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.

An Energy Efficient Estimation Algorithm for Wireless Sensor Network

2011 ◽  
Vol 216 ◽  
pp. 176-180
Author(s):  
Yong Ding ◽  
Yue Mei Su
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Estimation Algorithm ◽  
Efficient Estimation ◽  
Wireless Sensor ◽  
Estimation Algorithms ◽  
Current Energy

Wireless Sensor Networks functionality is closely related to network lifetime which depends on the energy consumption, so require energy- efficient protocols to improve the network lifetime. According to the analysis and summary of the current energy efficient estimation algorithms in wireless sensor network An energy-efficient algorithm is proposed,. Then this optimization algorithm proposed in the paper is adopted to improve the traditional diffusion routing protocol. Simulation results show that this algorithm is to effectively balance the network energy consumption, improve the network life-cycle and ensure the communication quality.

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.

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