augmenting in-situ with mobile sensing for adaptive ...dsm/papers/2019/praveen_iccps19.pdf · iccps...

Augmenting In-situ with Mobile Sensing for AdaptiveMonitoring of Water Distribution Networks

Praveen VenkateswaranUniversity of California, Irvine

[email protected]

Mahima Agumbe SureshSan Jose State University

[email protected]

Nalini VenkatasubramanianUniversity of California, Irvine

[email protected]

ABSTRACTInstrumenting water distribution networks with sensors for moni-toring is critical to maintain adequate levels of water quality andquantity. Existing efforts to detect and localize adverse events inthe network have explored either installing in-situ sensors on junc-tions or deploying a number of mobile sensors through pipes. Theseapproaches have high costs, low sensing accuracy, lack sufficientcoverage or provide intermittent monitoring. In this paper, wecombine the benefits of in-situ and mobile sensing with variousgeosocial factors to develop a cost-effective hybrid monitoring ar-chitecture that minimizes the impact of adverse water events onthe community. The architecture can adaptively adjust sensingresolutions on-demand within the network, determine requiredsensing capabilities based on the event, and respond to varyingevent severities. We propose a two-phase planning and deploy-ment approach that first integrates network structure, event, andcommunity information with simulation based analytics to deter-mine locations to install in-situ sensors and mobile sensor insertioninfrastructure. We then incorporate network flow information to de-termine mobile sensor deployment locations and volume to quicklylocalize detected events to minimize their impact. We evaluate ourapproach using multiple real-world water networks for adversewater quality and loss events and compare it to existing approaches.Our results show that our proposed approach can achieve upto79% reduction in impact with upto 68% greater cost efficiency com-pared to approaches using traditional coverage heuristics, and upto30% reduction in impact while being upto 52% more cost efficientcompared to approaches that attempt to minimize impact.

CCS CONCEPTS• Computer systems organization→ Sensor networks;Main-tainability and maintenance;

KEYWORDSWater Distribution Systems, Sensor Placement, Mobile Sensing,In-situ SensingACM Reference Format:Praveen Venkateswaran, Mahima Agumbe Suresh, and Nalini Venkatasub-ramanian. 2019. Augmenting In-situ with Mobile Sensing for Adaptive

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permitted. To copy otherwise, or republish,to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from [email protected] ’19, April 16–18, 2019, Montreal, QC, Canada© 2019 Association for Computing Machinery.ACM ISBN 978-1-4503-6285-6/19/04. . . $15.00https://doi.org/10.1145/3302509.3311048

Monitoring of Water Distribution Networks. In 10th ACM/IEEE InternationalConference on Cyber-Physical Systems (with CPS-IoT Week 2019) (ICCPS ’19),April 16–18, 2019, Montreal, QC, Canada.ACM, New York, NY, USA, 12 pages.https://doi.org/10.1145/3302509.3311048

1 INTRODUCTIONWater distribution networks (WDNs) constitute one of the mostcritical urban infrastructures and are an important communitylifeline. The monitoring of water networks is essential to ensurethe availability of sufficient quantity and quality of water. Today’swater networks are often decades old, and their growing scale andcomplexitymake them increasingly vulnerable to adverse events [1].Large pipe failures or leaks and the introduction of contaminantsare the most common events affecting the quantity and qualityof water in WDNs. Pipe leaks can result from stress caused byfactors such as corrosion, pipe displacements, extreme weather,disaster events and can cause contaminants like nitrates, metals,and pesticides from the soil to enter the water pipelines throughbackflows [5]. The impact of physical damages to the infrastructureand compromises to the quality of suppliedwater can be devastatingto society and cause huge economic and public health issues suchas massive flooding, outbreak of waterborne epidemics, shortagesof clean drinking water, damage to property, etc [28]. Having aneffective methodology in place to monitor water networks is thusessential to localize and resolve these adverse events, in particular,those that disrupt and impact the community at large.

There have been several efforts towards instrumenting waterdistribution networks with sensors to detect and localize eventsin a timely manner. Some systems like PIPENET [26], WaterWise[30], WaterBox [10], and AquaSCALE [8], install in-situ or staticsensors to measure network parameters like pressure, flow rate,turbidity, etc to detect the occurrence of events. Figure 1(a) showsthe WaterWise multi-sensor probe that holds several commercial-off-the-shelf sensors for hydraulics and is inserted into the flow onpressured pipes. Static sensors provide continuous monitoring withone time installation and continuous communication costs. High-end static sensors provide deterministic performance, larger sensingranges, and good accuracy. However, they are expensive (> $1000)[32] and the instrumentation of civic water infrastructures at largerequires significant investments (millions of dollars).

Systems like SmartBall [6] and PipeProbe [12] drop mobile sen-sors into the network which traverse and monitor pipes by movingwith the water flow. Figure 1(b) shows the deployment of a Smart-Ball into a water pipe. Mobile sensors detect events by traversingnear them while flowing through the pipes and incur operationalcosts in terms of the manual effort or automated infrastructurerequired to deploy or retrieve them. Operating mobile sensors typi-cally incurs lower cost than trenching and installing static sensors

https://doi.org/10.1145/3302509.3311048

https://doi.org/10.1145/3302509.3311048

ICCPS ’19, April 16–18, 2019, Montreal, QC, Canada Praveen Venkateswaran, Mahima Agumbe Suresh, and Nalini Venkatasubramanian

to retrofit existing pipe networks. Mobile sensors also allow foradaptive sensing on-demand as sensors can be deployed at differentlocations, at different times, and with different sensing capabilitiesbased on the need. However, they do not provide continuous mon-itoring and have low sensing ranges. They also require a largernumber of sensors since they have probabilistic movement throughnetwork junctions, and also need infrastructure support for theirinsertion into the network.

