Inline wireless mobile sensors and fog nodes placement for leakage detection in water distribution systems

Burst or leakage in drinkable water distribution system has occurred frequently in recent years, causing severe damages, economic loss, and long-lasting society impact. A viable solution is to use agile inline mobile sensors to detect and so as to mitigate the burst or leakage. Distinguishing from online fixed sensors, mobile sensors can swim freely along the piles in water distribution network, thus giving a more precise detection. To combat the low power, low computation, and low communication capability of mobile sensors, the newly emerged fog computing provides a promising means to gather and preprocess the sensing data. In practice, due to the budget limitation, we can deploy a limited number of sensors and fog nodes in the system. This introduces a challenging problem on how to deploy them in the system, ie, sensor and fog node placement. We first formulate mobile sensor placement (MSP) as a path cover problem and prove it as NP-complete, and then we propose a customized genetic algorithm and a mixed greedy algorithm to solve MSP and fog node placement, respectively. The correctness and efficiency of the proposed algorithm are illustrated by a comprehensive experiment. Moreover, some critical factors, eg, sensor battery lifetime and movement pattern, are all extensively investigated and the results show the coverage ratio is sensitive to these factors.     

Mobile sensor networks for optimal leak and backflow detection and localization in municipal water networks

Leak and backflow detections are essential aspects of Water Distribution Systems (WDSs) monitoring and are commonly fulfilled using approaches that are based on static sensor networks and point measurements. Alternatively, we propose a mobile, wireless sensor network solution composed of mobile sensor nodes that travel freely inside the pipes with the water flow, collect and transmit measurements in near-realtime (called sensors) and static access points (called beacons). This study complements the tremendous progress in mobile sensor technology. We formulate the sensor and beacon optimal placement task as a Mixed Integer Nonlinear Programming (MINLP) problem to maximize localization accuracy with budget constraint. Given the high time complexity of MINLP formulation, we propose a disjoint scheme that follows the strategy of splitting the sensor and beacon placement problems and determining the respective number of sensors and beacons by exhaustive search in linear time.     

Robust sensor placement for pipeline monitoring: Mixed integer and greedy optimization

Intelligent water systems – aided by sensing technologies – have been identified as an important mechanism towards ensuring the resilience of urban systems. In this work, we study the problem of sensor placement that is robust to intermittent failures of sensors, i.e. sensor interruptions. We propose robust mixed integer optimization (RMIO) and robust greedy approximation (RGA) solution approaches. The underlying idea of both approaches is to promote solutions that achieve multiple detectability of events, such that these events remain detectable even when some sensors are interrupted. Additionally, we apply a previously proposed greedy approximation approach for solving the robust submodular function optimization (RSFO) problem. We compare scalability of these approaches and the quality of the solutions using a set of real water-networks. Our results demonstrate that considering sensor interruptions in the design stage improves sensor network performance. Importantly, we find that although the detection performances of RMIO and RGA approaches are comparable, RMIO generally has better performance than RGA, and is scalable to large-scale networks. Furthermore, the results demonstrate that RMIO and RGA approaches tend to outperform the RSFO approach.     

New formulation and optimization methods for water sensor placement

Optimal sensor placement for detecting contamination events in water distribution systems is a well explored problem in water distribution systems security. We study herein the problem of sensor placement in water networks to minimize the consumption of contaminated water prior to contamination detection. For any sensor placement, the average consumption of contaminated water prior to event detection amongst all simulated events is employed as the sensing performance metric. A branch and bound sensor placement algorithm is proposed based on greedy heuristics and convex relaxation. Compared to the state of the art results of the battle of the water sensor networks (BWSN) study, the proposed methodology demonstrated a significant performance enhancement, in particular by applying greedy heuristics to repeated sampling of random subsets of events.     

On the problem of k-coverage in mission-oriented mobile wireless sensor networks

The problem of sensor deployment to achieve k-coverage of a field, where every point is covered by at least k sensors, is very critical in the design of energy-efficient wireless sensor networks (WSNs). It becomes more challenging in mission-oriented WSNs, where sensors have to move in order to k-cover a region of interest in the field. In this type of network, there are multiple missions (or monitoring tasks) to be accomplished, each of which has different requirements, particularly, in terms of coverage. In this paper, we consider the problem of k-coverage in mission-oriented mobile WSNs which we divide into two sub-problems, namely sensor placement and sensor selection. The sensor placement problem is to identify a subset of sensors and their locations in a region of interest so it is k-covered with a small number of sensors. The sensor selection problem is to determine which sensors should move to the above-computed locations in the region while minimizing the total energy consumption due to sensor mobility and communication. Specifically, we propose centralized and distributed approaches to solve the k-coverage problem in mission-oriented mobile WSNs. Our solution to the sensor placement problem is based on Helly’s Theorem and the geometric analysis of the Reuleaux triangle. First, we consider a deterministic (or disk) sensing model, where the sensing range is modeled as a disk. Then, based on the above analysis, we address the k-coverage problem using a more realistic sensing model, known as probabilistic sensing model. The latter reflects the stochastic nature of the characteristics of the sensors, namely sensing and communication ranges. Our centralized and distributed protocols enable the sensors to move toward a region of interest and k-cover it with a small number of sensors. Our experiments show a good match between simulation and analytical results. In particular, simulation results show that our solution to the k-coverage problem in mission-oriented mobile WSNs outperforms an existing one in terms of the number of sensors needed to k-cover a region of interest in the field and their total energy consumption due to communication, sensing, and mobility for the correct operation of the protocol.     

