services envisioned have been significantly enhanced with the proliferation of Internet-of-Things (IoT) technology offering real-time and ubiquitous monitoring capability. In this paper, we focus on the short-term traffic flow prediction problem based on real-world traffic data as one critical component of a smart city. In contrast to long-term traffic prediction, accurate prediction of short-term traffic flow facilitates timely traffic management and rapid response. We develop and study a novel ensemble model (EM) based on long short term memory (LSTM), deep autoencoder (DAE) and convolutional neural network (CNN) models. Our approach takes into account both temporal and spatial characteristics of the traffic conditions. We evaluate our proposal against well-known existing prediction models. We use two real traffic data (California and London roadways) with different characteristics to train and test the models. Our results indicate that our proposed ensemble model achieves the most accurate predictions (approx. 97.50% and approx. % accuracy) and is robust against high variance traffic flow.

Traditional network functions (NFs) such as firewalls are implemented in costly dedicated hardware. By decoupling NFs from physical devices, network function virtualization enables virtual network functions (VNF) to run in virtual machines (VMs). However, VNFs are vulnerable to various faults such as software and hardware failures. To enhance VNF fault tolerance, the deployment of backup VNFs in stand-by VM instances is necessary. In case of stateful VNFs, stand-by instances require constant state updates from active instances during its operation. This will guarantee a correct and seamless handover from failed instances to stand-by instances after failures. Nevertheless, such state updates to stand-by instances could consume significant network bandwidth resources and lead to potential admission failures for VNF requests. In this paper, we study the fault-tolerant VNF placement problem with the optimization objective of admitting as many requests as possible. In particular, the VNF placement of active/stand-by instances, the request routing paths to active instances, and state transfer paths to stand-by instances are jointly considered. We devise an efficient heuristic algorithm to solve this problem, and propose a bicriteria approximation algorithm with performance guarantees for a special case of the problem. Simulations with realistic settings show that our algorithms can significantly improve the request admission rate compared to conventional approaches.

The dominant application in today's Internet is content streaming, which is increasingly relying on caches to meet the stringent conditions on the latency between content servers and end-users. These systems routinely face the challenges of limited bandwidth capacities and network server failures, which degrade caching performance. In this paper, we study the problem of optimally allocating content over a resilient caching network, in which each cache may fail under some situations. Given content request rates and multiple routing paths, we formulate an optimization problem to maximize the expected caching gain, i.e., the reduction of latency due to intermediate caching. The offline version of this problem is NP-hard. We first propose a centralized, offline algorithm and show that a solution with (1-1/e) approximation ratio to the optimal can be constructed. We then propose a distributed ascent algorithm based on the concave relaxation of the expected gain. Informed by the results of our analysis, we finally propose a distributed resilient caching algorithm (DR-Cache) that is simple and adaptive to network failures. We show numerically that DR-Cache significantly outperforms other candidate algorithms under synthetic requests, as well as real world traces over a class of network topologies.

HTTP-based live streaming has become increasingly popular in recent years, and more users have started generating 4K live streams from their devices (e.g., mobile phones) through social-media service providers like Facebook or YouTube. If the audience is located far from a live stream source across the global Internet, TCP throughput becomes substantially suboptimal due to slow start and congestion control mechanisms. This is especially the case when the end-to-end content delivery path involves radio access network at the last mile. As a result, the data rate perceived by a mobile receiver may not meet the high requirement of 4K video streams, which causes deteriorated quality-of-experience (QoE). In this paper, we propose a scheme named edge-based transient holding of live segment (ETHLE), which addresses the above-mentioned issue by performing context-aware transient holding of video segments at the mobile edge with virtualized content caching capability. Through holding the minimum number of live video segments at the mobile edge cache in a context-aware manner, the ETHLE scheme is able to achieve seamless 4K live streaming experiences across the global Internet by eliminating buffering and substantially reducing initial startup delay and live stream latency. It has been deployed as a virtual network function at an LTE-A network, and its performance has been evaluated using real live stream sources that are distributed around the world. The significance of this paper is that by leveraging virtualized caching resources at the mobile edge, we address the conventional transport-layer bottleneck and enable QoE-assured Internet-wide live streaming services with high data rate requirements.

Mobile edge-cloud (MEC) aims to support low latency mobile services by bringing remote cloud services nearer to mobile users. However, in order to deal with dynamic workloads, MEC is deployed in a large number of fixed-location micro-clouds, leading to resource wastage during stable/low workload periods. Limiting the number of micro-clouds improves resource utilization and saves operational costs, but faces service performance degradations due to insufficient physical capacity during peak time from nearby micro-clouds. To efficiently support services with low latency requirement under varying workload conditions, we adopt the emerging network function virtualization (NFV)-enabled MEC, which offers new flexibility in hosting MEC services in any virtualized network node, e.g., access points, routers, etc. This flexibility overcomes the limitations imposed by fixed-location solutions, providing new freedom in terms of MEC service-hosting locations. In this paper, we address the questions on where and when to allocate resources as well as how many resources to be allocated among NFV-enabled MECs, such that both the low latency requirements of mobile services and MEC cost efficiency are achieved. We propose a dynamic resource allocation framework that consists of a fast heuristic-based incremental allocation mechanism that dynamically performs resource allocation and a reoptimization algorithm that periodically adjusts allocation to maintain a near-optimal MEC operational cost over time. We show through extensive simulations that our flexible framework always manages to allocate sufficient resources in time to guarantee continuous satisfaction of applications' low latency requirements. At the same time, our proposal saves up to 33% of cost in comparison to existing fixed-location MEC solutions.

