A Study on Wireless Sensor Network Deployment and ...

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Because of their inherent and other advantages, Wireless. Sensor Nodes have been deployed for environmental monitoring applications especially in gas fields.
Journal of Communication Engineering & Systems ISSN: 2249-8613(online), ISSN: 2321-5151(print) Volume 6, Issue 3 www.stmjournals.com

A Study on Wireless Sensor Network Deployment and Lifetime Maximization of Wireless Sensor Nodes in Natural Gas Pipeline Monitoring System Md. Fahad Monir1, Sarjo Das1,*, Priyankar Roychowdhury2 1

School of Information and Communication Technology, KTH Royal Institute of Technology, Stockholm, Sweden 2 Department of Electrical and Computer Engineering, The George Washington University, Washington, DC, USA

Abstract Leakage in Natural Gas Pipeline is a very common problem that may lead to economic losses, environmental hazards and pollution. Therefore, the maintenance and security monitoring of the natural gas pipeline has remained a big concern for many years. In the recent times, implementation of Wireless Sensor Networks (WSNs) has become very popular. This is due to the low cost and sustainability of WSNs. Since, the implementation of WSNs in different applications has increased rapidly; the energy consumption of WSN’s has also become an important part of study as nodes are powered by battery. In this paper, an architectural model of WSN implementation will be presented. This model can be used for identifying the leakage or other kind of damage monitoring in gas/oil pipelines as well as a process of lifetime maximization of the operating nodes. Moreover, we have studied the effectiveness of equal distance placement scheme, based on Ideal power model and Tmote power model, for WSN’s lifetime maximization in gas pipeline monitoring system. Keywords: Wireless sensor networks, gas pipeline monitoring, WSN application, linear sensor placement, lifetime maximization

*Author for Correspondence E-mail: [email protected]

INTRODUCTION Natural Gas is a type of fossil fuel that is used extensively to generate energy. As the demand of energy is increasing day by day, oil and natural gas producing countries are ramping their production and building up infrastructures. Based on the reference case of International Energy Outlook 2016 (IEO2016), the consumption of natural gas increases by 1.7% per year in average for industrial use and 2.2% per year in electric production. It has been projected that the total worldwide consumption of natural gas will be increased by approximately 95 trillion cubic feet (Tcf) in a time frame of 2012–2040. As a result, natural gas producers, through the worldwide, increase their production and the supplies is predicted to be raised by 69% by 2040 (as shown in Figures 1 and 2) [1]. Natural gas production industries always seek for a safe, economical and stable infrastructure

for their industries. An important component of this infrastructure is oil/gas pipelines. There is a need for a system that can monitor the gas pipeline 24*7 for security purpose and to detect any accidental leakages [2]. This system can be implemented, using Wireless Sensor Network that will help to prevent wastage of precious resources and will allow uninterrupted flow of gas through the pipeline. WSNs have already been used for many useful applications. For example, WSNs have already achieved a great success in natural gas pipeline monitoring systems [3–5]. Because of their inherent and other advantages, Wireless Sensor Nodes have been deployed for environmental monitoring applications especially in gas fields. Such a power efficient monitoring system will help a nation protect its assets and also help to prevent accidents or disasters and thus avoiding environmental pollution.

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WSN and its Lifetime Maximization in Natural Gas Pipeline Monitoring System

Fig. 1: World Natural Gas Consumption and Prediction, 2012–40 (trillion cubic feet).

WIRELESS SENSOR NETWORK FOR PIPELINE MONITORING AND CONTROL Over the past decade, wireless sensor networks have achieved a great success in pipeline monitoring system [6]. There are still many studies being carried on in this field, for building better wireless networks for structural monitoring (in term of scalability, redundancy, performance, safety, security and manageability) [7]. Some researchers examined WSNs implementations in various types of monitoring systems [8]. Various types of models have been proposed by different researchers in order to make this monitoring system more feasible. In Ref. [9], authors discussed about the current technologies of WSNs and its potentiality based on different aspects. Pakzad et al. came with an integrated software and hardware system design of wireless sensor network (WSN) in which they have developed an accelerometer sensor node for modal identification and structural vibration monitoring [10]. Sun et al. introduced a WSN architecture for pipeline monitoring that is magnetic induction-based [11]. Based on this architecture, sensors were placed both inside and outside of the pipeline for detecting and localizing leakages in underground oil/gas pipelines. In Ref. [12–15], the ground penetrating radar (GPR) system is proposed as a pipeline monitoring technique for detecting the underground pipeline leakages. GPS can find

