1. Development of IoT-based Applications for Smart Cities
Carlos Kamienski

2. Agenda Motivation IoT & Smart Cities Software Development for IoT
Related Work: IoTLink, IoTSuite, Node-RED, FIWARE
The IMPReSS Project
An SDP for IoT-based Applications
Energy-Efficiency Management in Smart Buildings
Research Challenges
Final Remarks

3. About Me
Professor of Computer Science in the Federal University of ABC (UFABC): new university (10 years)
PhD in Computer Science (UFPE/Brazil – 2003)
+20 years working with networking and telecom
18 years experience with research in computer networks and distributed systems
Last three years working with IoT-based application development for energy efficiency management in smart buildings (IMPReSS Project)
Current research interests: IoT, Smart Cities, network softwarization, cloud/fog computing
Technologies for supporting Smart Cities

4. Development of IoT-based Applications for Smart Cities
Motivation
Development of IoT-based Applications for Smart Cities

5. Motivation Application development for IoT is challenging
It involves a wide range of related issues
Lack of separation of concerns (division of roles and skills)
Ex.: domain expertise, deployment-specific knowledge, application design and implementation knowledge, platform- specific knowledge
Lack of high-level abstractions
Large scale & Heterogeneity
Different lifecycle phases have to be addressed
Development, deployment, maintenance
Patela, P., Cassoub, D., Enabling high-level application development for the Internet of Things, The Journal of Systems and Software 103(2015) 62–84.

6. Motivation
Many challenging issues still need to be addressed before a highly effective software platform for Smart Cities can be created
Enabling interoperability between city’s multiple systems
Guaranteeing citizens’ privacy
Managing large amounts of data
Supporting the required scalability
Dealing with a large variety of sensors
Santana, E., Chaves, A., Gerosa, M., Kon, F., Milojicic, D., Software Platforms for Smart Cities: Concepts, Requirements, Challenges, and a Unified Reference Architecture, arXiv: v2, 2016.

7. Motivation IoT-based applications face many challenges
Huge numbers of sensors and actuators
Mixed-critical applications that require real-time actions
Need of automated systems based on context-aware management for adapting their behavior to current environment conditions
Kamienski, C., et. al, Application Development for the Internet of Things: A Context-Aware Mixed Criticality Systems Development Platform, Computer Communications, October 2016.

8. Motivation
The development of IoT-enabled applications still depends on specific knowledge and tools that prevents it to be widespread
Many software components have to be engineered from scratch requiring huge amounts of effort
Developers must have a deep understanding of
Technologies
New application domains
The interplay with legacy systems
Kamienski, C., et. al, Application Development for the Internet of Things: A Context-Aware Mixed Criticality Systems Development Platform, Computer Communications, October 2016.

9. Motivation
IoT development presents significant challenges for inexperienced developers
Wide range of expertise required, to incorporate hardware, software and networking issues
Lack of integrated development tools
Different programming languages required
Available tools aimed at professional developers with high flexibility and extensive features
Steep learning curve for entry-level developers
Pramudianto, F., Eisenhauer, M., Kamienski, C., Sadok, D., Souto, E., Connecting the Internet of Things Rapidly Through a Model Driven Approach, WF-IoT 2016.

10. Development of IoT-based Applications for Smart Cities
IoT and Smart Cities
Development of IoT-based Applications for Smart Cities

11. Internet of Things (IoT)
The basic idea of IoT is the pervasive presence around us of a variety of things or objects (such as Radio-Frequency IDentification (RFID), tags, sensors, actuators, mobile phones, etc.) – which, through unique addressing schemes, are able to interact with each other and cooperate with their neighbors to reach common goals
Atzoria, L., Ierab, A., Morabitoc, G., "The Internet of Things: A survey", Computer Networks, 54(15), pp , October 2010.

12. Internet of Things (IoT)
Sensors & Actuators
Connectivity
People & Processes

13. Internet of Things (IoT)
Sensors & Actuators
Connectivity
People & Processes

14. Array of Things (Chicago) (arrayofthings.github.io)

15. Smart Cities
Cities are smart when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory governance
Caragliu, A., Del Bo, C., Nijkamp, P. (2011) “Smart Cities in Europe”, Journal of Urban Technology, vol. 18, no. 2, pp

16. Smart Cities (by Cisco)

17. Urban Computing
Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human, to tackle the major issues that cities face, e.g., air pollution, increased energy consumption and traffic congestion
Zheng, Y. et. al, "Urban Computing: Concepts, Methodologies, and Applications", ACM Transactions on Intelligent Systems and Technology, 6(2), July 2014.

