Intermediate Nodes Research Articles

Functional and spatial rewiring principles jointly regulate context-sensitive computation.

Adaptive rewiring provides a basic principle of self-organizing connectivity in evolving neural network topology. By selectively adding connections to regions with intense signal flow and deleting underutilized connections, adaptive rewiring generates optimized brain-like, i.e. modular, small-world, and rich club connectivity structures. Besides topology, neural self-organization also follows spatial optimization principles, such as minimizing the neural wiring distance and topographic alignment of neural pathways. We simulated the interplay of these spatial principles and adaptive rewiring in evolving neural networks with weighted and directed connections. The neural traffic flow within the network is represented by the equivalent of diffusion dynamics for directed edges: consensus and advection. We observe a constructive synergy between adaptive and spatial rewiring, which contributes to network connectedness. In particular, wiring distance minimization facilitates adaptive rewiring in creating convergent-divergent units. These units support the flow of neural information and enable context-sensitive information processing in the sensory cortex and elsewhere. Convergent-divergent units consist of convergent hub nodes, which collect inputs from pools of nodes and project these signals via a densely interconnected set of intermediate nodes onto divergent hub nodes, which broadcast their output back to the network. Convergent-divergent units vary in the degree to which their intermediate nodes are isolated from the rest of the network. This degree, and hence the context-sensitivity of the network's processing style, is parametrically determined in the evolving network model by the relative prominence of spatial versus adaptive rewiring.

A Multi-Objective Tri-Level Algorithm for Hub-and-Spoke Network in Short Sea Shipping Transportation

Hub-and-Spoke (H&S) network modeling is a form of transport topology optimization in which network joins are connected through intermediate hub nodes. The Short Sea Shipping (SSS) problem aims to efficiently disperse passenger flows involving multiple vessel routes and intermediary hubs through which passengers are transferred to their final destination. The problem contains elements of the Hub-and-Spoke and Travelling Salesman, with different levels of passenger flows among islands, making it more demanding than the typical H&S one, as the hub selection within nodes and the shortest routes among islands are internal optimization goals. This work introduces a multi-objective tri-level optimization algorithm for the General Network of Short Sea Shipping (GNSSS) problem to reduce travel distances and transportation costs while improving travel quality and user satisfaction, mainly by minimizing passenger hours spent on board. The analysis is performed at three levels of decisions: (a) the hub node assignment, (b) the island-to-line assignment, and (c) the island service sequence within each line. Due to the magnitude and complexity of the problem, a genetic algorithm is employed for the implementation. The algorithm performance has been tested and evaluated through several real and simulated case studies of different sizes and operational scenarios. The results indicate that the algorithm provides rational solutions in accordance with the desired sub-objectives. The multi-objective consideration leads to solutions that are quite scattered in the solution space, indicating the necessity of employing formal optimization methods. Typical Pareto diagrams present non-dominated solutions varying at a range of 30 percent in terms of the total distance traveled and more than 50 percent in relation to the cumulative passenger hours. Evaluation results further indicate satisfactory algorithm performance in terms of result stability (repeatability) and computational time requirements. In conclusion, the work provides a tool for assisting network operation and transport planning decisions by shipping companies in the directions of cost reduction and traveler service upgrade. In addition, the model can be adapted to other applications in transportation and in the supply chain.

Multi-parameter optical performance monitoring based on single-channel convolutional neural network

For the first time, a combination of grayscale maps and single-channel convolutional neural networks (CNN) is used in the direct detection optical performance monitoring (OPM) technique. Due to the faster monitoring speed of the direct detection OPM technique, it is very suitable for intermediate nodes of optical networks. The OPM technique proposed in this paper is an improvement based on the previous asynchronous delay-tap plot (ADTP) techniques. By reducing the redundant color channel information, the training process of the ADTP feature-based monitoring model is improved. In the simulation stage, three high-order modulated signals, 16quadrature amplitude modulation (QAM), 32QAM and 64QAM, are used as sample signals. The ADTP color maps, ADTP pseudo-color maps and ADTP grayscale maps generated by these three modulated signals with different signal-to-noise ratios and cumulative dispersion (CD) are collected. The accuracy of the monitoring model based on these three feature maps is close to 100% for both cumulative dispersion identification and signal modulation formats identification (MFI). However, the monitoring models based on grayscale maps and pseudo-color maps perform slightly better in estimating optical signal-to-noise ratio (OSNR) compared to the monitoring models based on color maps. And the grayscale map-based model slightly outperforms the other two models in terms of training complexity and monitoring speed. The superiority of grayscale maps and single-channel convolutional neural networks compared to previous ADTP-based OPM techniques is demonstrated.

