With the surging demand for data-intensive applications, ensuring seamless content delivery in Satellite-Terrestrial Integrated Networks (STINs) is crucial, especially for remote users. Dynamic Ad Insertion (DAI) enhances monetization and user experience, while Mobile Edge Computing (MEC) in STINs enables distributed content caching and ad insertion. However, satellite mobility and time-varying topologies cause service disruptions, while excessive or poorly placed ads risk user disengagement, impacting revenue. This paper proposes a novel framework that jointly addresses three challenges: (i) service continuity-and topology-aware content caching to adapt to STIN dynamics, (ii) Distributed DAI (D-DAI) that minimizes feeder link load and storage overhead by avoiding redundant ad-variant content storage through distributed ad stitching, and (iii) revenue-aware content distribution that explicitly models user disengagement due to ad overload to balance monetization and user satisfaction. We formulate the problem as two hierarchical Integer Linear Programming (ILP) optimizations: one content caching that aims to maximize cache hit rate and another optimizing content distribution with DAI to maximize revenue, minimize end-user costs, and enhance user experience. We develop greedy algorithms for fast initialization and a Binary Particle Swarm Optimization (BPSO)–based strategy for enhanced performance. Simulation results demonstrate that the proposed approach achieves over a 4.5% increase in revenue and reduces cache retrieval delay by more than 39% compared to the benchmark algorithms.

peer reviewed

Quantum key distribution (QKD) offers exceptionally high levels of data security during transmission by using principles of quantum physics. It is renowned for its provable security features. However, a gap between theoretical models and real-world applications, known as quantum hacking, challenges the reliability of QKD networks. Trojan-horse attacks represent a significant threat to the Bob subsystem in QKD, allowing Eve to infer Alice’s basis choices through back-reflected pulses. This can compromise security without detection in severe cases, especially when quantum bit error rates (QBER) fall below the abort threshold. The proposed method combines a category-based Gaussian Mixture Model (GMM) with the Kolmogorov-Smirnov test to estimate the posterior QBER distribution and assess risks in practical QKD systems. By processing the QBER, the approach also evaluates the dependability of the QKD scenario. Numerical results are presented using a state-of-the-art point-to-point QKD device operating over optical quantum channels of 1 m, 1 km, and 30 km lengths. The results of the experimental analysis of a 30 km optical link suggest that the QKD device provided prior information to the proposed learner. Consequently, our proposed trustworthy monitor offers a defensive mechanism that identifies potential Eve attacks, effectively mitigating the risk of security vulnerabilities.

As the demand for remote sensing increases, it is gradually becoming possible for remote sensing satellites to access large-scale communication constellations through laser inter-satellite links, enabling high-throughput data backhaul. This paper addresses the inter-layer topology planning problem using potential game theory for the first time, strategically framing it as a decision-making problem for remote sensing satellites to access communication satellites. Then, theoretical derivation confirms that the above problem is a potential game and verifies the existence of Nash equilibrium. Additionally, considering factors such as laser link establishment time, cross-layer link visibility time consumption rate, mission transmission delay, and communication satellite loading level, a potential game strategy selection probability update algorithm (PG) based on revenue contribution is proposed. Finally, the superior performance of the PG algorithm in terms of task completion, transmission delay, and transport layer network load is effectively demonstrated through two scenarios involving the Starlink&DOVE and GW&R-SAT constellations.

Satellite Edge Computing (SEC) is seen as a promising solution to content caching onboard by reducing retrieval latency and improving user experience. Deciding content type, number of copies, and where to cache it in a satellite con-stellation remains challenging, requiring careful consideration of many factors, such as satellite coverage, content popularity, and resource constraints. In this paper, we study resource-aware onboard content caching strategies in a multi-layer hierarchical satellite network that includes Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary Orbit (GEO) satellites. The primary objective is to design a cache placement strategy that maximizes resource utilization ratio while determining the optimal number of content copies, all within the context of a time-varying network topology that primarly occurs due to the mobility of non-geostationary (NGSO) satellites. We model a novel proximity-based hierarchical hybrid content popularity model and formulate the problem as an Integer Linear Programming (ILP) problem to utilize the resources in a network. To solve the ILP problem, we propose two algorithms: Greedy based Content Cache Placement (G_CCP) and Simulated Annealing based Content Cache Placement (SA_CCP). Extensive simulations demonstrate that both G_CCP and SA_CCP are near-optimal and outperform the benchmark in terms of cache-hit ratio, resource utilization ratio, and cache fetching duration.

