Ahmed Abdelgawad

Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks

This book introduces resource-aware data fusion algorithms to gather and combine data from multiple sources (e.g., sensors) in order to achieve inferences. These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. The algorithms described in this book are evaluated with simulation and experimental results to show they will maintain data integrity and make data useful and informative. Describes techniques to overcome real problems posed by wireless sensor networks deployed in circumstances that might interfere with measurements provided, such as strong variations of pressure, temperature, radiation, and electromagnetic noise; Uses simulation and experimental results to evaluate algorithms presented and includes real test-bed; Includes case study implementing data fusion algorithms on a remote monitoring framework for sand production in oil pipelines.

Data Fusion in WSN

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.

Remote Measuring of Flow Meters for Petroleum Engineering and Other Industrial Applications

Reliable remote measuring of flow meters for the petroleum gas industry is proposed in this work. The monitoring of flow rates and the total amount of the fluid flow is collected using a manual process. The main goal of this work is to implement a mechanism that avoids human error and achieves reliable, continuous, and accurate monitoring. We employed the NuFlo Measurement System Model MC-II Flow Analyzer to prototype our monitoring mechanism for measuring the liquid flow and a Crossbow Technology MICA2 mote and MDA300CA Data Acquisition Board to transmit collected data via a wireless sensor network (WSN). The flow analyzer generates a pulse signal whose frequency depends on the flow rate. The mote is used to count the number of pulses and send it to the host computer. An amplifier lets the mote detect the voltage level differences and overcome signal weakness. The host computer stores the data received from the mote into a PostgreSQL database for use in preparing Excel sheets and graphical displays in real time. The flow rate and the total flow amount collected by the host computer match those shown on analyzer. The design and implementation of our prototype serves as a proof of concept of how existing analog sensors used to monitor the flow rate and volume of the oil and water in petroleum production can be integrated with other devices in a WSN.

Remote Measuring for Sand in Pipelines Using Wireless Sensor Network

Sand production is considered one of the major problems facing the petroleum industry since a small amount of sand in the produced fluid can result in significant erosion in a very short time period. Installation of a system to monitor and quantify sand production from a well would be valuable to assist in optimizing well productivity and to detect sand as early as possible. In this paper, we present a framework for sand detection and sand production rate measurement. The framework combines two modules: 1) a wireless sensor data acquisition (WSDA) module and 2) a central data fusion (CDF) module. The framework is designed to collect data from oil pipeline using acoustic sensors (SENACO AS100), flow analyzer (MC-II), and differential pressure transmitter (EJA110A) in real time. A test bed is established from ten acoustic sensors mounted on a closed-loop pipeline. The flow rate and the differential pressure are monitored as well. The sand is injected in the test bed with a constant flow and pressure. The output of the acoustic sensor is analyzed in order to calculate the sand production rate. The sand rate, flow rate, and pressure are digitized for wireless transmission using the WSDA module. The data are collected in the gateway, i.e., a laptop in our case. The CDF module is implemented in the gateway. The purpose of data fusion is to improve the system performance. Three different fusion methods, fuzzy art, maximum-likelihood estimator, and moving average filter are evaluated throughout the simulation and experimental results. The proposed framework is successfully tested and evaluated.

Low-power distributed Kalman filter for wireless sensor networks

Distributed estimation algorithms have attracted a lot of attention in the past few years, particularly in the framework of Wireless Sensor Network (WSN). Distributed Kalman Filter (DKF) is one of the most fundamental distributed estimation algorithms for scalable wireless sensor fusion. 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. This paper proposes a low-power DKF. The proposed DKF is based on a fast polynomial filter. The idea is to apply a polynomial filter to the network matrix that will shape its spectrumin order to increase the convergence rate by minimizing its second largest eigenvalue. Fast convergence can contribute to significant energy saving. In order to implement the DKF in WSN, more power saving is needed. Since multiplication is the atomic operation of Kalman filter, so saving power at the multiplication level can significantly impact the energy consumption of the DKF. This paper also proposes a novel light-weight and low-power multiplication algorithm. The proposed algorithm aims to decrease the number of instruction cycles, save power, and reduce the memory storage without increasing the code complexity or sacrificing accuracy.