SoftCaching: A framework for caching node selection and routing in Software-Defined Information Centric Internet of Things

A considerable demand for Internet of Things (IoT) services is causing exponential growth in Internet traffic. Software-Defined Networking (SDN) and Information-Centric Networking (ICN) are complementary technologies for IoT services provisioning that reduce network traffic and service provisioning delays by caching content at intermediate nodes in the network. Existing research on Software-Defined Information-Centric Networking (SDICN) for IoT service provisioning suffers from two major challenges: (a) optimal caching node selection and (b) optimal path computation from content consumers to caching nodes based on end-to-end delay and link utilization constraints. We propose a SoftCaching framework that optimizes caching node selection for content publishers and computes the optimal routing path to caching nodes for content consumers. It computes optimal locations for caching nodes and finds optimal routing paths for IoT services to access cached content. SoftCaching analyzes network traffic on all nodes in the network and collects traffic metrics. It estimates the best possible caching nodes by using Waypoint Enforcement (WPE) and solving the traffic matrix using Singular Value Decomposition (SVD) and QR Factorization. WPE and SVD-based QR factorization provide optimal locations to cache the content for IoT services. Subsequently, SoftCaching uses a constraint-based shortest path algorithm to compute the optimal routing path from content consumers to caching nodes. We make sure that the caching node and the routing path selection follow delay and link utilization constraints. We perform extensive experiments for evaluation, and the results show that SoftCaching outperforms state-of-the-art caching schemes in terms of delay, link load, hop count, path stretch, and energy consumption metrics.

Gradual Route Modification in Mobile Wireless Multihop Network with Combination of Carrying and Forwarding

In a wireless multihop network, data messages are transmitted along a wireless multihop transmission route composed of a sequence of intermediate wireless nodes forwarding the data messages. However, it is difficult to detect a wireless multihop transmission route from a source wireless node to a destination one if a wireless nodes are not evenly distributed and there are some areas in which wireless nodes are sparsely distributed. Some methods have been proposed to solve this problem by combination of forwarding data messages between neighbor intermediate wireless nodes as in a usual wireless multihop network and carrying data messages to pass through such areas. In the conventional methods with the combination of forwarding and carrying data messages, some dedicated intermediate mobile wireless nodes serve the role of carrying. Hence, such nodes are required to consume more battery power which results in lower connectivity of the network. In addition, transmissions of data messages might be suspended for waiting the carrying intermediate wireless nodes which results in longer end-to-end transmission delay. In order to solve the problems, this paper proposes a novel carrying and forwarding method which gradually modifies a wireless multihop transmission route to evenly share the required mobility overhead among all the intermediate mobile wireless nodes and to reduce end-to-end transmission delay of data messages by making the route shorter. The proposed method ensures that no successive intermediate wireless nodes pass each other without neighboring.

Watchdog malicious node detection and isolation using deep learning for secured communication in MANET

Mobile Ad-hoc Networks (MANETs) are wireless networks formed dynamically by connecting or leaving nodes to and from the network without any fixed infrastructure. These categories of wireless networks are susceptible to different attacks based on their dynamic topological structure. Due to this, security is a primary constraint in MANETs to preserve communication between mobile nodes. A Deep Neural Learned Projective Pursuit Regression-based Watchdog Malicious Node Detection and Isolation (DNLPPR-WMNDI) technique is proposed and modelled in this paper to improve the security feature of MANETs. The newly proposed DNLPPR-WMNDI technique initially selects the neighbouring nodes by applying the projection pursuit regression function. In multicasting, the route paths are established through the intermediate node with the help of control commands named RREQ and RREP. After then, Watchdog Malicious Node Detection and Isolation (WMNDI) technique is applied to detect malicious nodes based on the data packet forwarding time. Basically, a malicious node is affected by a node isolation attack. For better communication, a malicious node is isolated from the network and multicast routing is carried out by selecting the next neighbouring node and this improves the communication security. Simulation is done for the developed technique based on different performance metrics.

