Upload
others
View
3
Download
0
Embed Size (px)
Citation preview
1
UNIVERSITY OF MANCHESTER
SCHOOL OF COMPUTER SCIENCE
COMP 60990: Research Methods and Professional Skills
A "traffic light" Decision Support Tool for Water Engineers
Progress Report
Student Name: Muhammad Zuhairi Bin Zainal
Supervisor: Dr. John Brooke
Programme: MSc Advanced Computer Science
2
ABSTRACT
Dependability of Decision Support System (DSS) to the availability of communication
network has occasionally limits the capability of Water Distribution System (WDS)'s
field workforce in decision making. Communication network serves as transportation
mechanism to transfer data between client's device and server systems. As a fact,
some factors have impact on the performance of communication network, for
instance network loads and geographical constraints. Some of the factors are
uncontrollable by human capabilities. Therefore, there is a need to have alternative
to these uncontrollable factors.
Cache technology is considered as one of the alternative solution for communication
network unavailability. Cached data stored in client's local memory will enable the
DSS's application to works under minimal resources and would still enable to
support field workforce in making decision.
3
Table of Contents
ABSTRACT ..................................................................................................................... 2
Table of Contents ............................................................................................................ 3
1. INTRODUCTION ...................................................................................................... 5
1.1. Problem Statement ............................................................................................ 6
1.2. Project Objectives .............................................................................................. 6
1.3. Project Challenges ............................................................................................. 6
1.4. Report Outline .................................................................................................... 8
2. BACKGROUND RESEARCH ................................................................................... 9
2.1. Water Distribution Systems ................................................................................ 9
2.1.1. Overview of Water Distribution System ..................................................... 10
2.1.2. Leakage in WDS network .......................................................................... 12
2.1.3. District Meter Area ..................................................................................... 13
2.2. Decision Support System ................................................................................. 14
2.2.1. Decision Support Tool for WDS Field Operation ....................................... 15
2.2.2. Static Contextual Knowledge ..................................................................... 16
2.2.3. Dynamic Contextual Knowledge ................................................................ 17
2.3. Distributed Systems, Wireless Communications and Cyber-Physical Systems 18
2.3.1. Introduction to Distributed Systems ........................................................... 18
4
2.3.2. Introduction to Wireless Communication ................................................... 21
2.3.3. Introduction to Cyber-Physical System ...................................................... 23
3. METHODOLOGY ................................................................................................... 25
3.1. Study of overall project ..................................................................................... 25
3.2. Software Development and Testing Methodology ............................................ 25
3.3. Updated project plan ........................................................................................ 29
4. RESULT AND DISCUSSION .................................................................................. 30
4.1. Review and study of existing project documentations ...................................... 30
4.2. Server system set-up for testing and development .......................................... 32
4.3. Hands on existing prototype client application ................................................. 33
4.4. Proposed improvements in technology, implementation, functionality and
usability ...................................................................................................................... 34
5. CONCLUSION ........................................................................................................ 36
6. REFERENCES ....................................................................................................... 37
5
1. INTRODUCTION
Operational activities for field water engineer are crucial and require thorough pre-
execution analysis and studies. A trivial mistake at the field during operation may
lead to disastrous consequences to many of components (for example valves and
pipes) involved in the Water Distribution System (WDS) network, Therefore, a
predictive Decision Support Tool (DST) that is able to predict accurate result from
any on-field activities carried out is necessary to the WDS stakeholders to ensure
the stability of WDS.
A solid distributed computing application framework was established in previous
research. Thus the current research will be an extension from the existing
framework. Collaboration with Thames Water Utilities LTD, a UK's largest water and
wastewater provider, was formed to enable research in WDS area to be conducted
with real-world data and scenario.
In current research context, DST is an application that runs on mobile devices,
and equipped with functionalities to assist field workforce in making decision of
proceeding with scheduled planned provided by central control room. The examples
of functionalities are listed in Section 4.3 and further explanation of the usage of the
functionalities can be found in [2]. The DST application in this project offers support
services to components scattered in a WDS network. Main purposes of using DST in
WDS area are [1]:
to provide access to the static and dynamic contextual information, that are
generated from computational model, in order to locate a problematic area
to assist in analyzing the structure and attributes of physical system and
its components
to help in determining the impact of possible decisions on customers,
regulatory requirements and state of the system
6
to assist in rectification plan preparation (based on up-to-date system state),
and scheduling planned decisions in order to coordinate concurrent
engineering decisions, and so forth
1.1. Problem Statement
Availability of communication network between central servers with distributed
DST applications on the field is considered to be a problem to be analysing in
this research. Communication network availability might vary from one location to
another. Consequently, it affects the reliability of DST application to access
loads of data in the server. This issue might cause the field workforce in failing to
retrieve accurate and real-time data and leads to a delay in actions since they
have to search for available network connection and in some worst case, they
might have to go back to the office and reassess the scenario. This can be
considered as time lost in business operational perspective since it involves time
wasted to collaborate between field workforce and remote personnel.
1.2. Project Objectives
To propose a solution that able to support field workforce's decision making
process in any possible condition in the field.
A study about the network communication reliability to cater for different
possibilities of wireless network availability.