Figure 1: (a) WaterWiSe in-situ probe (b) SmartBall mobiledeployment

In this paper, we propose to leverage the advantages provided bystatic sensors (continuous monitoring, sensing range, accuracy) andthose by mobile sensors (adaptive sensing, on-demand monitoring,low cost) to develop a hybrid (i.e. in-situ and mobile) sensing archi-tecture to provide adaptive monitoring of water networks. Our goalis to first plan and augment the placement of in-situ sensors withmobile sensor insertion infrastructure to quickly detect adverseevents in the network and then determine locations from whichto deploy mobile sensors to localize these events. Since the impactof events on the community are tied to their severity and the timetaken to localize them, our planning and deployment methodologyneeds to be able to quickly detect and localize high impact events.In this paper, we focus on large pipe failures and contaminationevents.

There has been work on combining in-situ and mobile sensingin smart cities for pollution monitoring [15], community data col-lection [33], public safety [14] etc. However, achieving this in waternetworks presents several challenges - (1) Most of the networkinfrastructure is below ground, making it hard to deploy and oper-ate sensors and involves other engineering efforts like developingmobile sensor insertion infrastructure, (2) The movement of mobilesensors is constrained by the direction and speed of the water flowthat change over time, and (3) Communication is a challenge inunderground networks where wireless networks suffer from atten-uation while wired approaches require much cost and effort. Ourcontributions in this paper are as follows -•Wepresent a novel hybrid architecture that leverages the strengthsof both in-situ and mobile sensors and combines it with various

community geosocial factors to provide real-time adaptive moni-toring of underground water distribution networks (Section 2).•We design approaches to model the components of the architec-ture, the occurrence and propagation of events, and their resultingimpact on the community structure (Section 3).•We develop novel algorithms to (a) perform network planningto determine the placement of static sensors and mobile sensor in-sertion infrastructure and (b) determine mobile sensor deploymentlocations with the goal of reducing costs and ensuring low com-munity impact while maintaining event detection and localizationaccuracy (Section 4).•We evaluate the performance of our proposed hybrid architec-ture on three real-world water networks from Maryland, Colorado,and Richmond, and perform comparisons with existing approachesin detecting dynamic events resulting in the loss of water qual-ity/quantity (Section 5).

2 HYBRID ADAPTIVE MONITORINGARCHITECTURE

In this section, we present our hybrid (in-situ plus mobile) archi-tecture for the adaptive monitoring of water networks as shown inFigure 2. The physical infrastructure consists of the water distribu-tion network, the surrounding community structure and the sensordeployments. In-situ or static sensors are installed on the pipes incontact with the water flow. Mobile sensors on the other hand, areinserted into, and extracted from the water flow through pointsthat we denote as Insertion/Extraction (I/E) points. These could bemanhole covers, fire hydrants or other specialized infrastructure.While the measurements from static sensors are uploaded as con-tinuous data streams, the data from mobile sensors are uploadedonce they are extracted at an (I/E) point.

Our system architecture has two phases. The first phase involvesnetwork planning where we leverage network information, commu-nity structure (terrain, population, locations of key infrastructure),and event propagation models in order to model the impact ofdifferent events occurring at various locations in the network onthe surrounding community. We then use these impact models todrive our planning algorithm to determine the locations for urbanplanners and water agencies to install static sensors and mobilesensor (I/E) points. The static sensors continuously monitor thewater network and whenever they detect the occurrence of a pipefailure or contamination event, they determine a region of interest,which constitutes a subset of junctions and pipes where the sen-sors believe that an event has occurred. In the second phase, wedetermine the Insertion/Extraction points from which to deploymobile sensors to quickly cover the region of interest so as to ensurethe minimal impact of events on the surrounding community.

The adaptive monitoring capabilities of our hybrid architectureresults from (a) the ability to dynamically adjust the sensing res-olution of different areas of the network on-demand through thedeployment of mobile sensors, (b) being able to pick and choosethe exact sensing capabilities (sensing rate, types of sensors) ofthe mobile sensors required to localize each event based on theinput provided by the static sensor deployment, and (c) catering tothe severity of different events by incorporating information fromstatic sensors to determine the number of mobile sensors required

Augmenting In-situ with Mobile Sensing for Adaptive Monitoring of WDNs ICCPS ’19, April 16–18, 2019, Montreal, QC, Canada

Figure 2: Hybrid Adaptive Monitoring Architecture

to provide adequate coverage and measurements from the region ofinterest.Related Work. Existing work on combining mobile and in-situsensor deployments in water networks typically assume the priorplacement of static infastructure - either static sensors [18, 19,22] that cover a predetermined portion of the network, or sinknodes/beacons [4, 27] in the junctions that communicate with themobile sensors, with the objective of determining the number of mo-bile sensors needed and their release locations to cover the network.However, we argue that it is essential to consider the deploymentof both types of sensors simultaneously since the placement ofone type directly affects the performance of the other. Also, theseprior approaches assume that all events are uniform and do notdistinguish between them. However, (1) different events can im-pact a community in different ways and require unique sensingcapabilities, (2) events of the same type can have varying severity,and (3) different communities can be impacted to differing extentsby the same event based on their location, structure, demograph-ics, built infrastructure, urbanization, etc. In our previous work[29], we developed a methodology to localize pipe failures usingstatic sensors by characterizing the differing impacts caused byleaks on the community. We showed how this impact-driven ap-proach can ensure low impact of pipe leaks on the community.However, our scope was limited to static sensor deployments andour methodology was tailored to failure detection. In contrast, thispaper presents a more general adaptive monitoring framework forwater distribution networks leveraging the strengths of both in-situand mobile sensing and provides more cost-effective planning anddeployment solutions to model and mitigate the impact of bothfailure and contamination events.

