With weather becoming more extreme both in terms of longer dry periods and more severe rain events, municipal water networks are increasingly under pressure. The effects include damages to the pipes, flash floods on the streets and combined sewer overflows. Retrofitting underground infrastructure is very expensive, thus water infrastructure operators are increasingly looking to deploy IoT solutions that promise to alleviate the problems at a fraction of the cost. In this paper, we report on preliminary results from an ongoing joint research project, specifically on the design and evaluation of its data analytics platform. The overall system consists of energy-efficient sensor nodes that send their observations to a stream processing engine, which analyzes and enriches the data and transmits the results to a GIS-based frontend. As the proposed solution is designed to monitor large and critical infrastructures of cities, several non-functional requirements such as scalability, responsiveness and dependability are factored into the system architecture. We present a scalable stream processing platform and its integration with the other components, as well as the algorithms used for data processing. We discuss significant challenges and design decisions, introduce an efficient data enrichment procedure and present empirical results to validate the compliance with the target requirements. The entire code for deploying our platform and running the data enrichment jobs is made publicly available with this paper.

Wireless sensor networks consist of a large number of sensor nodes that have low power and limited transmission range and can be used in various scenario. The nodes can be deployed in the long and narrow region, such as road, bridge, tunnel and pipeline, to get some interesting information. The linear topology of these network application is different other application and have special feature, such as multi-hop, long delay, long distance and low reliability. This paper introduces the concept of linear wireless sensor networks and discusses the classification of topology and key issue of this network. The application of the linear wireless sensor network, such as road, bridge, tunnel and pipeline is presented. The research challenges are discussed at last in this paper.

Wireless sensor networks are used to monitor water/oil pipelines. The excising schemes for pipeline monitoring are not energy efficient. Network operators have to daily travel across pipelines to change batteries of sensor nodes. In this paper, we propose a novel sensorbased pipeline monitoring protocol called FPMN. FPMN utilizes float nodes to reduce energy consumption in sensor nodes by reducing tasks of sensor nodes. FPMN tries to spread best route information along the pipelines in a way to reduce bandwidth and energy consumption. Our simulation experiments show that FPMN reduces energy consumption by 68.3% without change in packet delay and network traffic.

Wireless sensor networks are often utilized in different kinds of applications like healthcare, military operations, border security, road and pipeline monitoring etc. The majority of these applications use fixed network topology and place sensors in a linear form defined as a linear sensor network (LSN). The Linear sensor networks have gained much attraction of the researchers due to their several positive aspects including easy deployment for linear structures and robustness in different environments. The details about the LSN models and routing techniques are mostly overlooked in the previous studies. This study, therefore, explores the concept of LSNs models and routing techniques in various types of applications. Besides, it highlights the LSN design, classification, challenges and issues as well. The core motivation for this study is to summarize the types of LSNs and analyze their routing parameters i.e. energy efficiency, reliability, route discovery, route maintenance, coverage, delay, heterogeneity and mobility of sensors. Moreover, recent LSN techniques are classified according to the similarities of routing procedures. Further, this study has investigated and discussed various proposed solutions, future directions and opportunities that would serve as a milestone for other researchers so that they could explore further the LSN routing techniques more deeply.

Wireless sensor networks (WSNs) are a target technology for oil and gas pipeline monitoring because they offer benefits of low cost, ease of deployment and ability to cater for data acquisition at great spatial and temporal scales. In order for WSN to achieve trademark performance in remote monitoring of pipelines, and surpass the performance of present-day traditional monitoring systems, certain design requirements must be met. In this paper, we identify vital design issues that must be considered to facilitate the employment of WSN for pipeline monitoring. We classify these design issues into five different categories namely; sensing modality, power efficiency, energy harvesting, network reliability and localization. In addition, we discuss the concept of cooperative communication for pipeline-monitoring sensor networks deployed in sub-sea environments. We also study the employment of sensor networks for monitoring underground pipelines. Our findings are based on extensive study of the recent literature and comprehensive survey of existing WSN technologies. The WSN design considerations presented in this paper are particularly prolific for pipeline monitoring scenarios, they can however be easily extended to other oil and gas infrastructures. For example; well-head and heat exchanger monitoring, oil platform process monitoring, monitoring of natural gas storage facilities and data collection on coastal infrastructures that could support oil and gas exploration.

Wireless Sensor Networks (WSNs) consist of a large number of sensor nodes deployed in a wide area for monitoring applications. For some of these applications, as pipeline or road monitoring, wireless sensor nodes have to be deployed in a linear manner. We refer to these WSNs as Linear Sensor Networks (LSNs). Due to specificity of LSNs, MAC protocol designed for WSNs, as contention or TDMA based protocols, are often not suitable. Furthermore, wireless node deployment can provide a certain form of redundancy to prevent link or node failures. In this paper, we propose a token based MAC protocol for linear sensor networks in order to improve the network performance. We evaluate the effect of the redundancy on the number of packets delivered to the sink. We show that the redundancy induces a significant improvement both on the delivered traffic and on the FIFO queue size of the nodes.

