Single Sink Node Research Articles

HCAR: A Hybrid-Coding-Aware Routing Protocol for Underwater Acoustic Sensor Networks

Because of the characteristics of the underwater acoustic sensor networks (UASNs), such as long propagation delay, narrow bandwidth, limited resources, and intermittent connectivity, the design of the routing protocol has always been the focus and difficulty in UASNs research. Most of the existing coding-based underwater routing protocols only use the error correction function of network coding to improve the reliability of data transmission, ignoring that the network coding can effectively optimize the performance of packet transmission. In this paper, we propose a hybrid coding-aware routing protocol (HCAR). HCAR introduces inter-flow network coding and designs a new routing framework that combines reactive routing with opportunistic routing. It exhaustively explores potential coding opportunities in the network and proactively creates coding opportunities during the entire routing process, which enables HCAR to take full advantage of the broadcast nature of the wireless medium, thereby effectively reducing the number of transmissions. In addition, we design a new encoding method that does not rely on opportunistic listening, and thus avoids introducing excessive delay and overhead. The simulation results show that HCAR can effectively optimize the routing problem of the networks with multiple source nodes and a single sink node, further reducing the transmission overhead while ensuring reliability. We also implemented the key mechanism of HCAR protocol to verify its feasibility through sea tests.

Exploring propagation delay and mobility in underwater acoustic networks

This work investigates the impact of mobility on underwater acoustic communication networks where the propagation delay is comparable to or larger than the packet duration. A new simulator is proposed to implement the acoustic communication timing model when the nodes are mobile or static. Synchronous and asynchronous Medium Access Control (MAC) protocols are employed with ALOHA, TDMA (Time-Division Multiple Access), and Artificial Intelligent (AI) agent nodes. Extensive simulations are conducted to study the throughput of a MAC network where a large number of source nodes transmit to a single sink node. A network of 2–15 source nodes are assumed to be randomly located in a 1500 m cubic space and some of the nodes are moving in random directions at 2–4 m/s speed. The simulation results show that the asynchronous MAC protocols achieve much higher throughput than the synchronous protocols by allowing time slots to be much shorter than the maximum propagation delay among nodes and allowing asynchronous transmission time. High mobility of a few mobile nodes also favors asynchronous protocols and improves the network throughput in a manner similar to an AI agent.

A Low-Power Distributed Visual Sensor Network for Real-Time Barcode Localization and Identification.

A novel low-power distributed Visual Sensor Network (VSN) system is proposed, which performs real-time collaborative barcode localization, tracking, and robust identification. Due to a dynamic triggering mechanism and efficient transmission protocols, communication is organized amongst the nodes themselves rather than being orchestrated by a single sink node, achieving lower congestion and significantly reducing the vulnerability of the overall system. Specifically, early detection of the moving barcode is achieved through a dynamic triggering mechanism. A hierarchical transmission protocol is designed, within which different communication protocols are used, depending on the type of data exchanged among nodes. Real-Time Transport Protocol (RTP) is employed for video communication, while the Transmission Control Protocol (TCP) and Long Range (LoRa) protocol are used for passing messages amongst the nodes in the VSN. Through an extensive experimental evaluation, we demonstrate that the proposed distributed VSN brings substantial advantages in terms of accuracy, power savings, and time complexity compared to an equivalent system performing centralized processing.

Throughput optimization of multi-hop and multi-path cooperation in WPSNs with hardware noises

This paper proposes a novel multi-path and multi-hop wireless powered sensor network in case of hardware impairment, constituting an energy node, one source node, single sink node, and a series of distributed relay sensor nodes, where the energy node transmits wireless energy to all terminals in the first stage, and the relay sensor nodes relay the information of the source node to the sink node in the second stage. There exists M available paths between the source node and sink node, one of which is chosen for serving source-sink communication. To enhance the minimum achievable data rate, we propose a multi-hop communication protocol based on time-division-multiple-access and an optimal throughput path algorithm. We formulate the time allocation optimization problem about energy and information transmission of the proposed multi-hop cooperation, and confirm through abundant simulation experiments that the proposed scheme can availably improve user unfairness and spectral efficiency, and thus enhance its throughput performance.