18. Urban Computing
Zheng, Y. et. al, "Urban Computing: Concepts, Methodologies, and Applications", ACM Transactions on Intelligent Systems and Technology, 6(2), July 2014.

19. Urban Computing
Zheng, Y. et. al, "Urban Computing: Concepts, Methodologies, and Applications", ACM Transactions on Intelligent Systems and Technology, 6(2), July 2014.

20. Software Development for IoT
Development of IoT-based Applications for Smart Cities

21. Application Development for IoT
What is needed to facilitate and streamline software/application development for IoT?
Broadly, two classes of software tools
Backend: Middleware Components
Frontend: GUI Components

22. Software Architectures for IoT
Software architecture encompasses the set of significant decisions about the organization of a software system
IoT-A Project
Architectural Reference Model
Preliminary set of buildings blocks
IoT Domain Model
IoT Services
Virtual Entities
IoT-A Functional Model
Different functionalities for IoT

23. IoT-A Architecture

24. Design Issues of Middleware for IoT
Rapid software development of interoperable IoT services and applications requires
Middleware components
System development tools
System deployment tools
Common middleware services used by different application domains are necessary
Avoid developing extremely focused and vertical applications not able to interact
Razzaque, M., Milojevic-Jevric, M., Palade, A., Clarke, S. "Middleware for Internet of Things: A Survey", IEEE Internet of Things Journal, 3(1), February

25. Middleware Functional Requirements
Resource discovery
Ex: sensors, actuators
Resource management
Ex: QoS
Data management
Acquisition, processing, storage
Event management
Code management
Code allocation, code migration
Razzaque, M., Milojevic-Jevric, M., Palade, A., Clarke, S. "Middleware for Internet of Things: A Survey", IEEE Internet of Things Journal, 3(1), February

26. Middleware Nonfunctional Requirements
Scalability
Millions of sensors
Real time or timeliness
Quick response time
Reliability and availability
Service continuity
Security and privacy
Monitoring systems may disclose personal information
Ease-of-deployment
Deployment should not require expert knowledge
Popularity
Continuous support and extensions
Razzaque, M., Milojevic-Jevric, M., Palade, A., Clarke, S. "Middleware for Internet of Things: A Survey", IEEE Internet of Things Journal, 3(1), February

27. Middleware Architectural Requirements
Programming abstraction (API)
Interoperable
Service oriented
Adaptive
Context-aware
Autonomous
Distributed

28. Data Management – Data Architectures
Data management is challenging in IoT-based Applications for Smart Cities
Many requirements
Real-time and batch processing
Scalability
Many existing frameworks
Apache Kafka, Hadoop, Spark, Storm, Samza, etc.
Data Architectures
Lambda Architecture
Kappa Architecture

29. Lambda Architecture (Inventor: Nathan Marz)

30. Lambda Architecture (Inventor: Nathan Marz)

31. Lambda Architecture (Inventor: Nathan Marz)

32. Apache Kafka

33. Lambda Architecture - Analysis
Advantages
Captures requirements of both batch and stream processing
Highlights the problem of data reprocessing when code changes
Retains input data unchanged
Disadvantage
Two different codes must be maintained: batch and stream

34. Kappa Architecture (Inventor: Jay Kreps)

35. Kappa Architecture (Inventor: Jay Kreps)

36. Kappa Architecture (Inventor: Jay Kreps)

37. Context Aware Systems
“Context-aware systems are able to adapt their operations to the current context without explicit user intervention and thus aim at increasing usability and effectiveness by taking environmental context into account”
Baldauf, M., Dustdar, S., Rosenberg, F., “A survey on context-aware systems”, Intl., Journal of Ad Hoc and Ubiquitous Computing, 2(4), 2007.

38. Context Aware Systems Context Entities
Usually “context” refers to locations
“any information that can be used to characterize the situation of entities that are considered relevant to the interaction between user and an application, including the user and the application themselves.”
Dey, A., Abowd, G., “Towards a better understanding of context and context-awareness”, Workshop on the What, Who, Where, When and How of Context-Awareness, 2007.
Entities
Places: rooms, buildings etc.
People: individuals, groups
Things: physical objects, computer components etc.