人工鱼群算法在桥梁传感器优化配置中的应用

为了实现桥梁结构健康监测传感器的优化配置,用尽可能少的传感器获取尽可能多的反映桥梁结构健康状况的信息,将人工鱼群算法应用于一座拱桥的传感器配置中,利用人工鱼的三种典型行为,解决桥梁传感器优化配置问题。结果表明,人工鱼群算法自适应能力强,收敛精度高,可以实现桥梁结构健康监测传感器优化配置。     

Distributed Deployment Schemes for Mobile Wireless Sensor Networks to Ensure Multilevel Coverage

One of the research issues in wireless sensor networks (WSNs) is how to efficiently deploy sensors to cover an area. In this paper, we solve the k-coverage sensor deployment problem to achieve multi-level coverage of an area I. We consider two sub-problems: k-coverage placement and distributed dispatch problems. The placement problem asks how to determine the minimum number of sensors required and their locations in I to guarantee that I is k-covered and the network is connected; the dispatch problem asks how to schedule mobile sensors to move to the designated locations according to the result computed by the placement strategy such that the energy consumption due to movement is minimized. Our solutions to the placement problem consider both the binary and probabilistic sensing models, and allow an arbitrary relationship between the communication distance and sensing distance of sensors. For the dispatch problem, we propose a competition-based and a pattern-based schemes. The former allows mobile sensors to bid for their closest locations, while the latter allows sensors to derive the target locations on their own. Our proposed schemes are efficient in terms of the number of sensors required and are distributed in nature. Simulation results are presented to verify their effectiveness.     

Information geometry of target tracking sensor networks

In this paper, the connections between information geometry and performance of sensor networks for target tracking are explored to pursue a better understanding of placement, planning and scheduling issues. Firstly, the integrated Fisher information distance (IFID) between the states of two targets is analyzed by solving the geodesic equations and is adopted as a measure of target resolvability by the sensor. The differences between the IFID and the well known Kullback–Leibler divergence (KLD) are highlighted. We also explain how the energy functional, which is the “integrated, differential” KLD, relates to the other distance measures. Secondly, the structures of statistical manifolds are elucidated by computing the canonical Levi–Civita affine connection as well as Riemannian and scalar curvatures. We show the relationship between the Ricci curvature tensor field and the amount of information that can be obtained by the network sensors. Finally, an analytical presentation of statistical manifolds as an immersion in the Euclidean space for distributions of exponential type is given. The significance and potential to address system definition and planning issues using information geometry, such as the sensing capability to distinguish closely spaced targets, calculation of the amount of information collected by sensors and the problem of optimal scheduling of network sensor and resources, etc., are demonstrated. The proposed analysis techniques are presented via three basic sensor network scenarios: a simple range-bearing radar, two bearings-only passive sonars, and three ranges-only detectors, respectively.     

Sleep–wake up scheduling with probabilistic coverage model in sensor networks1

Energy optimisation is one of the important issues in the research of wireless sensor networks (WSNs). In the application of monitoring, a large number of sensors are scattered uniformly to cover a collection of points of interest (PoIs) distributed randomly in the monitored area. Since the energy of battery-powered sensor is limited in WSNs, sensors are scheduled to wake up in a large-scale sensor network application. In this paper, we consider how to reduce the energy consumption and prolong the lifetime of WSNs through wake-up scheduling with probabilistic sensing model in the large-scale application of monitoring. To extend the lifetime of sensor network, we need to balance the energy consumption of sensors so that there will not be too much redundant energy in some sensors before the WSN terminates. The detection probability and false alarm probability are taken into consideration to achieve a better performance and reveal the real sensing process which is characterised in the probabilistic sensing model. Data fusion is also introduced to utilise information of sensors so that a PoI in the monitored area may be covered by multiple sensors collaboratively, which will decrease the number of sensors that cover the monitored region. Based on the probabilistic model and data fusion, minimum weight probabilistic coverage problem is formulated in this paper. We also propose a greedy method and modified genetic algorithm based on the greedy method to address the problem. Simulation experiments are conducted to demonstrate the advantages of our proposed algorithms over existing work.     