Preprocessing for segment routing optimization

AbstractIn this article we introduce a preprocessing technique to solve the Segment Routing Traffic Engineering Problem optimally using significantly fewer computational resources than previously introduced methods. Segment routing is a recently developed interior gateway routing protocol to be used on top of existing protocols that introduces more flexibility in traffic engineering. In practice, segment routing allows to deviate traffic from its original path by specifying a list of intermediate nodes or links, called segments, to visit before going to its destination. The issue we tackle in this article is that the number of segment paths scales exponentially with the maximum number of segments allowed leading to scalability issues in mathematical formulations. This article introduces the notion of dominated segment paths, these are paths that can be eliminated from the solution space when searching for an optimal solution. We propose a dynamic programming algorithm eliminating dominated paths for any number of segments. Numerical results show that respectively 50%, 90%, and 97% of paths are dominated when considering up to 2, 3, and 4 segments on benchmark network topologies.

Short-Term and Mid-Term Outcomes of Indocyanine Green Fluorescence Imaging Guided Laparoscopic Right-Sided Colectomy: A Propensity Score-Matched Cohort Study.

With the use of indocyanine green fluorescence imaging, intraoperative lymphatic flow assessment is possible. However, there is no report which indicates mid-term outcomes of indocyanine green fluorescence imaging guided laparoscopic right-sided colectomy. The purpose of this study was to examine the mid-term outcomes of indocyanine green fluorescence imaging guided laparoscopic right-sided colectomy. This was a retrospective, multi-institutional study with propensity score matching. We conducted this study within the framework of the Yokohama Clinical Oncology Group in Japan. A total of 921 patients who underwent laparoscopic right-sided colectomy with lymph node dissection for colon cancer with clinical stage I to III between January 2009 and December 2020 were included. The patients were divided into two groups: 233 patients who underwent the lymphatic flow evaluation (indocyanine green group) and 688 patients who did not undergo lymphatic flow evaluation (non-indocyanine green group). The 3-year relapse-free survival after laparoscopic right-sided colectomy with and without indocyanine green fluorescence imaging were compared. After propensity score matching, 231 patients were matched in each group. The numbers of dissected central lymph nodes (6 vs 4, p < 0.001), and intermediate lymph nodes (7 vs 6, p = 0.03), and the total number of dissected lymph nodes (31 vs 27, p = 0.047) were significantly higher in the indocyanine green group. The median follow-up was 36.9 months. The estimated respective 3-year relapse-free survival and overall survival rates were 88.8% and 94.5% in the indocyanine green group and 89.4% and 94.7% in the non-indocyanine green group (p = 0.721 and 0.300), with no difference between the two groups. The limitations of this study include its retrospective design. Indocyanine green fluorescence imaging guided laparoscopic right-sided colectomy could increase the number of totals, intermediate and central lymph nodes. However, there was no difference in mid -term outcomes.

Performance Study of AODV, GRP and OSPFv3 MANET Routing Protocols Using OPNET Modeler

A Mobile Ad hoc Network (MANET) is a collection of mobile nodes, that forms on the fly a temporary wireless multi-hop network in a self-organizing way, without relying on any established infrastructure. In MANET, a pair of nodes exchange messages either over a direct wireless link, or over a sequence of wireless links including one or more intermediate nodes. For this purpose, an efficient routing protocol is required. This paper introduced performance study of three of MANET protocols (AODV, GRP and OSPFv3). This study was one of the newer studies because wireless communication played an important role in today’s application and the field of mobile ad hoc network becomes very popular for the researchers in the last years. This study was done using OPNET Modeler v14.5 and the comparison between the protocols was done in terms of throughput, routing traffic received, traffic sent, load and delay with increased number of mobile nodes from 10, 25 and 50 mobile nodes. The results showed that AODV (reactive routing protocol) was better in delay and had smaller load and smaller throughput than GRP (reactive and proactive routing protocols) and OSPFv3 (proactive routing protocols).

A Software Framework for Intelligent Security Measures Regarding Sensor Data in the Context of Ambient Assisted Technology.