Quality of Service‐aware clustered triad layer architecture for critical data transmission in multi‐body area network environment

AbstractWireless Body Area Network (WBAN) is an important element of future smart healthcare services in smart cities. For this reason, many research works have been undergone in WBAN. The significant research issue in the WBAN environment is Quality of Service (QoS) provisioning. In literature, some of the works have been focused on QoS aspect of WBAN. However, they have only marginal impact due to ineffective network design and the usage of a single sink node. Also, most of the research works have been tested within a single BAN and present intra‐BAN communication. In practical, an effective multi‐BAN communication system is emerging due to the need of large‐scale healthcare system. This paper addresses these issues and designs a novel QoS‐aware Clustered Triad Layer (QC‐TriL) architecture for multi‐BAN environment. The novel QC‐TriL uses dual sink nodes in each BAN and both sinks are deployed in the optimal positions. To determine the optimal position, Type‐II Fuzzy Logic (T2FL) is proposed with different criteria. Next, we form clusters based on the positions of dual sink nodes. QC‐TriL adopts criticality‐aware routing by presenting Delay‐aware One‐Hop Transmission and Fused Rank Scheme (FuRank) for critical data and normal data routing respectively. The data transmission is organized by a novel 1D‐Dragonfly topology‐based Priority Dual TDMA (PD‐TDMA) scheduling protocol. Finally, multi‐BAN communication is enabled with an optimal route selected by QoS‐aware Emperor Penguin Colony (QoS‐EPC) algorithm. The QC‐TriL multi‐BAN environment is modeled in OMNeT++ simulator and evaluated in terms of energy consumption, packet delivery ratio, throughput, and delay. The experiments show promising results for the multi‐BAN environment.

Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation

Segmentation of grayscale medical images is challenging because of the similarity of pixel intensities and poor gradient strength between adjacent regions. The existing image segmentation approaches based on either intensity or gradient information alone often fail to produce accurate segmentation results. Previous approaches in the literature have approached the problem by embedded or sequential integration of different information types to improve the performance of the image segmentation on specific tasks. However, an effective combination or integration of such information is difficult to implement and not sufficiently generic for closely related tasks. Integration of the two information sources in a single graph structure is a potentially more effective way to solve the problem. In this paper we introduce a novel technique for grayscale medical image segmentation called pyramid graph cut, which combines intensity and gradient sources of information in a pyramid-shaped graph structure using a single source node and multiple sink nodes. The source node, which is the top of the pyramid graph, embeds intensity information into its linked edges. The sink nodes, which are the base of the pyramid graph, embed gradient information into their linked edges. The min-cut uses intensity information and gradient information, depending on which one is more useful or has a higher influence in each cutting location of each iteration. The experimental results demonstrate the effectiveness of the proposed method over intensity-based segmentation alone (i.e. Gaussian mixture model) and gradient-based segmentation alone (i.e. distance regularized level set evolution) on grayscale medical image datasets, including the public 3DIRCADb-01 dataset. The proposed method archives excellent segmentation results on the sample CT of abdominal aortic aneurysm, MRI of liver tumor and US of liver tumor, with dice scores of 90.49±5.23%, 88.86±11.77%, 90.68±2.45%, respectively.

