Perceptual image compression has recently gained increasing attention, as it aims to reconstruct visually realistic images using generative models. Most existing methods adopt patch-based generative adversarial networks (PatchGAN) for one-step image generation, where adversarial training helps the decoder learn the distribution of natural images. However, this strategy is often coarse-grained, as it focuses mainly on patch-level consistency and overlooks global structural and semantic details. To address this limitation, we propose a simple yet effective Semantic and Spectral Consistency Learning (SSCL) strategy, which complements existing patch-based approaches for more accurate distribution alignment. For semantic consistency, we leverage semantic vision models to extract semantic features. The semantic discriminator, aware of the specific semantics of each image, provides more adaptive and precise feedback. This guides the encoder to retain meaningful information and helps the decoder synthesize detailed textures, without requiring explicit semantic transmission or additional modules. For spectral consistency, we introduce a frequency discriminator that focuses on high-frequency components, helping to reduce artifacts based on spectral priors. Experiments show that SSCL outperforms existing perceptual codecs in terms of visual quality. Compared to MS-ILLM, SSCL achieves 45% to 60% bit-rate savings on CLIC2020 and Kodak datasets, measured by FID and DISTS.

Mobile edge caching plays a crucial role in traffic offloading for access networks. By storing frequently requested content items close to subscribers, it significantly reduces data retrieval latency, mitigates backhaul congestion, and alleviates the load on remote servers. Among various caching strategies, the two-tier Least Recently Used (2-LRU) policy has been widely adopted due to its efficient popularity-aware filtering capability while maintaining$\mathcal {O}(1)$computational complexity. However, conventional 2-LRU often allocates an equal number of entries to both LRU tiers, overlooking the potential cache hit ratio gains achievable through asymmetric tier-size configurations. Therefore, in this paper, we present a comprehensive analysis of 2-LRU under asymmetric tier sizing, and leverage the obtained insights to further guide performance optimization. In particular, we first construct a discrete-time Markov chain model to characterize the state transitions of 2-LRU and derive a closed-form expression for its cache hit probability, i.e., the hit ratio of its second-tier LRU ($C_{2}$). We then perform extensive simulations to validate accuracy of the proposed model and investigate the optimal size settings for the first-tier LRU ($C_{1}$). Building on the key implications of the associated results, we further propose 2LRU-$\Delta C_{1}$, an enhanced 2-LRU scheme that dynamically adjusts the size of$C_{1}$to accelerate the population of$C_{2}$with popular content data, thereby improving the cache hit ratio of$C_{2}$. Finally, we implement 2LRU-$\Delta C_{1}$in NS-3 and evaluate its performance against several baseline strategies, including LRU, 2-LRU ($C_{1}=C_{2}$), LFU, and a DRL-based policy. Corresponding results have confirmed the efficiency of our proposed scheme.

With the rapid integration of the Internet of Things (IoT) and fifth-generation mobile communications (5G), the massive real-time computing demands generated on the terminal side have exceeded the processing capability of a single edge server (ES). How to efficiently offload computing tasks in parallel to multiple ESs for collaborative execution has emerged as a significant challenge in mobile edge computing (MEC). To address this, we propose a dynamic parallel offloading framework, CoESAC (Collaborative Edge with Soft Actor-Critic). On the one hand, we model the multi-objective offloading problem and the load allocation problem as a high-dimensional discrete decision-making task, demonstrating its intrinsic NP-hard complexity. On the other hand, based on a discrete Soft Actor-Critic (SAC) algorithm in deep reinforcement learning (DRL), the proposed method adopts a task sub-fragmentation and dynamic load adaptation mechanism to flexibly schedule the parallel computing capabilities of multiple ESs. Experimental results show that across diverse system conditions and execution scenarios, CoE-SAC reduces the average make-span by up to 50.16% compared with advanced baselines, and significantly lowers the failure rate by more than 30.73%. These improvements highlight the framework’s strong robustness and superior performance, offering new insights into multi-node collaborative computing under heterogeneous resources and high-concurrency workloads.

