Network congestion induces performance degradation and increases the uncertainty of service delivery, so it is essential to monitor it in real time. In this paper, we discuss the requirements of real-time monitoring of packet loss caused by congestion, present the problems and challenges faced by existing measurement techniques in monitoring congestion induced packet loss, and propose a comprehensive packet loss monitoring framework. The proposed framework is described in detail and its realizability is demonstrated. The proposed scheme is capable to not only determine the time and location of packet loss occurrence, make the accurate statistics of discarded packets, parse what traffic flows are contained in discarded packets and identify what traffic flows lead to microburst, but also obtain accurate packet loss ratio results with zero error. More importantly, our proposed scheme can achieve little or even no interference to network, and is applicable to any data plane without modifying the forwarding chip and packet header as existing measurement methods do. Experimental results have verified the effectiveness of our proposed scheme. Furthermore, we present three typical application scenarios to demonstrate the advantages of the proposed framework

The convergence of Blockchain and Internet of Things (BIoT) is fully considered as a paradigm for mitigating threats related to the trust, security, and privacy of Internet of Things (IoT) data. However, because the bandwidth across nodes and time varies in practical IoT networks, it is difficult for existing BIoT mechanisms guarantee blockchain consensus performances. The consensus time could become long owing to low-bandwidth nodes taking longer to download blocks than high-bandwidth nodes. Conventional wisdom holds that removing low-bandwidth nodes can decrease the consensus time, but the nodes could have high-bandwidth at another time owing to bandwidth variability; thus, kicking which nodes out of the consensus is a great challenge. In this article, a novel lightweight BIoT convergence (namely, E-Chain) is proposed to overcome bandwidth variability. The E-Chain first decouples the blockchain into on-chain validating and off-chain voting components. In the off-chain voting part, each node incurs a one-bit communication overhead for voting on a block based on a reputation index. This voting component does not need to download the full content of the block, and is therefore not affected by bandwidth variability. The reputation index was formulated using a rating algorithm with multidimensional IoT network metrics. In addition, the voting mechanism is secure and can still reach the correct consensus when suffering from byzantine attacks. By contrast, a block is validated and stored in a dispersed manner in the on-chain validating part. The E-Chain performances were then evaluated and compared with state-of-the-art mechanisms. Experimental results show that the E-Chain mechanism can significantly decrease both the consensus time and memory resources, and incur an acceptable memory overhead for resource-constrained IoT nodes.

State-of-the-art mechanisms against eavesdropping first encrypt all packet payloads in the application layer and then split the packets into multiple network paths. However, versatile eavesdroppers could simultaneously intercept several paths to intercept all the packets, classify the packets into streams using transport fields, and analyze the streams by brute-force. In this article, we propose a programming protocol-independent packet processors (P4)-based network immune scheme (P4NIS) against the intractable eavesdropping. Specifically, P4NIS is equipped with three lines of defenses to provide a softwarized network immunity. Packets are successively processed by the third, second, and first line of defenses. The third line basically encrypts all packet payloads in the application layer using cryptographic mechanisms. Additionally, the second line re-encrypts all packet headers in the transport layer to distribute the packets from one stream into different streams, and disturbs eavesdroppers to classify the packets correctly. Besides, the second line adopts a programmable design for dynamically changing encryption algorithms. Complementally, the first line uses programmable forwarding policies which could split all the double-encrypted packets into different network paths disorderly. Using a paradigm of programmable data planes-P4, we implement P4NIS and evaluate its performances. Experimental results show that P4NIS can increase difficulties of eavesdropping and transmission throughput effectively compared with state-of-the-art mechanisms. Moreover, if P4NIS and state-of-the-art mechanisms have the same level of defending eavesdropping, P4NIS can decrease the encryption cost by 69.85%-81.24%.

The Internet has made a significant success, which is on the basis of TCP/IP stacks. However, due to the dramatic development of the Internet of Things and 5G, giving rise to the continuous expansion of the network scale and the emergence of new applications, it becomes more and more complicated to manage the Internet. Specifically, the best-effort model and the device-centric working manner have become the inhibitors to meet the demands of the intelligent and coordinated transmission under heterogeneous networks in the future. In this paper, we proposed a novel Internet architecture named Smart Integration Identifier Networking (SINET-I) after comprehensively summarizing the related researches of the future Internet. SINET-I enhanced the ability of network automation and realized heterogeneous networks collaboration via introducing intent scheme and making full use of machine learning technologies. The experiment results show that SINET-I performs well in coordinated transmission across different heterogeneous protocols scenario and the available bandwidth of multi-paths is more than 2 times of that of single-path.

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.

Connected and autonomous vehicles (CAVs) are envisioned as a promising solution integrating the powerful AI and communication technologies to realize fully self-driving. However, there is few vehicular dataset open to study AI assisted self-driving. To make effectively use of AI technologies to optimize self-driving maneuver, we develop an open VeData platform to share the collected datasets. We also develop a Vehicular network Data harvester (VeData), which can collect various vehicular data at an arbitrary frequency. Based on this, we have incrementally collected diversified first-hand data in many different vehicular scenarios, including driving-in-campus, driving-around-campus, driving-in-downtown, and driving-on-highway. More datasets will be collected and shared to promote the research of AI assisted CAVs.