On redundant coverage maximization in wireless visual sensor networks: Evolutionary algorithms for multi-objective optimization

Wireless visual sensor networks can provide valuable information for a variety of monitoring and control applications. Frequently, a set of targets must be covered by visual sensors, as such visual sensing redundancy is a desired condition specially when applications have availability requirements for multiple coverage perspectives. If visual sensors become rotatable, their sensing orientations can be adjusted to optimize coverage and redundancy, bringing different challenges as there may be different coverage optimization objectives. Actually, the specific issue of redundant coverage maximization is inherently a multi-objective problem, but usual approaches are not designed accordingly to compute visual sensing redundancy. This article proposes two different evolutionary algorithms that exploit the multi-objective nature of the redundant coverage maximization problem: a lexicographic ”a priori” algorithm and a NSGA-II ”a posteriori” algorithm. The performance of both algorithms are compared, using a previously proposed single-objective greedy-based algorithm as a reference. Numerical results outline the benefits of employing evolutionary algorithms for adjustments of sensors’ orientations, potentially benefiting deployment and management of wireless visual sensor networks for different monitoring scenarios.     

Context aided pedestrian detection for danger estimation based on laser scanner and computer vision

Road safety applications demand the most reliable sensor systems. In recent years, the advances in information technologies have led to more complex road safety applications able to cope with a high variety of situations. These applications have strong sensing requirements that a single sensor, with the available technology, cannot attain. Recent researches in Intelligent Transport Systems (ITS) try to overcome the limitations of the sensors by combining them. But not only sensor information is crucial to give a good and robust representation of the road environment; context information has a key role for reliable safety applications to provide reliable detection and complete situation assessment. This paper presents a novel approach for pedestrian detection using sensor fusion of laser scanner and computer vision. The application also takes advantage of context information, providing danger estimation for the pedestrians detected. Closing the loop, the danger estimation is later used, together with context information, as feedback to enhance the pedestrian detection process.     

Approximate computational approaches for Bayesian sensor placement in high dimensions

Since the cost of installing and maintaining sensors is usually high, sensor locations should always be strategically selected to extract most of the information. For inferring certain quantities of interest (QoIs) using sensor data, it is desirable to explore the dependency between observables and QoIs to identify optimal placement of sensors. Mutual information is a popular dependency measure, however, its estimation in high dimensions is challenging as it requires a large number of samples. This also comes at a significant computational cost when samples are obtained by simulating complex physics-based models. Similarly, identifying the optimal design/location requires a large number of mutual information evaluations to explore a continuous design space. To address these challenges, two novel approaches are proposed. First, instead of estimating mutual information in high-dimensions, we map the limited number of samples onto a lower dimensional space while capturing dependencies between the QoIs and observables. We then estimate a lower bound of the original mutual information in this low dimensional space, which becomes our new dependence measure between QoIs and observables. Second, we use Bayesian optimization to search for optimal sensor locations in a continuous design space while reducing the number of lower bound evaluations. Numerical results on both synthetic and real data are provided to compare the performance of the lower bound with the estimate of mutual information in high dimensions, and a puff-based dispersion model is used to evaluate the sensor placement of the Bayesian optimization for a chemical release problem. The results show that the proposed approaches are both effective and efficient in capturing dependencies and inferring the QoIs.     

能耗均衡的移动传感器节点派遣算法

在混合无线传感器网络中,移动传感器节点最耗能的操作是移动,如何减少移动传感器节点的移动距离同时能让其完成任务是一个富有挑战性的研究课题。本文提出了一个移动传感器节点的派遣算法,旨在均衡各个移动传感器节点的移动负载,并且能按优先级响应事件地点,适用于任意数量的移动传感器节点和事件地点的情况。当移动传感器节点数量大于事件地点数量时,将其转化为一个带权完全二分图上的最大匹配问题。当事件地点数量大于移动传感器节点的数量时,本文提出的算法先将事件地点聚类分簇,然后派遣移动传感器节点到各个簇中分别完成访问任务。为了减少传感器节点之间的消息传输量,本文在集中式算法的基础上又提出了一个分布式算法。仿真实验结果表明本文提出的分布式算法能有效降低传感器节点之间的消息传输量,算法能够使得整个混合无线传感器网络的生存寿命延长20%左右。     

考虑可靠性的给水管网水质传感器多目标优化选址模型

针对给水管网水质传感器有可能失效的情况,提出了考虑传感器可靠性的多目标优化选址模型(SPM-R).该模型在最大限度监测突发或者蓄意的污染事件之外,期望传感器选址具有一定的可靠性,即若某一传感器发生故障,存在其它传感器能够继续监测污染事件,并且由此造成的监测时间增加值在允许范围之内.结合一管网算例,首先利用EPANET软件随机模拟管网节点被注入污染物后各个节点污染物浓度变化情况,得到监测时间矩阵,然后,基于非支配排序遗传算法-Ⅱ(NSGA-Ⅱ)详细阐述了SPM-R模型的求解过程,并对计算结果进行了参数敏感性分析.     