Conventional epidemic models assume omni-directional contact-based infection. This strongly associates the epidemic spreading process with node degrees. The role of the infection transmission medium is often neglected. In real-world networks, however, the infectious agent as the physical contagion medium usually flows from one node to another via specific directed routes (path-based infection). Here, we use continuous-time Markov chain analysis to model the influence of the infectious agent and routing paths on the spreading behavior by taking into account the state transitions of each node individually, rather than the mean aggregated behavior of all nodes. By applying a mean field approximation, the analysis complexity of the path-based infection mechanics is reduced from exponential to polynomial. We show that the structure of the topology plays a secondary role in determining the size of the epidemic. Instead, it is the routing algorithm and traffic intensity that determine the survivability and the steady-state of the epidemic. We define an infection characterization matrix that encodes both the routing and the traffic information. Based on this, we derive the critical path-based epidemic threshold below which the epidemic will die off, as well as conditional bounds of this threshold which network operators may use to promote/suppress path-based spreading in their networks. Finally, besides artificially generated random and scale-free graphs, we also use real-world networks and traffic, as case studies, in order to compare the behaviors of contact- and path-based epidemics. Our results further corroborate the recent empirical observations that epidemics in communication networks are highly persistent.

Inspired by the proliferation of content-centric applications in the Internet, Information-Centric Networking (ICN) has emerged as a promising networking paradigm. Focusing on the delivery of content instead of the pairwise communication between end-hosts, ICN inherently supports location-independent content/information distribution, through the means of in-network caching and multicast; as well as mobile computing. However, so far the vast majority of ICN research efforts have mostly focused on the design of sound and scalable architectures and protocols for the current Internet application landscape. In this paper, we revisit ICN in the context of a radically different application environment of smart grids and in particular, the case of smart charging of electric vehicles. Based on a thorough description of the currently forming application environment in the Netherlands, we highlight the inefficiencies resulting from a host-centric model. We then show how ICN can address these limitations and ultimately support quality and security in such application environment. Besides qualitative benefits, our preliminary analysis also demonstrates that ICN can substantially reduce communication and security complexity, thus fostering the development and widespread adoption of the smart charging application.

We introduce the concept of resource management for in-network caching environments. We argue that in Information-Centric Networking environments, deterministically caching content messages at predefined places along the content delivery path results in unfair and inefficient content multiplexing between different content flows, as well as in significant caching redundancy. Instead, allocating resources along the path according to content flow characteristics results in better use of network resources and therefore, higher overall performance. The design principles of our proposed in-network caching scheme, which we call ProbCache, target these two outcomes, namely reduction of caching redundancy and fair content flow multiplexing along the delivery path. In particular, ProbCache approximates the caching capability of a path and caches contents probabilistically to: 1) leave caching space for other flows sharing (part of) the same path, and 2) fairly multiplex contents in caches along the path from the server to the client. We elaborate on the content multiplexing fairness of ProbCache and find that it sometimes behaves in favor of content flows connected far away from the source, that is, it gives higher priority to flows travelling longer paths, leaving little space to shorter-path flows. We introduce an enhanced version of the main algorithm that guarantees fair behavior to all participating content flows. We evaluate the proposed schemes in both homogeneous and heterogeneous cache size environments and formulate a framework for resource allocation in in-network caching environments. The proposed probabilistic approach to in-network caching exhibits ideal performance both in terms of network resource utilization and in terms of resource allocation fairness among competing content flows. Finally, and in contrast to the expected behavior, we find that the efficient design of ProbCache results in fast convergence to caching of popular content items.

The current Internet does not enable easy introduction and deployment of new network technologies and services. This paper aims to progress the Future Internet (FI) by introduction of a service composition and execution environment that re-use existing components of access and core networks. This paper presents essential service-centric platforms and software systems that have been developed with the aim to create a flexible environment for an Autonomic Internet.

In this paper we focus on the provision of proportional class-based service differentiation to transmission control protocol (TCP) flows in the context of bandwidth on demand (BoD) split-TCP geostationary (GEO) satellite networks. Our approach involves the joint configuration of TCP-Performance Enhancing Proxy (TCP-PEP) agents at the transport layer and the scheduling algorithm controlling the resource allocation at the Medium Access Control (MAC) layer. We show that the two differentiation mechanisms exhibit complementary behavior in achieving the desired differentiation throughout the traffic load space: the TCP-PEPs control differentiation at low and medium system utilization, whereas the MAC scheduler becomes the dominant differentiation factor under high traffic load. The main challenge for the satellite operator is to appropriately configure those two mechanisms to achieve a specific differentiation target for the different classes of TCP flows. To this end, we propose a fixed-point framework to analytically approximate the achieved differentiated TCP performance. We validate the predictive capacity of our analytical method via simulations and show that our approximations closely match the performance of different classes of TCP flows under various scenarios for the network traffic load and configuration of the MAC scheduler and TCP-PEP agent. Satellite network operators could use our approximations as an analytical tool to tune their networks.

The rapid emergence of multimedia applications in the Internet has highlighted the need for service differentiation in broadband satellite networks, which aim at being an integral part of the broadband network infrastructure. This paper presents a novel scheduler, called SWTP (satellite waiting time priority), to provide relative service differentiation in a DVB-RCS geostationary (GEO) satellite system where the network is structured to support a finite number of ordered service classes. We advocate the adoption of the proportional service differentiation model in the satellite domain to provide proportional delay differentiation to different traffic classes. The lightweight nature of the model makes it especially suitable for satellite systems as it minimizes computational cost by doing away with mechanisms such as admission control and resource reservation. Simulation results suggest that the SWTP scheduler can effectively and consistently provide proportional delay differentiation in satellite networks.