scholarly journals Distributed Learning Fractal Algorithm for Optimizing a Centralized Control Topology of Wireless Sensor Network Based on the Hilbert Curve L-System

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1442 ◽  
Author(s):  
Jaime Moreno ◽  
Oswaldo Morales ◽  
Ricardo Tejeida ◽  
Juan Posadas ◽  
Hugo Quintana ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Physical Space ◽  
Distributed Learning ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Centralized Control ◽  
Hilbert Curve ◽  
Service Routing ◽  
Control Topology

Wireless sensor networks (WSNs) consist of a large number of small devices or nodes, called micro controller units (MCUs) and located in homes and/or offices, to be operated through the internet from anywhere, making these devices smarter and more efficient. Quality of service routing is one of the critical challenges in WSNs, especially in surveillance systems. To improve the efficiency of the network, in this article we proposes a distributed learning fractal algorithm (DFLA) to design the control topology of a wireless sensor network (WSN), whose nodes are the MCUs distributed in a physical space and which are connected to share parameters of the sensors such as concentrations of C O 2 , humidity, temperature within the space or adjustment of the intensity of light inside and outside the home or office. For this, we start defining the production rules of the L-systems to generate the Hilbert fractal, since these rules facilitate the generation of this fractal, which is a fill-space curve. Then, we model the optimization of a centralized control topology of WSNs and proposed a DFLA to find the best two nodes where a device can find the highly reliable link between these nodes. Thus, we propose a software defined network (SDN) with strong mobility since it can be reconfigured depending on the amount of nodes, also we employ a target coverage because distributed learning fractal algorithm (DLFA) only consider reliable links among devices. Finally, through laboratory tests and computer simulations, we demonstrate the effectiveness of our approach by means of a fractal routing in WSNs, by using a large amount of WSNs devices (from 16 to 64 sensors) for real time monitoring of different parameters, in order to make efficient WSNs and its application in a forthcoming Smart City.

scholarly journals An Energy Balancing Strategy Based on Hilbert Curve and Genetic Algorithm for Wireless Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Lingping Kong ◽  
Jeng-Shyang Pan ◽  
Tien-Wen Sung ◽  
Pei-Wei Tsai ◽  
Václav Snášel
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Evaluation Criteria ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sink Node ◽  
Hilbert Curve ◽  
Energy Balancing ◽  
Available Energy ◽  
Transmission Energy

A wireless sensor network is a sensing system composed of a few or thousands of sensor nodes. These nodes, however, are powered by internal batteries, which cannot be recharged or replaced, and have a limited lifespan. Traditional two-tier networks with one sink node are thus vulnerable to communication gaps caused by nodes dying when their battery power is depleted. In such cases, some nodes are disconnected with the sink node because intermediary nodes on the transmission path are dead. Energy load balancing is a technique for extending the lifespan of node batteries, thus preventing communication gaps and extending the network lifespan. However, while energy conservation is important, strategies that make the best use of available energy are also important. To decrease transmission energy cost and prolong network lifespan, a three-tier wireless sensor network is proposed, in which the first level is the sink node and the third-level nodes communicate with the sink node via the service sites on the second level. Moreover, this study aims to minimize the number of service sites to decrease the construction cost. Statistical evaluation criteria are used as benchmarks to compare traditional methods and the proposed method in the simulations.

Automated Object Detection and Tracking for Intelligent Visual Surveillance Based on Sensor Network

2011 ◽  
pp. 1263-1284
Author(s):  
Ruth Aguilar-Ponce ◽  
Ashok Kumar ◽  
J. Luis Tecpanecatl-Xihuitl ◽  
Magdy Bayoumi ◽  
Mark Radle
Keyword(s):  
Wireless Sensor Network ◽  
Object Detection ◽  
Sensor Network ◽  
Tracking System ◽  
Visual Surveillance ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Automated Object Detection

The aim of this research was to apply an agent approach to wireless sensor network in order to construct a distributed, automated scene surveillance. Wireless sensor network using visual nodes is used as a framework for developing a scene understanding system to perform smart surveillance. Current methods of visual surveillance depend on highly train personnel to detect suspicious activity. However, the attention of most individuals degrades after 20 minutes of evaluating monitor-screens. Therefore current surveillance systems are prompt to failure. An automated object detection and tracking was developed in order to build a reliable visual surveillance system. Object detection is performed by means of a background subtraction technique known as Wronskian change detection. After discovery, a multi-agent tracking system tracks and follows the movement of each detected object. The proposed system provides a tool to improve the reliability and decrease the cost related to the personnel dedicated to inspect the monitor-screens

scholarly journals The Algorithms of Distributed Learning and Distributed Estimation about Intelligent Wireless Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1302
Author(s):  
Fuxiao Tan
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Distributed Learning ◽  
Estimation Algorithm ◽  
Distributed Estimation ◽  
Wireless Sensor ◽  
Research Progress ◽  
Theoretical Frameworks

