Information-Centric Internet of Things (IC-IoT) enhances content distribution through its inherent in-network caching and multicast capabilities. However, effective data naming and disseminating in dynamic IoT environments remain challenging. In particular, each IoT device requires a routable name to facilitate data production and retrieval. Assigning and maintaining routable names for large numbers of sensors is cumbersome, restricting data management flexibility and scalability. Moreover, while data pushing is essential for IoT applications, such as real-time data uploads, the receiver-driven nature of ICN hinders push-based communication. In this context, we propose IC-Lite, an optimized framework built on IC-IoT, designed to provide lightweight, information-centric communication with simplified name assignment and data provisioning. Our approach introduces a modified forwarding plane that transmits data using simplified names and pushes data with the three-packet handshake, thereby enabling timely and efficient dissemination of real-time data from devices to application proxies. Evaluation results and theoretical analysis demonstrate that IC-Lite significantly reduces the overhead associated with name-based routing and namespace management, achieving an order of magnitude improvement in efficiency. Additionally, IC-Lite reduces data delivery delay by 60.8% in dynamic scenarios, making it an efficient solution for real-time IoT applications.

Integrated sensing and communication (ISAC) technology endows users with environmental awareness capabilities, which will play a crucial role in future mobile edge computing (MEC) systems. In this paper, we consider the design of sensingassisted beam alignment and investigate resource management for a task-oriented mmWave integrated sensing, communication, and computing (ISCC) system, which can be formulated as a preference-agnostic multi-objective optimization problem. To solve this problem, we first introduce a multi-objective Markov decision process (MOMDP) to reformulate the original problem and innovatively propose a preference-agnostic multi-objective soft actor-critic (PA-MOSAC) algorithm. To demonstrate the effectiveness of our proposed system architecture and resource management algorithm, we also introduce a traditional mmWave MEC (T-MEC) system based on the same set of system parameters as a benchmark. The proximal policy optimization (PPO) algorithm, known for its robustness, is used to address resource management in the T-MEC system. Through a comprehensive comparative analysis of the two systems and algorithms, we discover that our proposed sensing-assisted beam alignment can reduce task execution delay by 25% with only 1% increase in energy consumption. We also verify the convergence of our proposed PA-MOSAC algorithm and demonstrate its superior performance over the benchmark scheme.

Commercial data center networks enable low-latency services, but switch and link failures threaten stability. Existing precise port-level failure identification methods are limited to specific topologies, while general ones localize only at the switch level. We present LinkSonar, a system based on semi-controllable probe paths that pinpoints failures at the port level. By leveraging tunneling and ERSPAN technologies, the entire network is decomposed into individual links for probing, thereby enabling balanced full-link coverage while minimizing dependence on topology structures. By modeling the probing process and introducing a failure inference algorithm, LinkSonar estimates link transmission success rates without requiring routing information, while mitigating cascading effects caused by concurrent faults and network noise. Comparative experiments with two state-of-the-art methods demonstrate that LinkSonar achieves superior localization accuracy under diverse load-balancing settings, while reducing manual troubleshooting efforts by more than 50%. Simulations across two representative topologies show that LinkSonar maintains F1-scores above 0.95 even under large-scale failure scenarios. Deployed for six months in a production data center with hundreds of multi-vendor switches, LinkSonar successfully detected over 200 failures, among which 13 correspond to silent packet drops or misconfigurations that are difficult to detect using Pingmesh-like approaches.

Social networks are valuable tools for daily communication, information sharing, and news dissemination. For instance, on Twitter, users stay connected by following or mutually following each other. Prior works often study the characteristics of social networks using random graph models to capture the complexity of these relationships. To our knowledge, recent research not only generates directed social network graphs with reciprocal edges but also considers the rank correlations between different degree sequences. However, current generation methods are computationally expensive, particularly when applied to large-scale graphs. To address this, we propose a new graph generation model called FastGraph, designed to efficiently generate large-scale directed social network graphs with reciprocal edges and high clustering characteristics while significantly reducing computational costs. FastGraph introduces two key techniques: dynamic sampling based on current node degrees to reduce redundant computations and surplus degree rewiring to enhance local clustering. Compared to the latest Chung–Lu-based methods, FastGraph reduces the computational complexity from$O(n^{3})$to$O(n^{2})$. When generating large-scale graphs with the same number of nodes and similar edge counts, FastGraph achieves an average runtime reduction of nearly 12 times. Furthermore, FastGraph successfully replicates more than a dozen social network graphs and is capable of efficiently generating graphs of arbitrary size while preserving properties closely aligned with those of real-world networks.

