A virtual MIMO transmission scenarios for high energy efficiency smart wireless sensor networks over Rayleigh flat fading channel

2017 ◽  
Author(s):  
Fawaz Alassery
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fading Channel ◽  
High Energy ◽  
Wireless Sensor ◽  
Virtual Mimo ◽  
Flat Fading ◽  
High Energy Efficiency ◽  
Mimo Transmission

Data Gathering in Correlated Wireless Sensor Networks with Cooperative Transmission Data Gathering in Correlated Wireless Sensor Networks with Cooperative Transmission

2012 ◽  
pp. 944-966
Author(s):  
Laxminarayana S. Pillutla ◽  
Vikram Krishnamurthy
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Correlation Coefficient ◽  
Data Gathering ◽  
Optimal Number ◽  
Cooperative Transmission ◽  
Wireless Sensor ◽  
Virtual Mimo ◽  
Constellation Size ◽  
Mimo Transmission

This chapter considers the problem of data gathering in correlated wireless sensor networks with distributed source coding (DSC), and virtual multiple input and multiple output (MIMO) based cooperative transmission. Using the concepts of super and sub modularity on a lattice, we analytically quantify as how the optimal constellation size, and optimal number of cooperating nodes, vary with respect to the correlation coefficient. In particular, we show that the optimal constellation size is an increasing function of the correlation coefficient. For the virtual MIMO transmission case, the optimal number of cooperating nodes is a decreasing function of the correlation coefficient. We also prove that in a virtual MIMO based transmission scheme, the optimal constellation size adopted by each cooperating node is a decreasing function of number of cooperating nodes. Also it is shown that, the optimal number of cooperating nodes is a decreasing function of the constellation size adopted by each cooperating node. We also study numerically that for short distance ranges, SISO transmission achieves better energy-mutual information (MI) tradeoff. However, for medium and large distance ranges, the virtual MIMO transmission achieves better energy-MI tradeoff.

scholarly journals Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ru Huang ◽  
Xiaoli Chu ◽  
Jie Zhang ◽  
Yu Hen Hu
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Environmental Monitoring ◽  
Environmental Changes ◽  
High Energy ◽  
Wireless Sensor ◽  
Data Flows ◽  
Monitoring Applications

Software defined wireless networks (SDWNs) present an innovative framework for virtualized network control and flexible architecture design of wireless sensor networks (WSNs). However, the decoupled control and data planes and the logically centralized control in SDWNs may cause high energy consumption and resource waste during system operation, hindering their application in WSNs. In this paper, we propose a software defined WSN (SDWSN) prototype to improve the energy efficiency and adaptability of WSNs for environmental monitoring applications, taking into account the constraints of WSNs in terms of energy, radio resources, and computational capabilities, and the value redundancy and distributed nature of data flows in periodic transmissions for monitoring applications. Particularly, we design a reinforcement learning based mechanism to perform value-redundancy filtering and load-balancing routing according to the values and distribution of data flows, respectively, in order to improve the energy efficiency and self-adaptability to environmental changes for WSNs. The optimal matching rules in flow table are designed to curb the control signaling overhead and balance the distribution of data flows for achieving in-network fusion in data plane with guaranteed quality of service (QoS). Experiment results show that the proposed SDWSN prototype can effectively improve the energy efficiency and self-adaptability of environmental monitoring WSNs with QoS.

Data Gathering in Correlated Wireless Sensor Networks with Cooperative Transmission

2010 ◽  
pp. 471-496
Author(s):  
Laxminarayana S. Pillutla ◽  
Vikram Krishnamurthy
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Correlation Coefficient ◽  
Data Gathering ◽  
Optimal Number ◽  
Cooperative Transmission ◽  
Wireless Sensor ◽  
Virtual Mimo ◽  
Constellation Size ◽  
Mimo Transmission

This chapter considers the problem of data gathering in correlated wireless sensor networks with distributed source coding (DSC), and virtual multiple input and multiple output (MIMO) based cooperative transmission. Using the concepts of super and sub modularity on a lattice, we analytically quantify as how the optimal constellation size, and optimal number of cooperating nodes, vary with respect to the correlation coefficient. In particular, we show that the optimal constellation size is an increasing function of the correlation coefficient. For the virtual MIMO transmission case, the optimal number of cooperating nodes is a decreasing function of the correlation coefficient. We also prove that in a virtual MIMO based transmission scheme, the optimal constellation size adopted by each cooperating node is a decreasing function of number of cooperating nodes. Also it is shown that, the optimal number of cooperating nodes is a decreasing function of the constellation size adopted by each cooperating node. We also study numerically that for short distance ranges, SISO transmission achieves better energymutual information (MI) tradeoff. However, for medium and large distance ranges, the virtual MIMO transmission achieves better energy-MI tradeoff.

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