1.3. Project Challenges
Real-time aspect has been among recent goal in many Cyber-Physical System
(CPS) research. The emergence of Internet, advancement of communication
network and sophisticated computing devices are some of the factors that boost
the demand for real-time attribute in CPS. In addition to advancement in
technology, the fact that CPS is dealing with physical world adding the demand
for the real-time attribute in CPS implementation.
7
Reliable real-time data from physical world is expected to be sending to the
servers in cyber world to be compute and process. The data transportation
requires low latency, if not zero, between physical world and cyber server.
However, the availability of communication network itself is considered to be a
pre-requisite to have real-time CPS.
There are many factors have effects on the performance and the availability of a
network, namely signal sharing, network usage and load, local environment
characteristics, and network range and distance between devices [41]. Some of
these factors are beyond human's control. Thus, ensuring the availability of
communication network is one of the challenges in implementing real-time
distributed CPS.
Another identified challenge to this project is data consistency during cache
synchronization with database in case of device re-connected to communication
network. The flow of the cache implementation for this project will be discussed
in Section 4.4. As this research dealing with distributed CPS, there is tendency
that numbers of distributed field workforce make changes or updates to the
earlier identical cached copy stored inside each individual's device. The
complication may arise during data synchronization to the server once their
devices reconnected to communication network and may affect the consistency
of data in database server. In supporting this claim, Tanenbaum et al. 1 agree
that it is hard to ensure consistency in large-scale distributed system.
1 Tanenbaum, Andrew S, and Steen. Distributed Systems: Principles and Paradigms. Upper Saddle River, NJ: Pearson Prentice
Hall, 2007, p273
8
1.4. Report Outline
Chapter 1 provides an overview about the project, problem statement of the
project, objectives to be achieved by performing the research, and finally general
challenges anticipated for the project.
Chapter 2 describes the background research related to the project. This chapter
will cover elaboration on subjects about Water Distribution Systems, the needs
for Decision Support Systems in Water Distribution area, and technological
aspects that contributed to the development of Decision Support System.
Chapter 3 discuss the methodologies involved in this research. Performed and
planned activities such as project timeline, software development, and software
evaluation will be explored in depth.
Chapter 4 describes results and discussion of to-date project's progress, project's
improvement suggestions based on studies and analysis done from Chapter 3.
Chapter 5 and Chapter 6 serve as conclusion of overall project and list of
references respectively.
9
2. BACKGROUND RESEARCH
2.1. Water Distribution Systems
Demand for reliable and high-quality fresh water supply increases from time to
time. Apart from domestic consumption, water managers also have to be able to
handle staggering demand from industries and agricultural sectors. Metals, wood
and paper productions, gasoline and oils are only few numbers of industries that
heavily depending on water [5]. As a result, European Environment Agency
(EEA), an independent agency that studies, evaluates and provides sound
information about environment, anticipated that there will be a struggle to meet
water needs in the future due to appreciation of demand for water and because
of climate change[6].
Awareness about the importance of effective Water Supply System management
has been extensively developed by many parties. Various alternatives has been
discovered and been proposed to ensure the sustainability of water supply. A
team consists of top academics from Newcastle and Oxford University, has
published academic papers related to usage of nuclear power that could result in
increase of tidal and coastal water by almost 400% [7]. They had also studied
about the potential of using gas or other fossil fuels with high levels of carbon
capture and storage (CCS) that could lead to increment of fresh water
consumption by almost 70% [7]. In agricultural sectors, EEA highlighted several
alternatives, ranging from efficient water irrigation, educating farmers on best
practises on efficient water consumption and finally, the possibility of using
treated wastewater to meet agriculture's demand for water [6]. Daniel Loucks et
al [4] conducted a comprehensive study on how to use quantitative analysis, and
in particular computer models, to support and improve water resources planning
and management. Besides that, United Kingdom's government has also played
significant role in committing to secure water supplies, high standards of drinking
water and effective sewerage services by enforcing policy that require water
companies to update their plan every 5 years on [8]:
10
how to deal with future populations needs
climate changes
develop - where needed - new water supply resources such as reservoirs
Water Supply Systems comprise of 3 major components, which are [2] [10]:
raw extraction and transport
water treatment and storage
clear water transport and distribution
This research will be focusing only on third component as Water Distribution
Systems fall into this category.
2.1.1. Overview of Water Distribution System
Water Distribution System, or simply WDS, is a network of water supply
resources such as pumps, pipelines, storage tanks, and other appurtenances
[3][9]. It is a subsystem to a larger system which is Water Supply System. The
definition for Water Supply System is "infrastructure for the collection,
transmission, treatment, storage, and distribution of water for homes,
commercial establishments, industry, and irrigation, as well as for such public
needs as fire fighting and street flushing" [3]. Description about some of
elements inside WDS can be referring to Table 2.1. Main functionality of WDS
is to be able to carry water from treatment plant (or from the source in the
absence of treatment) to the consumer [9]. Optimal water pressure is
necessary to ensure the continuity of operating plumbing fixture and avoid
leaks and pipeline breaks [3].