In Internet of Things (IoT) networks, the IoT sensors collect valuable raw data required to sustain Artificial Intelligence (AI) based networks operation. AI models are data-driven as they use the data to make accurate network security, management, and operational decisions. Unfortunately, the sensors are deployed in harsh environments which affects the sensor behaviour and eventually the networks' operations. Further, IoT devices are typically vulnerable to a range of malicious events. Therefore, IoT sensor's correct operation including resilience to failure is essential for sustained operations. Naturally, the state variables of time-series data can be changed, i.e., the data streams generated in these situations can be incorrect, incomplete or missing, and sparse presenting a significant challenge for real-time decision-making ability of AI models to make explainable and intelligent management and control decisions. In this paper, we aim to alleviate this fundamental problem to predict the missing and faulty reading correctly so that the decision-making ability of the AI models should not deteriorate in the presence of incorrect, missing, and highly imbalanced data sets. We use a novel approach using fuzzy-based information decomposition to recover the missed data values. We use three data sets, and our preliminary results show that our approach effectively recovers the missed or compromised data samples and help AI models in making accurate decision. Finally, the limitations and future work of this research have been discussed.

Recent advances in Cloud Computing have shown great power in enhancing intelligent devices to support various applications. Nevertheless, conventional Cloud Computing fails to keep up with the ever-advancing requirements of efficient task execution, mainly resulting from its drawbacks in communication delay. To this end, multi-layer cloud computing with local, edge, and remote data centers has gained high interest yet remains challenging because of the inherent complexity of cross-layer orchestration. In particular, with more participants involved, it is nontrivial to achieve customized service provision while guaranteeing system stability. Hence, we address the workload scheduling issue in the multi-layer cloud paradigm in this paper, with task offloading and service reconfiguration considered jointly. We first formulate it as a stochastic optimization problem, where statistical service requirements are imposed on queue lengths. Then, we divide the original optimization into three individual low-complex sub-problems with optimal solutions provided. To improve system performance, we introduce a request-rejecting mechanism that augments our approach with delay-optimality. Theoretical analysis confirms that our approaches can guarantee system stability and are asymptotically optimal within a small gap from the optimum. Finally, we validate the efficiency of our approaches through extensive simulation results in performance guarantees and customized workload scheduling.

In smart farming sector, Internet of Things (IoT) based smart sensing systems are vulnerable to failure, malfunction, and malicious attacks. Also, sensors are deployed often in an alien and harsh environment. Here, the conditions are not well supportive which either causes the sensor to fail prematurely or gives unusual and erroneous readings, known as outliers. This effects the smart network's performance and decision-making ability in many ways. Therefore, it is important to accurately detect the IoT sensor behaviour in legitimate, faulty, and compromised or attack scenarios. To distinguish the sensor behaviour in different scenarios we have proposed a feasible approach using spatial correlation theory which is validated using Moran'sIindex tool. We have used Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) models to test our approach. For real-time anomaly detection we have used an edge computing technology. We have compared the proposed approach, using Forest Fire real dataset, with the three existing recent works. Our results are promising in terms of accurate detection of IoT sensor behaviours in real-time. This will assist the precision farming industry in making better decisions to securely manage IoT field network, increase productivity, and improves operational efficiency.

Edge caching has been regarded as a promising technique for low-latency, high-rate data delivery in future networks, and there is an increasing interest to leverage Machine Learning (ML) for better content placement instead of traditional optimization-based methods due to its self-adaptive ability under complex environments. Despite many efforts on ML-based cooperative caching, there are still several key issues that need to be addressed, especially to reduce computation complexity and communication costs under the optimization of cache efficiency. To this end, in this paper, we propose an efficient cooperative caching (FDDL) framework to address the issues in mobile edge networks. Particularly, we propose a DRL-CA algorithm for cache admission, which extracts a boarder set of attributes from massive requests to improve the cache efficiency. Then, we present an lightweight eviction algorithm for fine-grained replacements of unpopular contents. Moreover, we present a Federated Learning-based parameter sharing mechanism to reduce the signaling overheads in collaborations. We implement an emulation system and evaluate the caching performance of the proposed FDDL. Emulation results show that the proposed FDDL can achieve a higher cache hit ratio and traffic offloading rate than several conventional caching policies and DRL-based caching algorithms, and effectively reduce communication costs and training time.

