3rd IEEE International Workshop on SmArt COmmunications in NEtwork Technologies
A Novel Wireless Sensor and Actor Network Framework for Autonomous Monitoring and Maintenance of Lifeline Infrastructures Muhammad Imran1, Mohamed A. Alnuem2, Waleed Alsalih2, Mohamed Younis3 1
Deanship of e-Transactions and Communication, King Saud University, Saudi Arabia,
[email protected] College of Computer and Information Sciences, King Saud Univ., Saudi Arabia, {malnuem, wsalih}@ksu.edu.sa 3 Dept. of Computer Science & Electrical Eng., University of Maryland Baltimore County, USA,
[email protected] 2
Abstract-This position paper introduces a novel wireless sensor and actor network (WSAN) framework for autonomous monitoring and maintenance of pipe and power line (oil, gas, water, electricity) infrastructures in an efficient and cost-effective manner. The main focus is on boosting the availability of lifeline infrastructures through advancements in the WSAN technology. First, we categorize and classify the existing lifeline monitoring systems. Second, we identify the requirements for effective and efficient monitoring and maintenance of lifeline infrastructures. Third, we propose a novel WSAN architecture that combines sensing with distributed decision-making and acting capabilities through advanced robotics. Two operational models for the proposed architecture are also presented. The first is a push-up model that employs low-cost, multi-functional sensors along the lifeline to observe certain phenomena of interest, e.g., leakage, ruptures, clogs, etc., in real time and reports to actors over wireless links. The actors process the received data, coordinate with each other in order to identify the most appropriate response. The second is a pull-down model that capitalizes the resources of elite nodes (i.e. actors) in the network. Keywords: Wireless sensor and actor network, Autonomous monitoring and maintenance, Leakage detection.
I.
INTRODUCTION
Due to ever increasing demand, the pipe and power line infrastructures have become the veins of modern societies and economies because they are the most efficient means of transporting indispensable commodities such as oil, gas, water, and electricity. The pipe and power lines carrying these lifeline commodities become the lifeline infrastructures for the economy and modern societies. For example, only oil accounts for more than 90% of the Saudi Arabia's exports and nearly 75% of government revenues. While the world’s demand for water has tripled over the last half-century, Saudi Arabia is the largest reliant on desalinated water in the world transported through long pipelines. Oil and gas pipelines in the US extend over more than 500,000 miles in length. The inexorable population increase, economic growth, prosperity, etc. require the additional deployment of lifeline infrastructures across the world that is already extended over thousands of miles. In most cases, such a massive network of lifelines carrying commodities in various forms passes through hazardous areas and inhospitable terrains that expose the lifeline infrastructures
to various kinds of threats. These threats can disrupt the operation and results in dire consequences. Some recent real world examples include the latest oil spill accident, in Gulf of Mexico [1] and the recent electricity blackouts occurred in the US, UK, Switzerland and Italy in 2003 resulted in billions of dollar of losses besides disturbing the live of the population. The one-day long blackout in the entire north-west of the US in 2003 caused an economic loss of 7 to 10 billion US dollar. Autonomous monitoring and maintenance of these infrastructures in real time is extremely challenging due to the large geographic dispersion, diverse environmental conditions, etc. This requires autonomous, unattended and distributed mechanisms to continuously monitor and detect the presence of anomalies or bottlenecks in the lifeline and report to the nearest available substation so that an appropriate action can be taken. Most of the existing lifeline monitoring and maintenance systems are inefficient, costly and lack responsiveness. In this position paper, we introduce a novel wireless sensor and actor network (WSAN) framework for autonomous monitoring and maintenance of pipe and power line (oil, gas, water, electricity) infrastructures in an efficient and costeffective manner. The prime objective is to boost the availability of lifeline infrastructures through advancements in the WSAN technology. We first classify the existing lifeline monitoring systems. Then, we define the requirements for the autonomous monitoring and maintenance of lifeline infrastructures and propose a novel WSAN architecture that combines sensing with distributed decision-making and acting capabilities through advanced robotics. Two operational models for the proposed architecture are also presented. The first is a push-up model, in which sensors placed along the lifeline continuously monitor, detect and report an event of interest, e.g., leakage, ruptures, etc., through multi-hop communication to actors in the vicinity. The actors such as mobile robots process the received sensors’ data and collaborate with each other in order to determine an appropriate response and identify the most suitable set of actors that will participate in the maintenance operation. The second is a pull-down model that actively engages resourcerich actors and regional control centers to identify the suspicious segments of the lifeline. Then the sensors in those particular segments are only used to confirm and locate the
1 978-1-4577-2053-6/12/$31.00 ©2012 IEEE
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problem. The qualitative comparison with other published schemes shows that the proposed framework would provide distinct features. We believe the proposed framework will help oil, gas, water and electricity companies across the globe to provide reliable services. To the best of our knowledge, this is the first WSAN framework that is specifically designed for monitoring and maintaining pipe and power line infrastructures. The paper is organized as follows. Section II discusses the related work in the literature. The system requirements, the proposed architecture and the operational models are presented in section III. The key advantages of the proposed framework are highlighted in Section IV. Section V concludes the paper and point out our future plan. II.
