Request Success Rate Research Articles

A Resource-Adaptive Routing Scheme with Wavelength Conflicts in Quantum Key Distribution Optical Networks.

Quantum key distribution (QKD) has great potential in ensuring data security. Deploying QKD-related devices in existing optical fiber networks is a cost-effective way to practically implement QKD. However, QKD optical networks (QKDON) have a low quantum key generation rate and limited wavelength channels for data transmission. The simultaneous arrival of multiple QKD services may also lead to wavelength conflicts in QKDON. Therefore, we propose a resource-adaptive routing scheme (RAWC) with wavelength conflicts to achieve load balancing and efficient utilization of network resources. Focusing on the impact of link load and resource competition, this scheme dynamically adjusts the link weights and introduces the wavelength conflict degree. Simulation results indicate that the RAWC algorithm is an effective approach to solving the wavelength conflict problem. Compared with the benchmark algorithms, the RAWC algorithm can improve service request success rate (SR) by up to 30%.

In-TFK: a scalable traditional food knowledge platform, a new traditional food dataset, platform, and multiprocess inference service

Traditional Food Knowledge (TFK) is needed to define the acculturation of culture, society, and health in the context of food. TFK is essential for a human’s cultural, economic, and health aspects. Variations of ethnicity, culture, and lifestyle affect the diversity of traditional Indonesian food. Recognition of food is needed to maintain the sustainability of traditional food. Nowadays, there are many food dataset collections, but there needs to be a dataset that specifically collects standard food datasets. Our main contributions to the TFK research field are professional food image data acquisition, innovative development of an automatic, scalable food recognition system, and multi-process inference service. There are 34 variations of traditional foods from all regions in Indonesia that were acquired in this dataset. The dataset comprises 1644 high-quality images captured by professional cameras and 1020 by a smartphone. Several deep learning models are implemented in food recognition systems. This system can accommodate the addition and reduction of food variations in the knowledge recognition system and is capable of multiple concurrent requests at a time. The current prototype incorporates traditional types of food from Indonesia. However, the food model can also be expanded to other countries traditional foods. The automatic recognition systems are evaluated using several deep-learning network models. The experiment results have shown that the AUROC score is 0.99, and the request success rate can be improved by 70% with a multiprocess inference service.

Research on Detection and Early Warning Mechanism of Emergency Public Health Medical Education System Based on Internet of Things Technology.

Sudden public health and medical education events have tested the stability of society to a great extent. The government need to strengthen capacity building, make use of system dynamic supervision, warn public health events in advance, and minimize the impact scope and related harmfulness of events. This not only facilitates the rapid mobilization of resources by the later government but also facilitates the comprehensive and detailed deployment and arrangement of decision-makers. As we all know, the Internet of Things is used by all walks of life because of its outstanding advantages of low power consumption, low cost, and wide range. Therefore, this article takes the Internet of Things as the technical basis of the system. According to the actual demand and resource design, it includes two system functions: detection and early warning. The results show that: (1) considering the practical principle, the evaluation system interface found that the scores of font size and color style are all below 80%, which need to be optimized and adjusted; the overall interface basically meets the needs of users. (2) The throughput of the three methods is different. The CoAP-E has superior throughput. (3) With the increase in packet loss rate, the request success rate of the CoAP method decreases in a “drop” manner. The CoAP-E method in this article has the best performance. (4) When the packet loss rate is 25%, the network adaptability of this method is the strongest, and the retransmission rate is less than 18%; the CoAP method is as high as 35%. (5) When the number of concurrent requests is less than 2500, there is no obvious difference between the three methods; the optimal performance of the dynamic load balancing method is 10.1 s. (6) The system comprehensively considers various factors of five site selections. The highest comprehensive score of Final Site, 5 is 8.7, which can be used as the resettlement place of emergency rescue facilities. This article starts from the characteristics and needs of public emergencies, and the final set of the system runs well. It can quickly reflect public health emergencies and medical education events. Use the most effective system functions for risk control, and maximize the analysis, organization, and coordination of events. The follow-up optimization of system details needs to be studied.

Intelligent Cruise Guidance and Vehicle Resource Management With Deep Reinforcement Learning

The emergence of new business and technological models for urban-related transportation has revealed the need for transportation network companies (TNCs). Most research works on TNCs optimize the interests of drivers and passengers, and the operator assuming vehicle resources remain unchanged, but ignore the optimization of resource utilization and satisfaction from the perspective of flexible and controllable vehicle resources. In fact, the load of the scene is variable in time, which necessitates the flexible control of resources. Drivers wish to effectively utilize their vehicle resources to maximize profits. Passengers desire to spend minimum time waiting and the platform cares about the commission they can accrue from successful trips. In this article, we propose an adaptive intelligent cruise guidance and vehicle resource management model to balance vehicle resource utilization and request success rate, while improving platform revenue. We propose an advanced deep reinforcement learning (DRL) method to autonomously learn the statuses and guide the vehicles to hotspot areas where they can pick orders. We assume the number of online vehicles in the scene is flexible and the learning agent can autonomously change the number of online vehicles in the system according to the real-time load to improve effective vehicle resource utilization. An adaptive reward mechanism is enforced to control the importance of vehicle resource utilization and request success rate at decision steps. The simulation results and analysis reveal that our proposed DRL-based scheme balances vehicle resource utilization and request success rate at acceptable levels while improving the platform revenue, compared with other baseline algorithms.

QA2: QoS-Guaranteed Access Assistance for Space–Air–Ground Internet of Vehicle Networks

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%.