11
Table 2.1:Basic elements inside WDS
Figure 2.1:Water Supply Distribution System[9]
12
2.1.2. Leakage in WDS network
Water Distribution System is among the largest distributed infrastructures built
in UK and due to its scale and component's age, daily operational problems
has never been easier to the stakeholders. Examples of the issues that have to
be solved are pipes leakages, malfunctioning of valves, and discoloration and
water contamination [17]. Leakage in WDS context is water loss in a WDS
network that may caused by burst pipes, sudden rupture of a joint and fittings,
and overflowing service reservoirs [14]. Two groups of factors identified that
influenced leakage, which are [13][14]:
Technical factors. In this group, qualitative and quantitative technical
aspect of the WDS infrastructure that cause the leakage. For instance,
conditions of the infrastructure such as pressure, soil movement, pipe
condition, poor quality material, fittings and workmanship (e.g. poor
installation activities), traffic loading and also due to leakage control method
itself.
Social factors. This factor is perceived to be human incompetency to have
proper infrastructure planning, institutional (e.g. government, national and
local agencies) attitude towards leakage problems, and policies to control
leakage.
Leakage in WDS may affect in several ways [12][14]:
consumer inconveniences
collateral damage to larger WDS network and infrastructure
substantial amount of cost incurred for compensation and repairing
requirement to over-design sewer capacity as a result from increased
loading on infiltrated sewers
error in water bills because of introduction of air in WDS network
13
pollution in pipe network that may also lead to health risks
Given numbers of inconvenience consequences due to leakage in WDS, there
is a need to have leakage monitoring system and rectification plan to overcome
leakage. Basically, three identified strategies in leakage management, which
are [14]:
Passive control. Rectification activities after bursts or drop in pressure
Regular survey. This includes listening to pipe work, and manual inspection
to the distribution system
Leakage monitoring. Monitoring the water flows into zones to measure
leakage and prioritize detection activities
District Meter Area technique falls into third strategy and its introduction has
been considered as one of best leakage monitoring technique that able to
produce the best result [11].
2.1.3. District Meter Area
International Water Association (IWA), an association that gather water
professionals and has ambition to provide innovative solutions for water
challenges, had produced an international standard for terminology of Water
Supply System to avoid misunderstanding about terms in Water Supply System
domain. This initiative is a consequent from the increment of attention and
interest from water experts all around the world that having difficulties to
understand each other because of differences of definitions used by individual
countries [12]. In one of its published documents, John Morisson et al. [13]
define District Meter Area (DMA) as a "discrete area of a distribution system
usually created by the closure of valves or complete disconnection of
pipe work in which the quantities of water entering and leaving the area
are metered."
14
DMA approach has been one of heavily used approach in water distribution
leakage management. The rationale underlying this approach is to divide large
WDS network into smaller and scalable areas or zones, and being able to
regulate the flow measuring system in these zones. In this approach, flow
meters are installed at strategic points throughout the WDS [11][13]. Main
purpose of the flow meters are to record flows of water that flow into a zone.
Figure 2.2 depicts example of DMA installation in WDS network.
Figure 2.2:Complete Water Supply System inputs and outputs [12]
2.2. Decision Support System
Researches in Artificial Intelligence(AI) has contributed to undisputable advanced
technology development to various area in human life, including pharmaceutical,
gas production plant, fermentation process, telecommunication, and
environmental problems managements system [18][19]. Among the main
objectives of AI development is to have capability of computer to function like
15
human brain by being able to gather information, develop logical hypotheses and
thus complement human's decision making ability [20]. Environment Decision
Support Systems (EDSS) are one of the systems that applied the advancement
of AI in environment field, which related to WDS.
At glance, EDSS is an intelligent information software system that integrate
environmental models, databases and assessment tools which are integrated
under a graphical user interface (GUI), often realized by using spatial data
management functionalities provided by geographical information systems (GIS)
[18][21][22][23]. Ideal EDSS tools should incorporate two components as its
source of information, which are [1][2][18]:
Qualitative or analytical component. This includes personal knowledge,
obtained through education and experiences, that produce personal
knowledge
Quantitative component. Mathematical calculations and computer
modelling that produce contextual knowledge, a type of knowledge that is
specific to system involved.
2.2.1. Decision Support Tool for WDS Field Operation
Eventhough there has many DSS and tools develop to solve environmental
problems, the chances of developed DSS failing to meet with real world
problems are reported to be high [24]. Uncertainty and approximate knowledge
are some reasons behind the failing. In some environmental DSS test cases,
data inputs to be process in environmental field are too many, yet with limited
available knowledge that able to digest the data correctly and accurately [23].
In the current research project of WDS domain, field operation decision making
process receives both type of knowledge that was previously mentioned in
Section 2.2, personal and contextual knowledge. Contextual knowledge is
perceived to be complement knowledge for field personnel that possess
16
personal knowledge, in their decision making process. Combination of these is
an ideal approach as to reduce the probability of DSS failing because of
knowledge limitations.
Figure 2.3:Components inside EDSS [23]
2.2.2. Static Contextual Knowledge
Static contextual knowledge is a kind of knowledge that will not vary over time
such as location of pumps, size of pipes, and type of valves. In contrast, a
dynamic contextual knowledge has the tendency to vary from time to time,
depending on many factors, for instance consumption demand from
consumers, and water pressure and flows that differ if some elements in WDS
infrastructure are altered.
