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In the context of 5G platoon communications, the Platoon Leader Vehicle (PLV) employs groupcasting to transmit control messages to Platoon Member Vehicles (PMVs). Due to the restricted transmission power for groupcasting, it may need to pick one PMV as the Platoon Relay Vehicle (PRV) to be responsible for re-groupcasting messages of PLVs. To optimize the usage of limited spectrum resources, resource sharing can adopted to enhance spectrum efficiency within the platoon. This study proposes a resource allocation method, which is called Resource Sharing for Platoon Groupcasting (RSPG), for platoon groupcasting based on transmission reliability. RSPG utilizes tripartite matching to assign a subchannel to either a PLV or PRV that shares the assigned subchannel with the corresponding individual entity (IE), which does not belong to any platoon. The simulation results show that the proposed method performs better in terms of the QoS satisfaction rate of IEs, the number of allocated subchannels for platoons, and spectral efficiency., Comment: 9 Pages, 11 Figures

Automatic modulation classification (AMC) is an effective way to deal with physical layer threats of the internet of things (IoT). However, there is often label mislabeling in practice, which significantly impacts the performance and robustness of deep neural networks (DNNs). In this paper, we propose a meta-learning guided label noise distillation method for robust AMC. Specifically, a teacher-student heterogeneous network (TSHN) framework is proposed to distill and reuse label noise. Based on the idea that labels are representations, the teacher network with trusted meta-learning divides and conquers untrusted label samples and then guides the student network to learn better by reassessing and correcting labels. Furthermore, we propose a multi-view signal (MVS) method to further improve the performance of hard-to-classify categories with few-shot trusted label samples. Extensive experimental results show that our methods can significantly improve the performance and robustness of signal AMC in various and complex label noise scenarios, which is crucial for securing IoT applications., Comment: 8 pages, 7 figures

Unmanned Aerial Vehicles (UAVs) provide agile and safe solutions to communication relay networks, offering improved throughput. However, their modeling and control present challenges, and real-world deployment is hindered by the gap between simulation and reality. Moreover, enhancing situational awareness is critical. Several works in the literature proposed integrating UAV operation with immersive digital technologies, such as Digital Twin (DT) and Extended Reality (XR), to address these challenges. This paper provides a comprehensive overview of current research and developments involving immersive digital technologies for UAVs, including the latest advancements and emerging trends. We also explore the integration of DT and XR with Artificial Intelligence (AI) algorithms to create more intelligent, adaptive, and responsive UAV systems. Finally, we provide discussions, identify gaps in current research, and suggest future directions for studying the application of immersive technologies in UAVs, fostering further innovation and development in this field. We envision the fusion of DTs with XR will transform how UAVs operate, offering tools that enhance visualization, improve decision-making, and enable effective collaboration., Comment: 12 pages

Nowadays, the new network architectures with respect to the traditional network topologies must manage more data, which entails having increasingly robust and scalable network structures. As there is a process of growth, adaptability, and change in traditional data networks, faced with the management of large volumes of information, it is necessary to incorporate the virtualization of network functions in the context of information content networks, in such a way that there is a balance between the user and the provider at cost level and profit, the functions on the network. In turn, NFVs (Network Functions Virtualization) are considered network structures designed based on IT virtualization technologies, which allow virtualizing of the functions that can be found in the network nodes, which are connected through routing tables, which allows offering communication services for various types of customers. Therefore, information-centric networks (IC, unlike traditional data networks which proceed to exchange information between hosts using data packets and TCP/IP communication protocols, use the content of the data for this purpose where the data travels through the network is stored in a routing table located in the CR (Content Router) of the router temporality to be reused later, which allows for reducing operation costs and capital costs . The purpose of this work is to analyze how the virtualization of network functions is integrated into the field of information-centric networks. Also, the advantages and disadvantages of both architectures are considered and presented as a critical analysis when considering the current difficulties and future trends of both network topologies., Comment: 7 pages,7 figures, conference paper, index in Scopus

User experience on mobile devices is constrained by limited battery capacity and processing power, but 6G technology advancements are diving rapidly into mobile technical evolution. Mobile edge computing (MEC) offers a solution, offloading computationally intensive tasks to edge cloud servers, reducing battery drain compared to local processing. The upcoming integrated sensing and communication in mobile communication may improve the trajectory prediction and processing delays. This study proposes a greedy resource allocation optimization strategy for multi-user networks to minimize aggregate energy usage. Numerical results show potential improvement at 33\% for every 1000 iteration. Addressing prediction model division and velocity accuracy issues is crucial for better results. A plan for further improvement and achieving objectives is outlined for the upcoming work phase.

Connecting multiple processors via quantum interconnect technologies could help to overcome issues of scalability in single-processor quantum computers. Transmission via these interconnects can be performed more efficiently using quantum multiplexing, where information is encoded in high-dimensional photonic degrees of freedom. We explore the effects of multiplexing on logical error rates in surface codes and hypergraph product codes. We show that, although multiplexing makes loss errors more damaging, assigning qubits to photons in an intelligent manner can minimize these effects, and the ability to encode higher-distance codes in a smaller number of photons can result in overall lower logical error rates. This multiplexing technique can also be adapted to quantum communication and multimode quantum memory with high-dimensional qudit systems., Comment: 12 pages + 12-page appendices, 19 figures

Increasing content consumption by users and the expectation of a better Internet experience requires Internet service providers (ISPs) to expand the capacity of the access network continually. The ISPs have been demanding the participation of the content providers (CPs) in sharing the cost of upgrading the infrastructure. From CPs' perspective, investing in the ISP infrastructure, termed as \emph{public investment}, seems rational as it will boost their profit. However, the CPs can alternatively invest in making content delivery more efficient, termed as \emph{private investment}, as it also boosts their profit. Thus, in this work, we investigate this trade-off between public and private investment of the CPs for a net-neutral ISP. Specifically, we consider centralized decision and non-cooperative forms of interaction between CPs and an ISP and determine the optimum public and private investments of the CPs for each model. In the non-cooperative interaction, we find that at most one CP contributes to the public infrastructure, whereas all invest in their private infrastructure.