We study a new variant of consensus problems, termed `local average consensus', in networks of agents. We consider the task of using sensor networks to perform distributed measurement of a parameter which has both spatial (in this paper 1D) and temporal variations. Our idea is to maintain potentially useful local information regarding spatial variation, as contrasted with reaching a single, global consensus, as well as to mitigate the effect of measurement errors. We employ two schemes for computation of local average consensus: exponential weighting and uniform finite window. In both schemes, we design local average consensus algorithms to address first the case where the measured parameter has spatial variation but is constant in time, and then the case where the measured parameter has both spatial and temporal variations. Our designed algorithms are distributed, in that information is exchanged only among neighbors. Moreover, we analyze spatial frequency response and noise propagation associated to the algorithms. The tradeoffs of using local consensus, as compared to standard global consensus, include higher memory requirement and degraded noise performance.

We propose a novel energy-aware approach to detect a leak and estimate its size and location in a noisy water pipeline using least-squares and various pressure measurements in the pipeline network. The novelty in our work hinges on the fusion of the duty-cycling (DC) and data-driven (DD) strategies, both well-known techniques for energy reduction in a wireless sensor network (WSN). To maximize the information gain and minimize the energy consumed by the WSN, we first study the effects of (a) various levels of sensor measurement uncertainty and (b) the use of the smallest possible number of pressure sensors on the overall accuracy of our approach. Using the DD strategy only, a noisy environment, and a small number of sensors, the performance of our scheme shows that, for small leak sizes, the estimation error in both leak location and size becomes unacceptably high. Next, using as few sensors as possible for an acceptable accuracy, we fused the DD strategy with the DC one to minimize the sensing, processing, and communication energies. The fusion approach yielded a better performance with significant energy saving, even in noisy environments. EPANET was used to model the pipeline network and leak and MATLAB to implement, analyze, and evaluate our fusion approach.

Universal serial bus (USB) technology has been used in various applications as a universal interface. Wireless USB (WUSB) based on USB technology provides USB’s speed and compatibility in addition to wireless technology’s convenience. In order to provide internet of things service, the technology evolution is necessary to expand to a large-scale network through a combination of multiple local WUSB networks. However, the current WUSB standard does not support the multi-hop communication. In addition, when the number of WUSB devices in the WUSB network increases, and numerous WUSB devices transmit data, a bottleneck occurs in WUSB networks, and WUSB devices may be affected by interference from the mobile WUSB devices in the adjacent network. When the number of WUSB devices sharing the same superframe increases, the available resource also becomes insufficient. Therefore, in this paper, we propose a new multi-channel cluster tree algorithm to solve this resource shortage problem and prevent collision by the mobile WUSB devices.

Underwater Wireless Linear Sensor Networks (UW-LSNs) possess unique features as compared to the terrestrial sensor networks for pipeline monitoring. Other than long propagation delays for long range underwater pipelines and high error probability, homogeneous node deployment also makes it harder to detect and locate the pipeline leakage efficiently. Determining the exact leakage position with minimum delay stays a major issue where pipelines length is extremely long and expensive to deploy many underwater sensors. In order to tackle the problem of large scale pipeline monitoring and unreliable underwater link quality, many algorithms have been proposed and even some of them provided good solutions for these issues but the scalable nodes deployments still need focus and prime attention. In order to handle the problem of nodes deployment, we therefore propose a dynamic nodes deployment algorithm where every node in the network is assigned location in a quick and efficient way without needing any localization scheme. It provides an option to handle the heterogeneous types of nodes, distribute topology and mechanism in which new nodes are easily added to the network without affecting the existing network performance. The proposed distributed topology algorithm divides the pipeline length into segments and sub-segments in order to manage the higher delay issue. Normally nodes are randomly deployed for the long range underwater pipeline inspection yet it requires some proper dynamic nodes deployment algorithm assigning unique position to each node

To solve problems that existed in wired water monitoring system, such as high cost and restricted monitoring water range, a kind of Lijiang River water environment monitoring system is designed based on WSN. In order to ensure long survival time of the sensor node and a high performance of routing node, MSP430 and ARM32F107 processor are employed respectively. After preprocessing data, MU709 3G module will upload them to the remote data management center. Test results show that the system runs stably, its measurements are accurate and reliable, thus the system is suitable for harsh conditions.

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.