High Speed and Area-Efficient Multiply Accumulate (MAC) Unit for Digital Signal Prossing Applications

A high speed and area-efficient merged multiply accumulate (MAC) units is proposed in this work. To realize the area-efficient and high speed MAC unit proposed in this work, first we examine the critical delays and hardware complexities of conventional MAC architectures to derive at a unit with low critical delay and low hardware complexity. The new architecture is based on binary trees constructed using a modified 4:2 compressor circuits. Reducing the overall area is achieved by the full utilization of the compressors instead of putting zeros in free inputs. Increasing the speed of operation is achieved by avoid using the modified compressor in the critical path. Feeding the bits of the accumulated operand into the summation tree before the final adder helps to increase the speed too. The proposed MAC unit and the previous merged MAC unit are mapped on a field programmable gate array (FPGA) chip, in order to compare between them. The simulation result shows that the proposed system for 8-bit, 16-bit, and 32-bit MAC unit reduces area by 6.25%, 3.2 %, and 2.5% and increases the speed by 14%, 16%, and 19% respectively. The experimental test for the proposed 8-bit MAC is done using XESS demo board (XSA-100, Spartan-X2S100tq144).

Internet of Things (IoT) Platform for Structure Health Monitoring

Increase in the demand for reliable structural health information led to the development of Structural Health Monitoring (SHM). Prediction of upcoming accidents and estimation of useful life span of a structure are facilitated through SHM. While data sensing is the core of any SHM, tracking the data anytime anywhere is a prevailing challenge. With the advancement in information technology, the concept of Internet of Things (IoT) has made it possible to integrate SHM with Internet to track data anytime anywhere. In this paper, a SHM platform embedded with IoT is proposed to detect the size and location of damage in structures. The proposed platform consists of a Wi-Fi module, a Raspberry Pi, an Analog to Digital Converter (ADC), a Digital to Analog Converter (DAC), a buffer, and piezoelectric (PZT) sensors. The piezoelectric sensors are mounted as a pair in the structure. Data collected from the piezoelectric sensors will be used to detect the size and location of damage using a proposed mathematical model. Implemented on a Raspberry Pi, the proposed mathematical model will estimate the size and location of structural damage, if any, and upload the data to Internet. This data will be stored and can be checked remotely from any mobile device. The system has been validated using a real test bed in the lab.

An Application-Driven Modular IoT Architecture

Building upon the advancements in the recent years, a new paradigm in technology has emerged in Internet of Things (IoT). IoT has allowed for communication with the surrounding environment through a multitude of sensors and actuators, yet operating on limited energy. Several researchers have presented IoT architectures for respective applications, often challenged by requiring major updates for adoption to a different application. Further, this comes with several uncertainties such as type of computational device required at the edge, mode of wireless connectivity required, methods to obtain power efficiency, and not ensuring rapid deployment. This paper starts with providing a horizontal overview of each layer in IoT architecture and options for different applications. Then it presents a broad application-driven modular architecture, which can be easily customized for rapid deployment. This paper presents the diverse hardware used in several IoT layers such as sensors, embedded processors, wireless transceivers, internet gateway, and application management cloud server. Later, this paper presents implementation results for diverse applications including healthcare, structural health monitoring, agriculture, and indoor tour guide systems. It is hoped that this research will assist the potential user to easily choose IoT hardware and software as it pertains to their respective needs.

Low power multiply accumulate unit (MAC) for future Wireless Sensor Networks

Wireless Sensor Network (WSN) presents significant challenges for the application of distributed signal processing and distributed control. These systems will challenge us to apply appropriate techniques to construct capable processing units with sensing nodes considering energy constraints. Digital Signal Processing (DSP) is one of the capable processing units, but it is not commonly used in WSN because of the power constraint. The Multiply-Accumulate Unit (MAC) is the main computational kernel in DSP architectures. The MAC unit determines the power and the speed of the overall system; it always lies in the critical path. Developing high speed and low power MAC is crucial to use DSP in the future WSN. In this work, a fast and low power MAC Unit is proposed. The proposed architecture is based on examination of the critical delays and hardware complexities of merged MAC architectures to design a unit with a low critical path delay and low hardware complexity. The new architecture reduces the hardware complexity of the summation network, thus reduces the overall power. Increasing the speed of operation is achieved by feeding the bits of the accumulated operand into the summation tree before the final adder instead of going through the entire summation network. The ASIC implementation of the proposed 32-bit MAC unit saves 5.5% of the area, 9% of the energy, and reduces the delay by 13% compared to the regular merged MAC unit.

Target localization in Wireless Sensor Network based on Time Difference of Arrival

One of the most prominent challenges in Wireless Sensor Network (WSN) is target localization. As majority of the decisions made in navigation and path planning are dependent on current information available, target localization is one of the fundamental requirements. This paper presents accuracy studies on target localization using the Time Difference of Arrival (TDOA) method. An evaluation of the TDOA is presented through a random reference node by using four stationary sensors in a sensor network. Some of the fundamental advantages in the presented method are its simplicity through requiring only four reference nodes, tolerating errors in node positioning and time differences. Simulations results show that proposed TDOA method outperforms the centroid TDOA method in different test environments, with an average localization error of 2.36 m.