Although the known maximum total generalized correntropy (MTGC) and generalized maximum blakezisserman total correntropy (GMBZTC) algorithms can maintain good performance under the errors-in-variables (EIV) model disrupted by generalized Gaussian noise, their requirement for manual ad-justment of parameters is excessive, greatly increasing the practical difficulty of use. To solve this problem, the total arctangent based on logical distance metric (TACLDM) algo-rithm is proposed by utilizing the advantage of few parameters in logical distance metric (LDM) theory and the convergence behavior is improved by the arctangent function. Compared with other competing algorithms, the TACLDM algorithm not only has fewer parameters, but also has better robustness to generalized Gaussian noise and significantly reduces the steady-state error. Furthermore, the analysis of the algorithm in the generalized Gaussian noise environment is analyzed in detail in this paper. Finally, computer simulations demonstrate the outstanding performance of the TACLDM algorithm and the rigorous theoretical deduction in this paper., Comment: 10 page

Unmanned Aerial Vehicle (UAV) swarms play an effective role in timely data collection from ground sensors in remote and hostile areas. Optimizing the collective behavior of swarms can improve data collection performance. This paper puts forth a new mean field flight resource allocation optimization to minimize age of information (AoI) of sensory data, where balancing the trade-off between the UAVs movements and AoI is formulated as a mean field game (MFG). The MFG optimization yields an expansive solution space encompassing continuous state and action, resulting in significant computational complexity. To address practical situations, we propose, a new mean field hybrid proximal policy optimization (MF-HPPO) scheme to minimize the average AoI by optimizing the UAV's trajectories and data collection scheduling of the ground sensors given mixed continuous and discrete actions. Furthermore, a long short term memory (LSTM) is leveraged in MF-HPPO to predict the time-varying network state and stabilize the training. Numerical results demonstrate that the proposed MF-HPPO reduces the average AoI by up to 45 percent and 57 percent in the considered simulation setting, as compared to multi-agent deep Q-learning (MADQN) method and non-learning random algorithm, respectively., Comment: 13 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:2312.09953

This paper considers networks where user traffic is regulated through deterministic traffic profiles, e.g., token buckets, and requires hard delay bounds. The network's goal is to minimize the resources it needs to meet those bounds. The paper explores how reprofiling, i.e., proactively modifying how user traffic enters the network, can be of benefit. Reprofiling produces ``smoother'' flows but introduces an up-front access delay that forces tighter network delays. The paper explores this trade-off and demonstrates that, unlike what holds in the single-hop case, reprofiling can be of benefit} even when ``optimal'' schedulers are available at each hop.

Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. However, new approaches to FL often discuss their contributions involving small deep-learning models only and focus on training full models on clients. In the wake of Foundation Models (FM), the reality is different for many deep learning applications. Typically, FMs have already been pre-trained across a wide variety of tasks and can be fine-tuned to specific downstream tasks over significantly smaller datasets than required for full model training. However, access to such datasets is often challenging. By its design, FL can help to open data silos. With this survey, we introduce a novel taxonomy focused on computational and communication efficiency, the vital elements to make use of FMs in FL systems. We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications, elaborate on the readiness of FL frameworks to work with FMs, and provide future research opportunities on how to evaluate generative models in FL as well as the interplay of privacy and PEFT., Comment: Accepted for publication at IJCAI 2024. Please cite the published paper via https://doi.org/10.24963/ijcai.2024/919

Traditional wisdom for network management allocates network resources separately for the measurement and data transmission tasks. Heavy measurement tasks may take up resources for data transmission and significantly reduce network performance. It is therefore challenging for interference graphs, deemed as incurring heavy measurement overhead, to be used in practice in wireless networks. To address this challenge in wireless sensor networks, we propose to use power as a new dimension for interference graph estimation (IGE) and integrate IGE with concurrent flooding such that IGE can be done simultaneously with flooding using the same frequency-time resources. With controlled and real-world experiments, we show that it is feasible to efficiently achieve IGE via concurrent flooding on the commercial off-the-shelf (COTS) devices by controlling the transmit powers of nodes. We believe that efficient IGE would be a key enabler for the practical use of the existing scheduling algorithms assuming known interference graphs., Comment: Accepted by International Conference on Embedded Wireless Systems and Networking 2023 (EWSN'23), 7 pages with 9 figures, equal contribution by Haifeng Jia and Yichen Wei

With the growing need for automation and the ongoing merge of OT and IT, industrial networks have to transport a high amount of heterogeneous data with mixed criticality such as control traffic, sensor data, and configuration messages. Current advances in IT technologies furthermore enable a new set of automation scenarios under the roof of Industry 4.0 and IIoT where industrial networks now have to meet new requirements in flexibility and reliability. The necessary real-time guarantees will place significant demands on the networks. In this paper, we identify IIoT use cases and infer real-time requirements along several axes before bridging the gap between real-time network technologies and the identified scenarios. We review real-time networking technologies and present peer-reviewed works from the past 5 years for industrial environments. We investigate how these can be applied to controllers, systems, and embedded devices. Finally, we discuss open challenges for real-time communication technologies to enable the identified scenarios. The review shows academic interest in the field of real-time communication technologies but also highlights a lack of a fixed set of standards important for trust in safety and reliability, especially where wireless technologies are concerned., Comment: IEEE Internet of Things Journal 2023 | Journal article DOI: 10.1109/JIOT.2023.3332507