Vehicular Named Data Networking (VNDN) is a promising information-centric network architecture to achieve efficient data delivery among vehicles. With a subscription-based communication paradigm, it can disseminate event-triggered data like traffic updates or road accident notifications in a timely way. However, due to vehicle mobility and network dynamics, the data subscription path is normally fragile and consequently the data dissemination efficiency would be diminished. To address this problem, we propose SegSub, an adaptive segmented subscription mechanism which can maintain robust subscription path for efficient data dissemination in mobile scenarios. SegSub divides the whole subscription path into several segments and different strategies are employed to maintain subscription status for each segment path. The frequency of subscription update on each segment is calculated based on the prediction of vehicle mobility and network status to reach a trade-off between subscription updating overhead and expected data dissemination delay. Furthermore, a subscription migration mechanism is proposed to alleviate the data loss and redundant pending overhead caused by subscription failure when vehicles move. Evaluation results demonstrate that SegSub can decrease dissemination delay by 91.9% and 58.7% compared to native VNDN and the state-of-the-art subscription method.

Segmentation ambiguity in generative linguistic steganography could induce decoding errors. One existing dis-ambiguating way is removing the tokens whose mapping words are the prefixes of others in each candidate pool. However, it neglects probability distribution of candidates and degrades imperceptibility. To enhance steganographic security, meanwhile addressing segmentation ambiguity, we propose a secure and disambiguating approach for linguistic steganography. In this letter, we focus on two questions: (1) Which candidate pools should be modified? (2) Which tokens should be retained? Firstly, we propose a secure token-selection principle that the sum of selected tokens' probabilities is positively correlated to statisti-cal imperceptibility. To meet both disambiguation and optimal security, we present a lightweight disambiguating approach that is finding out a maximum weight independent set (MWIS) in one candidate graph only when candidate-level ambiguity occurs. Experiments show that our approach outperforms the existing method in various security metrics, improving 25.7% statistical imperceptibility and 11.2% anti-steganalysis capacity averagely.

Network covert timing channels can be maliciously used to exfiltrate secrets, coordinate attacks and propagate malwares, posing serious threats to cybersecurity. Current covert timing channels normally conduct small-volume transmission under the covers of various disguising techniques, making them hard to detect especially when a detector has little priori knowledge of their traffic features. In this article, we propose a generic and sensitive detection approach, which can simultaneously (i) identify various types of channels without their traffic knowledge and (ii) maintain reasonable performance on small traffic samples. The basis of our approach is the finding that the short-term timing behavior of covert and legitimate traffic is significantly different from the perspective of inter-packet delays’ variation. This phenomenon can be a generic reference to detect various channels because it is resistant to major channel disguising techniques which only mimic long-term traffic features, while it is also a sensitive reference to spot small-volume covert transmission since it can capture traffic anomalies in a fine-grained manner. To obtain the inner patterns of inter-packet delays’ variation, we design a context-sensitive feature-extraction technique. This technique transforms each raw inter-packet delay into a discrete counterpart based on its contextual properties, thus extracting its variation features and reducing traffic data complexity. Then we learn legitimate variation patterns using a neural network model, and identify samples showing anomalous variation as covert. The experimental results show that our approach effectively detects all currently representative channels in the absence of their knowledge, presenting once to twice higher sensitivity than the state-of-the-art solutions.

Space–air–ground Internet of Vehicle networks is a promising network paradigm to support diverse vehicular services. Exploiting the unique advantages of spatial, aerial, and terrestrial network segments, the Quality of Service (QoS) of different services can be met by smart access network selection. However, the integrated network inevitably has to face many challenges, such as dynamic network topology, heterogeneous resources, and long propagation latency, which can seriously degrade the QoS performance. In order to provide QoS guarantee to vehicles, we propose a novel architecture, called QoS-guaranteed access assistance (QA2) serve vehicles with access assistance. To be specific, a virtualized layer is established in the network, which contains the logical resources of ground network infrastructures. Using that layer, Access Assistants are flexibly deployed at a dynamic set of ground infrastructures, and then help vehicles to acquire network services with satisfied QoS requirements. Furthermore, to accommodate temporal–spatial-varying network demands of vehicles, QA2 adopts a cost-effective deployment scheme, which updates the locations of Assistants based on the real-time traffic flow with the minimum operation cost. The evaluation results demonstrate the effectiveness of QA2, which increases request success rate by more than 50%. The results also prove that the cost-effective deployment scheme can efficiently adapt to the change of traffic flow and achieve a good balance between QoS guarantee and cost saving. The operation cost is saved by more than 25% and the QoS performance is improved by more than 22%.