RELATED WORK
Various technologies have been proposed to monitor, detect, control and maintain lifeline infrastructures. Broadly, we categorize the published pipe and power line monitoring systems into periodic off-line and continuous on-line depending on whether the inspection is performed at regular intervals or in real time, respectively. Periodic Off-line Monitoring: The idea is to perform periodic inspection of lifeline infrastructures in order to detect and locate an anomaly. Some approaches rely on a maintenance crew that is equipped with measurement devices [2] to carry out patrolling in order to determine an anomaly through visual inspection. However, these schemes are very slow, monotonous, very expensive and impractical given that lifeline infrastructures are deployed across thousands of miles while passing through inaccessible and hazardous environments. Some approaches employ helicopters [3] to perform visual inspection but they are expensive and often less accurate. Few schemes have exploited the availability of mobile robots [4] to perform regular inspection of some lifeline infrastructures. Robot-assisted periodic inspection schemes provide various advantages over visual inspection and helicopter-based approaches. Robot-assisted periodic inspection may not be feasible for extremely large scale infrastructure. Most importantly, they are unable to monitor, detect and report an anomaly in real time. Continuous On-line Monitoring: A number of techniques have been proposed to monitor lifeline infrastructures in real time. Most of them are designed with the aim to detect, locate and report pipeline leakage to a remote center through communication networks. Various network architectures have been proposed for reliable transfer of the collected data from the monitoring modules to the control centers. These network architectures were based on wired, wireless or hybrid links. Most of the existing lifelines monitoring systems use copper or fiber cables [5] to connect sensing devices that measure some attributes from the pipeline such as flow rate, temperature, pressure, etc. to the remote control centers. These systems are expensive and difficult to install, and suffer short comings such as physical security, lack of robustness, etc. that may disrupt the communication. Some approaches like [6-9] employ wireless sensor networks (WSNs). The idea is to place sensor nodes along the lifeline to detect an anomaly and report it to
base station via multi-hop communication. Such a flat WSN architecture is not suitable for lifeline infrastructures that require linear placement of sensors over thousands of miles and introduce several issues like uneven energy dissipation, scalability, communication robustness, etc. The large scale deployment necessitates specialized hierarchical network organization, routing, aggregation and fault tolerant schemes. Moreover, sensors in WSNs are unable to interpret the data and perform appropriate action against the detected event. On the other hand, applications like monitoring lifeline infrastructures require autonomous, unattended and intelligent interaction with the environment such that the network is expected to respond to monitored events by performing appropriate actions such as repairing the damage and cutting off the power supply to avoid severe consequences. The requirement for the coexistence of sensors and actors has led to the emergence of a new class of networks capable of performing both sensing and acting on the environment, referred to as wireless sensor and actor networks (WSANs). Unlike a generic WSAN architecture [10], monitoring lifeline infrastructures requires an applicationcentric design that should consider the unique characteristics of these environments. III. WSAN FRAMEWORK FOR AUTONOMOUS MONITORING AND MAINTENANCE OF LIFELINE INFRASTRUCTURES
As mentioned earlier, none of the existing lifeline monitoring and maintenance system has been specifically designed for lifeline infrastructures. In this section, we first enumerate the basic requirements for the application-specific design. Then, we present our proposed application-centric architecture with two operational models. A. System requirements Lifeline infrastructures have been growing with the continuous population increase, economic growth, prosperity, etc. especially in last fifty years. Autonomous monitoring and maintenance of these infrastructures have become a major challenge in recent years due to size, age, cost and growing threats. A particular lifeline monitoring system is mainly expected to perform two functions. First, it should continuously monitor the infrastructure for possible anomaly and report it to the local control center (s). Second, it should immediately respond to such incident and carry out necessary maintenance to prevent negative, and sometimes catastrophic, consequences. The existing pipe and power line monitoring and maintenance systems have their own limitations. To be highly effective, an autonomous monitoring and maintenance system should meet the following design goals: o Application-specific requirements: A generic design might not be suitable for monitoring lifeline infrastructures as they pass through different environments (sea, desert, mountains, etc.) with different configurations (above ground, underground, underwater, overhead). Unlike, most other applications, lifeline infrastructures have linear layout. Therefore, lifeline monitoring system should consider application-specific requirements and constraints.