Ambient assisted technology (AAT), which has the potential to enhance patient care and productivity and save costs, has emerged as a strategic goal for developing e-healthcare in the future. However, since the healthcare sensor must be interconnected with other systems at different network tiers, distant enemies have additional options to attack. Data and resources integrated into the AAT are vulnerable to security risks that might compromise privacy, integrity, and availability. The gadgets and network sensor devices are layered with clinical data since they save personal information such as patients' names, addresses, and medical histories. Considering the volume of data, it is difficult to ensure its confidentiality and security. As sensing devices are deployed over a wider region, protecting the privacy of the collected data becomes more difficult. The current study proposes a lightweight security mechanism to ensure the data's confidentiality and integrity of the data in ambient-assisted technology. In the current study, the data are encrypted by the master node with adequate residual energy, and the master node is responsible for encrypting the data using the data aggregation model using a node's key generated using an exclusive basis system and a Chinese remainder theorem. The integrity of the data is evaluated using the hash function at each intermediate node. The current study defines the design model's layered architecture and layer-wise services. The model is further analyzed using various evaluation metrics, such as energy consumption, network delay, network overhead, time in generating hash, tradeoff between encryption and decryption, and entropy metrics. The model is shown to adequately perform on all measures considered in the analysis.

Secure cross-layer routing protocol with authentication key management scheme for manets

MANET (Mobile ad-hoc networks) is typically a no-infrastructure multi-hop network where every node interacts with other network nodes either indirectly or directly via intermediate nodes. A lot of research is being undertaken to save the energy of mobile nodes at different levels. Power-relevant issues can have an effect on every layer of the stack, making the traditional layered approach ineffective. In this work, cross layered routing protocol based on PSO (Particle swarm optimization) with adapted contention window technique is proposed. To form consistent and energy efficient routing paths, PSO algorithm uses Traffic index, Average energy load, data success rate & trust value parameters that are computed from network layer. After establishing routing paths, network's contentions are measured MAC layers for communications and contention with measured contentions and average energy loads. The trust of nodes is computed using the following constraints: group trust-provided by neighbour nodes & quality trust-computed by node QoS. Dual authentications with EnDA (enhanced dual authentication) using key management strategies for enhanced data security and integrity. ECC (Elliptical curve cryptography) and Diffie-Hellman key exchanges with bilinear maps improve security of communications. The suggested protocol when measured in terms of energy usage and secure key agreements during network's transmissions, showed satisfactory performances.

Design and Analysis of Systematic Batched Network Codes.

Systematic codes are of important practical interest for communications. Network coding, however, seems to conflict with systematic codes: although the source node can transmit message packets, network coding at the intermediate network nodes may significantly reduce the number of message packets received by the destination node. Is it possible to obtain the benefit of network coding while preserving some properties of the systematic codes? In this paper, we study the systematic design of batched network coding, which is a general network coding framework that includes random linear network coding as a special case. A batched network code has an outer code and an inner code, where the latter is formed by linear network coding. A systematic batched network code must take both the outer code and the inner code into consideration. Based on the outer code of a BATS code, which is a matrix-generalized fountain code, we propose a general systematic outer code construction that achieves a low encoding/decoding computation cost. To further reduce the number of random trials required to search a code with a close-to-optimal coding overhead, a triangular embedding approach is proposed for the construction of the systematic batches. We introduce new inner codes that provide protection for the systematic batches during transmission and show that it is possible to significantly increase the expected number of message packets in a received batch at the destination node, without harm to the expected rank of the batch transfer matrix generated by network coding.

BAR: Blockwise Adaptive Recoding for Batched Network Coding.

Multi-hop networks have become popular network topologies in various emerging Internet of Things (IoT) applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging to the same batch; BNC has much smaller computational and storage requirements at intermediate nodes compared with direct application of random linear network coding. In this paper, we discuss a practical recoding scheme called blockwise adaptive recoding (BAR) which learns the latest channel knowledge from short observations so that BAR can adapt to fluctuations in channel conditions. Due to the low computational power of remote IoT devices, we focus on investigating practical concerns such as how to implement efficient BAR algorithms. We also design and investigate feedback schemes for BAR under imperfect feedback systems. Our numerical evaluations show that BAR has significant throughput gain for small batch sizes compared with existing baseline recoding schemes. More importantly, this gain is insensitive to inaccurate channel knowledge. This encouraging result suggests that BAR is suitable to be used in practice as the exact channel model and its parameters could be unknown and subject to changes from time to time.