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Fig. 2: World Increase in Natural Gas Production by Country Grouping, 2012–40 (trillion cubic feet). out the exact point of the leakage in underground pipeline without digging. GPR could be installed in pipelines by integrating with portable devices. However, this method needs intensive human involvement. Furthermore, leakages cannot be detected in time as the monitoring is not real-time based. In Ref. [16,17], authors proposed Mass balance methods to detect the leakages. They have proposed to introduce flow sensors inside gas pipelines which can detect the leakage by examining the flow difference of liquids between an upstream and downstream flow. Though, the installing and maintenance cost of mass balance method is very low, however, this technique is not feasible as the rate of error is very high. This is because the flow difference of gas pipeline's liquid could be the reason of other factors such as temperature and density change etc. Jawhar et al. discuss about the use of wireless sensor networks in monitoring and protection of pipelines carrying water, oil, gas etc. [7]. Their paper proposes an architectural framework that can be used for monitoring of pipeline and other control functions. Moreover, their work also provides an overview of the routing protocols required for the necessary communication. Akkas et al. discussed about different types of challenges for underground oil pipeline monitoring system by using electromagnetic wave and wireless sensor networks [18]. They also propose a general framework for continuous monitoring of oil pipeline using wireless

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Journal of Communication Engineering & Systems Volume 6, Issue 3 ISSN: 2249-8613(online), ISSN: 2321-5151(print)

sensor networks. Yu et al. came with a MAC protocol (EEDA-MAC) that is energy efficient and delay aware for monitoring of oil and gas pipeline [19]. Authors also discussed some ways to improve EEDA-MAC protocol that can lead to network’s lifespan prolongation and improve delay performance of the network.

SYSTEM MODEL In our paper, for our work, we are focusing on the leak detection system model of Wan et al. [20]. The Natural Gas Pipeline Sensor Monitoring Networks are usually made up of a huge number of sensor nodes, sink and the management and control center. Along the pipelines the sensor nodes are installed that are responsible for collecting and processing signals data. A multihop route is used to send the processing results to the sink. For message exchange between the sensor nodes, a tube communication mode is used by the

monitoring networks [20]. The sink acts as the cluster leader that is tasked with the management of the sensor nodes in its cluster. It is also responsible for the combining the processing results for the final decision and to localize the position of the leak if a leak has taken place. Finally, the control and management center receives the diagnosis result which judges whether it is required to sound a leak warning alarm [20]. The Leak point localization is accomplished at the sink and this process of localization is divided into the following parts: Signal Grouping We have noticed from the previous related research that the increase in transmission distance causes the energy of the leak signal to decline gradually which is reflected in the average amplitude decrease of the signal waveform.

Fig. 2: Leak Point Localization.

Fig. 3: Integrated Localization of Multinode.

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WSN and its Lifetime Maximization in Natural Gas Pipeline Monitoring System

From Figure 2, if N= no of sensor nodes near the leak point which detects the abnormal signal produced when leak happens, D0=leak point distance from two nearest nodes from both sides X1 and X2, Sensor nodes that detect the leakage send some information (single mode signals, their ID and location) to the base station [20]. From Figure 3, if X1 < X2, The equation used to calculate the RMS amplitude of each signal 1

∆𝑇

𝑉 = √∆𝑇 ∫0 𝑉 2 (𝑡)𝑑𝑡

(1)