This paper presents a novel distributed data analytic architecture, and corresponding algorithms that apply to infrastructure anomaly detection. The proposed method mainly focuses on smart water networks, demonstrating that the highest possible sensor rate analytic performs at on-edge nodes without requiring the whole date to send back to the server. This approach saves communication costs and lengthens the lifetime of the battery-powered nodes. A complex set of tasks is developed on a single-core Intel Curie processor, Arduino101 and the raw sensor data is compressed using a customized Lempel-Ziv compression algorithm tailored to resource-constrained embedded systems. The compression rate figures are then analyzed but only the compressed data which is associated with the anomalous condition is sent back to the server by means of a LoRa platform. The developed system is evaluated experimentally and the results verify the high resource utilization.

This paper presents a development of water pipeline monitoring system using vibration sensor. The MPU6050 sensor is selected to measure the vibration occurred along the pipes. In this study, the water pipeline used is high pressure pipe, an inch of the Acrylonitrille Butadience Styrene (ABS) pipe size with 10 meters length. The Arduino controller board then processes the vibration data from the accelerometer sensor and transmits (Tx) the data over wireless ZigBee networks at one second ellipse time. The water pipeline system is examined based on two states, which are (i) no pipe leakage, and (ii) a 1mm size of pipe leakage. In our experiments, the pressure is varied for three different levels, 0.6, 0.8 and 1.0 kgf/cm2. Results show that at water pressure of a 0.6 kgf/cm2 a significant difference in vibration signal is demonstrated between no pipe leakage and 1mm size of pipe leakage. However, this is not the case for the other two water pressure of 0.8 and 1.0 kgf/cm2 in which the vibration signals achieved are similar for both pipe leakage and normal pipe. This is due to the fact that high water pressure resulted to low water flow and thus the vibration of the pipe is not that considerable when the pipe leaks.

This paper deals with the problem of guaranteed distributed estimation under an asynchronous communication scheme. The objective is to estimate the state of a perturbed linear plant using a set of agents which communicate using a common network. The agents have access to a limited set of system outputs affected by bounded noises, so collaboration among them is needed. Both measurement noises and disturbances are bounded, but no stochastic distribution is assumed to be known. This work proposes a guaranteed estimator based on the use of zonotopes, which constitutes a useful way to represent the estimated set. The transmissions between agents are event-triggered. A novel triggering rule is presented. It weights, on the one hand, the error with past transmissions, and on the other, the uncertainty of the estimation. Simulation examples illustrate the achieved results.

This brief proposes an innovative estimation and control scheme that enables the distributed monitoring and control of large-scale processes. The proposed approach considers a discrete linear time-invariant process controlled by a network of agents that may both, collect information about the evolution of the plant, and apply control actions to drive its behavior. The problem makes full sense when local observability/controllability is not assumed and the communication between agents can be exploited to reach system-wide goals. The design procedure guarantees system stability, allowing the designer to tradeoff performance and control effort. The obtained controllers and observers are implemented in a fully distributed fashion. To show the performance of the proposed technique, simulation results on a water delivery system are provided.

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

The smart water quality monitoring, regarded as the future water quality monitoring technology, catalyzes progress in the capabilities of data collection, communication, data analysis, and early warning. In this article, we survey the literature till 2014 on the enabling technologies for the Smart Water Quality Monitoring System. We explore three major subsystems, namely the data collection subsystem, the data transmission subsystem, and the data management subsystem from the view of data acquiring, data transmission, and data analysis. Specifically, for the data collection subsystem, we explore selection of water quality parameters, existing technology of online water quality monitoring, identification of the locations of sampling stations, and determination of the sampling frequencies. For the data transmission system, we explore data transmission network architecture and data communication management. For the data management subsystem, we explore water quality analysis and prediction, water quality evaluation, and water quality data storage. We also propose possible challenges and future directions for each subsystem.

The recent advancements in the field of Wireless Sensor Networks (WSN) make the sensors smaller, cheaper and more energy efficient. These innovations made it attractive to use sensor networks in new applications like Border monitoring, Railway track monitoring, Oil and Gas pipeline monitoring etc. In the above mentioned applications, a common characteristic is their linear topology. Since last few years, a new subclass of sensor networks known as linear sensor networks (LSN) is emerging as a major focus area of research. Due to the special topology of LSNs, the issues and solutions required for LSNs are significantly different from ordinary sensor network. In the present exposition, the applications of LSN, major issues related to LSN, and the major research trends in this area have been summarized.

The paper proposes an innovative estimation and control scheme that enables the distributed monitoring and control of large-scale processes. The proposed approach considers a discrete linear time-invariant process controlled by a network of agents that may both collect information about the evolution of the plant and apply control actions to drive its behaviour. The problem makes full sense when local observability/controllability is not assumed and the communication between agents can be exploited to reach system-wide goals. Additionally, to reduce agents bandwidth requirements and power consumption, an event-based communication policy is studied. The design procedure guarantees system stability, allowing the designer to trade-off performance, control effort and communication requirements. The obtained controllers and observers are implemented in a fully distributed fashion. To illustrate the performance of the proposed technique, experimental results on a quadruple-tank process are provided.