In this study, the Active Control in an Intelligent and Distributed Environment (ACIDE) media distribution model solution and algorithms are proposed for livestreaming in capacity efficient mobile wireless networks. The elements of the ACIDE model are a base station and a cluster formed by a number of peers able to establish peer to peer communications. The cluster peers are selected from a group of users interested in livestreaming the same media. The ACIDE model solution minimizes the bandwidth allocated to a cluster of n peers such that an uninterrupted media play for all peers is guaranteed. The livestream media is sent to the peers in packages and every media package is divided into n blocks. The blocks are distributed to the n peers of a cluster in two phases, such that the base station bandwidth is utilized during first phase only. The allocated bandwidth, the amount of bandwidth the base station has to allocate to a cluster, is minimized and its lower bound is equal to the bandwidth required for multicasting. In this study, the ACIDE model is used to address the problem of how to find the maximum number of peers n, chosen from a group of N users, that can be admitted to a cluster knowing the given allocated bandwidth, the amount of bandwidth that a base station allocates to a cluster in advance, prior to admitting users. When users become peers of an ACIDE cluster, the network capacity, the total number of users who are able to access live media, increases meaning that network resources are used more efficiently. The problem of finding the maximum number of peers n is addressed as an optimization problem, with the objective of having the entire given allocated bandwidth used by the peers admitted to the cluster. This problem is NP-complete and a non-optimal solution is proposed for peers selection such that all admitted peers play media continuously., Comment: 8 pages, 6 figures, 3 tables; Accepted for publication in: Proceedings of the 2023 International Conference on Computational Science and Computational Intelligence (CSCI'23: December 13-15, 2023, Las Vegas, Nevada, USA); Publisher: IEEE Computer Society (CPS)

Quantum entanglement is the key to quantum communications over considerable distances. The first step for entanglement distribution among quantum communication nodes is to generate link-level Einstein-Podolsky-Rosen (EPR) pairs between adjacent communication nodes. EPR pairs may be continuously generated and stored in a few quantum memories to be ready for utilization by quantum applications. A major challenge is that qubits suffer from unavoidable noise due to their interaction with the environment, which is called decoherence. This decoherence results in the known exponential decay model of the fidelity of the qubits with time, thus, limiting the lifetime of a qubit in a quantum memory and the performance of quantum applications. In this paper, we evaluate the fidelity of the stored EPR pairs under two opposite dynamical and probabilistic phenomena, first, the aforementioned decoherence and second purification, i.e. an operation to improve the fidelity of an EPR pair at the expense of sacrificing another EPR pair. Instead of applying the purification as soon as two EPR pairs are generated, we introduce a Purification scheme Beyond the Generation time (PBG) of two EPR pairs. We analytically show the probability distribution of the fidelity of stored link-level EPR pairs in a system with two quantum memories at each node allowing a maximum of two stored EPR pairs. In addition, we apply a PBG scheme that purifies the two stored EPR pairs upon the generation of an additional one. We finally provide numerical evaluations of the analytical approach and show the fidelity-rate trade-off of the considered purification scheme.

In this paper, a media distribution model, Active Control in an Intelligent and Distributed Environment (ACIDE), is proposed for bandwidth efficient livestreaming in mobile wireless networks. Two optimization problems are addressed. The first problem is how to minimize the bandwidth allocated to a cluster of n peers such that a continuous media play for all peers is guaranteed. The second problem is how to find the maximum number of peers n, chosen from a group of N users, that can be admitted to a cluster knowing the given allocated bandwidth, the amount of bandwidth that a base station allocates to a cluster prior to admitting users. Media is sent in packages and each package is divided into n blocks. The distribution of blocks to the peers follows a two-phase, multi-step approach. For the first problem a solution is proposed to find the optimal block sizes such that the allocated bandwidth is minimized, and its lower bound is the bandwidth required for multicasting. The second problem is NP-complete and a greedy strategy is proposed to calculate a near optimal solution for peer selection such that the network capacity, the total number of users who are able to access livestream media, increases., Comment: 13 pages, 12 figures, 3 tables, This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Relevance of research on telecommunications networks is predicated upon the implementations which it explicitly claims or implicitly subsumes. This paper supports researchers through a survey of Communications Service Providers current implementations within the metro area, and trends that are expected to shape the next-generation metro area network. The survey is composed of a quantitative component, complemented by a qualitative component carried out among field experts. Among the several findings, it has been found that service providers with large subscriber base sizes, are less agile in their response to technological change than those with smaller subscriber base sizes: thus, copper media are still an important component in the set of access network technologies. On the other hand, service providers with large subscriber base sizes are strongly committed to deploying distributed access architectures, notably using remote access nodes like remote OLT and remote MAC-PHY. This study also shows that the extent of remote node deployment for multi-access edge computing is about the same as remote node deployment for distributed access architectures, indicating that these two aspects of metro area networks are likely to be co-deployed., Comment: 70 pages

This paper discusses challenges and opportunities of considering the Metaverse as an Information-Centric Network (ICN). The Web today essentially represents a data-centric application layer: data named by URLs is manipulated with REST primitives. However, the semantic gap with the underlying host-oriented transport is significant, typically leading to complexity, centralization, and brittleness. Popular interest in "the Metaverse" suggests that the end-user experience of the Web will evolve towards always-on eXtended Reality (XR). With the benefit of a historical perspective, computing advances, and decades of experience with a global network, there is an opportunity to holistically consider the Metaverse not as an application of the current network, but an evolution of the network itself, reducing rather than widening the gap between network architecture and application semantics. An ICN architecture offers the possibility to achieve this with less overhead, low latency, better security, and more disruption tolerance suitable to diverse uses cases, even those facing intermittent connectivity., Comment: The final version of this paper has been accepted for publication in the proceedings of ACM ICN-2023. Please cite the published version (https://doi.org/10.1145/3623565.3623761)

In large-scale data analysis, near-duplicates are often a problem. For example, with two near-duplicate phishing emails, a difference in the salutation (Mr versus Ms) is not essential, but whether it is bank A or B is important. The state-of-the-art in near-duplicate detection is a black box approach (MinHash), so one only knows that emails are near-duplicates, but not why. We present DarkDiff, which can efficiently detect near-duplicates while providing the reason why there is a near-duplicate. We have developed DarkDiff to detect near-duplicates of homepages on the Darkweb. DarkDiff works well on those pages because they resemble the clear web of the past.

Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This paper presents mmHawkeye, a passive approach for UAV detection with a COTS millimeter wave (mmWave) radar. mmHawkeye doesn't require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV's periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low-SNR and uncertain reflected signals from the UAV. mmHawkeye can further track the UAV's position with dynamic programming and particle filtering, and identify it with a Long Short-Term Memory (LSTM) based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80m., Comment: 9 pages, 14 figures, IEEE SECON2023

With the rapid development of the Internet of Things (IoT) and the rise of 5G communication networks and automatic driving, millimeter wave (mmWave) sensing is emerging and starts impacting our life and workspace. mmWave sensing can sense humans and objects in a contactless way, providing fine-grained sensing ability. In the past few years, many mmWave sensing techniques have been proposed and applied in various human sensing applications (e.g., human localization, gesture recognition, and vital monitoring). We discover the need of a comprehensive survey to summarize the technology, platforms and applications of mmWave-based human sensing. In this survey, we first present the mmWave hardware platforms and some key techniques of mmWave sensing. We then provide a comprehensive review of existing mmWave-based human sensing works. Specifically, we divide existing works into four categories according to the sensing granularity: human tracking and localization, motion recognition, biometric measurement and human imaging. Finally, we discuss the potential research challenges and present future directions in this area., Comment: 30 pages, 17 figures, IEEE Survey & Tutorial

Long range wireless transmission techniques such as LoRa are preferential candidates for a substantial class of IoT applications, as they avoid the complexity of multi-hop wireless forwarding. The existing network solutions for LoRa, however, are not suitable for peer-to-peer communication, which is a key requirement for many IoT applications. In this work, we propose a networking system - 6LoRa, that enables IPv6 communication over LoRa. We present a full stack system implementation on RIOT OS and evaluate the system on a real testbed using realistic application scenarios with CoAP. Our findings confirm that our approach outperforms existing solutions in terms of transmission delay and packet reception ratio at comparable energy consumption.

FAIR Digital Object (FDO) is an emerging concept that is highlighted by European Open Science Cloud (EOSC) as a potential candidate for building a ecosystem of machine-actionable research outputs. In this work we systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself. We compare the FDO approach with established Linked Data practices and the existing Web architecture, and provide a brief history of the Semantic Web while discussing why these technologies may have been difficult to adopt for FDO purposes. We conclude with recommendations for both Linked Data and FDO communities to further their adaptation and alignment., Comment: 39 pages, submitted to PeerJ CS

It turns out that some empirical facts in Big Data are the effects of properties of large numbers. Zipf's law 'noise' is an example of such an artefact. We expose several properties of the power law distributions and of similar distribution that occur when the population is finite and the rank and counts of elements in the population are natural numbers. We are particularly concerned with the low-rank end of the graph of the law, the potential of noise in the law, and with the approximation of the number of types of objects at various ranks. Approximations instead of exact solutions are the center of attention. Consequences in the interpretation of Zipf's law are discussed., Comment: 7 pages, 4 figures

IoT networks are increasingly becoming target of sophisticated new cyber-attacks. Anomaly-based detection methods are promising in finding new attacks, but there are certain practical challenges like false-positive alarms, hard to explain, and difficult to scale cost-effectively. The IETF recent standard called Manufacturer Usage Description (MUD) seems promising to limit the attack surface on IoT devices by formally specifying their intended network behavior. In this paper, we use SDN to enforce and monitor the expected behaviors of each IoT device, and train one-class classifier models to detect volumetric attacks. Our specific contributions are fourfold. (1) We develop a multi-level inferencing model to dynamically detect anomalous patterns in network activity of MUD-compliant traffic flows via SDN telemetry, followed by packet inspection of anomalous flows. This provides enhanced fine-grained visibility into distributed and direct attacks, allowing us to precisely isolate volumetric attacks with microflow (5-tuple) resolution. (2) We collect traffic traces (benign and a variety of volumetric attacks) from network behavior of IoT devices in our lab, generate labeled datasets, and make them available to the public. (3) We prototype a full working system (modules are released as open-source), demonstrates its efficacy in detecting volumetric attacks on several consumer IoT devices with high accuracy while maintaining low false positives, and provides insights into cost and performance of our system. (4) We demonstrate how our models scale in environments with a large number of connected IoTs (with datasets collected from a network of IP cameras in our university campus) by considering various training strategies (per device unit versus per device type), and balancing the accuracy of prediction against the cost of models in terms of size and training time., Comment: 18 pages, 13 figures

With the advancement of blockchain systems, many recent research works have proposed distributed ledger technology~(DLT) that employs Byzantine fault-tolerant~(BFT) consensus protocols to decide which block to append next to the ledger. Notably, BFT consensus can offer high performance, energy efficiency, and provable correctness properties, and it is thus considered a promising building block for creating highly resilient and performant blockchain infrastructures. Yet, a major ongoing challenge is to make BFT consensus applicable to large-scale environments. A large body of recent work addresses this challenge by developing novel ideas to improve the scalability of BFT consensus, thus opening the path for a new generation of BFT protocols tailored to the needs of blockchain. In this survey, we create a systematization of knowledge about the novel scalability-enhancing techniques that state-of-the-art BFT consensus protocols use. For our comparison, we closely analyze the efforts, assumptions, and trade-offs these protocols make., Comment: 18 pages, accepted to appear in the proceedings of the 5th IEEE International Conference on Blockchain and Cryptocurrency