GIS is a computer system that enable spatial data handling, map creation,
geographical modelling and simulation that can be use to assist in
understanding some complex interrelationships of natural resources, human
population and their ecological interactions [26]. GIS has been among the
biggest contributors in EDSS field and continuous research of implementation
17
of GIS in EDSS has started even before 1980's. Examples of GIS application in
EDSS field are study of prevention of groundwater deterioration, visualization
and mathematical calculation of the distribution of predicted environment
concentration (PEC) of down-the-drain chemicals in European water surface on
a river, and a study of GIS/remote-sensing techniques to estimate oil erosion
losses from intensive agricultural activities in a watershed in Sao Paulo
State [27]. In this research, GIS application is one of static contextual
knowledge provider as it provides the built application with the capability to
display and zooming geographic map, and functionalities to create simulation
using static data of water network [2].
2.2.3. Dynamic Contextual Knowledge
According to [1], one of the functional requirements of the currently developed
DST prototype for this thesis is to be able to run water quality modelling, a
computer modelling applications that consist of two types of simulations, which
are hydraulic and water quality simulation. The result from running hydraulic
simulation is to be used to predict spatial and temporal behaviour of water
quality, while running the water quality simulation provides insight on the
criticality of water properties such as concentration of chemical in the water and
water age [2]. The simulations are using dynamic data that may change from
any point of times because of many factors. Table 2.2 shows the identified
sources for dynamic data and its usages:
Data Sources Usage in Hydraulic simulation
Supervisory Control And Data
Acquisition (SCADA) system
Set status & speed of pumps
Set status & settings of valves
Set water level in tanks
Wireless Sensor Networks (WSN) Set pressures at junctions
Set flow rates in pipes
18
Field workforce Set status of pipes
Set status & speed of pumps
Set status & setting of valves
Set water level in tank
Table 2.2:Dynamic data sources and their usages in currently developed system [1]
2.3. Distributed Systems, Wireless Communications and Cyber-Physical
Systems
2.3.1. Introduction to Distributed Systems
Recently, development of distributed system has been in hasty pace had
resulted many advancement in technological area. Ranging from high-
performance mainframe computers to small nodes in sensors network,
distributed system has been one of the backbone behind their operations.
Although there were many definition about Distributed System defined by
scholars, but the most famous is [35]:
A distributed system is a collection of independent computers that appears to
its users as a single coherent system.
The underlying concepts behind the definition of distributed system are very
interesting to be elaborated. Two main aspects from the definition that worth
mentioning are [35]:
computers as components that are autonomous
abstraction of distributed system's components from user's perspective as
user perceived that they only deal with single system instead of with
various components inside the system
Typically, three common layers used in describing components in distributed
systems which are:
19
outer layer that handle interaction with user
middle layer, that sometimes called middleware
lower layer that constitutes operating system and networks
Figure below depicts an example of distributed system, sometimes known as
middleware.
Figure 2.4:Four networked computers, with three applications. Application B is
distributed between two computers [35]
Optimization of distributed system may benefit an organization in several ways.
For instance, installation of network printers in an organization enable
operational cost reduction as the network printers are shared among users in
an organization. Another benefit of optimized distributed system is the systems
have the capability to handle concurrent request from multiple clients, which
lead to increase in organizational productivity and operational efficiency. The
existence of Internet has helped to extend distributed system's capability by
providing mechanism for users to communicate, to collaborate and to exchange
files.
Current developed DST for WDS area has embraced the distributed system
approach by having structured components' architecture, that separating client-
end application and server-side systems. As the end-user, field workforce used
their handheld devices to retrieve information without knowing about the
20
computational operation handled by servers that are located in remote area and
not inside their handheld devices. Figure 2.5 illustrates the proposed project
system's architecture that embodies distributed system.
Figure 2.5: Proposed high-level system architecture for WDS field workforce[1]
21
2.3.2. Introduction to Wireless Communication
Two form of communications between computers, servers, printers and nodes
in a network are wired and wireless. Wired communication imposed limitations
to the development and implantation in distributed system economically,
practicality and feasibility. These limitations hinder wired communication to be
used in large-scale distributed system. As opposed to wired communication's
limitation, wireless communication offers lower implementation cost and most
importantly scalable for large system.
In distributed system environment, selecting suitable type of wireless
communication is essential. Latency, data rate, resilience and security are
some example of the criteria in choosing suitable type of wireless
communication. Table 2.3 depicts matrix of some of the communication and
their criteria.
Table 2.3: Type of wireless communications and their criteria weightage [36]
Above matrix table also applicable for DST used in WDS area. As referring to
Figure 2.5, the DST application lies as intermediate layer between end-user
and system-core layer. Criteria such as data rate, resilience, distance and
22
scalability are considered as crucial for the field workforce that used DST in
executing their task. Data rate or bandwidth criterion defines information
transfer capacity of the channel [36]. Current DST prototype possesses the
ability to load GIS map and huge amount of data to-and-from server side. Thus,
bandwidths required are expected to be medium to high, since lower bandwidth
may result in high latency. Distance coverage is also expected to be high since
field workforce will be anywhere around the globe and should still be able to
communicate with centralized server side. The scalability criterion of whole
DSS determines the capability of the system to be expanded in future to cater
for wider coverage expansions. Other criteria are equally important; however
there are still room to tolerate if they are not being met.
Field workforces are expecting information from DST to be available in almost
real-time. However, the accurate output from server-side computations and
availability of information are more importance. Therefore, there is trade-off
between latency and accuracy since sometime servers might require longer
processing time in order to produce accurate output. Security criterion has
relationship with data integrity. Data tempering and data exploit are examples
of security concerns that might occur in wireless communication area. A DSS
require high-level of support commitment from wireless service provider to
provide support in ensuring high level of communication's security between
DST in a field with server-side environment. The least expected criteria for DST
in WDS environment to possess is resilience.