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o Flexibility: The lifeline monitoring system should be flexible and customizable such that it can accommodate heterogeneous devices and technologies. o Resource optimization: The system design should make efficient resource utilization in order to reduce the deployment, operation and maintenance cost. o Autonomous operation: Given the large geographical area and the often inhospitable surroundings, the monitoring system should support unattended operation. o Energy efficiency: Again, the unattended operation requires the system to be conscious of energy usage and to harvest energy from the environment in order to sustain its operation without human intervention. o Robustness: The system should be able to accurately detect an anomaly or hotspot in real time in order to avoid serious consequences. The system should be able to locate and identify the cause of the problem and take an appropriate action. o Responsiveness: The system should immediately detect and respond to an anomaly before it causes significant damage. o Scalability: As mentioned earlier, lifeline infrastructures are expanded over thousands of miles. Therefore, a lifeline monitoring system should be able to operate at large scale regardless of their size. o Fault tolerance: Given the criticality of the role that the system plays, it should be highly available and sustain its operation despite the failure of some of its components. The unattended operation requires that the system self-heals and continues to serve, probably at a degraded level until additional resources are provided. In other words, fault detection, localization and recovery are to be performed without external intervention. o Backward and forward compatibility: Minimal infrastructure support should be required in order to apply to existing lifeline networks. In addition, the system design should employ open standard and be able adopt advanced technology without a need to redesign. B. Application-centric WSAN architecture As introduced in Section 1, our proposed architecture employs low cost sensors in order to accurately detect, localize and report a problem to nearby actors. The corresponding actors respond to sensors data and perform the appropriate procedure. Both the sensors and actors are complementary to each other for the effective and efficient monitoring and maintenance. Although, sensors can accurately detect the presence of an anomaly, they have limited resources to perform maintenance. On the other hand, actors are more capable nodes that can perform maintenance tasks, yet they cannot ensure the spatial and temporal coverage to detect the presence of an anomaly in real time. We propose a hierarchical architecture that consists of four layers: the sensing layer, the action layer, the regional control center (RCC) layer and the command and control center (CCC) as depicted in Fig. 1. Each layer of the framework is detailed as follows: At the sensing layer, heterogeneous wireless sensor nodes such as pressure, temperature, acoustic, etc. are placed along
the lifeline to detect a phenomenon of interest. The type of sensors depends on the carrier (pipe or power line), commodity (oil, water, gas, and electricity), configuration (overhead, underground, underwater, etc.) and phenomena to be detected (leakage, corrosion, burst, etc.). For example, temperature sensors can be used to monitor, identify and locate the potential problems, e.g., insulator melting or fire in the underground power lines. The sensors collaboratively identify and locate the problem and report it to actors in the vicinity through multi-hop communication. Multiple types of sensors can be employed to confirm the accuracy or avoid false alarms. For instance, in addition to temperature sensors, acoustic sensors can be used to detect sparks and identify potential cause of damage to underground power lines. The temperature and acoustic sensors can partially process the sensed data and send it to the corresponding actor for onward processing and taking appropriate action. Since, sensors have limited energy and communication range, they use multi-hop communication to send their aggregated data to actors as shown in Figure 1. In the action layer, actors with various action capabilities such as fire extinguisher, multimedia, lifeline repair, etc. are placed at the substations. Unlike the typical flat WSN architecture, where sensors directly send their data to the BS either directly or through multi-hop communication, we divide the WSAN into segments with substations at their terminals. The actors are more capable in terms of resources. Therefore, they receive data from sensors, process it, share with peer actors, perform appropriate action (if required) and report to RCC (s) through long-range communication. At the regional control center layer, each station receives the status of lifeline from the actors and sends it to CCC as shown in Figure 1. A generic WSAN architecture may not be suitable for monitoring lifeline infrastructures expanded over thousands of miles in linear configuration. It may not be feasible for the actors to directly report to the base station (BS) or CCC. Therefore, we have introduced an intermediate layer (i.e. RCCs) to locally monitor the status of lifeline and report it to CCC. In addition, the maintenance staff is located at the RCC to assist the actors and/or conduct sophisticated maintenance and repair. Heterogeneous sensor A