Although information-centric networking (ICN) can boost the performance of sensor data delivery in wireless sensor networks (WSNs), its energy efficiency may be significantly impaired due to the receiver-driven request-response transmission model. ICN sensors have to keep long-time active mode to wait for the requests that may arrive at any time, resulting nontrivial energy waste for energy-constrained WSNs. An intuitive method to conserve energy is to schedule partial nodes to fall asleep, but at the cost of extra data fetching delay. In this work, we investigate multihop cooperative caching for green WSN over receiver-driven ICNs. By leveraging regional caches, not only can the energy be saved, but also the data fetching delay can be reduced. We formulate those two cooperation gains and capture the tradeoff between energy saving and delay reduction. Based on the optimal tradeoff, we present a solution of scoped multihop cooperative caching and elaborate a green cooperation policy for each sensor to determine its operation scope and conduct a proper cooperation decision. Our simulation results show that our proposed multihop cooperative caching can save more than 91.0% energy and achieve 28.9% delay reduction than the basic ICWSN, and also reduce 60.2% energy consumption than the existing cooperative caching schemes.

Energy efficient routers are important and promising devices in the roadmap toward green networking. In this paper, we explore this topic with a traffic aware design that can automatically adapt the router's energy consumption to the network traffic in real time with transitions at the scale from microseconds to nanoseconds. In this paper, we make three contributions. First, we propose an energy efficient router architecture using frequency scaling, which is extended from the contemporary line card architecture of the router. Second, we prototype our architecture on a gigabit NetFPGA platform, which extends a router design on that platform to dynamically work at six frequencies. Then, we discuss the implementation in detail and present a per-packet-based power consumption model from real-world measurements. Third, we further propose four algorithms, which efficiently and automatically adapt power grades under the methodology of periodical and threshold-based scaling. Our experiments are carried out on two platforms, a NetFPGA prototype for feasible scenarios and a simulator for other scenarios in excess of the NetFPGA port limit with parameters synthetically generated to the real-world measurement. Experimental results show that approximately 25% power can be saved with our architecture and algorithms. Conclusions are also given to summarize our findings.

Energy efficient routers are a promising step forward in the road-map towards energy efficient networking. In this paper, we use gigabit routers to measure and evaluate the impacts of green technologies in different scenarios. These programmable NetFPGA based routers come with the inbuilt feature of frequency scaling which can reduce the operational frequency of the board by half from 125MHz to 62.5MHz. Router power consumption is compared for standard and low frequency modes with different numbers of activated ports. Perpacket and per-byte energy costs in different cases with different packet sizes are measured and analyzed to evaluate the effects of frequency adjustment. Finally, a simple energy model for real traffic is proposed and evaluated. This paper reveals the internal power usage of a gigabit router at the granularity of packet, and header and payload byte level, and offers the reader a clear understanding of the potential power savings at different operational router frequencies, for different numbers of activated ports, various traffic loads and packet sizes, which would assist the green data center networks.

Recently, energy expenditures of the Internet have increased dramatically, raising energy issue of routers an urgent problem in relative research areas. In fact, much device surplus and redundancy are introduced during network planning for rarely appeared traffic peak hours and device failures, wasting energy most of the time. In this work, an energy-aware architecture is proposed for routers, which could trade system performance for energy savings while traffic is low by scaling frequencies of its inner components. We also explore multi-frequency modulation strategies to optimize the energy saving effect. The result shows that our prototype router could save about 40\% of its peak power consumption.

Future Internet requires re-thinking of network infrastructure towards the balance between computing capacities and energy sustainable techniques. As one of computing intensive components, Bloom filters are widely used for network packet processing. In this paper, an energy efficient self-adaptive Bloom filter, EABF, is devoted to a balance of power and performance especially for high performance networks. The basic idea is to give the Bloom Filter the capability to adjust the number of active hash functions according to the current workload automatically. This adaption depends on its control policies. Three policies are presented and compared. We also give the method to implement EABF in hardware for higher performance. It is presented in a two-stage platform based on FPGA where Stage 1 is always active and Stage 2, a secondary stage, is only active when necessary. The platform can also be extended to multi-stages. A control circuit is designed for flexibly changing working stage and reducing both dynamic and static power consumption. Analysis and experiments show that our dynamic two-stage EABF can achieve almost the best power savings as that of the fixed schemes; unlike the fixed schemes that might have much longer latency, EABF maintains nearly 1 clock cycle latency as that of a regular Bloom filter.