Enhanced Fuzzy Logic Zone Stable Election Protocol for Cluster Head Election (E-FLZSEPFCH) and Multipath Routing in wireless sensor networks

A Wireless Sensor Network (WSN) is a system of sensor nodes that communicate with one another and with their surroundings. Wireless sensor nodes are now used as the foundation for the higher-level industrial internet of things applications. Any type of real-time Internet of Things (IoT) application, as long as the data is collected utilizing the device's sensors. WSN are severely limited in terms of energy. Routing is one of the most challenging components of WSNs. Unbalanced energy utilization during data packet routing operations is one of the most serious difficulties with WSNs. This issue must be considered because the energy levels of sensor nodes are limited. We used WSNs multipath routing to save energy, improve scalability, and balance load. Many investigations are being undertaken to increase sensor node energy efficiency and network lifetime. More attention must be devoted to the clustering and routing components of communication in order to conserve energy. Clustering, also known as node clustering is a network organizing approach which helps with energy efficiency, workload distribution, data collection, and a connected hierarchy. The network is made up of numerous sensor nodes that must connect with one another in order for data to reach its goal, and when messages are not sent to intermediary nodes, problems arise. With multipath routing, numerous paths between the source and destination can be created, improving the possibility of data reaching the target and providing additional copies via alternate paths. The proposed routing solution uses less energy and extends the life of the network while compared with well-known approaches i.e., LEACH, ECPF, CHEF, UCR, DFLC, ACAWT, and Gupta. According to the simulation results and overall analysis, the proposed technique is much better than all the previous approaches under different number of nodes and simulation time in terms of end-to-end time reduced by 17.09% to 68.13%, energy consumption reduced by 13.23% to 29.12%, packet loss rate declined by 16.33% to 82.38% and drop rate of alive sensor nodes are much better shows high network resilience.

Path planning and collision avoidance based on the RRT*FN framework for a robotic manipulator in various scenarios

AbstractIn this article, we present a new path planning algorithm based on the rapidly exploring random tree-fixed node (RRT*FN) algorithm for manipulators. It addresses the problem that RRT*FN complex environment processing is not fast enough to meet real-time requirements, and RRT*FN is basically impossible to search for an effective manipulator path in a narrow-channel environment. In the new path planning algorithm, a heuristic sampling method is adopted and the leaf nodes outside the ellipsoids are preferentially deleted to address the node removal problem, resulting in better search paths and faster search speeds. When the nodes are expanded, new nodes are generated due to goal gravity and random point gravity, with the weight values dynamically adjusted via dichotomy, and the new nodes are expanded to the goal point twice to more rapidly obtain a tree extension direction that is closer to the goal point. For boundary points, the issues of narrow channels and stepped obstacles can be effectively solved by extending the local environment sampling boundaries. To optimize the paths, the redundant intermediate nodes are simplified based on the triangle inequality. Simulation analyses show that the proposed planner can adapt to a variety of scenarios in real time while satisfying optimal path conditions.

Performance analysis of UAV assisted triple-hop mixed FSO/RF/UWOC system with altitude optimization

Abstract In this paper, we propose a unmanned aerial vehicle (UAV) assisted triple hop mixed FSO/RF/UWOC system to establish a communication between distant users. In the proposed system, the source sends information signal towards a hovering UAV such as drone acting as a first intermediate node via FSO link modelled by Gamma-Gamma distribution. The drone further transmits the received signal to the second intermediate node located on a floating vehicle (FV) on the ocean surface via a RF link modelled by the Nakagami-m fading distribution and from where it is finally forwarded towards the deep ocean destination via visible light channel (VLC) link which is modelled by Exponential Generalized Gamma (EGG) fading distribution. Both the relay nodes act as decode and forward relays. For this asymmetric triple hop mixed communication system, we obtain a new exact closed form analytical expression for the outage probability and also study its behaviour at high signal to noise ratio (SNR) regime. Additionally, the UAV altitude optimization is also carried out for the proposed system to find the optimal altitude of the UAV and the optimal elevation angle to maximize the system’s performance. The numerical simulation results illustrate the accuracy of the proposed system model and present the influence of the various link parameters such as atmospheric path loss, turbulence, pointing errors, angle-of-arrival fluctuations, multipath fading parameter, water turbulence, and scintillation on the performance of the proposed system.