39. Development Tools for IoT
Tools for supporting developers to create IoT applications
Need
A fully integrated tool that frees IoT developers from dealing with specific technological challenges
Network protocols, data formats, data management
The process of developing IoT applications is still a complex task
While the number of connected things increases rapidly and the volume of data is getting overwhelming

40. Development Tools: IoTLink
IoTLink is designed to support inexperienced developers connecting different IoT components rapidly
IoTLink adopts a model-driven development (MDD) approach
Once the model has been defined, IoTLink is able to generate the software artifacts, representing the physical objects
Implemented as an Eclipse plugin
IoTLink is faster than writing Java code for linking components
PhD Thesis
MDD Increase the abstraction level for developers.
Why this is better?
Solution & problem are defined in the same language
Communication between domain experts and developers are easier to maintain.
Why this is worst?
Lost control of implementation details
MDD tools are not as advanced as 3rd gen programming language
Mashup development
End user developments
Similar to MDD for composing, but for Web based service composition
Based on flow based programming (Morrison, 2010)
IBM’s Node-RED (IoT)

41. Development Tools: IoTLink
Support CEP: SQL-like language to recognize event patterns, sum, avg streams of data
Support different input : Web Services, MQTT, Serial, OPC Client
Support different output: Web Services, MQTT, Drools Rule engine, actuations
Support Template:
OOP paradigm (classes as templates). Convert an object to a class.
Discover the available Arduino

42. Development Tools: IoTSuite
IoTSuite aims to make IoT application development easy for stakeholders
Objectives
To separate IoT application development into different concerns
To provide high-level modeling languages for IoT
To automate IoT application development where possible
Compiler, mapper and linker provide automation
Developer and User versions
PhD Thesis

43. Development Tools: IoTSuite

44. Development Tools: Node-RED
A visual tool for wiring the Internet of Things
hardware devices, APIs and online services
Browser-based flow editing
Flows can be deployed to the runtime in a single-click
Built on Node.js
Node.js allows the creation of Web servers and networking tools using JavaScript
Social Development
Flows are stored using JSON which can be easily imported and exported for sharing with others
Supported by IBM

45. Development Tools: Node-RED
A visual tool for wiring the Internet of Things
hardware devices, APIs and online services
Browser-based flow editing
Flows can be then deployed to the runtime in a single-click
Built on Node.js
Node.js allows the creation of Web servers and networking tools using JavaScript
Social Development
Flows are stored using JSON which can be easily imported and exported for sharing with others

46. Development Tools: Node-RED
A visual tool for wiring the Internet of Things
hardware devices, APIs and online services
Browser-based flow editing
Flows can be then deployed to the runtime in a single-click
Built on Node.js
Node.js allows the creation of Web servers and networking tools using JavaScript
Social Development
Flows are stored using JSON which can be easily imported and exported for sharing with others

47. Development Tools: FIWARE
FIWARE’s mission: “to build an open sustainable ecosystem around public, royalty-free and implementation-driven software platform standards that will ease the development of new Smart Applications in multiple sectors”
FIWARE: making it easier to connect to the Internet of Things and to process and analyze Big Data
FIWARE provides
An OpenStack-based cloud environment
A set of open standard APIs
European project

48. Development Tools: FIWARE
FIWARE’s mission: “to build an open sustainable ecosystem around public, royalty-free and implementation-driven software platform standards that will ease the development of new Smart Applications in multiple sectors”
FIWARE: making it easier to connect to the Internet of Things and to process and analyze Big Data
FIWARE provides
An OpenStack-based cloud environment
A set of open standard APIs

49. Cloud-based Commercial IoT Tools
Salesforce IoT Cloud
Amazon AWS IoT
SensorCloud
ThingWorx IoT
Xively IoT Platform

50. Cloud-based Commercial IoT Tools
Salesforce IoT Cloud
Amazon AWS IoT
SensorCloud
ThingWorx IoT
Xively IoT Platform

51. Cloud-based Commercial IoT Tools
Salesforce IoT Cloud
Amazon AWS IoT
SensorCloud
ThingWorx IoT
Xively IoT Platform

52. Development Tools for Smart Homes: openHAB
openHAB is a software for integrating different home automation systems and technologies into one single solution
Features
Automation: rule engine, triggers and scripts
Single user interface for all systems
Vendor-neutral and hardware/protocol-agnostic
Developed in Java (runs in JVM)
Different home automation technologies
Fully open source

