Total Power Minimization Using Dual Power Assignment in Wireless Sensor Networks

2015 ◽  
Author(s):  
Nitesh Sisodiya ◽  
D. Pushparaj Shetty
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Total Power ◽  
Power Minimization ◽  
Power Assignment ◽  
Dual Power Assignment ◽  
Dual Power

Energy Efficient Mobile Wireless Sensor Networks

Aerospace ◽  
2006 ◽  
Author(s):  
Shashank Priya ◽  
Dan Popa ◽  
Frank Lewis
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Low Cost ◽  
Electric Energy ◽  
Cmos Technology ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Total Power ◽  
Gas Temperature ◽  
Traditional Use

Wireless sensor networks (WSN) have tremendous potential in many environmental and structural health monitoring applications including, gas, temperature, pressure and humidity monitoring, motion detection, and hazardous materials detection. Recent advances in CMOS-technology, IC manufacturing, and networking utilizing Bluetooth communications have brought down the total power requirements of wireless sensor nodes to as low as a few hundred microwatts. Such nodes can be used in future dense ad-hoc networks by transmitting data 1 to 10 meters away. For communication outside 10 meter ranges, data must be transmitted in a multi-hop fashion. There are significant implications to replacing large transmission distance WSN with multiple low-power, low-cost WSN. In addition, some of the relay nodes could be mounted on mobile robotic vehicles instead of being stationary, thus increasing the fault tolerance, coverage and bandwidth capacity of the network. The foremost challenge in the implementation of a dense sensor network is managing power consumption for a large number of nodes. The traditional use of batteries to power sensor nodes is simply not scalable to dense networks, and is currently the most significant barrier for many applications. Self-powering of sensor nodes can be achieved by developing a smart architecture which utilizes all the environmental resources available for generating electrical power. These resources can be structural vibrations, wind, magnetic fields, light, sound, temperature gradients and water currents. The generated electric energy is stored in the matching media selected by the microprocessor depending upon the power magnitude and output impedance. The stored electrical energy is supplied on demand to the sensors and communications devices. This paper shows the progress in our laboratory on powering stationary and mobile untethered sensors using a fusion of energy harvesting approaches. It illustrates the prototype hardware and software required for their implementation including MEMS pressure and strain sensors mounted on mobile robots or stationary, power harvesting modules, interface circuits, algorithms for interrogating the sensor, wireless data transfer and recording.

Power Assignment for Symmetric Communication in Wireless Sensor Networks

2009 ◽  
Vol 5 (2) ◽  
pp. 185-200
Author(s):  
Joongseok Park ◽  
Sartaj Sahni
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Cost Reduction ◽  
Spanning Trees ◽  
Minimum Cost ◽  
Approximation Ratio ◽  
Wireless Sensor ◽  
Power Assignment ◽  
Power Cost

We show that two incremental power heuristics for power assignment in a wireless sensor network have an approximation ratio 2. Enhancements to these heuristics are proposed. It is shown that these enhancements do not reduce the approximation ratio of the considered incremental power heuristics. However, experiments conducted by us indicate that the proposed enhancements reduce the power cost of the assignment on average. Further, the two-edge switch enhancements reduce the power-cost reduction (relative to using minimum cost spanning trees) that is, on average, twice as much as obtainable from any of the heuristics proposed earlier.

scholarly journals Capacity of Data Collection in Wireless Sensor Networks Based on Mutual Information and MMSE Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ajib Setyo Arifin ◽  
Tomoaki Ohtsuki
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Mutual Information ◽  
Power Allocation ◽  
Wireless Sensor ◽  
Total Power ◽  
Multiple Access Channel ◽  
Optimal Power ◽  
Equal Power

We investigate the properties of data collection in wireless sensor networks, in terms of both capacity and power allocation strategy. We consider a scenario in which a number of sensors observe a target being estimated at fusion center (FC) using minimum mean-square error (MMSE) estimator. Based on the relationship between mutual information and MMSE (I-MMSE), the capacity of data collection in coherent and orthogonal multiple access channel (MAC) models is derived. Considering power constraint, the capacity is derived under two scenarios: equal power allocation and optimal power allocation of both models. We provide the upper bound of capacity as a benchmark. In particular, we show that the capacity of data collection scales as Θ((1/2)log(1+L)) when the number of sensors L grows to infinity. We show through simulation results that for both coherent and orthogonal MAC models, the capacity of the optimal power is larger than that of the equal power. We also show that the capacity of coherent MAC is larger than that of orthogonal MAC, particularly when the number of sensors L is large and the total power P is fixed.

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