Abdulfattah Mohammad Obeid

A Proposed Scalable Design and Simulation of Wireless Sensor Network-Based Long-Distance Water Pipeline Leakage Monitoring System

Anomalies such as leakage and bursts in water pipelines have severe consequences for the environment and the economy. To ensure the reliability of water pipelines, they must be monitored effectively. Wireless Sensor Networks (WSNs) have emerged as an effective technology for monitoring critical infrastructure such as water, oil and gas pipelines. In this paper, we present a scalable design and simulation of a water pipeline leakage monitoring system using Radio Frequency IDentification (RFID) and WSN technology. The proposed design targets long-distance aboveground water pipelines that have special considerations for maintenance, energy consumption and cost. The design is based on deploying a group of mobile wireless sensor nodes inside the pipeline and allowing them to work cooperatively according to a prescheduled order. Under this mechanism, only one node is active at a time, while the other nodes are sleeping. The node whose turn is next wakes up according to one of three wakeup techniques: location-based, time-based and interrupt-driven. In this paper, mathematical models are derived for each technique to estimate the corresponding energy consumption and memory size requirements. The proposed equations are analyzed and the results are validated using simulation.

A review on wireless sensor network for water pipeline monitoring applications

Wireless sensor networks (WSNs) have gained a lot of attention from researchers both from academia and industry during the past decade. This key technology enables a wide range of potential applications and services including monitoring of physical environments, enhanced industrial control, surveillance, remote health care and logistics. Real-time monitoring of water pipeline network is one such application where WSN plays significant role. The issue of water is considered to be one of the largest and most serious challenges. It is expected to aggravate over time, given the scarcity of available traditional water resources and the massive costs of providing fresh potable water from non-traditional sources such as desalination plants. Therefore, a robust and reliable WSN technique is required to monitor leaks, bursts and other anomalies in the water pipeline systems. This paper presents a consolidated review on WSN for water pipeline monitoring applications.

A review on wireless sensor node architectures

During the last few years, WSN (Wireless Sensor Network) has emerged and deployed in a wide range of applications attracting many research and industrial communities. Low power is one such crucial constraint where these systems are supplied with small battery. Several technologies and architectures have been proposed for design ultra-low power motes. In this paper, we present a survey on wireless sensor node architectures proposed to optimize the performances in particular real-time, low power, cost and time to market. Despite their variety, these architectures are consisted basically of processing unit, acquisition unit, transmission unit and power supply unit. The difference resides on components and technologies that constitute these units. Each technology is discussed in relation to its advantages and limitations.

EARNPIPE: A Testbed for Smart Water Pipeline Monitoring Using Wireless Sensor Network☆

Abstract Large quantities of water are wasted daily due to leakages in pipelines. In order to decrease this waste and preserve water, advanced systems could be used. In this context, a Wireless Sensor Network (WSN) is increasingly required to optimize the reliability of the inspection and improve the accuracy of the water pipeline monitoring. A WSN solution is proposed in this paper with a view to detecting and locating leaks for long distance pipelines. It combines powerful leak detection and localization algorithms and an efficient wireless sensor node System on Chip (SoC) architecture. In fact, a novel hybrid Water Pipeline Monitoring (WPM) method has been proposed using Leak detection Predictive Kalman Filter (LPKF) and Modified Time Difference of Arrival (TDOA) method based on pressure measurements. The data collected from sensors are filtered, analyzed and compressed with the same Kalman Filter (KF) based algorithm instead of using various algorithms that deeply damage the battery of the node. The local low power pre-processing is efficient to save the power of the sensor nodes. Moreover, a laboratory testbed has been constructed using plumbing components and validated by an ARM-based prototyping platform with pressure sensors.

Outlier detection approaches for wireless sensor networks: A survey

Over the past few years, wireless sensor networks have gained significant attention. They have been distributed in the real world in order to collect valuable raw sensed data. Due to high density, WSNs (Wireless Sensors Networks) are exposed to faults and nasty attacks. Likewise, the sensors readings are inaccurate and unreliable, which make Wireless Sensor Networks vulnerable to outliers. Abnormal data, outliers or anomalies are usually considered to be those sensor readings that have deviated significantly from normal behavior. However, the challenge is to ensure data quality, secure monitoring and reliable detection of interesting and critical events. In this survey, we describe a comprehensive overview of existing outlier detection techniques specifically used for the wireless sensor networks. Moreover, we present a comparative table used as a guideline to select which technique is adequate for the application in terms of characteristics such as detection mode, architectural structure and correlation extraction.