Faster Dynamic-Consistency Checking for Conditional Simple Temporal Networks

A Conditional Simple Temporal Network (CSTN) is a structure for representing and reasoning about time in domains where temporal constraints may be conditioned on outcomes of observations made in real time. A CSTN is dynamically consistent (DC) if there is a strategy for executing its time-points such that all relevant constraints will necessarily be satisfied no matter which outcomes happen to be observed. The literature on CSTNs contains only one sound-and-complete DC-checking algorithm that has been implemented and empirically evaluated. It is a graph-based algorithm that propagates labeled constraints/edges. A second algorithm has been proposed, but not evaluated. It aims to speed up DC checking by more efficiently dealing with so-called negative q-loops.This paper presents a new two-phase approach to DC-checking for CSTNs. The first phase focuses on identifying negative q-loops and labeling key time-points within them. The second phase focuses on computing (labeled) distances from each time-point to a single sink node. The new algorithm, which is also sound and complete for DC-checking, is then empirically evaluated against both pre-existing algorithms and shown to be much faster across not only previously published benchmark problems, but also a new set of benchmark problems. The results show that, on DC instances, the new algorithm tends to be an order of magnitude faster than both existing algorithms. On all other benchmark cases, the new algorithm performs better than or equivalently to the existing algorithms.

A 3-D Topology Evolution Scheme With Self-Adaption for Industrial Internet of Things

The complex factory environment of the Industrial Internet of Things (IIoT) greatly increases the energy consumption of sensor nodes and reduces production profits. Especially, in mines, the terrain will change continuously as the mining progresses. Additionally, the heavy traffic load on a single sink node and the unbalanced load on multiple sink nodes also reduce the battery life. Therefore, how to build an energy-efficient topology based on the unique mine terrain characteristics is a critical issue. To address this problem, this article proposes a 3-D topology evolution scheme with self-adaption for mining areas (3D-TES) to reduce energy consumption. We build the multipeak terrain model according to the characteristics of the mining environment. Blocked by the undulating peaks on the mine, the strength of the node signal is quantified by the slope and aspect. The 3D-TES is then applied to determine the optimal number of sink nodes and find the best data transmission path between sensor nodes and multiple sink nodes. The experimental results show that 3D-TES outperforms the directed angulation toward the sink node model (DASM) in terms of reliability, average path length, and data load on sink nodes.

Optimal Sink Node Placement in Large Scale Wireless Sensor Networks Based on Harris’ Hawk Optimization Algorithm

Large-scale wireless sensor network (LSWSN) is composed of a huge number of sensor nodes that are distributed in some region of interest (ROI), to sense and measure the environmental conditions like pressure, temperature, pollution levels, humidity, wind, and so on. The objective is to collect data for real-time monitoring so that appropriate actions can be taken promptly. One of the sensor nodes used in an LSWSN is called the sink node, which is responsible for processing and analyzing the collected information. It works as a station between the network sensor nodes and the administrator. Also, it is responsible for controlling the whole network. Determining the sink node location in an LSWSN is a challenging task, as it is crucial to the network lifetime, for keeping the network activity to the most possible extent. In this paper, the Harris' hawks optimization (HHO) algorithm is employed to solve this problem and subsequently the Prim's shortest path algorithm is used to reconstruct the network by making minimum transmission paths from the sink node to the rest of the sensor nodes. The performance of HHO is compared with other well-known algorithms such as particle swarm optimization (PSO), flower pollination algorithm (FPA), grey wolf optimizer (GWO), sine cosine algorithm (SCA), multi-verse optimizer (MVO), and whale optimization algorithm (WOA). The simulation results of different network sizes, with single and multiple sink nodes, show the superiority of the employed approach in terms of energy consumption and localization error, and ultimately prolonging the lifetime of the network in an efficacious way.

On the Use of Graphs for Node Connectivity in Wireless Sensor Networks for Hostile Environments

Wireless sensor networks (WSNs) have been extensively studied in the literature. However, in hostile environments where node connectivity is severely compromised, the system performance can be greatly affected. In this work, we consider such a hostile environment where sensor nodes cannot directly communicate to some neighboring nodes. Building on this, we propose a distributed data gathering scheme where data packets are stored in different nodes throughout the network instead to considering a single sink node. As such, if nodes are destroyed or damaged, some information can still be retrieved. To evaluate the performance of the system, we consider the properties of different graphs that describe the connections among nodes. It is shown that the degree distribution of the graph has an important impact on the performance of the system. A teletraffic analysis is developed to study the average buffer size and average packet delay. To this end, we propose areference nodeapproach, which entails an approximation for the mathematical modeling of these networks that effectively simplifies the analysis and approximates the overall performance of the system.