3 MODELING THE NETWORK, EVENTS ANDASSOCIATED COMMUNITY IMPACT

In this section, we describe our methodology for modeling thevarious components of the proposed hybrid infrastructure, theoccurrence and propagation of contamination and failure events,the community structure and the associated impact of these events.

3.1 Modeling Infrastructure ComponentsSince the propagation of different events is dependent on the net-work flow, an event may manifest itself at only a subset of junctionsin the network. There is also a time delay associated with themanifestation that increases with distance from the event source.Therefore, it is important to model the water network and the sens-ing capabilities of both types of sensors to ensure the continuousmonitoring of the network and the timely detection and localizationof events. We use a hydraulic simulator EPANET [23] developed bythe United States Environmental Protection Agency, which simu-lates the hydraulic behavior within pressurized water distributionpipe networks, to model the sensing capabilities of the sensors.Modeling theWater Network: Awater distribution network canbe represented as a graph, where the vertices represent nodes andjunctions, while the edges represent links (pipes, valves, and pumps).We denote the set of potential locations for the occurrence of eventE in the network as E = {e1, e2, ..., en }, where ej refers to anevent occurring at location j. We also define the set of potentialstatic sensor locations and mobile sensor Insertion/Extraction pointsas Sstat = {sstat1 , sstat2 , ..., sstatn } and Smob = {smob

1 , smob2 , ...,

smobn } respectively, where sstati , smob

i refer to a static sensor and an(I/E) point at location i respectively. There could be locations deepunderground where installing in-situ sensors is not possible butcould be reached by mobile sensors deployed from existing (I/E)infrastructure. Similarly, there could be places where installingspecialized (I/E) infrastructure is infeasible due to network accessor cost, where in-situ deployments are more useful. Figure 3 showsa sample water network with five nodes that we use as a runningexample to illustrate our modeling approach.

Figure 3: Running example of water network with its (a) el-evation map and (b) key infrastructure information

Figure 4: Running examplematrices for event detection andsensing capabilities of (a) static sensors and (b) mobile sen-sors, (c) Determining event propagation

Modeling In-Situ Sensors: In our hybrid architecture, the staticsensors are responsible for detecting the occurrence of any eventand determine a region of interest by continuously monitoring the


network. An event would cause static sensors to return measure-ments depending on their ability to detect the event. The combi-nation of these measurements can be used to identify junctionspossibly affected by the event and hence track its propagation. Ourgoal is to then determine the potential event locations ej ∈ E thatcan be detected by each static sensor location sstati ∈ Sstat andthe corresponding time taken to do so. To do this, we introduce anevent at each potential location (E) in EPANET. We then determinethe sensor locations that can detect each event by monitoring thevalues of the requisite hydraulic variables for the event (pressurechange for failures and contaminant concentration for contamina-tion events). We build a detection capability matrix Mdc , wherethe rows represent the potential static sensor locations and thecolumns represent the event locations. The entries of the matrixare binary valued (0 or 1) depending on whether the static sensor iscapable of detecting the event [21]. We denote the observed valuesof the hydraulic variable under normal conditions as vi and as vionce the event is introduced. We also set a detection threshold ϵfor each event. Then the values ofMdc for a particular event canbe computed as -

Mdc [sstati , ej ] =

{1, if vi − vi ≥ ϵ

0, otherwise

We then store the corresponding time taken for the static sensorsto detect an event in a detection time matrixMdt as -

Mdt [sstati , ej ] =

{η(sstati )ej , ifMdc [s

stati , ej ] = 1

∞, otherwise

where η(sstati )ej is the time taken for a static sensor sstati to detectan event at ej measured in seconds. The values of Mdt are setto infinity for the locations where a static sensor is incapable ofdetecting the event.Example. The detection capability and detection time matrices inFig. 4(a) show the capability of static sensors to detect failures inthe sample network. For instance, a static sensor at junction 1 candetect a leak at junction 2 with a delay of 12 seconds.Modeling Mobile Sensors: Once the static sensors determine aregion of interest, the mobile sensors are then deployed in order tolocalize the event. Since the mobile sensors flow along with thewater, at each junction that connects multiple pipes, a mobile sensormay flow into any one of the outlet pipes. Thus, sensors released atthe same time and location can take a number of possible traversalpaths. We account for this uncertainty by adopting a probabilisticapproach to model the flow of mobile sensors in the network [27].