Software-defined network assisted packet scheduling method for load balancing in mobile user concentrated cloud

Cloud Network provides a variety of applications and services to meet the requirements of increasing user demands. With its dedicated components, the cloud enables anywhere anytime access of resources to its associated users. A significant factor that holds the performance of the cloud is its congestion, due to unpredictable traffic and asynchronous user demands. Though load balancers serve the purpose of optimizing network traffic, congestion is unavoidable when the level of user demand increases. In this manuscript, a Resource-aware Packet-level Scheduling with Load Balancing (RPS-LB) algorithm is proposed to minimize congestion. In RPS, the incoming network traffic is disseminated through instantaneous neighbors by analyzing their Store and Forward (SF) factor. SF factor is pre-estimated through Estimated Transmission Count (ETX) metric. With this analysis, the reliability of the instantaneous neighbor for handling the current packet-level transmission is verified. This algorithm addresses the issues in user-level and flow-level to mitigate stagnancy and overflow of network traffic. Besides, the proposed load-balancing algorithm is defined as a linear optimization problem within definite constraints to prevent degradation proportional to user density. The performance of the proposed system is assessed by using the performance evaluation parameters such as Queue Utilization, Request Success Rate, Request Failure Rate, Response Time and Link Utilization. The simulation results show that the proposed system performs well than the existing systems.

Design of an Internet of Brain Things Network Security System Based on ICN

Network data traffic has increased dramatically with the rapid development of the Internet of Brain Things, and the host-based IP network Internet of Brain Things architecture is gradually unable to adapt to the new situation, and the information-centric network (ICN) centered on the information is proposed as a new network architecture. ICN-based Internet of Brain Things has greater flexibility to ensure scalable and efficient content distribution based on large-scale data transmission efficiency. With deep research on ICN, the key issue currently faced is the Internet of Brain Things network security problem based on ICN network, which is of great significance to the future development of the Internet of Brain Things network security. Aiming at this problem, and according to the research needs at this stage, this paper first analyzes the current research status of ICN network security based on ICN, and focuses on the attack and privacy issues in the current Internet of Brain Things operation mechanism. Then, according to the security requirements of ICN-based Internet of Brain Things integrity, confidentiality, availability, authentication and rights management, the ICN-based Internet of Brain Things network and security architecture is designed. From the perspective of mitigating attacks and protecting privacy, an interval-based authentication and a popularity-based caching mechanism are designed. Finally, in order to further verify the system performance, this paper is equipped with the simulation platform in ndnSIM environment to simulate the ICN-based Internet of Brain Things network security’s overall security architecture and its authentication and caching mechanism. The system performance is evaluated by four technical indicators: round-trip time delay, network throughput, cache hit rate, and request success rate. The results show that the architecture and mechanism have good network security performance.

A novel bartering exchange ring based incentive mechanism for peer-to-peer systems

Peer-to-Peer (P2P) networking is an alternative to the cloud computing for relatively more informal trade. One of the major obstacles to its development is the free riding problem, which significantly degrades the scalability, fault tolerance and content availability of the systems. Bartering exchange ring based incentive mechanism is one of the most common solutions to this problem. It organizes the users with asymmetric interests in the bartering exchange rings, enforcing the users to contribute while consuming. However the existing bartering exchange ring formation approaches have inefficient and static limitations. This paper proposes a novel cluster based incentive mechanism (CBIM) that enables dynamic ring formation by modifying the Query Protocol of underlying P2P systems. It also uses a reputation system to alleviate malicious behaviors. The users identify free riders by fully utilizing their local transaction information. The identified free riders are blacklisted and thus isolated. The simulation results indicate that by applying the CBIM, the request success rate can be noticeably increased since the rational nodes are forced to become more cooperative and the free riding behaviors can be identified to a certain extent.

ARMR: Anonymous routing protocol with multiple routes for communications in mobile ad hoc networks

A mobile ad hoc network consists of mobile nodes that communicate in an open wireless medium. Adversaries can launch analysis against the routing information embedded in the routing message and data packets to detect the traffic pattern of the communications, thereby obtaining sensitive information of the system, such as the identity of a critical node. In order to thwart such attacks, anonymous routing protocols are developed. For the purposes of security and robustness, an ideal anonymous routing protocol should hide the identities of the nodes in the route, in particular, those of the source and the destination. Multiple routes should be established to increase the difficulty of traffic analysis and to avoid broken paths due to node mobility. Existing schemes either make the unrealistic and undesired assumption that certain topological information about the network is known to the nodes, or cannot achieve all the properties described in the above. In this paper, we propose an anonymous routing protocol with multiple routes called ARMR, which can satisfy all the required properties. In addition, the protocol has the flexibility of creating fake routes to confuse the adversaries, thus increasing the level of anonymity. In terms of communication efficiency, extensive simulation is carried out. Compared with AODV and MASK, our ARMR protocol gives a higher route request success rate under all situations and the delay of our protocol is comparable to the best of these two protocols.

Architecture and Task Scheduling of Video Streaming on Multi-core Platform

Compared with traditional video streaming server, streaming on multi-core platform has many advantages: flexible and configurable on the number of executing core according to system requirements; fault-tolerant; and fitting well to future process technologies, more cores will be available in advanced process technologies, meanwhile the complexity per core does not increase. In this paper, we focused on the video streaming issues on multi-core processor, including architecture and task scheduling. We proposed a pipeline-parallel hybrid multi-core architecture and service migration based task scheduling strategy on multi-core processor to improve the efficiency of video streaming and increase the number of concurrence. We implemented the task scheduling algorithm with proposed architecture, and provided evidences of 48% outperformance based on Cavium OCTEON CN5860 multi-core processor than full parallel architecture, meanwhile, request success rate higher than REM algorithm. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4491 Full Text: PDF