Energy efficient adaptive transmission strategy using cooperative diversity for wireless sensor networks

Energy saving strategies pose challenges for wireless Sensor Networks communication, as the nodes have limited energy resources and are battery powered. Thus, there is a strong interest in developing energy efficient transmission techniques. In this paper, we present a new approach to minimize the energy consumption of clustered wireless sensor networks, while achieving the required level of reliability. We consider clusters where sensors transmit monitored data to nearby cluster-head using cooperative communication. The goal of our approach is to minimize the transmit power energy based on Bit Error Probability (BEP) criterion for selective digital relaying (SDR) schemes whose closed form expression was recently derived in previous works. Therefore, the proposed scheme can efficiently allocate the least transmission power among the source and the relays that satisfy the predefined level of reliability while decreasing the energy consumption of the node. Simulation results show that our relay selection algorithm achieves significant energy savings (more than 50% compared with direct transmissions for moderate SNR values and up to 90% in high SNR regions).

Flexible reconfigurable architecture for DSP applications

As the flexibility offered by fine-grained field programmable gate array (FPGA) comes at a significant cost of area, speed, and power, there is a trend to use coarse-grained architecture (CGA) for dataflow applications requiring high computational resources. Existing CGA solutions are characterized according to the general organization, the processing element architecture, the basic interconnect structure and their reconfiguration characteristics. Based on the study of several digital signal processing (DSP) applications and their implemented VLSI architectures, we propose a CGA with parameterizable and flexible blocks based on a generic matrix and interconnected by a configurable network. The proposed architecture provides a good tradeoff between flexibility and performance-density. A CAD tool is developed to automate the implementation of the design on the architecture. A synthesizable VHDL code generator is also developed in order to further explore and validate the proposed architecture. The unification of coarse-grained logic block and bus-based interconnection bridges the gap between application-specific integrated circuit (ASIC) and CGA, resulting in area reduction from 40× to 6×, respectively. Experimental results demonstrate the performance and efficiency of the proposed architecture to implement DSP designs.

A Cost-Effective Dynamic Partial Reconfiguration Implementation Flow for Xilinx FPGA

Reconfigurability of Field Programmable Gate Array (FPGA) makes it one of the most promising approaches in the implementation of reconfigurable systems. Partitioning the reconfigurable system to many Reconfigurable Modules (RMs) and allocating them into Reconfigurable Regions (RRs) on the FPGA is a challenging task for the system designer. Partitioning choices impact the area efficiency and the time of reconfiguration of the reconfigurable systems. In this paper, different partitioning techniques are studied and evaluated according to their impact on reconfiguration time and the area utilization. Also, a new proposed Dynamic Partial Reconfiguration (DPR) tool flow is presented that automates and optimizes the partitioning procedure based on a graph clustering algorithm, modifies the designs HDL files as per the partitioning results, and implements a routing switch to dynamically change routing between Reconfigurable Regions (RRs) during reconfiguration.

One class outlier detection method in wireless sensor networks: Comparative study

Recent advances in communication technology have enable the emergency of new types of wireless networks: Wireless Sensor Networks (WSN). It consists of a huge number of tiny and low cost devices with sensing and communication capabilities. They are emerging recently as a key solution to monitor remote environments and concern a wide range of applications from the environmental and military surveillance to home automation. However, these models are not suitable for the energy constrained WSNs because they assumed the whole data is available in a central location for further analysis. In this paper, we present a comparative study between Centralised and Distributed One-class Outliers Detection Classifier (COODC & DOODC) based on Mahalanobis Kernel used for outlier detection in wireless sensor networks (WSNs). For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. Recently, Kernel Principal component analysis (KPCA) has used for nonlinear case which can extract higher order statistics. On account of the attractive capability, KPCA-based methods have been extensively investigated, and have showed excellent performance. Within this setting, we propose Kernel Principal Component Analysis based Mahalanobis kernel as a new outlier detection method using Mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate outlier points from normal pattern of data distribution. The use of Distributed One-class Outliers Detection Classifier based on Mahalanobis Kernel on real word data obtained from Intel Berkeley are reported showing that the proposed method performs better in finding outliers in wireless sensor networks when compared to the Centralised One-class Outliers Detection Classifier (COODC).

Scalable Design of Microprogrammed Digital FIR Filter for Sensor Processing Subsystem

In this letter, a novel scalable and modular design of direct form sequential finite impulse response (FIR) filter using microprogrammed control unit is proposed that can be efficiently realized in field programmable gate array (FPGA) or application specific integrated circuit (ASIC). The proposed design is suitable for sensor processing subsystem used in wireless sensor network (WSN) nodes. This is demonstrated by evaluating a sample 4-tap FIR filter on various FPGA platforms and ASIC technologies. The evaluation result shows good area/power efficiency and flexibility by using microprogrammed architecture for such applications.