At each junction, we assign the probability of the sensor flowingthrough an outlet pipe connecting junction i and j as pi j=fi j/Ti ,where fi j is the flow rate through the outlet pipe andTi is the totalflow rate out of junction i . We maintain this junction-to-junctiontransition probability information in a matrix (M)which reflects theprobability that a mobile sensor at junction i would reach junctionj in a single step. This also implies that the probability of a mobilesensor from junction i reaching junction k after two steps can becomputed as pik=

∑j pi j∗pjk for all intermediate junctions j , which

translates to computingM2. We repeat this for n steps until there

are no more transitions (i.e., all the probabilities are 0) and create atraversal probability matrix as -

T =

n∑k=1

Mk , such thatMn = 0 (1)

where 0 denotes the zero matrix and each entry of T denotesthe probability of a mobile sensor traversing from one junction toanother in any number of steps. Our goal is to then translate theseprobabilities into finding the number of mobile sensors required totraverse a junction with a minimum coverage probability pc . Thiscan be modeled as a binomial distribution b(n,p), where p is theprobability that a mobile sensor will traverse to a junction, (1 − p)the probability that it will not, and n the number of mobile sensorsdeployed. Hence, the probability that nomobile sensorswill traverseto a junction can be represented as (1−p)n . We defineA as the eventin which at least one mobile sensor traverses to a given junction.Our goal is to then determine n such that Prob(A)=Prob(1−A′)=1−(1 − p)n ≥ pc . We thus obtain -

n ≥

⌈ln(1 − pc )ln(1 − p)

⌉(2)

We see that for low traversal probabilities, a larger number ofmobile sensors need to be deployed. We then use Equation (2) tobuild a traversal capability matrix Mtc where the rows denotethe potential mobile sensor (I/E) points (Smob ) and the columnsdenote the event locations (E). The entries ofMtc represent theminimum number of mobile sensors required to traverse to eachevent location with probability pc -

Mtc [smobi , ej ] =

{nei ej , if T[smob

i , ej ] > 0∞, otherwise

We also build the corresponding traversal time matrixMt t usingthe network flow rate information as the time taken for the mobilesensor to traverse from one junction to another.

Mt t [smobi , ej ] =

{θ (smob

i , ej ), ifMtc [smobi , ej ] , ∞

∞, otherwise

where θ (smobi , ej ) is the time taken for a mobile sensor to traverse

from mobile sensor location smobi to event location ej as a sum

of the time taken to traverse each intermediate pipe which wecompute using the flow rates and pipe lengths.Example. Fig. 4(b) shows the traversal capability and traversaltime matrices for mobile sensors in detecting contamination eventsin the sample network. We see that traversing from junction 1to junction 2 requires at least 7 mobile sensors to achieve a 95%coverage probability and they take 44 seconds to reach junction 2.

3.2 Modeling Events - Water Quality andQuantity

The occurrence of different events in the network can result in mul-tiple hydraulic variables being affected. It is therefore important toaccurately model the events to determine the manner of propaga-tion of each event. Since different events can affect different partsof the community, we partition the community into smaller regionsusing a delaunay triangulator, Triangle [25]. Triangular grids allowfor localized grid refinement and can easily conform to terrains


with irregular shapes [2]. We denote the set of triangular regionsas ∆ = {△1,△2, ...,△n }. We use EPANET to model the occurrenceand propagation of contamination and failure events.

3.2.1 Contamination Events. In contamination events, a dissolvedcontaminant travels down the network with the same averagevelocity as the carrier fluid while at the same time reacting (eithergrowing or decaying) at some given rate. Hence, contaminationevents can be detected by monitoring the chemical concentrationlevels in the water. We assume that at junctions, the mixing of fluidis complete and instantaneous.Definition.(Contaminant Spread) The spreading of contaminantsin the network can be computed as -

∂Ci∂t= −ui

∂Ci∂x+ r (Ci ) (3)

where Ci is the concentration in pipe i as a function of distancex and time t , ui is the flow velocity in pipe i and r is the rate ofreaction as a function of concentration [23].Contamination levels The degree of contamination at each junc-tion is an important factor to consider while measuring the impactof a contamination event on the community. People consumingwater from a contaminated junction within a region would be ad-versely affected. We measure this using EPANET by injecting acontaminant at each potential event location ej ∈ E and simulatingits spread throughout the network using Equation (3). We thenbuild a contaminant level matrixMcl where the rows and columnscorrespond to the contamination event locations (E) and the trian-gular regions (∆) respectively. We compute the entries ofMcl as-

Mcl [ej ,△k ] =

{0, △k does not consume from ej

C(ej )△k , otherwise

where C(ej )△k denotes the concentration levels at △k due to thecontaminant intrusion at ej .

3.2.2 Failure Events. Physical infrastructure failures, such as pipeleaks/breaks, cause a disturbance in the water flow resulting in apressure wave that moves through the network with high velocity[16]. Past work has shown that the the velocity of water exiting fromthe leak orifice is faster thanwithin the pipe causing a pressure drop,implying that pipe bursts can be identified by detecting changes inhydraulic pressure [20, 29].Definition.(Outflow Rate) The outflow rate of water from a leakcan then be computed as -

Q = Ec × pβ (4)

where Q is the outflow rate from the leak, Ec is the effective leakarea of the orifice, p is the pressure head at the leak and β is aconstant [13].Flood levels One of the main factors influencing the impact ofpipe failures on the community is the flooding resulting from wateroutflow and seepage from leaks. We capture this by simulatingthe outflow of water from a leak as well as its propagation alongthe surrounding terrain using a hydrodynamic flood simulationalgorithm BreZo [24]. We simulate leak events in EPANET by in-troducing emitters. We then compute the outflow rate for each leak

event ej ∈ E using Equation (4) and provide this as input to theBreZo simulator in addition to the triangular regions (∆) and theleak location (ej ). The BreZo simulator returns the regions affectedby flooding and the corresponding flood levels. We use this informa-tion to build a flood level matrixMf l consisting of the leak eventlocations as the rows and the triangular regions as the columns.The entries ofMf l can be computed as -

Mf l [ej ,△k ] =

{0, leak at ej does not impact △kH (ej )△k , otherwise

where H (ej )△k denotes the maximum flood level at △k due to aleak at ej .Example. For the sample network, we measure the effects of failureand contamination events introduced at each junction for fourtriangular regions denoted by △ in Fig. 3(a). The resulting flood andcontamination level matrices are shown in Fig. 4(c).