Recently, there is a growing interest in Double-Layered Satellite Networks (DLSN) which integrate Medium-Earth-Orbit (MEO) and Low-Earth-Orbit (LEO) satellites for provision of mobile and personal services. However, it is still in the early stage with several challenges unaddressed, and one of the key problems is the inter-layer link allocations between MEO and LEO satellites, as DLSN topology is dynamically changed over the time and satellites are with the limited number of connections onboard. To this end, we propose a corresponding Inter-layer Link Allocation (ILA) scheme in this paper, taking the visible duration between satellites, transmitting power consumed onboard and geographical distributions of user load into account, aiming to maximize the utilization efficiency of DLSN inter-layer links. Then, we formulate it as a constrained multi-objective linear programming problem and evaluate its performance with other three benchmarks. Numerical results have demonstrated that the proposed ILA scheme can decrease the number of ILL handovers and average inter-satellite distance, with load balanced between LEO and MEO satellites.

Since Internet of Things (IoT) communications can enjoy many advantages brought by content-centric networking (CCN) in nature, there is an increasing interest on their integration for better information retrieval and distribution. Nevertheless, different from the conventional multimedia traffic of which contents are hardly changed, IoT data are always transient and updated by their producers according to the actual situation. As a result, if without any effective countermeasures, outdated copies are inevitably stored by CCN routers and then distributed to the associated consumers, degrading both caching efficiency and user experience. In fact, most of related policies take little account of information freshness for cached contents, and how to tackle transient IoT data in CCN is still an ignored but crucial issue required for further explorations. Therefore, in this article, we propose an efficient popularity-based cache consistency management scheme, which aims to guarantee freshness of IoT data returned by on-path routers and avoid heavy signalling costs introduced at the same time. Extensive simulations were performed under both real-world scare-free and binary-tree topologies, and corresponding results have proved the efficiency of the proposed scheme in timely evictions of outdated IoT data stored by CCN in-network caching.

Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies facilitate deploying Service Function Chains (SFCs) at clouds in efficiency and flexibility. However, it is still challenging to efficiently chain Virtualized Network Functions (VNFs) in overlay networks without knowledge of underlying network configurations. Although there are many deterministic approaches for VNF placement and chaining, they have high complexity and depend on state information of substrate networks. Fortunately, Reinforcement Learning (RL) brings opportunities to alleviate this challenge as it can learn to make suitable decisions without prior knowledge. Therefore, in this article, we propose an RL approach for efficient SFC provision in overlay networks, where the same VNFs provided by multiple vendors are with different performance. Specifically, we first formulate the problem into an Integer Linear Programming (ILP) model for benchmarking. Then, we present the online SFC path selection into a Markov Decision Process (MDP) and propose a corresponding policy-gradient-based solution. Finally, we evaluate our proposed approach with extensive simulations with randomly generated SFC requests and a real-world video streaming dataset, and implement an emulation system for feasibility verification. Related results demonstrate that performance of our approach is close to the ILP-based method and better than deep Q-learning, random, and load-least-greedy methods.

Recent advances in the Internet of Things (IoT) technologies have enabled ubiquitous smart devices to sense and process various kinds of data. However, these innovations also raise the concern of efficient data transmission. Tackling the above issue is nontrivial since the resource constraints and environmental randomness in IoT require a lightweight transmission scheme while guaranteeing system stability. In this paper, we formulate the transmission scheduling problem of multi‐interface IoT devices as a concave optimization, aimed at accommodating the randomness of the IoT environment within the network capacity. By applying the Lyapunov optimization technique, we divide the stochastic problem into a series of low‐complex subproblems, which can be individually solved per time slot, and develop a dynamical control algorithm that does not require a priori knowledge such as link states. Theoretical analysis shows that our algorithms nicely bound the average queue length and are asymptotically optimal. Finally, extensive simulation results verify the theoretical conclusions and validate the effectiveness of the proposed algorithm.