Resilience criterion, as according to [36] is ability to resist interferences and
recover during catastrophic circumstances such as earthquake. This criterion is
considered as low for DST in workforce area since a DST is handheld device
and assumption of the ability of field workforce to avoid performing task during
catastrophic events. Table 2.4 summarizes level of importance of several
communication criteria from an expected DSS in WDS area.
Criteria Weightage
23
Latency Medium
Bandwidth High
Resilience Low
Security Medium
Distance High
Scalability High
Table 2.4: Expected criteria from a DSS in WDS area and its weightage
It can be inferred from information in Table 2.3 and Table 2.4, suitable wireless
communication to be used are either WiMax or cellular network (2G, 3G and
4G).
2.3.3. Introduction to Cyber-Physical System
Cyber-Physical System (CPS) is defined as integrations of computational and
physical process that able to interact with humans through many modalities
[37][38]. From the definition, there are three identified aspects inside CPS,
which are:
Computational capability expected from technology
Physical process in real world
Human involvement as source of input
Uncertainty and unpredictability of physical processes in real world has caused
tension to engineers in finding predictable solutions. Generic medical
prescription to obese patient will not really help without really knowing factors
24
(e.g. family traits, diet and lifestyle) that lead to the obesity. These factors vary
from every human. Earthquake, volcano eruption and tsunami are example of
unpredictable natural disasters. Behaviours of these disasters have becoming
interesting subjects to be studied.
Edward Lee [38] believes that CPS is not optimized if it is operated in controlled
environment as the environment does not provide robust and unpredictable
conditions. Advancement in technologies is expected to be able to support
computations for solution for complex physical problem in real world. Human
interaction with system is essential as source of faithful inputs in order to
produce accurate computations output. With all the stated aspects inside CPS,
it came down to the question on how to design CPS that able to handle
unpredictability in physical process problems? Current research's CPS design
can be used as an example because the design met some criteria of good CPS
design. The design will be elaborated briefly in Section 4.
25
3. METHODOLOGY
3.1. Study of overall project
Thorough study about the overall existing system was conducted in order to
understand underlying technical concept behind the project and the system
architecture. Some of the activities including:
Reading publications and thesis of previous researchers that related to
current project. This is to ensure:
o comprehensive understanding about WDS domain concept, EDSS and
technical aspect of system architecture is build
o that the suitable objectives, and proposed solutions are selected for
current system prototype
Revision and discussion with other researcher to understand technical
aspects especially about server-side component of the prototype system.
Read-then-run the existing system from source code. This helps to
understand system and the syntax.
3.2. Software Development and Testing Methodology
Current research will adopt iterative and incremental software development
methodology together with several Agile practises. This methodology was
chosen on the basis that current research is expected to provide additional
functionalities to the existing developed DST for WDS. Briefly, iterative and
incremental software development methodology emphasis on functionality's
gradual enhancement, with cyclical release to the system. Basically, there are
four major phases involved [32]:
Inception phase. Define scope, general requirement and risk
associated.
26
Elaboration phase. Working unit that fulfil requirement and minimize
risk associated from inception phase.
Construction phase. Incrementally integrate working unit into existing
overall system
Transition. Deploy system to production environment
Figure 3.1: Iterative development model [33]
In proposing client-side caching technique (will be further discussed in Section
4.4) as a solution to less reliable network communication between DST and
central server in WDS environment, two round of iterations are identified as
necessary. Table 3.1 below describe the planned iterations and phases involved.
Inception Elaboration Construction Transition
Scope Requirement Risk Estimated
Completion
Time (weeks)
Estimated
Completion
Time (weeks)
Estimated
Completion
Time(weeks)
Iteration 1 Cache DST able to Loads of 4 1 1
27
development in
client device
detect network
connectivity
Latest cached
data is stored
Ability to re-
run simulation
manager
data will affect
DST
performance
especially
handphone
devices
Iteration 2 Data
synchronization
between DST and
central server
database
Correct data
updated to
central server
Incorrect
data updated
to database
(data
consistency)
1 1 1
Table 3.1: Software development iteration
Test-Driven Development (TDD) practise will be used in each of iteration
regularly. TDD is an evolutionary approach in agile software practise that
emphasize on creating test script or test code before creating the actual
implementation code. The development of the implementation code will have to
ensure that the code will passed in all test scripts. Main advantages of using this
approach are:
implementation code fulfilled required functionalities
as growing implementation code expanded, it does not lost its
previous tested functionalities
reduce efforts to document the functionalities as test cases can be
considered as documented functionality
Thus, it can be expected that at the end of the research, many test cases will be
shown as one type of proof of system functionalities.
28
Another agile software development practise that will be used is time boxing.
This is a time management approach is crucial to avoid uncertainty related to
time. All tasks, regardless of its scale, will be identified and will be assigned with
weightage and anticipated time required to accomplish each tasks.