∆T = Signal Sampling Time. On comparing all signal’s average amplitude Vi (i=1,2,…,N) Vi > Vi-1 > Vi+1 > Vi-2 > ∙∙∙ > V1 (2) From the Figure 3, node i is the closest to the point of leak so the amplitude of this node is the largest. As node 1 is situated at a distance farthest from the point of leak its amplitude is the smallest. Now, according to the interval grouping criterion the signals are divided into two groups: One group contains signals that sensor nodes collected on the left of the point of leak and the other on the right of the point of leak. U = {Si-1,Si-2, ∙∙∙ S1} (3) W={Si,Si+1, ∙∙∙, SN} (4) where, Si (i-1,2,…,N) denote the signal that went to the base station by node i. Let the signaling number in group U and W is denoted by NU and NW. The no of signal pairs obtained is: NP=NU*NW=(i-1)*(N-i+1) (5) Determination of Time Difference As the original leak signal is a type of continuity signal, it is very difficult to obtain the time exactly when the nodes received the leak signals. So cross correlation analysis of signals is used to get the time difference of the signals arriving from at the upstream and the downstream nodes. 1 𝑡 RAB(τ)=𝑇 ∫0 𝐴(𝑡)𝐵(𝑡 + 𝜏)𝑑𝑡 (6) where, RAB(τ) is the cross correlation function between wave A(t) and wave B(t+τ) with

Monir et al.

delay time τ and the finite time interval is denoted by T. Weighted Average Localization Signal grouping algorithm divides all the single mode signals into NP pairs, which can lead to the generation of NP coordinates based on the correlation time deference localization method [20]. But during signal processing it may generate errors and as result to improve the localization accuracy weighted average localization algorithm is used to find out the leak point’s coordinates. Np

Pleak=∑𝑗=1 𝜇𝑗 𝑃𝑗 (1≤ 𝑗 ≤ N𝑝 ) Pleak represents the final leak point coordinate, Pj denotes position coordinate of leak point calculated by signals in pair j, μj is the weight factor, which signifies credibility of leak point position determined by signals in pair j. Monitoring System The sensor board of the nodes contains temperature, acceleration, magnetic sensors and light sensors. The sensor board should be connected with the radio node (Figure 4). The conversion of analog signals to digital form is carried out in the sensor board by microprocessors. It ensures that the data has been taken in correct interval, the data are processed and packaged into messages and then the message is routed via radio hardware. For programming wireless nodes, an opensource platform (TinyOS operating system) with nesC computer language has been used. Publicly available software is used to set up the sensors in wireless mesh network [6]. This provides the algorithms required for operating the radio and routing messages. The monitoring model is organized based on the works of Jang et al. and Yu and Guo [6,19]. Yu and Guo proposed three tier network architecture for pipeline data collection [19]. The first tier consists of pipeline sensor nodes (PSN), second layer consists of intermediate stations sink nodes (ISSN) and in final layer we have pipeline monitoring control center as shown in Figure 5.

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Journal of Communication Engineering & Systems Volume 6, Issue 3 ISSN: 2249-8613(online), ISSN: 2321-5151(print)

In primary layer, nodes will be physically installed by the equal distance placement with Tmote power model. Sensor nodes are divided into groups. Pipeline sensor nodes are appointed to sense the real time information from pipeline such as quantity of flow, oil and gas leak, pressure, wax precipitation, pipeline corrosion and environmental pollution etc. Nodes will be grouped in a zone. The data will be collected by each zone that is awake and an average value will be extracted, which will be transmitted to the data collection center. This mechanism is followed in order to minimize the transmitted data which can play an important role in energy saving. Distance between two neighboring nodes should be smaller than the transmitting range of nodes.

Nevertheless, a number of nodes will be installed in the transmission range of each node in order to enhance the reliability of the sensor nodes which may also play an important role to avoid network failure. In the middle tier, ISSN will be used. Here, each ISSN has the ability of computing and resource storage compared to PSN. The main jobs of ISSN are examining the data coming from PSNs and uploading data to the control center. This data transfer takes place via satellite communication or internet. In the top tier, control center connects all ISSNs by satellite or internet communication. The overall analysis of data takes place in control center [20].

Fig. 4: Wireless Sensor Node and Sensor Board.