The rapid deployment of Low Earth Orbit (LEO) satellites, driven by technological advancements and cost reductions, has led to the emergence of mega-constellations for satellite-based Internet services. Among them, the networking problem has become a significant area of research. Specifically, breaking away from the traditional Grid-Mesh + topology schemes has been a focal point. Although some related studies have been conducted, there are still three major challenges in optimizing the topology of laser inter-satellite links: the lack of theoretical derivation for satellite visibility, the need for comprehensive modeling goals, and the absence of network simulation verification. To address these challenges, we introduce the theory of visibility analysis of laser terminals in real scenarios and propose a theoretical model of topologically feasible solutions for integer linear programming. Furthermore, a mathematical model is developed that considers time delay, hop count, and link load as optimization objectives. The Many-objective Non-Grid-Mesh Topology Optimization (M-NGTO) algorithm, based on Non-dominated Sorting Genetic Algorithm III (NSGA-III), is then designed to effectively optimize the topology. The optimized Non-Grid-Mesh topology is validated through packet-level simulations on the Hypatia platform. Additionally, consistency analysis is performed to establish agreement between theory and simulation results. The results of simulations conducted on two megaLEO satellite Internet constellations, GW and Starlink, demonstrate that the performance of the Non-Grid-Mesh topology in the above three optimization objectives is approximately 39.11% better than the average of the Grid-Mesh + topology, confirming the effectiveness of the M-NGTO algorithm. The findings have significant implications for enhancing the communication performance and load balancing of satellite Internet systems.

This paper addresses the virtual network embedding (VNE) problem in integrated satellite-terrestrial networks (STNs). VNE consists of mapping virtual network functions (VNFs) in a service function chain (SFC) to physical nodes and mapping virtual links connecting the VNFs to physical links. Compared to terrestrial networks, VNE in STNs is challenging due to the movement of the non-geostationary orbit (NGSO) satellites and limited onboard processing resources. A static VNE strategy fails to efficiently address the diverse requirements of heterogeneous service requests in such a highly dynamic topology. In addition, existing solutions do not consider the capacity limitation and connectivity duration of inter-satellite links (ISLs) and ground-to-satellite links, which are essential parameters for deploying a VNE strategy in STNs. This work proposes a heuristic solution and reinforcement learning (RL)-based improved solution for the VNE scheme that can dynamically modify the existing VNF deployment strategy to maximize the average service acceptance rate and revenue. The RL agent selects a suitable VNE strategy for each service request considering the time-varying network topology and service’s requirements. The proposed scheme increases the service acceptance ratio by 19.95% compared to the benchmark TS-MAPSCH.

Satellite Edge Computing (SEC) is seen as a promising solution for deploying network functions in orbit to provide ubiquitous services with low latency and bandwidth. Software Defined Networks (SDN) and Network Function Virtualization (NFV) enable SEC to manage and deploy services more flexibly. In this paper, we study a dynamic and topology-aware VNF mapping and scheduling strategy within an SDN/NFV-enabled SEC infrastructure. Our focus is on meeting the stringent requirements of mission-critical (MC) applications, recognizing their significance in both satellite-to-satellite and edge-to-satellite communications while ensuring service delay margin fairness across various time-sensitive service requests. We formulate the VNF mapping and scheduling problem as an Integer Nonlinear Programming problem (INLP), with the objective ofminimaxfairness among specified requests while considering dynamic satellite network topology, traffic, and resource constraints. We then propose two algorithms for solving theINLPproblem: Fairness-Aware Greedy Algorithm for Dynamic VNF Mapping and Scheduling (FAGD_MASC) and Fairness-Aware Simulated Annealing-Based Algorithm for Dynamic VNF Mapping and Scheduling (FASD_MASC) which are suitable for low and high service arrival rates, respectively. Our extensive simulations demonstrate that bothFAGD_MASCandFASD_MASCapproaches are very close to the optimization-based solution and outperform the benchmark solution in terms of service acceptance rates.