The intelligent wireless sensor network is a distributed network system with high “network awareness”. Each intelligent node (agent) is connected by the topology within the neighborhood which not only can perceive the surrounding environment, but can adjusts its own behavior according to its local perception information to constructs a distributed learning algorithms. Therefore, three basic intelligent network topologies of centralized, non-cooperative, and cooperative are intensively investigated in this paper. The main contributions of the paper include two aspects. First, based on algebraic graph, three basic theoretical frameworks for distributed learning and distributed parameter estimation of cooperative strategy are surveyed: increment strategy, consensus strategy, and diffusion strategy. Second, based on classical adaptive learning algorithm and online updating law, the implementation process of distributed estimation algorithm and the latest research progress of above three distributed strategies are investigated.

Automated Object Detection and Tracking for Intelligent Visual Surveillance Based on Sensor Network

2011 ◽  
pp. 206-228 ◽  
Author(s):  
Ruth Aguilar-Ponce ◽  
Ashok Kumar ◽  
J. Luis Tecpanecatl-Xihuitl ◽  
Magdy Bayoumi ◽  
Mark Radle
Keyword(s):  
Wireless Sensor Network ◽  
Object Detection ◽  
Sensor Network ◽  
Tracking System ◽  
Visual Surveillance ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Automated Object Detection

The aim of this research was to apply an agent approach to wireless sensor network in order to construct a distributed, automated scene surveillance. Wireless sensor network using visual nodes is used as a framework for developing a scene understanding system to perform smart surveillance. Current methods of visual surveillance depend on highly train personnel to detect suspicious activity. However, the attention of most individuals degrades after 20 minutes of evaluating monitor-screens. Therefore current surveillance systems are prompt to failure. An automated object detection and tracking was developed in order to build a reliable visual surveillance system. Object detection is performed by means of a background subtraction technique known as Wronskian change detection. After discovery, a multi-agent tracking system tracks and follows the movement of each detected object. The proposed system provides a tool to improve the reliability and decrease the cost related to the personnel dedicated to inspect the monitor-screens

scholarly journals A Load Balancing Routing Mechanism Based on SDWSN in Smart City

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 273 ◽  
Author(s):  
Xin Cui ◽  
Xiaohong Huang ◽  
Yan Ma ◽  
Qingke Meng
Keyword(s):  
Wireless Sensor Network ◽  
Load Balancing ◽  
Sensor Network ◽  
Network Architecture ◽  
Smart Cities ◽  
Routing Algorithm ◽  
Wireless Sensor ◽  
Centralized Control ◽  
Traffic Scheduling ◽  
Elman Neural Network

In the wireless sensor network infrastructure of smart cities, whether the network traffic is balanced will directly affect the service quality of the network. Because of the traditional WSN (wireless sensor network) architecture, load balancing technology is difficult to meet the requirements of adaptability and high flexibility. This paper proposes a load balancing mechanism based on SDWSN (software defined wireless sensor network). This mechanism utilizes the advantages of a centralized control SDN (software defined network) and flexible traffic scheduling. The OpenFlow protocol is used to monitor the running status and link load information of the network in real time. According to the bandwidth requirement of the data flow, the improved load balanced routing is obtained by an Elman neural network. The simulation results show that the improved SDSNLB (software-defined sensor network load balancing) routing algorithm has better performance than LEACH (Low Energy Adaptive Clustering Hierarchy) protocol in balancing node traffic and improving throughput.

Wireless Sensor Network Localization Algorithm Based on Hop-Count and Distributed Learning

2014 ◽  
Vol 940 ◽  
pp. 457-460
Author(s):  
Ying Zhang ◽  
Yi Wang ◽  
Ying Ze Ye
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Distributed Learning ◽  
Training Data ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Hop Count ◽  
Sensor Network Localization ◽  
Network Localization ◽  
Positioning Method

The wireless sensor network localization algorithm in this paper combines hop-count information and distributed learning. The network is classified into many classes based on sensors’ location, and then the class that each sensor falls into is specified. There are a certain number of beacon nodes with position coordinate in network, and they use their own locations as training data in performing above classification. This positioning method merely uses the partial hop-count information between target sensor and reference node in specifying the class of each node. The final simulation experiment will analyze the excellent performance of this method under different system parameters.

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