A study on the Development of a Korean Metabolic Syndrome Questionnaire

Segmentation of grayscale medical images is challenging because of the similarity of pixel intensities and poor gradient strength between adjacent regions. The existing image segmentation approaches based on either intensity or gradient information alone often fail to produce accurate segmentation results. Previous approaches in the literature have approached the problem by embedded or sequential integration of different information types to improve the performance of the image segmentation on specific tasks. However, an effective combination or integration of such information is difficult to implement and not sufficiently generic for closely related tasks. Integration of the two information sources in a single graph structure is a potentially more effective way to solve the problem. In this paper we introduce a novel technique for grayscale medical image segmentation called pyramid graph cut, which combines intensity and gradient sources of information in a pyramid-shaped graph structure using a single source node and multiple sink nodes. The source node, which is the top of the pyramid graph, embeds intensity information into its linked edges. The sink nodes, which are the base of the pyramid graph, embed gradient information into their linked edges. The min-cut uses intensity information and gradient information, depending on which one is more useful or has a higher influence in each cutting location of each iteration. The experimental results demonstrate the effectiveness of the proposed method over intensity-based segmentation alone (i.e. Gaussian mixture model) and gradient-based segmentation alone (i.e. distance regularized level set evolution) on grayscale medical image datasets, including the public 3DIRCADb-01 dataset. The proposed method archives excellent segmentation results on the sample CT of abdominal aortic aneurysm, MRI of liver tumor and US of liver tumor, with dice scores of 90.49±5.23%, 88.86±11.77%, 90.68±2.45%, respectively.

Evolutionary Software Defined Networking-Inspired Routing Control Strategies for the Internet of Things

The Routing over Low Power and Lossy Networks (RPL) protocol is an important standardized routing solution for the Internet of Things (IoT), characterized by significant benefits, including IPv6 support and efficient low-power operation over noisy channels, especially for many-to-one communication scenarios (i.e., a set of deployed devices gathering periodic measurements destined to a single sink node). However, the RPL faces performance issues in networks with mobility or point-to-point communication requirements, raising applicability constraints in a number of real-world IoT applications, spanning from environmental monitoring to industrial and urban networks. In this paper, we approach RPL from the Software-Defined Networking (SDN) perspective, exploiting its high customization features to address the above inefficiencies. We apply an evolutionary methodology, i.e., building over the widely deployed RPL protocol, while maintaining compliance with its standard. More precisely, we investigate two routing control strategies exploiting the global view of the network: (i) the Moderate RPL control that enables dynamic reconfigurability of crucial protocol parameters to improve its operation in mobile environments; and (ii) the Deep RPL control that utilizes a new RPL Objective Function (OF) we proposed that enforces direct point-to-point paths through link-coloring. We implemented and evaluated the two strategies based on a novel centralized routing control facility. Our experimental analysis considers hybrid scenarios with both fixed and mobile nodes, as well as many-to-one and point-to-point communication. Compared to the standard RPL in mobile topologies, the proposed solution achieves improved packet delivery ratio of up to 33 percent and 21 percent for the mobile nodes and for the whole network, respectively, while maintaining RPL compliance. In case of point-to-point communication in a random topology, the improvements rise up to 32.7 percent for the trip-time and 42 percent for the round-trip time, while the packet loss ratio for the same experiment is improved up to 75 percent in the non-storing mode.