Non-Geostationary Orbit satellite (NGSO) is an essential element in 5G Non-Terrestrial Networks (NTNs), which can operate either independently or as complementary parts to terrestrial systems to boost the network capacity, coverage and resilience. Due to the highly dynamic topologies, one of the challenges in NGSO is how to harmonize the network virtualized resources to satisfy diverse quality of service requirements in an efficient manner. In this paper, we investigate Virtual Network Embedding (VNE) for integrated NGSO-terrestrial systems while considering dynamic topologies. We propose a Dynamic Topology-Aware VNE (DTA-VNE) algorithm which, given priori information about the network’s evolution over time, can plan the embedding for each Virtual Network Request (VNR) over its lifetime. In a highly dynamic environment, the VNE decision can be varying for different VNRs at the expense of a considerable cost of migrating traffic and reconfiguring resources. The proposed DTA-VNE aims at minimizing this migration cost and thus avoids unnecessary re-mappings. In numerical results, the effectiveness of the proposed DTA-VNE is demonstrated with much lower migration cost than the conventional implementations. We show the benefit of planning for more time slots, in terms of migration cost, and the impact on the computation time, due to the increasing problem complexity. The trade-off between these two performance metrics is studied. Furthermore, we verify the efficiency of DTA-VNE in a MultI-layer awaRe SDN-based testbed for SAtellite-Terrestrial networks (MIRSAT). Finally, the application of DTA-VNE to novel scenarios such as mega LEO constellations is discussed, highlighting the challenges and possible solutions.

Satellite edge computing (SEC) has emerged as a promising technology to deliver network services to remote users. Coupled with software-defined networking (SDN) and network function virtualization (NFV), SEC can provide flexibility, agility, and efficiency when allocating computing and storage resources. However, there still remain a number of technical challenges in terms of fairness and efficiency of the allocation of physical resources in service provisioning, especially in a satellite network with limited resources and dynamic traffic demands. In this paper, we investigate a dynamic virtual network function (VNF) mapping and scheduling in an SDN/NFV-enabled SEC environment to maximize the fairness between competing services in terms of the E2E delay safe margin to enhance the service acceptance rates in the network. We mathematically formulate the VNF mapping and scheduling problem as a nonlinear integer optimization problem, which is NP-hard. In order to effectively solve the problem, this paper proposes a two-stage heuristic dynamic VNF mapping and scheduling algorithm: i) the path selection algorithm returns all possible paths for a given service request with multiple VNFs, which are sorted in ascending order based on their E2E service delay and executed offline, and ii) the dynamic VNF mapping and scheduling algorithm performs online dynamic remapping and rescheduling of VNFs. Finally, numerical results are provided to demonstrate that the proposed algorithm offers a higher service acceptance rate, computing resource utilization efficiency, and higher fairness compared to a benchmark scheme.

Before the advent of High-Throughput Satellites (HTSs), the satellite capacity was not enough to accommodate a large amount of data. Thanks to HTS, beyond 5G networks will boost the cooperation between space, air and terrestrial networks. The coexistence of heterogeneous QoS traffic demands, (e.g., emergency services, In-Flight Connectivity (IFC), Earth Observation Missions (EOMs)), over a dynamic environment, such as Multi-layer satellite-terrestrial networks, made the routing complex to handle. Additionally, due to numerous Inter-Satellite Links (ISLs) and their frequent changes, a testbed with real network emulation is challenging to develop.It is relevant not only to optimize the dynamic routing, but also to emulate the network in a testbed. This allows to consider systems constraints such as communication and technology delays in the most realistic manner. This paper investigates the future coexistence of the mentioned use cases for integrated Non-Terrestrial Networks (NTN)-terrestrial networks. We use Software Defined Networking (SDN) to monitor the substrate network with traffic statistics and apply routing decisions, via traffic handovers, during unexpected situations (congestion, link unavailability), in a reactive manner. Furthermore, we show that, for traditional handovers due to loss of Line of Sight (LoS), the SDN controller manages the procedure proactively to minimize traffic losses.

In this paper, we address the virtual network embedding (VNE) problem in non-terrestrial networks (NTNs) enabling dynamic changes in the virtual network function (VNF) deployment to maximize the service acceptance rate and service revenue. NTNs such as satellite networks involve highly dynamic topology and limited resources in terms of rate and power. VNE in NTNs is a challenge because a static strategy under-performs when new service requests arrive or the network topology changes unexpectedly due to failures or other events. Existing solutions do not consider the power constraint of satellites and rate limitation of inter-satellite links (ISLs) which are essential parameters for dynamic adjustment of existing VNE strategy in NTNs. In this work, we propose a dynamic VNE algorithm that selects a suitable VNE strategy for new and existing services considering the time-varying network topology. The proposed scheme, D-ViNE, increases the service acceptance ratio by 8.51% compared to the benchmark scheme TS-MAPSCH.