Actor-actor communication Wireless multi-hop link
Command and Control Center
Actor
Hotspot Regional Control center
Regional Control center
A
Segment
Substation A
A
A
Lifeline Lifeline right-of-way
Figure 1: An autonomous Wireless Sensor and Actor Network architecture for lifeline monitoring.
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The CCC receives information from the RCCs and maintains the overall status of the lifeline infrastructure. In order to avoid central point of failure, we have proposed the idea of RCCs that interact with the actors located at substations in particular segments of the lifeline. C.
Operational models
This subsection describes the two operational models for the proposed WSAN architecture to realize the basic functionalities of monitoring and maintenance of lifeline infrastructures. In the push-up model, sensors continuously monitor the lifeline and report about the anomaly to the actors for onward processing and taking appropriate action. However, it might not be possible for the sensors to stay operational for the long time with limited energy resources. In addition, replacing failed sensors or replenishing their energy may not be practical. Furthermore, it may not be feasible to deploy redundant sensors with high density. On the other hand, the pull-down model exploits resources of the capable nodes, i.e., the actors and RCC by employing software-based methods to predict the suspicious segments of the lifeline and using sensors only to confirm and locate the problem. Both models are explained below: Push-up model: The following describes how the system would operate in this model: The sensors would detect a problem, such as leakage, corrosion, burst, melting, etc., and report it to the actors in the vicinity through multi-hop communication. While sensing component continuously monitor the lifeline, the transmission and processing circuitry of sensors are kept in sleep mode most of the time to conserve energy. However, as soon as there is an event, a sensor detects it, partially processes the readings, and then forwards them it to the sensor on the routing path leading to the actor in the vicinity. Moreover, sensors that detect the same event will collaboratively identify and locate the problem, and aggregate the data before forwarding it to the actor in order to avoid duplicate transmission. The corresponding actors located at the substation receive event notification from the sensors, process and share it with peer actors in order to determine the most appropriate set of actors that will participate in the maintenance operation. The response of an actor depends on the intensity of the event and the capability of actors [11]. For instance, an actor located at substation immediately cut off the power supply once it receives a report from temperature and acoustic sensors about the blast in the power line. Unmanned vehicles (actors) with on board robots move to the location of the problem. A mobile robot equipped with multimedia capabilities, e.g., [12], may move to the location of the failure for detailed inspection. Thus, multiple robots will get engaged in performing a repair the damaged parts. Recent advancements in technology have enabled robots to carry out autonomous maintenance [13]. Actors periodically report their action to the corresponding RCC which update the status to the CCC. In case of large scale damage such as pipeline breakage, the actors may just take the safety
precautions to limit the spill and notify RCC to send the maintenance crew. The maintenance staff located at the RCC monitors the actors operation and may assist depending upon the scope of the problem. For example, staff may control the robots remotely, i.e. tele-robotics [14, 15]. However, in case of large scale damage, the crew stationed at the RCC may need to move to the site to carry out the require repair. Pull-down model: This model operates as follows: The actors located at terminals (substations) of each segment perform checks and detect unusual patterns. For example in the context of leak detection, actors may continuously quantify the commodity and share with adjacent actors to detect difference between an upstream and downstream flow and use mass balance methods (software based) to determine the leakage [16]. Once the actors observe the abnormal change in flow, they report it to the RCC. The RCC predicts the suspicious segments of