Optical backbone networks are required to be highly dynamic in supporting requests with flexible bandwidth granularities to cope with the demands of new broadband wireless and fixed access networks. To provide this flexibility, services are offered by taking requested bandwidth profile into consideration, instead of assigning a fixed amount of bandwidth to each request. New techniques are developed for the resource management of the elastic optical networks to realize services with a specified bandwidth profile, consisting of minimum, average, and maximum required number of spectrum slots, in addition to holding time. In this work, two new schemes are proposed to realize such services, exploiting a probabilistic spectrum partitioning approach. This new probabilistic spectrum partitioning scheme is devised to enhance the chance of accommodating requests and consequently lower request blocking probability. It enforces different probabilities to contributing spectrum partitions in a certain service realization. Taking advantage of this probabilistic spectrum partitioning and a profile-based routing, we introduce two multistage spectrum assignment methods to make a certain lightpath meet the requested service profile constraints, considering the time-weighted average of the assigned spectrum slots. The results indicate that our algorithms can successfully realize the requests with the probability of 0.993 for the offered loads less than 400 erlang., Comment: 12 pages, 13 figures

Network slicing is a crucial enabler to support the composition and deployment of virtual network infrastructures required by the dynamic behavior of networks like 5G/6G mobile networks, IoT-aware networks, e-health systems, and industry verticals like the internet of vehicles (IoV) and industry 4.0. The communication slices and their allocated communication resources are essential in slicing architectures for resource orchestration and allocation, virtual network function (VNF) deployment, and slice operation functionalities. The communication slices provide the communications capabilities required to support slice operation, SLA guarantees, and QoS/ QoE application requirements. Therefore, this contribution proposes a networking slicing conceptual model to formulate the optimization problem related to the sharing of communication resources among communication slices. First, we present a conceptual model of network slicing, we then formulate analytically some aspects of the model and the optimization problem to address. Next, we proposed to use a SARSA agent to solve the problem and implement a proof of concept prototype. Finally, we present the obtained results and discuss them., Comment: 8 pages, 9 figures, ADVANCE conference paper

Decentralized finance (DeFi) is known for its unique mechanism design, which applies smart contracts to facilitate peer-to-peer transactions. The decentralized bank is a typical DeFi application. Ideally, a decentralized bank should be decentralized in the transaction. However, many recent studies have found that decentralized banks have not achieved a significant degree of decentralization. This research conducts a comparative study among mainstream decentralized banks. We apply core-periphery network features analysis using the transaction data from four decentralized banks, Liquity, Aave, MakerDao, and Compound. We extract six features and compare the banks' levels of decentralization cross-sectionally. According to the analysis results, we find that: 1) MakerDao and Compound are more decentralized in the transactions than Aave and Liquity. 2) Although decentralized banking transactions are supposed to be decentralized, the data show that four banks have primary external transaction core addresses such as Huobi, Coinbase, and Binance, etc. We also discuss four design features that might affect network decentralization. Our research contributes to the literature at the interface of decentralized finance, financial technology (Fintech), and social network analysis and inspires future protocol designs to live up to the promise of decentralized finance for a truly peer-to-peer transaction network.

Cloud computing represents an appealing opportunity for cost-effective deployment of HPC workloads on the best-fitting hardware. However, although cloud and on-premise HPC systems offer similar computational resources, their network architecture and performance may differ significantly. For example, these systems use fundamentally different network transport and routing protocols, which may introduce network noise that can eventually limit the application scaling. This work analyzes network performance, scalability, and cost of running HPC workloads on cloud systems. First, we consider latency, bandwidth, and collective communication patterns in detailed small-scale measurements, and then we simulate network performance at a larger scale. We validate our approach on four popular cloud providers and three on-premise HPC systems, showing that network (and also OS) noise can significantly impact performance and cost both at small and large scale., Comment: To appear in SIGMETRICS 2023

Netizens, Michael and Ronda Hauben's foundational treatise on Usenet and the Internet, was first published in print 25 years ago. In this piece, we trace the history and impact of the book and of Usenet itself, contextualising them within the contemporary and modern-day scholarship on virtual communities, online culture, and Internet history. We discuss the Net as a tool of empowerment, and touch on the social, technical, and economic issues related to the maintenance of shared network infrastructures and to the preservation and commodification of Usenet archives. Our interview with Ronda Hauben offers a retrospective look at the development of online communities, their impact, and how they are studied. She recounts her own introduction to the online world, as well as the impetus and writing process for Netizens. She presents Michael Hauben's conception of "netizens" as contributory citizens of the Net (rather than mere users of it) and the "electronic commons" they built up, and argues that this collaborative and collectivist model has been overwhelmed and endangered by the privatisation and commercialisation of the Internet and its communities., Comment: 24 pages, 0 figures

Pre-exascale High Performance Computers (HPC) can reach more than 400 Pflop/s real perfor-mance according the HPLinpack benchmarks. For nanoscience and quantum biology there are requirements for those program codes based on quantum physics algorithms which is difficult to ideally parallelize. Such parallel codes reach their limitations at terascale performance clus-ters. The standard Amdahl's law suggestions for code parallelization complicates focusing and planning for the next step the parallel code developments. In this report we focused on a three key applications domain which are highly parallelizable: HPC benchmarks, quantum compu-ting simulators and Car-Parinello molecular dynamics. According the results we summarize the Amdahl's Law & Parallel Speedup performance achievements with supercomputer with pre-petascale homogeneous HPC hardware. We conclude as an universal computer the pre-petascale supercomputing performance homogeneous hardware still has the basic challeng-es which must be addressed by the researchers or developer in order efficiently to use them.