PVL-Cartographer: Panoramic Vision-Aided LiDAR Cartographer-Based SLAM for Maverick Mobile Mapping System

The Mobile Mapping System (MMS) plays a crucial role in generating accurate 3D maps for a wide range of applications. However, traditional MMS that utilizes tilted LiDAR (light detection and ranging) faces limitations in capturing comprehensive environmental data. We propose the “PVL-Cartographer” SLAM (Simultaneous Localization And Mapping) approach for MMS to address these limitations. This proposed system incorporates multiple sensors to yield dependable and precise mapping and localization. It consists of two subsystems: early fusion and intermediate fusion. In early fusion, range maps are created from LiDAR points within a panoramic image space, simplifying the integration of visual features. The SLAM system accommodates both visual features with and without augmented ranges. In intermediate fusion, camera and LiDAR nodes are merged using a pose graph, with constraints between nodes derived from IMU (Inertial Measurement Unit) data. Comprehensive testing in challenging outdoor settings demonstrates that the proposed SLAM system can generate trustworthy outcomes even in feature-scarce environments. Ultimately, our suggested PVL-Cartographer system effectively and accurately addresses the MMS localization and mapping challenge.

On Performance of NOMA-Based Wireless Powered Communication Networks Assisted With Power Beacons and PPP Distributed IN

This work studies the performance of power beacon (PB) assisted downlink cooperative non-orthogonal multiple access (NOMA) in a wireless powered communication network (WPCN) with realistic non-linear energy harvesting (EH) model. The paper proposes an analytical methodology for improving performance at blocked user at the edge of network via an intermediate node (IN). IN deployed in the coverage area of the base station (BS), acts as decode-and-forward relay to perform cooperative transmission. IN assists the BS in communicating with the blocked Internet of Things (IoT) device after harvesting energy from PB mounted on the BS. IN employs NOMA to serve IoT devices with heterogeneous quality of service requirements in order to achieve higher spectral and energy efficiency. A practical non-linear EH model is employed at IN to power the cooperative transmission to the IoT devices. The performance of the proposed scheme is compared with the system employing orthogonal multiple access and it is shown that the proposed scheme attains improved performance in terms of outage probability, system throughput, and energy efficiency.

Sustainable Next-Generation Network Design using Social Aware and Delay Tolerant Approach

With the ever-growing popularity of smartphones, new services are emerging where the local and positioning aspects become more important. Additionally, new routing algorithms are being developed based on various networking technology such as Delay Tolerance Networks (DTN), Deviceto- Device communication, Opportunistic Network, etc. DTN is categorized as stable and unstable, a continuous path between the source and the destination node. Here, communication is done by carrying the message through the intermediate relay node on the store and carry forward paradigm. In this work, a Sustainable Energy Delay Tolerance Approach (SEDA) considers nodes mobility patterns for energy-efficient data transmission. This routing model is based on the appropriate usage of node information along with node mobility and its contacts for data dissemination. Next step is applying some techniques to predict the most likely position for next encounter. We will design and implement a social relation-aware routing relation-awareness the social relation as a part of the routing matrix. We assume that most of the movement of nodes are included in various numbers of social contacts; therefore, there is a higher probability of message delivery to the destination. Simulation results show that the proposed routing protocol improves the delivery ratio by reducing network size and distance, which also impacts energy saving.

ARR-GCN: Anatomy-Relation Reasoning Graph Convolutional Network for Automatic Fine-grained Segmentation of Organ's Surgical Anatomy.

Anatomical resection (AR) based on anatomical sub-regions is a promising method of precise surgical resection, which has been proven to improve long-term survival by reducing local recurrence. The fine-grained segmentation of an organ's surgical anatomy (FGS-OSA), i.e., segmenting an organ into multiple anatomic regions, is critical for localizing tumors in AR surgical planning. However, automatically obtaining FGS-OSA results in computer-aided methods faces the challenges of appearance ambiguities among sub-regions (i.e., inter-sub-region appearance ambiguities) caused by similar HU distributions in different sub-regions of an organ's surgical anatomy, invisible boundaries, and similarities between anatomical landmarks and other anatomical information. In this paper, we propose a novel fine-grained segmentation framework termed the "anatomic relation reasoning graph convolutional network" (ARR-GCN), which incorporates prior anatomic relations into the framework learning. In ARR-GCN, a graph is constructed based on the sub-regions to model the class and their relations. Further, to obtain discriminative initial node representations of graph space, a sub-region center module is designed. Most importantly, to explicitly learn the anatomic relations, the prior anatomic-relations among the sub-regions are encoded in the form of an adjacency matrix and embedded into the intermediate node representations to guide framework learning. The ARR-GCN was validated on two FGS-OSA tasks: i) liver segments segmentation, and ii) lung lobes segmentation. Experimental results on both tasks outperformed other state-of-the-art segmentation methods and yielded promising performances by ARR-GCN for suppressing ambiguities among sub-regions.