53. Development Tools for Smart Homes: openHAB
openHAB is a software for integrating different home automation systems and technologies into one single solution
Features
Automation: rule engine, triggers and scripts
Single user interface for all systems
Vendor-neutral and hardware/protocol-agnostic
Developed in Java (runs in JVM)
Different home automation technologies
Fully open source

54. A System Development Platform (SDP) for IoT-based Applications
Development of IoT-based Applications for Smart Cities

55. IMPReSS Project
IMPReSS project was a joint EU-Brazil project ( )
IMPReSS aimed at providing a Systems Development Platform (SDP) for enabling rapid and cost effective development of mixed criticality complex systems involving Internet of Things and Services (IoTS) and at the same time facilitates the interplay with users and external systems

56. IMPReSS Project
IMPReSS project was a joint EU-Brazil project ( )
IMPReSS aimed at providing a Systems Development Platform (SDP) for enabling rapid and cost effective development of mixed criticality complex systems involving Internet of Things and Services (IoTS) and at the same time facilitates the interplay with users and external systems

57. IMPReSS Domain Model

58. IMPReSS Architecture: Stakeholders (roles)
Creator: IMPReSS Creator, who contributes to the development of the IMPReSS SDP
Developer: Application Developer, who uses IMPReSS SDP to develop Applications
Integrator: Solution Integrator, who installs, configures and deploys the Application and connects it with other external services and hardware components
Recipient: Final Recipient, person affected by the solution, such as owner of a house or audience of a theater

59. IMPReSS Architecture Views

60. IMPReSS Architecture: Creator’s View

61. IMPReSS Architecture: Developer’s View

62. IMPReSS Architecture: Integrator’s View

63. IMPReSS Architecture: Recipient’s View

64. IMPReSS Data Manager
The Data Manager provides data and knowledge storage and features retrieval, as well as implements data analysis, stream management and data mining
The Data and Knowledge Storage maintains information such as historical sensor data and inferred knowledge, enabling the storage and retrieval of both raw data and enhanced information for other components

65. IMPReSS Data Manager

66. IMPReSS Context Manager
The Context Manager is responsible for adapting system's behavior according to the current context conditions without explicit user intervention, and providing different features such as context templates, context models, context reasoning engine, and algorithms for sensor and data fusion

67. IMPReSS Context Model: Entities

68. IMPReSS Context Manager

69. Adapting Behavior Dynamically
Rules: Drools (JBoss)
When rules are changed in Drools Guvnor, it is possible to reload them into the application executing Drools Expert
Fusion: Esper (EsperTech)
Fusion criteria (SQL like) can be dynamically changed
Actions: Java
Actions can be dynamically loaded by Java
Tracker
Finds implicit contexts and counts context execution

70. Context Manager Demo

71. IMPReSS Resource Manager
The Resource Manager provides mixed criticality resource management at device and application levels
Device-level Resource Manager
Controls the power feed to IoT devices
More critical devices are provided with power in case of a power outage
Application-level Resource Manager
Controls how IoT applications access distributed IoT Resources (i.e., sensors and actuators)

72. IMPReSS Resource Manager

73. IMPReSS Communication Manager
XMPP-based Communication Manager
Provides means for abstraction, discovery and seamlessly interconnection of the physical world, i.e. sensors and actuators, with logical entities in order to allow the development and deployment of IoT applications even to non-technical users
Provides security features
IoT communication infrastructure
Resource Adaptation Interface (RAI)
IoT Resource Catalogue
Lightweight Middleware Infrastructure
Network Management Infrastructure
IoT communication infrastructure consists of four components:
a) The Resource Adaptation Interface (RAI) enables seamless and automatic virtualization of heterogeneous physical IoT resources;
b) The IoT Resource Catalogue discovers and keeps track of available IoT resources in the network;
c) The Lightweight Middleware Infrastructure allows the system to scale down or up easily for different types of physical communication gateways;
d) The Network Management Infrastructure reduces network management effort by automating the association between the physical deployment of IoT gateways and services that access them.