3.3 Modeling Community Structure and EventImpact

Estimating the impact of any event in the network on the commu-nity requires developing a model of the community structure. Wethen extract the following community information from each ofthe triangular community regions (∆) -1) Critical Infrastructure: We use mapping services to identifythe presence of critical infrastructure such as healthcare, transporta-tion, government facilities, education, etc within the boundaries ofeach region and assign relative importance scores to each of thesecategories. We then compute the critical infrastructure score △infk ,of region △k , as the sum of the scores of the infrastructure locatedin △k .2) Population Information: We obtain the population densityinformation of each of the triangular regions using census data anddenote it as △popk .3) Elevation Information: We build elevation maps for each ofthe triangular regions as the average elevation of its vertices anddenote it as △elek .4) Demand:We determine the average consumption of water byeach triangular region △k by averaging the total supply througheach of the network junctions in △k , and denote this by △demk .Example. Figure 3(a) shows the triangular grids and the terraininformation used to obtain △elek while Figure 3(b) shows the pres-ence of critical infrastructure around the water network to compute△infk .Measuring the impact of an event on the community (IE ) is

dependent on the type of event that occurs in the network. Here,we present our methodology for measuring the impact of both pipefailures and contamination events.Impact of Pipe Failures A large pipe failure can cause floodingin the surrounding area, thus affecting the population present aswell as the functioning of critical infrastructure. We thus computethe impact of a leak event at location j on region △k as -

Ileakej =Mf l [eleakj ,△k ] ∗ (△

popk + △

infk ) (5)


whereMf l [eleakj ,△k ] is the level of flooding caused by the leak

event, △popk is the population density, and △infk is the critical in-frastructure score of the region.Impact of Contamination Events The impact of a contamina-tion event would depend on the amount of contaminated waterconsumed as well as the number of people consuming the water.We estimate the impact of a contamination event at location j onregion △k as -

Icontej =Mcl [econtj ,△k ] ∗ (△

popk + △demk ) (6)

whereMcl [eleakj ,△k ] is the contamination levels caused by the

event, △popk is the population density, and △demk is the consumptiondemand of the region.

4 NETWORK PLANNING AND DEPLOYMENTALGORITHMS

As described in Section 2, given a water network, our proposedarchitecture determines the placement of static sensors and Inser-tion/Extraction points to quickly identify the regions of interest andthen identifies the deployment locations of mobile sensors to pro-vide rapid localization of events. In this section, we present ournetwork planning and deployment algorithms. The goal of our plan-ning algorithm is to simultaneously determine locations to placeboth static sensors and mobile sensor Insertion/Extraction pointswhile providing high utility and incurring low costs. We define asensor placement P to consist of a set of static sensor locations andmobile sensor (I/E) points (i.e.) P ⊆ (Sstat

⋃Smob ). The utility

of a placement can be measured in terms of the impact caused byevents in the network due to the delay in their detection and local-ization. A sensor placement that provides high utility is thus onethat 1) quickly detects and localizes high impact events, 2) resultsin low overall impact on the community, and 3) incurs low costs.

We denote Cstati ,Cmobi as the set of event locations (E) that can

be detected by a static sensor placed at, or traversed to by mobilesensors deployed from location i respectively. We also define costs: costm as the cost ratio of static and mobile sensors and Ccov asthe set of event locations detectable by the deployment. We ac-knowledge that modeling the various costs involved is complex anddependent on the event type and severity, and use sensor cost ra-tios to determine the relative utility provided by in-situ and mobilesensors. The objective of our planning algorithm is to then identifya minimum cost placement set P = (Pstat ∪Pmob ) that covers theset of all detectable events such that its overall utility is maximized.This is equivalent to the weighted set cover problem defined as -Definition.(Weighted Set Cover) Let L be a finite set of elementsand C={C1,C2, ...,Cn } be a set of subsets of L with weightsW =

{w1,w2, ...,wn }. The goal is to find a set Cs ⊆ C such that all theelements are covered by Cs (i.e.)

⋃Ck =

⋃Ci ,∀Ck ∈ Cs ,∀Cj ∈ C,

and the sumofweights inCs is minimized (i.e.)minimize(∑wk ,∀Ck

∈ Cs ).Using this definition, if C is the collection of event locations

covered by each sensor, andW the corresponding costs of staticand mobile sensors, finding a set cover Cs is a solution to the WSCproblem and hence shows the equivalence. The WSC problem isNP-hard [11] thus implying that finding a solution for the plan-ning problem is also computationally complex. We present our

approximate Hybrid Impact Driven network planning algorithm asdescribed in Algorithm 1 -• For a given budget B and set of event locations E, we iterateover the set of potential sensor locations and determine theutility of placing a static sensor or an Insertion/Extractionpoint at each location.• For a static sensor sstati , we compute its utility for event ej asa function of the impact caused in the time taken for the sen-sor to detect the event (i.e.)U stat

ej (sstati ) = Iej /Mdt [sstati , ej ].

