• Title/Summary/Keyword: Delivery latency

Search Result 113, Processing Time 0.017 seconds

A Method for Reducing Path Recovery Overhead of Clustering-based, Cognitive Radio Ad Hoc Routing Protocol (클러스터링 기반 인지 무선 애드혹 라우팅 프로토콜의 경로 복구 오버헤드 감소 기법)

  • Jang, Jin-kyung;Lim, Ji-hun;Kim, Do-Hyung;Ko, Young-Bae;Kim, Joung-Sik;Seo, Myung-hwan
    • Journal of IKEEE
    • /
    • v.23 no.1
    • /
    • pp.280-288
    • /
    • 2019
  • In the CR-enabled MANET, routing paths can be easily destroyed due to node mobility and channel unavailability (due to the emergence of the PU of a channel), resulting in significant overhead to maintain/recover the routing path. In this paper, network caching is actively used for route maintenance, taking into account the properties of the CR. In the proposed scheme, even if a node detects that a path becomes unavailable, it does not generate control messages to establish an alternative path. Instead, the node stores the packets in its local cache and 1) waits for a certain amount of time for the PU to disappear; 2) waits for a little longer while overhearing messages from other flow; 3) after that, the node applies local route recovery process or delay tolerant forwarding strategy. According to the simulation study using the OPNET simulator, it is shown that the proposed scheme successfully reduces the amount of control messages for path recovery and the service latency for the time-sensitive traffic by 13.8% and 45.4%, respectively, compared to the existing scheme. Nevertheless, the delivery ratio of the time-insensitive traffic is improved 14.5% in the proposed scheme.

EC-RPL to Enhance Node Connectivity in Low-Power and Lossy Networks (저전력 손실 네트워크에서 노드 연결성 향상을 위한 EC-RPL)

  • Jeadam, Jung;Seokwon, Hong;Youngsoo, Kim;Seong-eun, Yoo
    • Journal of Korea Society of Industrial Information Systems
    • /
    • v.27 no.6
    • /
    • pp.41-49
    • /
    • 2022
  • The Internet Engineering Task Force (IETF) has standardized RPL (IPv6 Routing Protocol for Low-power Lossy Network) as a routing protocol for Low Power and Lossy Networks (LLNs), a low power loss network environment. RPL creates a route through an Objective Function (OF) suitable for the service required by LLNs and builds a Destination Oriented Directed Acyclic Graph (DODAG). Existing studies check the residual energy of each node and select a parent with the highest residual energy to build a DODAG, but the energy exhaustion of the parent can not avoid the network disconnection of the children nodes. Therefore, this paper proposes EC-RPL (Enhanced Connectivity-RPL), in which ta node leaves DODAG in advance when the remaining energy of the node falls below the specified energy threshold. The proposed protocol is implemented in Contiki, an open-source IoT operating system, and its performance is evaluated in Cooja simulator, and the number of control messages is compared using Foren6. Experimental results show that EC-RPL has 6.9% lower latency and 5.8% fewer control messages than the existing RPL, and the packet delivery rate is 1.7% higher.

Prediction of Target Motion Using Neural Network for 4-dimensional Radiation Therapy (신경회로망을 이용한 4차원 방사선치료에서의 조사 표적 움직임 예측)

  • Lee, Sang-Kyung;Kim, Yong-Nam;Park, Kyung-Ran;Jeong, Kyeong-Keun;Lee, Chang-Geol;Lee, Ik-Jae;Seong, Jin-Sil;Choi, Won-Hoon;Chung, Yoon-Sun;Park, Sung-Ho
    • Progress in Medical Physics
    • /
    • v.20 no.3
    • /
    • pp.132-138
    • /
    • 2009
  • Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.

  • PDF