Functionality Level of difficulty (0-9)
*0=easiest,9=hardest
Estimated time (working days including
weekend) *will be based on Elaboration
phase + Construction phase
Iteration 1
(40days)
Detect network
unavailability
1 2
Store page cache
functionality
6 10
Retrieve page cache
functionality
3 4
Store data cache
functionality
6 10
Retrieve data cache
functionality
3 4
Perform simulation on
unavailable network
5 7
Iteration 2
(20days)
Detect network
availability(reuse function in
Iteration 1)
1 1
Update database data
correctly
9 19
Table 3.2: Initial timeboxing chart for this research
29
3.3. Updated project plan
30
4. RESULT AND DISCUSSION
This section describes activities executed as of until this report was written and
related issues since the research started.
4.1. Review and study of existing project documentations
As a result from reading related project materials, it is understandable that this
research will be focusing on the DST for field workforce of WDS. With respect to
system architecture, the DST is considered as one of subsystem inside a CPS
for WDS. Previously, Section 2.3.3 had briefly described about CPS and the
three aspects that may affect a CPS. In current research context, the DST acts
as interaction mechanism between the CPS's third aspects (i.e. human
involvement as source of input) with backend computations server.
Designing robust CPS requires abstraction of process from user's perspective,
distributed computations and networked control, and also validation and
verification supports [37]. Therefore, it is believed that current proposed CPS
design for decision support process for field workforce in WDS has taken into
account the previous mentioned criteria. Figure 4.1 shows the overall CPS
architecture of the current prototype system.
31
Figure 4.1: Proposed CPS of the monitoring and controlling the WDS
Generally, the CPS design has decoupled client-server architecture. The red
rectangle area, which is also the research area, is the client-side from the whole
system, while the remaining areas are the middleware and backend server.
From the selected red area, three resources used as control mechanisms of DSS
in the WDS field area, which are:
Communication networks
Field workforce engineering expertise
Handheld tools or supporting devices for field workforce
These three resources are essential and dependable on each others. Field
workforce engineering expertise is considered to be the major personal
knowledge provider to this CPS and the existence of handheld DST is to provide
reliable and faithful static and contextual information to the field workforce. In
32
order to retrieve contextual information from remote server to handheld devices,
communication network serves as the intermediary.
As mentioned in the Error! Reference source not found. section, this research
will propose an alternative solution for less reliable communication network in
client-side of the system. Setup for server-side for the system has completed and
will be discuss in Section 4.2. Progress work about the implementation in client-
side will be discussed in Section 4.4.
4.2. Server system set-up for testing and development
This activity is needed in order to prepare a Linux environment, Web application
server (Apache Tomcat) and JAVA development environment. In addition, VPN
software (Shrewsoft) was also installed to enable communication with database
servers and computational servers that located inside University of Manchester's
secured network zone. VPN is needed if the development will be done from
outside of University Of Manchester's wireless area.
The unfamiliarity with Linux operating system and tweaking computer's Operating
System (OS) had caused in longer time consumed since substantial research
through Internet was required. Several approaches tried including:
Setup Ubuntu in virtual machine hosted by virtual server (VMware)
Install Ubuntu OS with Windows 8. OS dual booting technique with
Windows 8 required.
Second option was chosen since according observation made found that it is
much faster when running the DST prototype program. Besides OS, the
Integrated Development Environment (IDE) installed was Netbeans Eclipse
(Kepler). All Eclipse's required plugins and extensions were installed including:
Subversion (SVN). This plugin is required to access to centralized
version controlling server
33
Google Web Toolkit. This plugin is to translate Java code to
JavaScript during compilation mode
The client application will be running from local machine and hosted in the same
server as web application server.
4.3. Hands on existing prototype client application
The source code was downloaded into development area and was run in debug
mode, to enable clearer understanding of system flow by adding breakpoint
remarks at several lines of codes.
The prototype system is exercising Model-View-Presenter (MVP) pattern, a
software design pattern that isolates user-interface from business logic. This
pattern is said to be originated from Model-View-Controller concept [28]. MVP is
suitable to be used for web development area since it requires substantial effort
of graphical user interface (GUI) development. Briefly, Model component involve
in structuring Data of the system, View component is used to provide user
interaction mechanism to system backend and finally Presenter component is
used as computation unit and as bridge between Model and View.
By studying the developed prototype MVP architecture in research [2] and
running the application using Eclipse IDE, several knowledge obtained, which
are:
Understand the flow between MVP components' classes.
Understand communication between client and server.
Understand data transferred between client and server.
Understand on how to operate the developed DST prototype
Moreover, this activity helps to understand functionalities and features of current
system prototype. Features identified are [2]:
34
DMA network model selector and loader
Display network map and user’s geolocation
Display WDS components and their attributes
WDS component searcher
Run simulations
Simulation status notification
Results summary
Visualization of results on network map
Results graph creator
Alerts notification
Automatic simulation re-run
4.4. Proposed improvements in technology, implementation, functionality
and usability
As mentioned in Section 1.2, one of the project objectives is to propose solution
to support decision making process for field workforce, particularly in any
wireless network communication conditions. As described in Section1.1,
dependency of current DST prototype to the availability of communication
network may limit the capability of decision making for workforce engineer while
in the operation field.
Therefore, one alternative proposed to encounter this limitation is to have client-
side caching technique. Briefly, cache technology is high speed buffer memory-
based storage used to store temporary portions of contents of computer's main
memory [30]. In current project, cached data and web pages are expected to be
stored in client's (in this research context, the field workforce) device memory.