Fig. 5: The Architecture of Pipeline Data Collection Algorithm based on WSN [20].

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WSN and its Lifetime Maximization in Natural Gas Pipeline Monitoring System

In the control center for data analysis, a software application is required. A desktop computer can be used for this data collection. The work of Jang et al. is also taken as model to design the database server, where a Java application transforms the digital data into engineering units and then inserts the data in database [6]. It should be taken in concern that all necessary data about the sensor is included. By following the work of Jang et al., a webbased viewer is proposed to be used accessing the data by which, users can view the data through a conventional web browser [6]. In order to build this Web interface, conventional scripting tools could be used for accessing the database and modifications as needed. PHP could be a good example to write down the scripting software for accessing the database and result displaying [20]. Based on their system, user can modify the database and can add more sensors in the network in order to find out the necessary data. There is a menu entry named as “Emergency records”, which can help to find any kind of emergency situation in gas pipeline such as leakage, fire etc.

LIFETIME MAXIMIZATION It is clear that WSNs play a very important role in oil/gas pipeline monitoring systems as WSN based monitoring structures have less maintenance cost, more efficiency and flexibility. However, one of the key challenges of wireless sensor networks is non-uniformity of energy consumption which leads to short lifetime of wireless sensors. Yu and Guo have proposed a data collection algorithm to design an efficient pipeline (oil/gas) monitoring system that increases sensors lifetime in pipeline areas [19]. Different schemes have been proposed by many researchers to maximize WSN’s lifetime [21–23]. The WSN lifetime can be enhanced by balancing the energy consumption in every node rather than having all sensor nodes transmit from the same distance [24]. Taking after this thought, an equivalent power greedy heuristic plan has been considered in [25], where the sensor hubs are unevenly disseminated along the gas pipeline. That is, for sensor hubs that are closer to the base station, a shorter distance for transmission is

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appointed which permits the sensor hubs to transmit at a lower force level, while the sensor hubs that are far from the base station can transmit longer separation to cover the same length of gas pipeline. In our paper, we have analyzed Tmote Power Model and Ideal Power Model for pipeline monitoring and lifetime maximization [26]. We have studied and summarized the equaldistance and equal power sensor placement schemes that have been presented by Guo et al. [26]. They have investigated the lifetime maximization of WSNs in oil/gas pipeline monitoring by examining the influence of linear sensor placement. According to them, the lifetime of WSN could be boosted significantly if we use right number of sensors in pipeline monitoring.

NORMALIZED LIFETIME OF WSN UNDER EQUAL DISTANCE PLACEMENT In our result part, we have shown the equal distance placement scheme of WSNs based on Ideal power model (which is widely used in today’s wireless sensor networks) and Tmote power model (derived from Tmote sky sensors) [10]. We have analyzed the figures of normalized lifetime of WSN of these two power models (Tmote and Ideal) which has been proposed by Guo et al. [26]. Following four graphs are showing Normalized Lifetime values of WSN against the number of sensor nodes under equal distance placement scheme in four cases [26]: 1. Ideal Power Model L=5000 m 2. Ideal Power Model L=15000 m 3. Tmote Power Model L=5000 m 4. Tmote Power Model L=15000 m The ideal power model has been considered for six different power levels as shown in Table 1. From the Figures 6, 7, 8 and 9, it can be noticed that the lifetime of the WSNs in Ideal Power Model increases up to a certain limit with the increase in number of sensor nodes and then it decreases gradually. It implies that once the sensor’s number increases and motes can transmit at basis of the next junior power level. But, if the increased number of the WSN

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Journal of Communication Engineering & Systems Volume 6, Issue 3 ISSN: 2249-8613(online), ISSN: 2321-5151(print)

sensors is not enough to transmit at more low power level, the life time of the sensors decreased steadily [10]. For Tmote Power Model, lifetime decreases with the increase in number of sensor nodes though sensor nodes

can transmit at lower power levels. This is because the distance and transmission power of Tmote power model is sublinear whereas it is quadratic or cubical for the ideal power model [10].