Network Performance Enhancement of Multi-sink Enabled Low Power Lossy Networks in SDN Based Internet of Things

Software Defined Network (SDN) brought revolution in the network field with the partnership of Academia and Industry. SDN bridges the gap to overcome issues of IoT deployment, optimization and better utilization of network resources. The escalation in resource congestion in Wireless Sensor Networks (WSNs) can usually lead to scalability, data computation or storage, and energy efficiency problems with only a single sink node for data acquisition. Internet of Things (IoT) has resource and energy constraints for WSN devices. Low Power and Lossy Networks (LLNs) ought to be optimized for traffic with multiple sinks. RPL routing has constraints to support this approach. However, RPL inherits the ability to offer features like Auto-Configuration, Self-Healing, Loop avoidance, and detection. These features of RPL can be transformed into the improved performance of a WSN by increasing the number of sinks with a linear increase of data transmitting nodes in the network. Further, to mitigate the escalated computing needs, edge computing has emerged as a new paradigm to resolve SDN-enabled IoT and localized computing needs. This study proposes an SDN-based solution to the interconnectivity of resource constraint LLN devices with edge computing routers in mesh and cluster topological scenario using RPL as IoT routing protocol. Performance evaluation concerning different routing metrics and objective functions: Minimum Rank with Hysteresis Function (MRHOF) and Zero (OF0) are analyzed. COOJA simulator is used for emulation of random as well as linear grid topologies for the creation of WSN static nodes. Simulation results confirm that the gradual increase of a number of nodes from 16, 32, 48, 64 and a simultaneous increase in sinks nodes as 1, 2, 3, 4 respectively in LLN network reflects the desired advantages with the stable network.

Adaptive Queuing Censoring for Big Data Processing

In the era of big data, adaptive censoring (AC) provides us a natural option of trimming data by only keeping the statistical informative data. However, the data chosen by AC may arrive in clusters, which do not relieve the computational resource requirement as expected. In this letter, we exploit queuing theory to model a single sink node with abundant sensor nodes. By adding a buffer to censored distributed wireless sensor networks (WSNs), the uncensored data can be modeled as a queue. With the buffer, the new algorithm entails simple, closed-form updates, and has no loss in terms of estimation accuracy comparing to the original AC method. The proposed model can further reduce the communication cost of distributed WSNs. The proposed model is illustrated in a linear regression setting. Numerical results validate the effectiveness of the proposed model in dealing with data congestion problem.

Optimal Scheduling and Fair Service Policy for STDMA in Underwater Networks with Acoustic Communications.

In this work, a multi-hop string network with a single sink node is analyzed. A periodic optimal scheduling for TDMA operation that considers the characteristic long propagation delay of the underwater acoustic channel is presented. This planning of transmissions is obtained with the help of a new geometrical method based on a 2D lattice in the space-time domain. In order to evaluate the performance of this optimal scheduling, two service policies have been compared: FIFO and Round-Robin. Simulation results, including achievable throughput, packet delay, and queue length, are shown. The network fairness has also been quantified with the Gini index.

QoS Oriented and Delay Tolerant WSN Routing Protocol for Data Gathering in IoT Ecosystem

In this paper, a robust and efficient delay tolerant and quality of service (QoS) oriented data gathering protocol for low power lossy wireless sensor network (WSN) has been proposed. Our proposed routing protocol applied received signal strength based forwarding node selection and transmission path formation scheme, which has been further armoured with multiple timer based handoff optimisation to enable optimal data transmission, particularly data gathering using single mobile sink node. To ensure backward compatibility of RPL, our proposed scheme has incorporated control message modification that makes our proposed routing protocol applicable for major low power lossy network commonly known as RPL based internet of things (IoT) ecosystem. The higher packet delivery ratio of 98.93% and lower delay and control packet requirements make our proposed routing protocol computationally efficient to be used for major WSN based IoT communication systems.

Cancel-and-tighten algorithm for quickest flow problems

Given a directed graph with a capacity and a transit time for each arc and with single source and single sink nodes, the quickest flow problem is to find the minimum time horizon to send a given amount of flow from the source to the sink. This is one of the fundamental dynamic flow problems. Parametric search is one of the basic approaches to solving the problem. Recently, Lin and Jaillet (SODA, 2015) proposed an algorithm whose time complexity is the same as that of the minimum cost flow algorithm. Their algorithm employs a cost scaling technique, and its time complexity is weakly polynomial time. In this article, we modify their algorithm by adopting a technique to construct a strongly polynomial time algorithm for solving the minimum cost flow problem. The proposed algorithm runs in O ( n m 2 ( log n ) 2 ) time, where n and m are the numbers of nodes and arcs, respectively. © 2016 Wiley Periodicals, Inc. NETWORKS, 2016