• The utility for an (I/E) point smobi depends on the impact

caused in the time taken for the existing static sensors to de-termine the region of interest and for themobile sensors to tra-verse to the event location (i.e.) Umob

ej (smobi ) = Iej /(δdt [ej ]

+Mt t [smobi , ej ]), where δdt [ej ] is the shortest time taken

for the placed static sensors to detect ej .• We then compute the total utility as a function of the costincurred to achieve the above utilities. This is

Ustat (sstati )=

|E |∑j=1

U statej (sstati )/costs

for static sensors, and

Umob (smobi )=

|E |∑j=1

Umobej (smob

i )/(Mtc [smobi , ej ] ∗ costm )

formobile sensor Insertion/Extraction points, whereMtc [smobi

, ej ] is the minimum number of mobile sensors needed to bedeployed from smob

i .• At the end of each iteration, we choose the sensor typeand location pairing with the largest utility and add it to theplacement set. We do this until either the budget is exhaustedor the network has been covered.

The algorithm is guaranteed to complete since every detectableevent location has at least one static sensor location that coversit. The worst case running time of the algorithm, O((|Sstat | +|Smob |)|E |2), occurs when at each iteration, only one new eventlocation is covered. This would result in long runtimes for largescale water networks.We however use the concept of submodularityto significantly reduce the number of utility computations in eachiteration, thus reducing the runtime [17].Definition. (Submodularity) Let C be a finite set and f be a setfunction. For all subsets Cs ⊆ Cr ⊆ C, and elements ci ∈ C \ Cr , fis submodular whenever f (Cs ∪ ci )−f (Cs ) ≥ f (Cr ∪ ci )−f (Cr ).Theorem 1. The utility functionsUstat ,Umob are submodular.We provide the proof in the appendix □Our greedy approach gives an approximation ratio of (1−1/e) sim-ilar to the weighted set cover problem [17]. Hence, in a given it-eration of the algorithm if U(s1) ≥ U(s2) ≥ U(s3) ≥ ..., thens1 would be added to the placement. Then in the next iteration ifU(s2) ≥ U(s3), we can conclude thatU(s2) ≥ U(si ),∀i ≥ 3, thusreducing the number of evaluations.

Once the locations of static sensors and mobile sensor Inser-tion/Extraction points have been determined, we need to identifythe mobile sensor deployment locations. This depends on the di-rection and velocity of water flow in the network as well as thejunctions that need to be localized. Given the junctions within


Algorithm 1 Network Planning Algorithm

1: Input: Sstat , Smob, E,Mdc ,Mdt ,Mtc ,Mt t , B, costs , costm,

Pstat , Pmob, Cstat , Cmob, Ccov

2: Output: P = (Pstat ∪ Pmob )

3: Initial Conditions: Pstat = ∅, Pmob = ∅, Ccov = ∅,

4: while Ccov , |E | and B > 0 do5: for i = 1→ |Sstat ∪ Smob | do6: if sstati < Pstat then7: for j = 1→ |E | do8: U stat

ej (sstati )=Iej /Mdt [sstati , ej ]

9: Ustat (sstati )=∑|E |j=1 U

statej (sstati )/costs

10: if smobi < Pmob then

11: for j = 1→ |E | do12: Umob

ej (smobi )=Iej /(δdt [ej ] +Mt t [smob

i , ej ])

13: Umob (smobi )=

∑|E |j=1 U

mobej (smob

i )/(Mtc [smobi , ej ]∗costm )

14: U(si )=max (Ustat (sstati ), Umob (smobi ))

15: Umax (sv )={U(si ) :max (U(si )), ∀i : 1→ |S |}16: if Umax (sv ) = Ustat (sv ) then17: B ← B − costs18: Ccov ← Ccov ∪ Cstatv19: Pstat ← Pstat ∪ sv20: else21: B ← B − (Mtc [smob

v , E] ∗ costm )22: Pmob ← Pmob ∪ sv

the regions of interest R, our objective is to identify the subset of(I/E) points determined by our planning algorithm at which mo-bile sensors need to be deployed (i.e.) D ⊆ Pmob . We describe ourdeployment algorithm (Algorithm 2) -

Algorithm 2Mobile Sensor Deployment Algorithm

1: Input: R,Mt t ,Mtc , Pmob

2: Output: D ⊆ Pmob

3: for all ej ∈ R do4: d = ∅, d t = ∅5: for i = 1→ |Pmob | do6: if Mtc [P

mobi , ej ] > 0 andMt t [P

mobi , ej ] < d t then

7: d t = Mt t [Pmobi , ej ]

8: d = Pmobj

9: D ← D ∪ d

• For every junction ej in the region of interest R, we identify the(I/E) points in the placement set Pmob , from which mobile sensorscan traverse to ej (i.e.)Mtc [P

mobi , ej ] > 0.

• We then determine the (I/E) point with the shortest traversaltime and add it to the deployment set D.•We repeat this till the entire region of interest has been localized.

More complex models for mobile sensor deployment are possiblethat exploit existing control systems in the network such as pumpsand valves to change the direction and speed of water flow thatcould result in fewer (I/E) points needed to ensure the reachabilityand coverage of mobile sensors. However, this requires detectingthe state of the systems and estimating their levels of functionalityin the aftermath of the event. Our proposed approach, though more

conservative, results in deployment solutions unaffected by effectsof the event on these systems.

5 EXPERIMENTAL RESULTSIn this section, we evaluate our proposed hybrid adaptive moni-toring architecture for both failure and contamination events. Wecompare the performance of our approach to existing sensor de-ployment approaches using water networks of varying scale andvalidate our approach under multiple event scenarios. While ourexperimental studies are conducted using simulators, we intend todevelop a testbed as part of our future work.