35
Identified format of data received from server side is in eXtensible Markup
Language (XML) and web pages are in Hypertext Markup Language (HTML)
format.
In order to have cached copy inside DST device, field workforce has to run DST
application at least once while there is communication network before going to
the assigned field location. Replication process of server's data will produce a
copy of server's data and then stored inside field workforce's device memory
during the running. While at the operation field, during network unavailability
circumstances, DST will inquire user before using stored cached data. In other
words, field workforce has the option in making decision by using the cached
data or not. However, as discussed in Section 1.3, there will be a challenge to
perform data synchronization. This research will have thorough look in this
matter.
Java Caching System (JCS) will be used in the cache development of this
research. JCS provide various features including, memory management, disk
overflow (and defragmentation), thread pool controls, element grouping, minimal
dependencies, data expiration (idle time and max life), and fully configurable
runtime parameters [39]. These features will be explored to produce optimized
cache memory management in client's devices. Testing of the development will
be carried out using laptop and possibly Android device. Laptop will be used to
test implementation using Wireless Fidelity (WiFi) network and Android device
will be used to test cellular network.
36
5. CONCLUSION
This study has clearly shown the needs to have reliable decision support tool in
assisting field workforce of Water Distribution System to make sound and accurate
decision. One identified improvement in current developed DST prototype is to have
an alternative solution for communication network dependency. As a result from
detailed reading and discussion, cache solution has been identified to be
implemented in the research.
Research will involve programming activities, testing and reflection on the stated
activities. These activities will be divided into two iterations. First iteration is to
implement the cache technology using Java Caching System into the existing DST
prototype. Second iteration will be focusing on updating cached data in the
37
6. REFERENCES
[1] Kashif Khan, "A Distributed Computing Architecture to Enable Advances in Field
Operations and Management of Distributed Infrastructure," Doctoral Thesis, The
University of Manchester, 2012.
[2] Pedro F. Jaramillo Hernandez, "Decision Support Tool for Field Water Engineers",
Master Thesis, University of Manchester, 2013.
[3] “Water Supply System :: Effluent Disposal.” Encyclopedia Britannica. Accessed
March 31, 2014. http://www.britannica.com/EBchecked/topic/637296/water-supply-
system/286138/Effluent-disposal.
[4] Loucks, Daniel P, Eelco van Beek, Jery R Stedinger, Jozef P. M Dijkman, and
Monique T Villars. "Water Resources Systems Planning and Management: An
Introduction to Methods, Models and Applications." Paris: UNESCO, 2005.
[5] “Industrial Water Use, the USGS Water Science School.” Accessed April 13, 2014.
http://water.usgs.gov/edu/wuin.html.
[6] “Water for Agriculture — European Environment Agency (EEA).” Article. Accessed
April 13, 2014. http://www.eea.europa.eu/articles/water-for-agriculture.
[7] Macalister, Terry. “Water Shortages Could Disrupt Britain’s Electricity Supply,
Researchers Warn.” The Guardian, February 16, 2014, sec. Environment.
http://www.theguardian.com/environment/2014/feb/16/water-shortages-electricity-
supply-climate-change.
[8] “Water Resource Management (current System) - Maintaining Secure Water
Supplies, High Standards of Drinking Water and Effective Sewerage Services -
Policies - GOV.UK.”Accessed April 14, 2014.
https://www.gov.uk/government/policies/maintaining-secure-water-supplies-high-
standards-of-drinking-water-and-effective-sewerage-services/supporting-
pages/water-resource-management.
38
[9] US EPA, OW. “Distribution System.” Accessed April 14, 2014.
http://water.epa.gov/lawsregs/rulesregs/sdwa/tcr/distributionsystems.cfm.
[10] Trifunovic, Nemanja. Introduction to Urban Water Distribution: Unesco-IHE Lecture
Note Series. CRC Press, 2006.
[11] Di Nardo, Armando, and Michele Di Natale. “A Heuristic Design Support
Methodology Based on Graph Theory for District Metering of Water Supply
Networks.” Engineering Optimization 43, no. 2 (February 2011): 193–211.
doi:10.1080/03052151003789858.
[12] Lambert, A., and W Hirner. “Losses from Water Supply Systems: Standard
Terminology and Recommended Performance Measures.” International Water
Association,
http://www.iwahq.org/contentsuite/upload/iwa/Document/Losses_from_Water_Sup
ply_Systems_2000.pdf.
[13] Morisson, John, Stephen Tooms, and Dewi Rogers. “District Metered Areas
Guidance Notes.” IWA Publishing, February 2007.
[14] Farley, Malcolm. “Leakage Management and Control. A Best Practice Training
Manual.” World Health Organization, 2001.
http://whqlibdoc.who.int/hq/2001/WHO_SDE_WSH_01.1_pp1-98.pdf.
[15] Khan, Kashif, Robert Haines, and John M. Brooke. “A Distributed Computing
Architecture to Support Field Engineering in Networked Systems.” 54–61. IEEE,
2010. doi:10.1109/CISIS.2010.179.
[16] “Water Distribution System Analysis Field Studies Modeling and Management
Reference Guide for Utilities.” National Environmental Publications Information
System, 2006.
http://nepis.epa.gov/Exe/ZyPDF.cgi/2000D2C2.PDF?Dockey=2000D2C2.PDF.