The Internet of Things (IoT) has gained great attention in recent years, due to its significant role in industry innovations and promotions. However, it is still facing many technical challenges before fully gaining ground, mainly resulting from limited computational and energy resources of IoT devices and best-effort underlying network paradigms. Thanks to the emerging edge computing that optimizes the cloud computing by processing data at edge networks, IoT devices can offload computation-intensive tasks to their assigned edge computing servers with response time guaranteed and energy consumption saved. As a result, how to perform task offloading by IoT devices has become a key challenge widely discussed. Nevertheless, most of the existing works focus on the tradeoff between executing a task locally and remotely through techniques, such as optimization and game theory, rather than related theoretical model to analyze communication procedures of offloading policies. Thus, in this paper, we propose a multiqueue model to explore the impact of offloading policies on performance of the IoT devices with their assigned edge computing server. Particularly, we consider two simple policies, namely Locality-First policy and Probability-based policy, and obtain their analytic solution of the task mean response time and energy consumption of the IoT devices and edge computing server. Extensive simulations are performed and related results have proved accuracy of the proposed model.

The great improvements in both satellite and terrestrial networks have motivated the academic and industrial communities to rethink their integration. As a result, there is an increasing interest on how to combine broadband satellite networks with the clean-slate terrestrial ones, especially with clouds leveraging SDN (Software-Defined Networking) and NFV (Network Functions Virtualization) techniques, for better network openness, flexibility, elasticity and controllability. In this way, customized SFCs (Service Function Chaining) can be deployed at terrestrial and satellite ground segment clouds on demand, significantly reducing OPEX and CAPEX (Operational and Capital Expense). Nevertheless, how to efficiently leverage cloud substrate resources and deploy required SFCs is still challenging, as many issues such as system cost and revenue are involved. Therefore, in this paper, we focus on SFC mappings at SDN/NFV-based terrestrial and satellite ground clouds, and propose a related approach that considers both SF (Service Function) multiplexing and SFC merging, aiming to improve resource utilization efficiency of underlying substrate networks. Extensive simulations are performed and numerical results have verified benefits of the proposed SFC mapping approach.

Information-Centric Networking (ICN) provides great potential to promote the development of the Internet of Things (IoT) due to its multicast nature and mobility support. However, the stateful forwarding peculiarity introduces new varietal attacks named Interest Flooding Attacks (IFA), which is stealthy but destructive for the resource-limited IoT devices. In this paper, we propose a lightweight BLoom-filter based Attack Mitigating (BLAM) mechanism to reduce the detecting memory cost, while guaranteeing both the detecting accuracy and delay. Specifically, each IoT node employs a small Bloom filter to check attack behaviors instead of the traditional memory-consuming operations, i.e., recording malicious requests. Bloom filter values by hashing the published data names with a set of hash functions, are encapsulated and distributed via a new message named Ba-NACK. Based on this design, two specific schemes are further proposed for the attack detecting and Bloom filter updating. We formulate the memory cost minimum problem and theoretically analyze that BLAM can reduce the memory cost. We also implement BLAM in a realistic network testbed to evaluate its performance. The results show that BLAM reduces the memory cost by 78.6%, and reduces the delay from millisecond to microsecond with slight sacrifice of the accuracy by 0.4% compared with other state-of-the-art mechanisms.