A Column Generation Method for Constructing and Scheduling Multiple Forwarding Trees in Wireless Sensor Networks

This paper considers the problem of jointly constructing and scheduling forwarding trees in a wireless sensor network, each to collect measurements from a group of sensor nodes at a single sink node. The goal is to construct such trees that gather measurements in the most energy efficient manner and with minimal gathering latency. We assume transmissions (carrying measurements) on wireless links interfere with one another, and thus, appropriate link scheduling is required to manage interference. We refer to this problem as forwarding tree construction and scheduling (FTCS). Each tree may be constructed independently, and then, its links are scheduled. However, when all trees are combined together, the shortest and energy efficient schedule may not be guaranteed. Furthermore, a large number of possible forwarding trees for each group of sensors may be considered. Both problems of enumerating forwarding trees and scheduling links for those trees are hard combinatorial problems. This is compounded by the fact that the two problems must be solved jointly, to guarantee the selection of the best forwarding trees that, when their links are scheduled, guarantee a shortest energy efficient schedule. After highlighting the complexity of the FTCS problem, we present a novel primal-dual decomposition method using column generation. We also highlight several challenges we faced when solving the decomposed problem and present efficient techniques for mitigating those challenges. One major advantage of this paper is that it can serve as a benchmark for evaluating the performance of any low complexity method for solving the FTCS problem for larger network instances, where no known exact solutions can be found.

Multi-sink Optimal Repositioning for Energy and Power Optimization in Wireless Sensor Networks

A wireless sensor network (WSN) plays a major role in many recent applications now such as surveillance and security, target tracking, agriculture, health and military purposes. The main problem with WSN is the energy resource for long lasting lifetime. Therefore an efficient methodology is to be implemented for improving the energy level of WSN. Also some efforts have focused on the mobility of a single or multiple sink nodes. The mobility of the sink node introduces a tradeoff between the need for frequent re-routing to optimize the performance and the minimization of the overhead resulting from this topology management. In this we propose a novel approach to increase the lifetime of a sensor network based on the mobility, static sink repositioning and multiplicity of sinks. Optimal sink position also identified using optimal search concepts and multipath routing in large scale sensor networks with multiple sink nodes for energy is implemented for entire WSN. Based on the evolution of network in terms of energy dissipation and distribution this approach reaches to find the optimal position for all the sinks in order to optimize the lifetime of the network and move according with intelligent sink positioning. The simulation result shows the efficiency of our approach in terms of energy gain.

On the routing protocol influence on the resilience of wireless sensor networks to jamming attacks.

In this work, we compare a recently proposed routing protocol, the multi-parent hierarchical (MPH) protocol, with two well-known protocols, the ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR). For this purpose, we have developed a simulator, which faithfully reifies the workings of a given protocol, considering a fixed, reconfigurable ad hoc network given by the number and location of participants, and general network conditions. We consider a scenario that can be found in a large number of wireless sensor network applications, a single sink node that collects all of the information generated by the sensors. The metrics used to compare the protocols were the number of packet retransmissions, carrier sense multiple access (CSMA) inner loop retries, the number of nodes answering the queries from the coordinator (sink) node and the energy consumption. We tested the network under ordinary (without attacks) conditions (and combinations thereof) and when it is subject to different types of jamming attacks (in particular, random and reactive jamming attacks), considering several positions for the jammer. Our results report that MPH has a greater ability to tolerate such attacks than DSR and AODV, since it minimizes and encapsulates the network segment under attack. The self-configuring capabilities of MPH derived from a combination of a proactive routes update, on a periodic-time basis, and a reactive behavior provide higher resilience while offering a better performance (overhead and energy consumption) than AODV and DSR, as shown in our simulation results.