Essex Junction, VT, United States
Essex Junction, VT, United States

Time filter

Source Type

Abdelgawad A.,54 Lavoie Drive | Bayoumi M.,University of Louisiana at Lafayette
Lecture Notes in Electrical Engineering | Year: 2012

A Wireless Sensor Network (WSN) is a network comprised of numerous small autonomous sensor nodes called motes. It combines a broad range of networking, hardware, software, and programming methodologies. Each node is a computer with attached sensors that can process and exchange sensed data, as well as communicates wirelessly among them to complete various tasks. Sensors attached to this node allow them to sense various phenomena within the surroundings.WSN has received momentous attention in recent years because of its titanic potential in applications. In this chapter, we introduced many applications of WSN, explained the sensor node evaluation metrics, brought in the sensor network architecture, and finally we discussed the WSN's challenges and constraints. © 2012 Springer Science+Business Media, LLC.


Abdelgawad A.,54 Lavoie Drive | Bayoumi M.,University of Louisiana at Lafayette
Lecture Notes in Electrical Engineering | Year: 2012

Experimentally, the proposed DKF using the proposed multiplication method and the proposed fast polynomial filter was evaluated. The DKF introduced by Olfati was experimentally tested as well. The results show that the proposed DKF achieves up to 33% energy saving. The results show also that one node can run the Olfati's DKF for up to five neighbors only, but the proposed DKF can run for up to seven neighbors. This different in the nodes numbers is because of the memory limitation, as Olfati's DKF exchange the measurements and the covariance, but the proposed DKF exchange the estimation only. Moreover the proposed multiplication method saves memory as well. © 2011 Springer Science+Business Media, LLC.


Abdelgawad A.,54 Lavoie Drive | Bayoumi M.,University of Louisiana at Lafayette
Lecture Notes in Electrical Engineering | Year: 2012

WSN is intended to be deployed in environments where sensors can be exposed to circumstances that might interfere with measurements provided. Such circumstances include strong variations of pressure, temperature, radiation, and electromagnetic noise. Thus, measurements may be imprecise in such scenarios. Data fusion is used to overcome sensor failures, technological limitations, and spatial and temporal coverage problems. Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather this information in order to achieve inferences, which will be more efficient and potentially more accurate than if they were achieved by means of a single source. The term efficient, in this case, can mean more reliable delivery of accurate information, more complete, and more dependable. The data fusion can be implemented in both centralized and distributed systems. In a centralized system, all raw sensor data would be sent to one node, and the data fusion would all occur at the same location. In a distributed system, the different fusion modules would be implemented on distributed components. Data fusion occurs at each node using its own data and data from the neighbors. This chapter briefly discusses the data fusion and a comprehensive survey of the existing data fusion techniques, methods and algorithms. © 2012 Springer Science+Business Media, LLC.


Abdelgawad A.,54 Lavoie Drive | Bayoumi M.,University of Louisiana at Lafayette
Lecture Notes in Electrical Engineering | Year: 2012

The trend in oil companies nowadays is to integrate the entire well sensors (modern and legacy sensors) with wireless sensor network (WSN). In this work, we introduced a new framework from such sensors using a heterogeneous network of sensors taking in our consideration the WSN's constraints. The framework combined two modules: a Wireless Sensor Data Acquisition (WSDA) module and a Central Data Fusion (CDF) module. A test bed was established from ten acoustic sensors mounted on a closed loop pipeline. The flow rate and the differential pressure were monitored as well. The CDF module was implemented in the gateway using four fusion methods; Fuzzy Art (FA), Maximum Likelihood Estimator (MLE), Moving Average Filter (MAF) and Kalman Filter (KF). The results show that the KF fusion method is the most accurate method. Unlike the other methods, Kalman filter algorithm does not lent itself for easy implementation; this is because it involves many matrix multiplication, division and inversion. Among these 17 matrix operations, there are 10 matrix multiplications, 2 matrix inversions, 4 matrix additions and 1 matrix subtraction. Moreover, these tasks are computationally intensive and strain the energy resources of any single computational node in a WSN. In other words, most sensor nodes do not have the computational resources to complete a central KF task repeatedly. Furthermore, the computational complexity of the centralized KF is not scalable in terms of the network size. © 2012 Springer Science+Business Media, LLC.


Abdelgawad A.,54 Lavoie Drive | Bayoumi M.,University of Louisiana at Lafayette
Lecture Notes in Electrical Engineering | Year: 2012

In this chapter, the DKF problem is addressed by reducing it into a dynamic consensus problem in term of weighted average estimates matrix that can be viewed as data fusion problem. We have presented a Distributed Kalman Filter based on polynomial filter to accelerate the distributed average consensus in the static network topologies. The proposed algorithm performs closely to the central filter, and also reduces the filter complexity at each node by reducing the dimension of the data. Thus, it scales computational complexity. Being based on sending only the estimates between neighbors, it also reduced radically the communication requirements. The proposed DKF contributes to significant energy saving. © 2012 Springer Science+Business Media, LLC.


Abdelgawad A.,54 Lavoie Drive | Bayoumi M.,University of Louisiana at Lafayette
Lecture Notes in Electrical Engineering | Year: 2012

An efficient and low-power multiplication algorithm has been proposed in this chapter. It reduces the number of add operations during multiplication by rounding any sequence of 1s in the fractional part. The impact of using the proposed multiplication method on FIR and IIR filters response has been studied. Experimental results show that the proposed algorithm achieves up to 17% power saving and 16% increasing in speed, with only 1% accuracy loss compared to Horner's algorithm. The new multiplication method has been validated experimentally using the eZ430-RF2500 wireless sensor board. In the next chapter, we will study the impact of using the proposed multiplication method on the power consumption of the proposed DKF. © 2012 Springer Science+Business Media, LLC.


Abdelgawad A.,54 Lavoie Drive | Bayoumi M.,University of Louisiana at Lafayette
Lecture Notes in Electrical Engineering | Year: 2012

This chapter has briefly discussed the need of the DKF and introduced the literature work of the DKF. Most DKF methods proposed in the literature rely on consensus filters algorithm. The convergence rate of such distributed consensus algorithms typically depends on the network topology and the weights given to the edges between neighboring sensors. The next chapter proposes a low power DKF. The proposed DKF is based on a fast polynomial filter to accelerate distributed average consensus in static network topologies. The idea is to apply a polynomial filter on the network matrix that will shape its spectrum in order to increase the convergence rate by minimizing its second largest eigenvalue. Fast convergence can contribute to significant energy saving. © 2012 Springer Science+Business Media, LLC.

Loading 54 Lavoie Drive collaborators
Loading 54 Lavoie Drive collaborators