We propose in this paper a coinvestment plan between several stakeholders of different types, namely a physical network owner, operating network nodes, e.g. a network operator or a tower company, and a set of service providers willing to use these resources to provide services as video streaming, augmented reality, autonomous driving assistance, etc. One such scenario is that of deployment of Edge Computing resources. Indeed, although the latter technology is ready, the high Capital Expenditure (CAPEX) cost of such resources is the barrier to its deployment. For this reason, a solid economical framework to guide the investment and the returns of the stakeholders is key to solve this issue. We formalize the coinvestment framework using coalitional game theory. We provide a solution to calculate how to divide the profits and costs among the stakeholders, taking into account their characteristics: traffic load, revenues, utility function. We prove that it is always possible to form the grand coalition composed of all the stakeholders, by showing that our game is convex. We derive the payoff of the stakeholders using the Shapley value concept, and elaborate on some properties of our game. We show our solution in simulation.

The report describes the digital architecture developed for the West Cambridge Digital Twin, particularly focussed on real-time sensor data collection and analysis with a privacy framework allowing occupants of the buildings to be first-class participants in the system. The implementation has some notable characteristics. In particular 'push' technology is used throughout such that information streams from the incoming asynchronous individual sensor events through to the end-user web pages with the absolute minimum latency, including real-time generated simple and complex events derived from the the underlying sensor data and the updating of visualisations such as an in-building heatmap. We believe the ability of the entire system to respond in the timescale of individual sensor messages to be unique. JSON structures are used to represent all data types including sensor readings, sensor types, building objects, organisations and people, with the idea that JSON-LD may represent a more suitable way than XML/RDF for managing relations between those objects (such as the 'occupies' relationship of people to offices, or the 'type' relationship of sensors to sensor types)., Comment: 72 pages. arXiv admin note: substantial text overlap with arXiv:2103.04924, arXiv:2103.09169

Cellular vehicle-to-everything (C-V2X) has been continuously evolving since Release 14 of the 3rd Generation Partnership Project (3GPP) for future autonomous vehicles. Apart from automotive safety, 5G NR further bring new capabilities to C-V2X for autonomous driving, such as real-time local update, and coordinated driving. These capabilities rely on the provision of low latency and high reliability from 5G NR. Among them, a basic demand is broadcasting or multicasting environment update messages, such as cooperative perception data, with high reliability and low latency from a Road Side Unit (RSU) or a base station (BS). In other words, broadcasting multiple types of automotive messages with high reliability and low latency is one of the key issues in 5G NR C-V2X. In this work, we consider how to select Modulation and Coding Scheme (MCS), RSU/BS, Forward Error Correction (FEC) code rate, to maximize the system utility, which is a function of message delivery reliability. We formulate the optimization problem as a nonlinear integer programming problem. Since the optimization problem is NP-hard, we propose an approximation algorithm, referred to as the Hyperbolic Successive Convex Approximation (HSCA) algorithm, which uses the successive convex approximation to find the optimal solution. In our simulations, we compare the performance of HSCA with those of three algorithms respectively, including the baseline algorithm, the heuristic algorithm, and the optimal solution. Our simulation results show that HSCA outperforms the baseline and the heuristic algorithms and is very competitive to the optimal solution., Comment: 13 pages, submitted to IEEE Transactions on Vehicular Technology

The prepare-and-measure theory is a new type of quantum paradox that reveals the incompatibility between classical theory and quantum mechanics in terms of the dimensionality of physical systems.Just as the Horodecki criterion can determine whether given quantum states are capable of exhibiting Bell nonclassicality, a similar criterion is needed for the prepare-and-measure theory to determine whether given uantum states can exhibit the prepare-and-measure nonclassicality. Recently, Poderini et al. [Phys. Rev. Research 2, 043106 (2020)] presented such a criterion for the prepare-and-measure nonclassicality. In this work, we experimentally validate this criterion -- 52 different sets of quantum states are prepared and tested one by one using this criterion to determine whether they can exhibit the prepare-and-measure nonclassicality, and the experimental results are in good agreement with the theoretical expectations. The criterion experimentally verified here has the potential to be widely used in future research on the prepare-and-measure nonclassicality.

Distributed ledger networks, chiefly those based on blockchain technologies, currently are heralding a next generation of computer systems that aims to suit modern users' demands. Over the recent years, several technologies for blockchains, off-chaining strategies, as well as decentralised and respectively self-sovereign identity systems have shot up so fast that standardisation of the protocols is lagging behind, severely hampering the interoperability of different approaches. Moreover, most of the currently available solutions for distributed ledgers focus on either home users or enterprise use case scenarios, failing to provide integrative solutions addressing the needs of both. Herein we introduce the OpenDSU platform that allows to interoperate generic blockchain technologies, organised - and possibly cascaded in a hierarchical fashion - in domains. To achieve this flexibility, we seamlessly integrated a set of well conceived OpenDSU components to orchestrate off-chain data with granularly resolved and cryptographically secure access levels that are nested with sovereign identities across the different domains. Employing our platform to PharmaLedger, an inter-European network for the standardisation of data handling in the pharmaceutical industry and in healthcare, we demonstrate that OpenDSU can cope with generic demands of heterogeneous use cases in both, performance and handling substantially different business policies. Importantly, whereas available solutions commonly require a pre-defined and fixed set of components, no such vendor lock-in restrictions on the blockchain technology or identity system exist in OpenDSU, making systems built on it flexibly adaptable to new standards evolving in the future., Comment: 18 pages, 8 figures