With the increasing global mobile traffic, there is a trend to deploy network services at mobile edge clouds. Benefiting from the techniques of Network Function Virtualization and Software-Defined Networking, service function chains are enabled to compose a series of required network functions dynamically. As a consequence, most of common-used and IT-based mobile network services can be deployed at MEC cloud networks under the 5G context, remarkably reducing user latency and network traffic. However, as resources in cloud networks are limited, it is challenging to promote the system utilization with guaranteed user experience. Thus, in this paper, we formulate the allocation problem of service functions in MECs as an Semi-Markov Decision Process model and present a value iteration algorithm to find the optimized solution, aiming to increase request acceptance rate. Additionally, we discuss the parameter settings of the proposed scheme under different cases to find higher rewards.

In recent years, the mobile edge computing (MEC) has achieved various of research interests. By offloading data from the user equipments (UEs) to the MEC servers, many computationally demanding applications can be processed at the edge of the mobile networks. However, the data offloading of MEC needs to share the bandwidth with the conventional traffic in the mobile edge link. Simply using TCP on MEC offloading causes bandwidth robbery to the conventional TCP traffic. On the other hand, in the highly lossy wireless link environments, TCP fails to satisfy the MEC's strict requirement on the short transport delay. Therefore, we propose the MEC offloading transport (MOT) mechanism. MOT uses the prioritized queueing to avoid bandwidth robbery to the conventional traffic, and also uses the per-hop reliability to achieve loss-insensitive bandwidth utilization. The evaluation results show that MOT successfully avoids degrading the QoS of the conventional services, and achieves almost full utilization on the remaining bandwidth.

Information-Centric Networking (ICN) introduces ubiquitous in-network caching to reduce network load and improve Quality of Service (QoS). This peculiarity requires high-speed caching technologies to support wire-speed and large-amount data forwarding, which brings new challenges to existing routers. To promote practical ICN deployment, many emerging researches focus on how to accelerate caching. In this paper, we propose a novel two-layer High-speed Caching scheme (HCaching), which leverages the characteristics of both SRAM and DRAM to accelerate caching for ICN routers. In particular, using DRAM as a primary cache and SRAM as a secondary one, HCaching is able to: (i) reduce excessive utilization of high-cost SRAM, (ii) speed up access of DRAM, (iii) and improve total network throughput. We implement and analyze HCaching performance by comparing with another two state-of-the-art solutions. The results show that HCaching achieves an improved throughput by 3-10 times faster than the compared solutions.

Information-Centric Networking (ICN) has emerged as a promising way for the efficient content delivery over the Internet, and it can be seen as a super large-scale caching distributed system. However, as one of the most important problems, the cache consistency issue, which refers to whether cached contents in routers are outdated, is still not investigated thoroughly in ICN. Thus, in this paper, we propose a cost-effective Popularity-based Cache Consistency (PCC) mechanism to guarantee the freshness of cached contents in ICN routers. PCC is able to balance the trade between the consistency strength and related costs since it only maintains the strong consistency for popular contents while the weak for unpopular ones. Besides, we improve another two cache consistency mechanisms used in the web caching, namely Polling-Every-Time (PET) and Time-To-Live (TTL), to be suitable for ICN, and use them as the benchmarks for comparisons with PCC. To evaluate their performance, we firstly analyse the costs of these mechanisms including the user latency in terms of hop counts and corresponding signaling overheads, and then conduct extensive simulations using a real topology. The simulation results show the high efficiency of PCC compared with the improved PET and TTL.

Since the satellite network plays an irreplaceable role in many fields, how to interconnect it with the ground network has received an unprecedented attention. However, with much more requirements imposed to the current terrestrial network, many serious problems caused by the IP dual-role exposed. In this context, their direct interconnection seems not the most appropriate way. Thus, in this paper, SAT-GRD, an incrementally deployable ID/Loc split network architecture is proposed, aiming to integrate the satellite and ground networks efficiently. Specifically, SAT-GRD separates the identity of both the host and network from the location. Then, it isolates the host from the network, and further divides the whole network into core and edge networks. These make SAT-GRD much more flexible and scalable to achieve heterogeneous network convergence and avoid problems resulting from the overloaded semantics of IP addresses. In addition, much work has been done to implement the proof-of-concept prototype of SAT-GRD, and experimental results prove its feasibility.