5.1 Experimental SetupWater Networks. We evaluate our proposed architecture usingthree real-world water networks of varying scale - (1) a subzone ofthe Washington Suburban Sanitary Commission’s (WSSC) waterservice area in Montgomery County, Maryland, (2) a model for theWolf-Cordera Ranch (WCR) in Colorado Springs, Colorado, and (3)the Richmondwater distribution system, part of the Yorkshire watersupply area in the U.K. The data for (1) was obtained from WSSCwhile for (2) and (3) from [9]. A summary of the network layouts isshown in Figure 5. Figure 6 shows the layout of the WSSC networkand its surrounding community and shows the placement of in-situsensors and mobile sensor (I/E) points determined by the proposedplanning algorithm for a subset of the network. The layouts of theRichmond and WCR networks are shown in the appendix.

Figure 5: Experimental Network Layout Summary

Figure 6: WSSC network and subset of sensor deploymentfor failure and contamination events

Comparison Approaches.We compare our proposed HybridImpact Driven planning and deployment approach (HID) to threeexisting sensor placement approaches in water networks - StaticCoverage Driven (SCD) [20], Hybrid Coverage Driven (HCD) [19],and Static Impact Driven (SID) from our previous work [29]. Here,


static and hybrid refer to the sensor types being used in the ap-proach. The SCD approach iteratively selects static sensor locationsbased on the number of event locations covered and their ability todistinguish between pairs of events. The HCD approach uses a crossentropy based methodology to select a percentage of junctions toinstall static sensors followed by determining mobile sensor releasepoints. The SID approach iteratively determines locations to installstatic sensors based on their achieved impact mitigation of the eventon the community. Figure 7 shows the number of in-situ sensors andmobile sensors in the deployments determined by each approach.We observe that sensor deployments use fewer sensors to detectand localize failure events since the event propagation (movementof pressure wave) is faster as compared to the contaminant flowwhich is restricted by the flow velocity of the water in the network.We also see that the hybrid approaches result in the deployment offar fewer in-situ sensors thus reducing the costs incurred. However,the deployments resulting from impact driven approaches use moresensors than their coverage driven counterparts since they attemptto quickly localize high impact events resulting in more sensorsbeing deployed to cover vulnerable regions.

Figure 7: Number of sensors deployed (in-situ/mobile) byeach approach for failure and contamination events

Event Scenarios.We compare the performance of the sensor de-ployments resulting from each of the approaches for the followingevent scenarios -1) Geo-correlated events:We simulate the cascading effects of failureand contamination events by introducing them in spatially clusteredlocations ranging from 5% to 50% of the network’s junctions wherethe number of events in each cluster is uniform.2) Critical events: We then introduce failure and contaminationevents in the top 5% to 30% junctions ordered by impact.

5.2 Evaluating Effectiveness of Hybrid SensorDeployments

We compare the effectiveness of the sensor deployments resultingfrom each approach using three metrics - (a) Detection and local-ization times, (b) Impact caused, (c) Cost effectiveness. In order todetermine the impact of failure and contamination events as de-scribed in Section 3.3, we obtain community structure informationby building the terrain elevation map (△ele ) using elevation datafrom [31], obtain population density information (△pop ) from cen-sus data [3], mine the coordinates of critical infrastructure (△inf )in the area using the OpenStreetMap service [7], and determine thedemand of water at each junction (△dem ) from the network model.

Evaluating Detection and Localization Times: For each in-troduced failure or contamination event in the above event scenar-ios, we determine the shortest time taken to detect and localize theevent by the sensors deployed by each approach. We then comparethe average of these shortest times for all the introduced events.Figure 10(a) shows the comparison of the average detection times

by each of the approaches for geo-correlated failure and contami-nation events. We see that in general, the detection and localizationof failure events (98 − 398 sec) is faster than contamination events(1010 − 7035 sec). Also, the detection and localization time of con-tamination events increases with the size of the network, whilethat of failure events remains consistent. This happens becausethe high speed pressure wave resulting from pipe failures can bedetected quickly even in larger networks as compared to the slowermoving contaminant flow. We observe that the coverage drivenapproaches (SCD,HCD) take a longer time on average to detect andlocalize events as compared to impact driven approaches (SID,HID)since their coverage based objective results in sparser sensor de-ployments. We also observe that the proposed hybrid HID approachtakes approximately 9% longer to detect and localize events ascompared to the in-situ based SID approach.

Extent of Impact Caused: We then compare the approachesbased on the impact caused to the community by the failure andcontamination event scenarios before their deployments can detectand localize the events. For each introduced event, we determineits impact (Section 3.3) caused as a function of the shortest timetaken for each approach’s sensor deployment to detect and localizeit. We then compute the average normalized impact caused over allthe introduced events (Figure 10(b)). We see that for geo-correlatedevents, the impact driven approaches (SID,HID) result inmuch lowerimpacts on average since they prioritize the quick detection of highimpact events and the coverage of critical regions. We observe thatthe proposed HID approach results in upto nearly 30% lesser impactthan the SID approach due to the faster localization of events usingmobile sensors and upto nearly 79% lesser impact than the coveragebased approaches.

Sensor type cost ratio: Here, we determine the influence of thecost ratio between mobile and static sensors on their proportion inthe sensor deployment resulting from the proposed HID approachby varying the cost ratio of mobile to static sensors from 1:1 to 1:10.Figure 8 shows the proportion of static and mobile sensors for theWSSC network for (a) failure and (b) contamination events. We seethat there is a stabilization in the proportions at a 1:5 cost ratio forthe WSSC network. We observe that this ratio increases with anincrease in the size of the network.