the lifeline based on the readings from the actors, and identifies some suspicious segments where lifeline is likely to have leakage or rupture. The RCC alerts the corresponding actors of the suspicious segment (s) by sending a message. Since software based methods, such as mass balance [16] in case of leak detection, cannot accurately detect and locate the problem, the corresponding actors send a wake-up call to the sensors in the segment and request measurements based on more accurate methodology, e.g. [17]. The sensors usually remain in the sleep mode in order to conserve energy and are activated by actor (s) or an event. The sensors wakeup from the sleep mode, collect the data, aggregate it, transmit it to the corresponding actor through multi-hop communication and goes back to sleep mode to conserve energy. The corresponding actors receive data from the sensors, process it and share with peer actors in order to collaboratively identify, locate and localize the problem. The actors coordinate with each other on the appropriate response. The actors involved in the operation will continuously update RCC about the action which further notifies the CCC. IV. QUALITATIVE COMPARISON WITH EXISTING SOLUTIONS We have identified the features or attributes that are desirable for WSAN architecture to effectively monitor and maintain lifeline infrastructures. In this section, we show through qualitative analysis that our proposed architecture have several advantages over contemporary schemes in terms of providing additional features that are not present in existing solutions. The quantitative analysis is the part of our future plan. Table 1 shows the qualitative comparative study in terms of providing various features. This clearly shows that our architecture is more comprehensive and provides several advantages. The most important advantage of our proposed architecture is it provides specialized features that are specifically designed for lifeline infrastructures. The detailed discussion is omitted due to space constraints.
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Table 1: Comparison of various pipeline monitoring schemes PIPENET Jawhar MISE-PIPE SPAMMS Akyildiz [6] [7] [8] [18] [10]
Features Application-specific design Targeted applications
Yes Water pipelines
Flexibility and Customization Resource optimization Autonomous monit & maintenance Energy Clustering efficient Specialized Routing operation Aggregation Active and Reactive Responsiveness (latency-aware) Scalability Fault tolerance Backward compatibility Deployment cost
No No No Yes Yes Yes No No Yes No No High
V.
Yes Oil, gas, water pipelines No No No No Yes No No No Yes Yes No Very high
Yes Underground Pipelines No No No No No No No No Yes No No High
CONCLUSION AND FUTURE WORK
This paper has presented a novel wireless sensor and actor network framework for autonomous monitoring and maintenance of lifeline infrastructures in an efficient and costeffective manner. The goal is to improve the reliability of lifeline infrastructures through advancements in the wireless sensor and actor network technology. We have categorized the existing lifeline monitoring systems and pointed out their shortcomings. In addition, we have identified the basic requirements for the autonomous monitoring and maintenance of lifeline infrastructures. Moreover, we have presented a WSAN architecture that combines sensing with distributed decision-making and acting capabilities through advanced robotics for monitoring and maintenance of lifeline infrastructures. Two operational models for the proposed architecture are also presented and their key advantages have been highlighted. We believe this framework will help oil, gas, water and electricity companies across the globe to provide reliable services. In future, we plan to implement the proposed framework and analyze the performance. REFERENCES [1] Alexandre Santos and M. Younis, "A Sensor Network for Non-Intrusive and Efficient Leak Detection in Long Pipelines," Proc. of the IFIP Wireless Days,Canada, pp. 1-6, 2011. [2] N. H. Ahmed and N. N. Srinivas, "On-line partial discharge detection in cables," in the IEEE Transactions on Dielectrics and Electrical Insulation,vol. 5 (2), pp. 181-188, 1998. [3] Lili Ma and Y. Chen, "Aerial surveillance system for overhead power line inspection," Center for Self-Organizing and Intelligent Systems (CSOIS), Utah State University, 2003. [4] R. Se-gon and C. Hyouk Ryeol, "Differential-drive in-pipe robot for moving inside urban gas pipelines," IEEE Transactions on Robotics, vol. 21(1), pp. 1-17, 2005. [5] W.-W. Lin, "Novel distributed fiber optic leak detection system," Optical Engineering, vol. 43, pp. 278-279, 2004. [6] I. Stoianov, L. Nachman, S. Madden, and T. Tokmouline, "PIPENETwireless sensor network for pipeline monitoring," Proc. of the 6thinternational conference on Information processing in sensor networks, pp. 264 – 273, Cambridge, Massachusetts, 2007.