74. IMPReSS Communication Manager

75. GUI: IMPReSS Context Modeler
A Graphical Web-based User Interface for configuring context entities: place, resource, fusion, rule, action, activity, context
Context Designer
Tool aimed at dealing with the Context Graph
Context Graph
Represents a context where vertices are entities and edges are data flow between entities

76. Context Modeler

77. Context Monitoring

78. Context Designer

79. Energy-Efficiency Management in Smart Buildings
Development of IoT-based Applications for Smart Cities

80. Scenario: Context-Aware Energy Saving

81. Context-Aware Energy Saving: Initial Rules

82. Context-Aware Energy Saving: Final Rules

83. Scenario: Context-Aware Energy Saving Instantiation of the IMPReSS Architecture

84. Scenario: Energy Consumption Monitoring in Historical Buildings (Teatro Amazonas)

85. Scenario: Energy Consumption Instantiation of the IMPReSS Architecture

86. Energy Consumption Monitoring in Historical Buildings (Teatro Amazonas)

87. Energy Consumption Monitoring in Historical Buildings (Teatro Amazonas)

88. Device-level Mixed-Criticality - Power Outage in a University Campus
Device-level Mixed-Criticality Resource Management can be utilized to handle power outages at UFPE
Rules
When 70% of the energy is available all the devices below criticality value 100 will be turned off
When 40% of the energy is available all the devices below criticality value 200 will be turned off
When 10% of the energy is available all the devices below criticality value 300 will be turned off

89. Device-level Mixed-Criticality - Power Outage in a University Campus

90. Scenario: Power Outage Instantiation of the IMPReSS Architecture

91. Development of IoT-based Applications for Smart Cities
Research Challenges
Development of IoT-based Applications for Smart Cities

92. Automated Development of IoT-based Applications for Smart Cities
Facilitating IoT-based application development is of paramount importance
Current state of the art is not enough for dealing with an ever-increasing demand
Smart Cities pose new and complex challenges to software development
Ex.: Huge amounts of data that must be collected, stored, processed and transformed into services for citizens
Ideas from automated service composition may be used

93. Applications for IoT with Cloud and Fog Computing
Future architectures of data collection, storage and processing will rely on a combination of cloud and fog
Fog is aimed at putting computing resources closer to users, to the network edge, thus decreasing delay and broadening coverage
How to manage the life cycle of IoT-based applications in a platform that combines cloud and fog?

94. (Big) Data Management for IoT-enabled Smart Cities
One of the main challenges motivated by IoT
Data model: how should be a data model to deal with IoT and Smart Cities?
Data collection: how should sensor data be collected from a networking point of view?
Data storage: how should data be stored efficiently and effectively?
Data Fusion: which data? when? how?
Data processing
Data architectures?
Analytics and context-awareness?
Event management?

95. IoT-based application development and the network softwarization approach
New technological advances in the networking area may impact or even impose new methodologies for dealing with application development
Networks are being softwarized and virtualized
Software Defined Networks (SDN)
Network Functions Virtualization (NFV)
Many network functions needed for in an IoT architecture will be softwarized
Middleware should be adapted to cope with these new developments

96. Considerations about connectivity for IoT applications
Connectivity is not available everywhere
Disconnections are very frequent
A pervasive connectivity infrastructure in our cities is still to come
How should applications deal with frequent disconnections?
Challenge been analyzed for more than 20 years in the mobile computing area
New requirements, new challenges, new solutions…

97. Development of IoT-based Applications for Smart Cities
Final Remarks
Development of IoT-based Applications for Smart Cities

98. Final Remarks
Application development for IoT and Smart Cities is challenging
Software components engineered from scratch
Huge numbers of sensors and actuators
Different programming languages
Data management
Streamlining software/application development for IoT-based Smart Cities
Backend: Middleware Components
Frontend: GUI Components

99. Final Remarks
Many initiatives exist, but a general solution is yet to come
Academic and Commercial
The IMPReSS did not achieve many of its ambitious goals
But raised a lot of interesting questions
Research Challenges
Automated Development
Cloud and Fog Computing
Data Management
Network softwarization
Connectivity

100. Development of IoT-based Applications for Smart Cities
Carlos Kamienski