In this paper, we present FedServ, a federated task service system for fog-enabled Internet of vehicles (IoV), using which we aim to minimize the delay of vehicle task service. FedServ considers different task service requirements such as delay, computation-intensiveness, processing units required, and energy consumption. The existing literature mainly focuses on the delay requirement of tasks and considers the energy consumption for task processing, whereas FedServ aims to achieve the Quality of Service (QoS) of the task while optimizing different resource requirements for task service. FedServ provisions the federated task service by fragmenting the tasks, which minimizes the task complexity while preserving task fragment dependency. To determine the suitable fog nodes for task fragment service, we formulate the problem as a coalition graph game. Analytical results depict that the proposed scheme minimizes the delay of the task service and the energy consumption while reducing the number of QoS violated tasks by 34.32%, 25.32%, and 22.78% compared to the existing schemes.

In this paper, we present a traffic engineering scheme for sustainable hybrid Software-Defined Networks (SDN) to reduce the overall energy consumption of the network and increase the amount of programmable traffic. Hybrid SDN involves both legacy and SDN switches because of the migration from a legacy network to an SDN. The primary reason for this migration is to increase the amount of programmable traffic and add flexibility to network management operations such as network monitoring, load distribution, and energy management. The energy management solutions in SDN include dynamic activation or deactivation of network elements and traffic rerouting. However, there exists a trade-off between energy-aware routing and programmable traffic, as unplanned traffic rerouting may transform programmable traffic into a non-programmable one. In this paper, we propose a scheme for dynamic activation of SDN links and optimal route selection of existing flows. In contrast to the previous works, we focus on reducing energy consumption, while maximizing the programmable traffic, as it serves the primary intent of transforming a legacy network into an SDN. The simulation results show that the proposed scheme, ETHoS, increases the energy savings by$28.91\%$compared to SENEtoR, an existing scheme.

A social fog-IoV network involves time-critical tasks because it is highly dynamic due to rapid changes in the network topology. Therefore, completing tasks within the allowable delay is a challenge for social fog-IoV networks. In this context, we present a latency-aware task offloading scheme, named LOAN, in a fog-enabled association-free social Internet of Vehicle (IoV) network that aims to minimize the delay of time-critical tasks while saving the starvation of best-effort tasks. Our work considers different priorities for the service of time-critical tasks while efficiently utilizing fog resources. Different from the works in the literature that provide privilege to the time-critical tasks, LOAN handles the service of best-effort tasks efficiently without making them suffer conditions such as starvation. LOAN manages the priority levels of the tasks by incrementing their priorities based on their waiting time for the task service. We formulate the problem of efficient task service by suitable fog node as a coalition formulation game. Numerical results show that the LOAN achieves a reduction in delay compared to Greedy method by 36.5%.

In this paper, we present a traffic-aware flow migration approach, which reduces data plane load in Software-Defined Networking (SDN) during a network update. SDN update involves rerouting of multiple traffic flows to accommodate new flows. An unplanned flow migration schedule overloads the data plane by burdening the data links and flooding the rule-space of capacity-constrained SDN switches. The overload of data links and switches blocks the update process, and the network fails to address the Quality of Service (QoS) demands of the traffic flows, especially latency-sensitive flows. Prior approaches migrate flows without considering load reduction of the data plane along with QoS demands of the flows. In this work, we propose a load reduction strategy that prioritizes traffic flows based on QoS demands and aims to avoid link congestion and rule-space overflow during flow migration. The proposed scheme significantly reduces the maximum data link bandwidth usage. In particular, the maximum data link bandwidth usage is 13.22% less than the two-phase update approach.