Mobile networks are becoming energy hungry, and this trend is expected to continue due to a surge in communication and computation demand. Multi-access Edge Computing (MEC), will entail energy-consuming services and applications, with non-negligible impact in terms of ecological sustainability. In this paper, we provide a comprehensive review of existing approaches to make edge computing networks greener, including but not limited to the exploitation of renewable energy resources, and context-awareness. We hence provide an updated account of recent developments on MEC from an energetic sustainability perspective, addressing the initial deployment of computing resources, their dynamic (re)allocation, as well as distributed and federated learning designs. In doing so, we highlight the energy aspects of these algorithms, advocating the need for energy-sustainable edge computing systems that are aligned with Sustainable Development Goals (SDGs) and the Paris agreement. To the best of our knowledge, this is the first work providing a systematic literature review on the efficient deployment and management of energy harvesting MEC, with special focus on the deployment, provisioning, and scheduling of computing tasks, including federated learning for distributed edge intelligence, toward making edge networks greener and more sustainable. At the end of the paper, open research avenues and challenges are identified for all the surveyed topics., Comment: 31 pages, submitted to ACM Computing Surveys

Confusion is a mental state triggered by cognitive disequilibrium that can occur in many types of task-oriented interaction, including Human-Robot Interaction (HRI). People may become confused while interacting with robots due to communicative or even task-centred challenges. To build a smooth and engaging HRI, it is insufficient for an agent to simply detect confusion; instead, the system should aim to mitigate the situation. In light of this, in this paper, we present our approach to a linguistic design of dialogue policies to build a dialogue framework to alleviate interlocutor confusion. We also outline our sketch and discuss challenges with respect to its operationalisation., Comment: 2pages, 1figure, 1 page literture, Semdial2022 Conference (https://semdial2022.github.io/?page=program#)

Modern agriculture is constantly evolving to increase production despite unfavorable environmental conditions. A promising approach is 'greenhouse cultivation' providing a microclimate to the cultivated plants to overcome unfavorable climate. However, massive-sized greenhouses develop non-uniform micro-climate throughout the complex requiring high degree of human supervision. We propose deploying an Agri-Bot to create and maintain positive ecological conditions in the greenhouse, reducing labor costs and increasing production. The prototype will contain two primary systems, the navigation system and the data analytics system. The navigation system will be controlled by an Arduino, and data analytics will be handled using an ESP8266 microchip. Numerous sensors for measuring the greenhouse parameters will be mounted on the robot. It will follow a predefined path, while taking readings at checkpoints. The microchip will collect and process data from sensors, transmit to the cloud, and give commands to the actuators. The soil and climate parameters like temperature, humidity, light intensity, soil moisture, pH will be measured periodically. When the parameters are not within a specified range, the Agri-Bot will take corrective actions like switching on blowers/heaters, starting irrigation etc. If external intervention is required, eg., fertilizer, it will indicate accordingly. Deploying such an Agri-Bot for monitoring and controlling microclimate in large-scale greenhouses can mitigate labor costs while increasing productivity. In spite of an initial cost, it can provide a high return on investment by providing flexibility, low power consumption and easy management to help greenhouse be water efficient, provide evenly dispersed and controlled sunlight intensity, temperature and humidity., Comment: Presented at Eureka Hackathon, India

We study a quantum switch that creates shared end-to-end entangled quantum states to multiple sets of users that are connected to it. Each user is connected to the switch via an optical link across which bipartite Bell-state entangled states are generated in each time-slot with certain probabilities, and the switch merges entanglements of links to create end-to-end entanglements for users. One qubit of an entanglement of a link is stored at the switch and the other qubit of the entanglement is stored at the user corresponding to the link. Assuming that qubits of entanglements of links decipher after one time-slot, we characterize the capacity region, which is defined as the set of arrival rates of requests for end-to-end entanglements for which there exists a scheduling policy that stabilizes the switch. We propose a Max-Weight scheduling policy and show that it stabilizes the switch for all arrival rates that lie in the capacity region. We also provide numerical results to support our analysis., Comment: arXiv admin note: text overlap with arXiv:2106.00831

In order to enhance levels of engagement with conversational systems, our long term research goal seeks to monitor the confusion state of a user and adapt dialogue policies in response to such user confusion states. To this end, in this paper, we present our initial research centred on a user-avatar dialogue scenario that we have developed to study the manifestation of confusion and in the long term its mitigation. We present a new definition of confusion that is particularly tailored to the requirements of intelligent conversational system development for task-oriented dialogue. We also present the details of our Wizard-of-Oz based data collection scenario wherein users interacted with a conversational avatar and were presented with stimuli that were in some cases designed to invoke a confused state in the user. Post study analysis of this data is also presented. Here, three pre-trained deep learning models were deployed to estimate base emotion, head pose and eye gaze. Despite a small pilot study group, our analysis demonstrates a significant relationship between these indicators and confusion states. We understand this as a useful step forward in the automated analysis of the pragmatics of dialogue., Comment: 8 figures, 10pages including 2pages reference. Conference: https://semdial2021.ling.uni-potsdam.de/, Paper link:https://semdial2021.ling.uni-potsdam.de/assets/semdial2021_potsdial_full_proceedings.pdf

Balanced energy consumption is a major research concern in sensor networks. If some sensor nodes spent energy rapidly compared to other sensor groups, then the energy consumption is not evenly distributed and the lifetime of the network is adversely affected. In cluster-based communication protocol, cluster-head nodes rapidly dissipate their energy compared to respective cluster members as it has additional duties of receiving data from cluster members and aggregation of received data. Clustering protocols periodically rotate the energy-intensive job of cluster-head among other sensors in order to evenly distribute the CH's energy load. In this paper, we review various cluster-head rotation approaches and classify them into different categories based on the number of participating nodes, rotation mechanism, and decision parameter(s). Finally, we discuss open research issues in developing an efficient cluster-head rotation approach, which reduces the premature death of cluster-head nodes and maximizes the network lifetime., Comment: Paper Accepted for SUSCOM2020

High performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable and high performance packet classification solution using hand-tuned heuristics approaches. In this paper, we present a scalable learning-based packet classification engine and its performance evaluation. By exploiting the sparsity of ruleset, our algorithm uses a few effective bits (EBs) to extract a large number of candidate rules with just a few of memory access. These effective bits are learned with deep reinforcement learning and they are used to create a bitmap to filter out the majority of rules which do not need to be full-matched to improve the online system performance. Moreover, our EBs learning-based selection method is independent of the ruleset, which can be applied to varying rulesets. Our multibit tries classification engine outperforms lookup time both in worst and average case by 55% and reduce memory footprint, compared to traditional decision tree without EBs., Comment: 6 pages. arXiv admin note: text overlap with arXiv:2205.07973

Scalable packet classification is a key requirement to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable packet classification solution using hand-tuned heuristics approaches. In this paper, we present a scalable learning-based packet classification engine by building an efficient data structure for different ruleset with many fields. Our method consists of the decomposition of fields into subsets and building separate decision trees on those subsets using a deep reinforcement learning procedure. To decompose given fields of a ruleset, we consider different grouping metrics like standard deviation of individual fields and introduce a novel metric called diversity index (DI). We examine different decomposition schemes and construct decision trees for each scheme using deep reinforcement learning and compare the results. The results show that the SD decomposition metrics results in 11.5% faster than DI metrics, 25% faster than random 2 and 40% faster than random 1. Furthermore, our learning-based selection method can be applied to varying rulesets due to its ruleset independence., Comment: 13 pages, published in IET Netw. arXiv admin note: substantial text overlap with arXiv:1902.10319 by other authors

In time-sensitive networks, bounds on worst-case delays are typically obtained by using network calculus and assuming that flows are constrained by bit-level arrival curves. However, in IEEE TSN or IETF DetNet, source flows are constrained on the number of packets rather than bits. A common approach to obtain a delay bound is to derive a bit-level arrival curve from a packet-level arrival curve. However, such a method is not tight: we show that better bounds can be obtained by directly exploiting the arrival curves expressed at the packet level. Our analysis method also obtains better bounds when flows are constrained with g-regulation, such as the recently proposed Length-Rate Quotient rule. It can also be used to generalize some recently proposed network-calculus delay-bounds for a service curve element with known transmission rate., Comment: 19 pages, 6 figures, submitted

Automatic Dependent Surveillance Broadcast (ADS-B) sensors deployed on the ground are central to observing aerial movements of aircraft. Their unsystematic placement, however, results in over-densification of sensor coverage in some areas and insufficient sensor coverage in other areas. ADS-B sensor coverage has so far been recognized and analyzed as an availability problem; it was tackled by sensor placement optimization techniques that aim for covering large enough areas. In this paper, we demonstrate that the unsystematic placement of ADS-B sensors leads to a security problem, since the realization and possible deployment of protective mechanisms is closely linked to aspects of redundancy in ADS-B sensor coverage. In particular, we model ADS-B sensor coverage as a multi-dimensional security problem. We then use multi-objective optimization techniques to tackle this problem and derive security-optimized near-optimal placement solutions. Our results show how the location of sensors play a significant role in reducing the success rate of attackers by providing a sufficient number of sensors within a specific geographical area to verify location claims and reducing the exposure to jamming attacks., Comment: 12 pages, 12 figures, accepted as a short paper in WiSec 2022. This is a technical report for more details about our accepted paper

The modern Internet is highly dependent on trust communicated via certificates. However, in some cases, certificates become untrusted, and it is necessary to revoke them. In practice, the problem of secure revocation is still open. Furthermore, the existing procedures do not leave a transparent and immutable revocation history. We propose and evaluate a new revocation transparency protocol that introduces postcertificates and utilizes the existing Certificate Transparency (CT) logs. The protocol is practical, has a low deployment cost, provides an immutable history of revocations, enables delegation, and helps to detect revocation-related misbehavior by certificate authorities (CAs). With this protocol, a holder of a postcertificate can bypass the issuing CA and autonomously initiate the revocation process via submission of the postcertificate to a CT log. The CAs are required to monitor CT logs and proceed with the revocation upon detection of a postcertificate. Revocation status delivery is performed independently and with an arbitrary status protocol. Postcertificates can increase the accountability of the CAs and empower the certificate owners by giving them additional control over the status of the certificates. We evaluate the protocol, measure log and monitor performance, and conclude that it is possible to provide revocation transparency using existing CT logs.

We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths. The GNN-based algorithm is trained only with random graphs generated with the Barab\'asi-Albert model. Even though, the obtained test results show that we can precisely model the penalties in a wide range of various existing topologies. GNNs eliminate the need to simulate complex outage scenarios for the network topologies under study. In practice, the whole design operation is limited by 4ms on modern hardware. This way, we can gain as much as over 12,000 times in the speed improvement., Comment: This paper is under consideration at Journal of Network and Systems Management

Within the past two decades, Augmented Reality (AR) applications have received increased attention. Augmented Reality is now widely used in the education sector at level K to 12. AR is expected to be generally adopted in two to three years in higher education and four to five years in K to 12. Applying AR technology in the education sector especially in STEM subjects, can result in having a smart campus. In adopting a SMART Campus strategy, education practitioners must address many intrinsic issues in science, technology, engineering, and mathematics (STEM) research. For example, in physics, there are expensive or insufficient laboratory systems, system faults, and difficulty simulating other experimental circumstances. In technology, many schools do not have enough computers. In engineering, there are only a few instructors who are knowledgeable in computer aided design (CAD). In mathematics, few teachers incorporate technology into their lessons often because they believe it is still better to teach through the traditional methods. Hence, In this paper we discuss how AR is being used now in different learning areas in STEM to open new doors to researchers and teachers as they transition their schools into SMART campuses with the use of AR apps. Aligned with this, a suggested framework for school administrators and policymakers is proposed based on a review of the positive benefits of different AR apps., Comment: 10 pages, 2 figures, 1 Table