Background Traffic-Based Retransmission Algorithm for Multimedia Streaming Transfer over Concurrent Multipaths

The content-rich multimedia streaming will be the most attractive services in the next-generation networks. With function of distribute data across multipath end-to-end paths based on SCTP's multihoming feature, concurrent multipath transfer SCTP (CMT-SCTP) has been regarded as the most promising technology for the efficient multimedia streaming transmission. However, the current researches on CMT-SCTP mainly focus on the algorithms related to the data delivery performance while they seldom consider the background traffic factors. Actually, background traffic of realistic network environments has an important impact on the performance of CMT-SCTP. In this paper, we firstly investigate the effect of background traffic on the performance of CMT-SCTP based on a close realistic simulation topology with reasonable background traffic in NS2, and then based on the localness nature of background flow, a further improved retransmission algorithm, named RTX_CSI, is proposed to reach more benefits in terms of average throughput and achieve high users' experience of quality for multimedia streaming services

Muscle activity-driven green-oriented random number generation mechanism to secure WBSN wearable device communications

Wireless body sensor networks (WBSNs) mostly consist of low-cost sensor nodes and implanted devices which generally have extremely limited capability of computations and energy capabilities. Hence, traditional security protocols and privacy enhancing technologies are not applicable to the WBSNs since their computations and cryptographic primitives are normally exceedingly complicated. Nowadays, mobile wearable and wireless muscle-computer interfaces have been integrated with the WBSN sensors for various applications such as rehabilitation, sports, entertainment, and healthcare. In this paper, we propose MGRNG, a novel muscle activity-driven green-oriented random number generation mechanism which uses the human muscle activity as green energy resource to generate random numbers (RNs). The RNs can be used to enhance the privacy of wearable device communications and secure WBSNs for rehabilitation purposes. The method was tested on 10 healthy subjects as well as 5 amputee subjects with 105 segments of simultaneously recorded surface electromyography signals from their forearm muscles. The proposed MGRNG requires only one second to generate a 128-bit RN, which is much more efficient when compared to the electrocardiography-based RN generation algorithms. Experimental results show that the RNs generated from human muscle activity signals can pass the entropy test and the NIST random test and thus can be used to secure the WBSN nodes

Let's Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference

APIs have intricate relations that can be described in text and represented as knowledge graphs to aid software engineering tasks. Existing relation extraction methods have limitations, such as limited API text corpus and affected by the characteristics of the input text.To address these limitations, we propose utilizing large language models (LLMs) (e.g., GPT-3.5) as a neural knowledge base for API relation inference. This approach leverages the entire Web used to pre-train LLMs as a knowledge base and is insensitive to the context and complexity of input texts. To ensure accurate inference, we design our analytic flow as an AI Chain with three AI modules: API FQN Parser, API Knowledge Extractor, and API Relation Decider. The accuracy of the API FQN parser and API Relation Decider module are 0.81 and 0.83, respectively. Using the generative capacity of the LLM and our approach's inference capability, we achieve an average F1 value of 0.76 under the three datasets, significantly higher than the state-of-the-art method's average F1 value of 0.40. Compared to CoT-based method, our AI Chain design improves the inference reliability by 67%, and the AI-crowd-intelligence strategy enhances the robustness of our approach by 26%

Evaluation of Performance of Background Traffic-based CMT-SCTP with Active Queue Management Algorithms

Abstract Existing researches on performance analysis of SCTP's Concurrent Multipath Transfer (CMT-SCTP) usually use DropTail algorithm as queue management algorithm without considering the impact of the background traffic. However, the background traffic of realistic network environments has an important impact on the QoS of SCTP. Besides, more and more Active Queue Management (AQM) algorithms have been proposed as a router-based mechanism for early congestion detection to keep the stability of the whole network. This paper investigates the effect of background traffic on the performance of CMT-SCTP, and evaluates the performance of CMT-SCTP under two realistic simulation topologies with reasonable background traffic and different AQM algorithms in NS-2. The simulation results show that: 1) the performance of CMT-SCTP depends on characteristic of background traffic; and 2) the different AQM algorithms used as queue management algorithm under same background traffic have the different effects. Finally, this paper summarizes the proposals to satisfy the QoS requirements in terms of throughput, end-to-end packet delay and loss rate. Since CMT-PF2 is recommended by RFC4960 but without taking impact of cross traffic into account. In the second part, we use the most promising topology which meets the developing network and base on result of analysis mentioned in the first part to analyze the performance CMT-PF1/2/3/4 played respectively, in this part, the most common scenario, symmetric CMT-SCTP, is adopted and CMT-PF algorithm is turned on. A conclusion had been nailed down that, CMT-PF3 can get more advantage in terms of average throughput than CMT-PF2 which is recommended by RFC4960. Per reasonable analyzing, we lastly recommend a more reasonable resolution for realistic network in order to reaching more satisfied QoS

Security Vulnerabilities and Countermeasures for Time Synchronization in TSCH Networks

Time-slotted channel hopping (TSCH), which can enable highly reliable and low-power wireless mesh networks, is the cornerstone of current industrial wireless standards. In a TSCH network, all nodes must maintain high-precision synchronization. If an adversary launches a time-synchronization attack on a TSCH network, the entire network communication system can be paralyzed. Thus, time-synchronization security is a key problem in this network. In this article, time synchronization is divided into single-hop pairwise, clusterwise, and three-level multihop according to the network scope. We deeply analyze their security vulnerabilities due to the TSCH technology itself and its high-precision synchronization requirements and identify the specific attacks; then, we propose corresponding security countermeasures. Finally, we built a test bed using 16 OpenMoteSTM nodes and the OpenWSN software to evaluate the performance of the proposed scheme. The experimental results showed that serious security vulnerabilities exist in time-synchronization protocols, and the proposed countermeasures can successfully defend against the attacks