Yes Oil, gas, water pipelines Yes No Yes No Yes No Yes No No No No Very high
No General applications Yes No Yes No No No Yes No Yes No No Low
Proposed WSAN Yes Pipe and power lines in various configurations Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Low
[7] N. Mohamed and I. Jawhar, "A Fault Tolerant Wired/Wireless Sensor Network Architecture for Monitoring Pipeline Infrastructures," Proc. of the 2nd International Conference on Sensor Technologies and Applications,(SENSORCOMM '08), Cap Esterel, France, pp. 179-184, 2008,. [8] Zhi Sun, Pu Wang, Mehmet C. Vuran, Mznah A. Al-Rodhaan, Abdullah M. Al-Dhelaan, and I. F. Akyildiz, "MISE-PIPE: Magnetic InductionBased Wireless Sensor Networks For Underground Pipeline Monitoring," Ad Hoc Networks, vol. 9 (3), pp. 218–227, 2011. [9] Y. Sunhee, Y. Wei, J. Heidemann, B. Littlefield, and C. Shahabi, "SWATS: Wireless sensor networks for steamflood and waterflood pipeline monitoring," in the IEEE Network, vol. 25(1), pp. 50-56. [10] I. F. Akyildiz and I. H. Kasimoglu, "Wireless sensor and actor networks: research challenges," Ad Hoc Networks, vol. 2(4), pp. 351-367, 2004. [11] M. Imran, A. M. Said, M. Younis, and H. Hasbullah, "Application-centric connectivity restoration algorithm for wireless sensor and actor networks," in Proceedings of the 6 th international conference on Advances in grid and pervasive computing, pp. 243-253, Oulu, Finland, 2010. [12] T. Melodia, "Communication and coordination in wireless multimedia sensor and actor networks," PhD thesis,School of Electrical and Computer Engineering, Georgia Institute of Technology, 2007. [13] Z. Wang, Q. Cao, N. Luan, and L. Zhang, "Development of an autonomous in-pipe robot for offshore pipeline maintenance," Industrial Robot: An International Journal, vol. 37 (2), pp. 177 - 184, 2010. [14] N. Dong, H. Li, H. Gao, L. Wu, T.-J. Tarn, S.-B. Chen, and G. Fang, "An Implementation of Seamless Human-Robot Interaction for Pipeline Welding Telerobotics," Robotic Welding, Intelligence and Automation. vol. 88: Springer Berlin Heidelberg, 2011, pp. 435-442. [15] W. Lin, L. Haichao, G. Hongming, and Z. Guangjun, "Development of Telerobotic System for Remote Welding Operations in Unstructured Environment," Solid State Phenomena, vol. 127, pp. 37-42, 2007. [16] J. C. Martins and J. Paulo Seleghim, "Assessment of the Performance of Acoustic and Mass Balance Methods for Leak Detection in Pipelines for Transporting Liquids," Journal of Fluids Engineering vol. 132 (1), 2010. [17] A. F. Colombo, P. Lee, and B. W. Karney, "A selective literature review of transient-based leak detection methods," Journal of Hydroenvironment Research, vol. 2, pp. 212-227, 2009. [18] K. Jong-Hoon, G. Sharma, N. Boudriga, and S. S. Iyengar, "SPAMMS: A sensor-based pipeline autonomous monitoring and maintenance system," in Proceedings of the 2 nd International conference on Communication Systems and Networks (COMSNETS),2010, pp. 1-10.
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