Multimodal information fusion for urban scene understanding

This paper addresses the problem of scene understanding for driver assistance systems. To recognize the large number of objects that may be found on the road, several sensors and decision algorithms have to be used. The proposed approach is based on the representation of all available information in over-segmented image regions. The main novelty of the framework is its capability to incorporate new classes of objects and to include new sensors or detection methods while remaining robust to sensor failures. Several classes such as ground, vegetation or sky are considered, as well as three different sensors. The approach was evaluated on real publicly available urban driving scene data.     

Motion fault detection and isolation in Body Sensor Networks

Significant amount of research and development is being directed on monitoring activities of daily living of senior citizens who live alone as well as those who have certain motion disorders such as Alzheimer’s and Parkinson’s. A combination of sophisticated inertial sensing, wireless communication and signal processing technologies has made such a pervasive and remote monitoring possible. Due to the nature of the sensing and communication mechanisms, these monitoring sensors are susceptible to errors and failures. In this paper, we address the issue of identifying and isolating faulty sensors in a Body Sensor Network that is used for remote monitoring of daily living activities. We identify three different types of faults in a Body Sensor Network and propose fault isolation strategies using history-based and non-history based approaches. The contributions of this paper are: (i) faulty sensor node identification in a small number of deployed body sensors (accelerometers); and (ii) identification of a faulty sensor node using a statically or dynamically bound group of sensor nodes that is sharing similar sensor signal patterns.     

Sensor virtualization for underwater event detection

Distributed event detection is a popular application in Underwater Wireless Sensor Networks (UWSNs). The Base Station (BS) collects the measurements from multiple sensor nodes, and makes a decision based on the sensors’ reports. However, due to the unpredictable moving of underwater sensor nodes and interference among multiple events, it is difficult to guarantee the accuracy of event detection. In this paper, we propose a sensor virtualization approach to deal with the event detection problem in UWSNs. The final decision making at the BS will be implemented with the reports of multiple virtual sensors. Although the events may happen in a large scale, the locations where the events happen are relatively sparse in the underwater environment. Consider the sparse property of events, we employ the technique of compressive sensing to recover the original signal from the correlated sensors’ measurements. Through a proper signal reconstruction, the accurate event detection can be reached with a remarkable low sensing overhead. We implement the sensor virtualization based on the compressive sensing technique. Our approach is suitable for the high dynamic topology of UWSN, and it can improve the accuracy of event detection and reduce energy consumption in UWSNs.     

Feature-based tactile object recognition

Tactile sensing offers powerful capabilities for robotic perception. Through the use of array-force sensors, precisely located surface information about objects in the workspace is available wherever the robot arm may reach. In order to use this information to identify objects and their placement, interpretation processes should employ proprioceptive information and should use tactile image features which reflect object characteristics. A technique is described for the generation of constraints on object identity and placement such that information from multiple sensor contacts may cooperate towards interpretation.     

Stochastically resilient extended Kalman filtering for discrete-time nonlinear systems with sensor failures

Missing sensor data is a common problem, which severely influences the overall performance of modern data-intensive control and computing applications. In order to address this important issue, a novel resilient extended Kalman filter is proposed for discrete-time nonlinear stochastic systems with sensor failures and random observer gain perturbations. The failure mechanisms of multiple sensors are assumed to be independent of each other with different failure rates. The locally unbiased robust minimum mean square filter is designed for state estimation under these conditions. The performance of the proposed estimation method is verified by means of numerical Monte Carlo simulation of two different nonlinear stochastic systems, involving a sinusoidal system and a Lorenz oscillator system.     

Adjoint-based numerical method using standard engineering software for the optimal placement of chlorine sensors in drinking water networks

To obtain representative water quality simulations, unknown model parameters have to be updated by combining information from the water quality model and the sensor outputs. An adjoint-based numerical method has been developed to determine the optimal placement of chlorine sensors in drinking water networks at a low computational cost. From a practical engineering perspective, the proposed optimal placement corresponds to the set of sensors that minimizes the area in which the unknown model parameters cannot be identified. The numerical strategy is implemented in the hydraulic software EPANET. Using the adjoint framework, we develop and apply an adaptive strategy in a French drinking water network that provides the optimal placement from 1 sensor to 6 sensors. We show that the highest reduction of the non-identifiable area is obtained at the first stages of the adaptive strategy. After 4 sensors, a plateau is reached.