In this paper, we propose a scheme to address the problem of load management in the control plane of Software-Defined Internet of Things (SDIoT) networks. In SDIoT, multiple controllers are deployed to enhance network scalability. With the growth of IoT, the number of devices is increasing rapidly. The management of control plane load is an essential issue for IoT networks because of the dynamic traffic characteristics. IoT traffic is highly dynamic due to the heterogeneity of IoT devices in terms of mobility, activation model, Quality of Service (QoS) demand, and flow generation rate. The challenge is to prevent controller overload and distribute traffic optimally under the consideration of heterogeneous IoT devices. The proposed scheme estimates control plane load based on the mobility and activation model of IoT devices. For mobility prediction, we use Order- m fallback Markov Predictor as it consumes less space and performs efficiently even for small values of m. Based on the prediction results, we implement a traffic-aware rule-caching mechanism and a master controller assignment scheme to reduce the control plane load. Simulation results show that the proposed scheme reduces the peak intensity of the control traffic by 23.08% and 16.67%, as compared to the considered benchmark schemes.

In this paper, we present a traffic-aware consistent approach for flow migration in Software Defined Networking (SDN). The proposed scheme considers heterogeneous traffic to determine a consistent flow migration schedule. In a large-scale network, majority of traffic flows are latency sensitive. These flows change path frequently to accommodate new traffic flows. The challenge is to reduce the time required to modify the flow-path of latency sensitive flows. Existing solutions do not consider specific flow characteristics to decide a consistent traffic flow migration schedule. In this work, we propose a coalition graph game-based strategy while prioritizing traffic flows based on latency sensitivity. The proposed scheme significantly reduces the migration duration of latency sensitive traffic flows. In particular, the average traffic flow migration duration is 15.43% less than existing timed two-phase update solution.

In this paper, we address the issue of rule duplication during network updates in software-defined networking (SDN). In SDN, network update involves the controller in sending update packets to desired set of switches, where the update rules are installed. To ensure update consistency, old flow rules are stored until the total update procedure is complete. Higher consumption of ternary content addressable memories (TCAMs) during update increases the cost of network update and decreases the scalability of SDN. In this paper, we propose an approach for consistent update with redundancy reduction, named CURE, which reduces the TCAM usage during update. CURE prioritizes switches according to their usage pattern and schedules updates based on priority zones. The proposed approach guarantees that highly loaded switches are updated first. CURE also maintains packet-level consistency by implementing a multilevel queuing approach. In this framework, each switch in the current update region stores the incoming packets in individual device queues until the switch completes update. Therefore, after the initiation of an update, packets are processed according to new rules only. The results of performance evaluation depict that the average rule space utilization during update using CURE is 29.954% less than using the two-phase update proposed in the existing literature.

Out of the several factors responsible for processor power dissipation, the branch prediction unit itself contributes to almost 10% of the overall processor power dissipation. The functioning of predictors is, therefore, to be more accurate especially for the CMPs (chip multiprocessors) with thousands of on-chip cores. This work reports that the design inaccuracy (fault) in a predictor can cause a huge power loss, even up to95%. The impact of faults causing additional loss in processor power is estimated to diagnose the faulty module of a predictor. This has been established through introduction of probable faults/design inaccuracies to each of the functional modules of a predictor. Exhaustive experimentations on the state-of-the-art predictors reveal that the amount of additional power loss during processor execution clearly points to the specific design inaccuracies. This effectively leads to identification of the faulty module of a predictor.

In CMPs (Chip Multi-Processors) with thousand of processors, the issue of power dissipation has emerged out as a matter of serious concern. Out of the several factors responsible for processor power dissipation, the branch prediction unit of a modern processor itself contributes to almost 10% of the overall power dissipation. It points to the fact that the functioning of predictors is to be more accurate as well as power efficient. In this work, we analyze the impact of inaccurate/faulty design on the branch predictors' power dissipation while realizing speculative execution. This issue has been addressed through introduction of probable faults, commonly arise out of the design inaccuracies, in a predictor that lead to mis-speculation. The evaluation of fault effect (design inaccuracy) is done by estimating the additional power consumed by a pipelined processor. The detail analysis reveals that the design inaccuracy/fault in a predictor can cause a huge power loss, even up to 95%.

In WSN (Wireless Sensor Network), the sensor nodes may become faulty due to low battery power or some other physical defects. The faulty nodes may badly affect the network performance while trying to reach an agreement on an event. This effectively leads to deviation from the desired outcome, ineffective utilization of network bandwidth and unproductive computational overhead. In this work, we propose a CA (Cellular Automata) based scheme that can efficiently identify the faulty nodes of a WSN in operation. The scheme is developed around the SACA (Single Attractor CA) to enable diagnosis of the network region with affected nodes, without consuming massive computational overhead and bandwidth.