Comparison of coverage and cost of deployment: We thenuse the 1:5 cost ratio to compare the costs of the deployments re-sulting from each of the four approaches. We vary the number ofsensors from 10% to 100% of the total number of sensors in eachdeployment and compare the costs incurred and the coverage ofthe network achieved at each step for the WSSC network. Figure9 shows that approaches using only in-situ sensors (SID,SCD) in-cur much larger costs than the hybrid approaches (HID,HCD). Wealso observe that the coverage driven approaches achieve highernetwork coverage using lesser number of sensors.

Cost effectiveness of deployments: We evaluate the normal-ized cost effectiveness of the deployments as a function of the totalimpact caused and the total cost incurred. We observe from Figure10(c) that for geo-correlated events, the proposed HID approachproves to be the most cost effective by upto nearly 52% over the SIDapproach and upto nearly 68% over the coverage based approaches.This would improve as the cost ratio between mobile and staticsensors increases.


Performance underCritical Events:Due to space constraints,we present the results of the comparison of the four approaches forthe critical events scenario in the appendix. We observe that thehybrid driven approaches detect critical events much faster thancoverage based approaches and result in lower impacts of events.We also see that the proposed HID approach remains the most costeffective by upto nearly 40% over the SID approach.

Figure 8: Proportion of in-situ and mobile sensors inproposed hybrid approach deployment with varying mo-bile:static sensor cost ratios for the WSSC network

Figure 9: Progression of sensor deployment costs andachieved event coverage with increasing number of sensorsby the approaches for the WSSC network

6 CONCLUSION AND FUTUREWORKIn this paper we presented a novel architecture for the adaptivemonitoring of water distribution networks to quickly detect and lo-calize adverse events like pipe breaks and chemical contamination.The architecture leverages in-situ and mobile sensing to provide acost-effective solution that minimizes the impact of these events onthe community. We presented a two-phase approach that incorpo-rates information about the network, events, and the community todetermine locations to install sensing infrastructure and to deploymobile sensors and evaluate its effectiveness using real-world waternetworks of varying scale. While we demonstrate that a hybridarchitecture provides improved cost-effectiveness, its biggest ad-vantage lies in the flexibility provided by its adaptive monitoringcapabilities. As part of our future work, we intend to provide moredetailed models of the installation and operational costs incurred byin-situ and mobile sensors and determine methods to leverage exist-ing control systems and infrastructure while maintaining reliabilityunder sensor measurement uncertainties.

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http://www.awwa.org/infrastructure


(a) Average detection and localization times of the deployments for geo-correlated events

(b) Average normalized impact caused by geo-correlated events in the time taken for the deployments to detect and localize them

(c) Normalized Cost Effectiveness of the sensor deployments for geo-correlated eventsFigure 10: Comparison of Approaches for Geo-correlated events


APPENDIXA PROOF OF THEOREM 1Proof - We see from the formulation of the event utility functionsUstat ,Umob that they depend on the impact caused by the eventand the time taken to detect and localize it. Since, the impact for-mulation derived from Section 3.3 is independent of the sensordeployment, the submodularity of the event utility functions de-pends on the detection timeMdt and traversal timeMt t .

For the static event utility functionUstat , consider two place-ment sets Pstat ⊆ Qstat ⊆ Sstat . Given an event ej ∈ E thatcan be detected by a static sensor installed at location i such thatsstati ∈ Sstat \ Pstat . Depending on the time taken for sstati todetect ej , there are three cases -(1)Mdt [s

stati , ej ] ≥min(Mdt [P

stat , ej ]).This impliesmin (Mdt [P

stat ∪ {si }, ej ]) = min(Mdt [Pstat , ej ])

andmin (Mdt [Qstat ∪ {si }, ej ]) = min(Mdt [Q

stat , ej ]).Hence,Ustat (Pstat ∪ {sstati }) -Ustat (Pstat ) =Ustat (Qstat ∪

{sstati })−Ustat (Qstat ) = 0.(2)min(Mdt [Q

stat , ej ]) ≤ Mdt [sstati , ej ] < min(Mdt [P

stat , ej ]).This implies Ustat (Qstat ∪ {sstati })=Ustat (Qstat ) and henceUstat (Pstat ∪{sstati })−Ustat (Pstat ) ≥ Ustat (Qstat ∪{sstati })

-Ustat (Qstat ).(3)Mdt [s

stati , ej ] < min(Mdt [Q

stat , ej ]).Here,Ustat (Pstat∪{sstati }) ≥Ustat (Qstat∪{sstati }) andUstat (Pstat )

≤ Ustat (Qstat ) due to the non-decreasing property of Ustat .Hence, we getUstat (Pstat∪{sstati }) −Ustat (Pstat ) ≥ Ustat (Qstat∪

{sstati })−Ustat (Qstat ).Hence, we show that Ustat is submodular. Similarly we can

show thatUmob is submodular.

B NETWORK LAYOUT OF RICHMOND ANDWOLF CORDERA RANCH

Figure 11: Network Layouts of WCR and Richmond

C CRITICAL EVENTS EXPERIMENTALSTUDY


(a) Average detection and localization times of the deployments for critical events

(b) Average normalized impact caused by critical events in the time taken for the deployments to detect and localize them

(c) Normalized Cost Effectiveness of the sensor deployments for critical eventsFigure 12: Comparison of Approaches for Critical events

augmenting in-situ with mobile sensing for adaptive ...dsm/papers/2019/praveen_iccps19.pdf · iccps...

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