[17] Haines, R., K. Khan, and J. Brooke. “Bringing Simulation to Engineers in the Field:
A Web 2.0 Approach.” Philosophical Transactions of the Royal Society A:
39
Mathematical, Physical and Engineering Sciences 367, no. 1898 (July 13, 2009):
2635–44. doi:10.1098/rsta.2009.0047.
[18] Cortés, Ulises, Miquel Sànchez-Marrè, Luigi Ceccaroni, Ignasi R-Roda, and Manel
Poch. “Artificial Intelligence and Environmental Decision Support Systems.”
Applied Intelligence 13, no. 1 (2000): 77–91.
[19] Burras, Simon. “A Real-Time Expert System for Industrial Environments.” In
Industrial Applications of AI (Artificial Intelligence), IEE Colloquium on (Digest No.
014), 2–1. IET, 1992.
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=167698.
[20] “Cognitive Systems Redefine Business Potential. Adaptive Intelligence Can Help
To Optimize Decision Making.” Ventana Research, 2012.
[21] Matthies, Michael, Carlo Giupponi, and Bertram Ostendorf. “Environmental
Decision Support Systems: Current Issues, Methods and Tools.” Environmental
Modelling & Software 22, no. 2 (February 2007): 123–27.
doi:10.1016/j.envsoft.2005.09.005.
[22] A.E., Rizzoli, and Young W.J. “Delivering Environmental Decision Support
Systems: Software Tools and Techniques” 12 (1997): 237–49.
doi:10.1016/S1364-8152(97)00016-9.
[23] Poch, Manel, Joaquim Comas, Ignasi Rodríguez-Roda, Miquel Sànchez-Marrè,
and Ulises Cortés. “Designing and Building Real Environmental Decision Support
Systems.” Environmental Modelling & Software 19, no. 9 (September 2004): 857–
73. doi:10.1016/j.envsoft.2003.03.007.
[24] Giupponi, Carlo. “Decision Support Systems for Implementing the European Water
Framework Directive: The MULINO Approach.” Environmental Modelling &
Software 22, no. 2 (February 2007): 248–58. doi:10.1016/j.envsoft.2005.07.024.
[25] Goodchild, Micheal F. “Geographic Information System,” 1991, 194–200.
40
[26] “Geographic Information Systems (GIS) | How We Use Data in the Mid-Atlantic
Region | US EPA.” Accessed April 19, 2014.
http://www.epa.gov/reg3esd1/data/gis.htm.
[27] Sweeney, Michael W. “Geographic Information Systems.” Water Environment
Research, 1999, 551–56.
[28] “Using the Model-View-Presenter (MVP) Design Pattern to Enable Presentational
Interoperability and Increased Testability - Dot Net Miscellany - Site Home - MSDN
Blogs.” Accessed April 28, 2014.
http://blogs.msdn.com/b/jowardel/archive/2008/09/09/using-the-model-view-
presenter-mvp-design-pattern-to-enable-presentational-interoperability-and-
increased-testability.aspx.
[29] “Cache.” Wikipedia, the Free Encyclopedia, April 21, 2014.
http://en.wikipedia.org/w/index.php?title=Cache&oldid=605177092.
[30] Smith, Alan Jay. “Cache Memories.” ACM Computing Surveys (CSUR) 14, no. 3
(1982): 473–530.
[31] Singhal, Vivek, Ian Emmons, and Richard Jensen. “Dynamic Web Page Cache,”
August 22, 2006.
[32] “What Is Iterative and Incremental Development? - Definition from Techopedia.”
Techopedia.com. Accessed April 30, 2014.
http://www.techopedia.com/definition/25895/iterative-and-incremental-
development.
[33] “Iterative and Incremental Development.” Wikipedia, the Free Encyclopedia, April
18, 2014.
http://en.wikipedia.org/w/index.php?title=Iterative_and_incremental_development&
oldid=591868914.
[34] “Introduction to Test Driven Development (TDD).” Accessed April 30, 2014.
http://www.agiledata.org/essays/tdd.html.
41
[35] Tanenbaum, Andrew S, and Steen. Distributed Systems: Principles and
Paradigms. Upper Saddle River, NJ: Pearson Prentice Hall, 2007.
[36] Akyol, B. A., Harold Kirkham, S. Clements, and M. Hadley. “A Survey of Wireless
Communications for the Electric Power System.” Prepared for the US Department
of Energy, 2010.
https://www.pnnl.gov/nationalsecurity/technical/secure_cyber_systems/pdf/power_
grid_wireless.pdf.
[37] Baheti, Radhakisan, and Helen Gill. “Cyber-Physical Systems.” The Impact of
Control Technology, 2011, 161–66.
[38] Lee, Edward A. “Cyber Physical Systems: Design Challenges.” 363–69. IEEE,
2008. doi:10.1109/ISORC.2008.25.
[39] “JCS - Java Caching System.” Accessed May 8, 2014.
http://commons.apache.org/proper/commons-jcs/.
[40] Sha, Lui, Sathish Gopalakrishnan, Xue Liu, and Qixin Wang. Machine Learning in Cyber Trust. Boston, MA: Springer US, 2009. http://www.springerlink.com/index/10.1007/978-0-387-88735-7.
[41] “Factors Affecting Wireless Networking Performance - 4Gon.” Accessed May 8,
2014.
http://www.4gon.co.uk/solutions/technical_factors_affecting_wireless_performance
.php.