Table 1: Transmission Power and Range for Tmote Sky Sensors [27]. Levels

1

2

3

4

5

6

Transmission range (meters)

5.49

15.85

39.01

60.96

71.02

87.48

Transmission Power (mW)

33.1

39.6

45.0

51.1

57.2

61.9

Fig. 6: Normalized Lifetime for the Ideal Power Model (L=5000 m).

Fig. 7: Normalized Lifetime for the Ideal Power Model (L=15000 m).

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WSN and its Lifetime Maximization in Natural Gas Pipeline Monitoring System

Monir et al.

Fig. 8: Normalized Lifetime for the Tmote Power Model L=5000.

Fig. 9: Normalized Lifetime for the Tmote Power Model L=15000.

In this paper, we have studied different architectural models of WSN based oil/gas pipeline monitoring system. Our paper work elaborates an architectural model for continuously monitoring gas pipeline using wireless sensor networks by reviewing the models of Jang et al. and Yu and Guo [6,19].

implemented to remotely perform continuous monitoring of gas pipeline. After that, we studied different architectural models for lifetime maximization of WSNs that have been proposed by different researchers. We illustrate the equal-distance sensor placement models that have been proposed by Guo et al. [26].

We have discussed the ways in which the data will be collected from the field level and will be delivered towards control room for examining any kind of fault report. The model explained in this paper gives an alternative way in which wireless sensor nodes can be

Based on their evaluation, lifetime is optimized only when if minimum number of sensor nodes got deployed. The lifetime decreases if more sensor nodes are being added based on the equal-distance placement scheme.

CONCLUSIONS

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ACKNOWLEDGEMENT We would like to thank Dr. Thomas Lind of KTH Royal Institute of Technology for his valuable suggestions and feedback that he provided during the development of this paper. This idea was first presented by Sarjo Das and Md. Fahad Monir in a seminar for the Subject: EP2950 Wireless Networks at KTH Royal Institute of Technology in December 2015. REFERENCES 1. Independent Statistics & Analysis. U.S. Energy Information Administration, International Energy Outlook May 2016. Available from: www.eia.gov/forecasts/ieo/pdf/0484(2016) .pdf (Last accessed 21st Aug 2016) 2. Petersen S, Doyle P, Vatland S, et al. Requirements, drivers and analysis of wireless sensor network solutions for the Oil & Gas industry. In IEEE Conference on Emerging Technologies and Factory Automation (EFTA 2007), 2007 Sep 25, 219–226p. 3. Allen CO. Wireless sensor network nodes: security and deployment in the niger-delta oil and gas sector. Int J Netw Secur Appl. (IJNSA), January 2011; 3(1): 68–79p. 4. Fasasi T, Maynard D, Nasr H, et al. Wireless sensors remotely monitor wells in Nigeria swamps. Oil Gas J. 2005; 103(18): 49–52p. 5. Jawhar I, Mohamed N, Shuaib K. A framework for pipeline infrastructure monitoring using wireless sensor networks. In Wireless Telecommunications Symposium, 2007. 2007 Apr 26, 1–7p. 6. Jang WS, Healy WM, Skibniewski MJ. Wireless sensor networks as part of a webbased building environmental monitoring system. Automat Constr. 2008 Aug 31; 17(6): 729–36p. 7. Chang KD, Chen JL. A survey of trust management in WSNs, internet of things and future internet. KSII T Internet Inform Syst. (TIIS). 2012 Jan; 6(1): 5–23p. 8. Healy WM. Lessons learned in wireless monitoring. ASHRAE J. 2005 Oct 1; 47(10): 54p. 9. Khan S, Pathan AS, Alrajeh NA, editors. Wireless Sensor Networks: Current Status

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http://www.moteiv.com/products/tmot esky.php, 2007. (Last accessed 21st Aug 2016). Cite this Article Md. Fahad Monir, Sarjo Das, Priyankar Roychowdhury. A study on Wireless Sensor Network deployment and Lifetime Maximization of Wireless Sensor Nodes in Natural Gas Pipeline Monitoring System. Journal of Communication Engineering & Systems. 2016; 6(3):

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