Internet of Things: Research Directions

Davide Nardone
May, 2016
Internet of Things:
Research Directions
PARTHENOPE
UNIVERSITY

Research Directions for IoT
1. Introduction
2. IoT vision and contexts
3. The main IoT research directions:
A. Massive scaling
B. Architecture and Dependencies
C. Creating knowledge and Big Data
D. Robustness
E. Openness
F. Security
G. Privacy
H. Humans in the Loop
4. Conclusion
Outline

• Using the Internet as a global platform for communication, dialogue,
calculation and the interconnection of devices has been shaping a new
world: a combination of new methods and technologies which are the
basis of a variety of smart context.
• The creation of these scenarios has been investigated from different
and disjointed scientific communities as:
• Mobile Computing (MB);
• Pervasive Computing (PC);
• Wireless Sensor Networks (WSN);
• Cyber-Physical-Systems (CPS).
Introduction

• Since the technology progresses, the overlapping and the combination
of topics and research questions has increased to such a point that
there are no more specific definitions for the previous mentioned fields.
• Many researches in the fields of IoT, PC, MC, WSN and CPS often rely
on underlying technologies such as Machine Learning, Real-Time
Computing, Signal Processing, Security, etc.
It’s clear the need for cooperation between the several communities for the
improvement and the resolution of various interesting research problem
regarding these environments.
Introduction (cont.)
Machine
Learnig
Security Privacy

• For many people, the concept of Smart City is no longer a vision so far
away.
• Many infrastructures are already equipped with no sensor and actuator
able to:
• Monitoring entire system and
• Interact with each other, in order to exchange information and so to
improve the quality of one or more services.
IoT vision and contexts

• IoT: an essential and integrated infrastructure on which
many application and services can be run.
• System of systems that synergistically interact with each other to
create new and not predicable services.
IoT vision and contexts (cont.)

What is that vision?
Examples
• Global transport systems
• City without traffic lights and 3D transport vehicles
• Buildings that will control energy, health, comfort
• People with bionic fragments for detecting
physiologic parameters.
• etc
Examples
• Input: Biometric systems
• Output: Holographic display
Some examples

• The continuing growth of the sensors and the improving of the
related processes will produce continuous qualitative changes in
different contexts.
IoT benefits

• Given the massive amount of devices connected today (~ 24 billion),
the research [1] has reported that by 2020 the revenues achieved from
the sale of smart devices and the services offered to them will be
about $ 2,500 billion dollars.
Industry /use case Economic value
1. Smart car $ 600 billions
2. Clinical Remote Monitoring $ 350 billions
3. Assisted Life $ 270 billions
4. Home and Buildings Security $ 250 billions
5. Car insurance “Pay-As-You-Drive” $ 245 billions
6. New business models for car usage $ 225 billions
7. Smart meters $ 105 billions
8. Traffic management $ 100 billions
9. Electric vehicle charging $ 75 billions
10. Building automation systems $ 40 billions
IoT benefits (cont.)

IoT challenges and benefits
? Challenges ✓ Benefits
• People / Society • Better and faster information
• Security / Privacy • Improving education
• Authority • Regulation / Legislation
• Standard / Policies • Life quality
• Resource Efficiency • Greater awareness
• Technology Architecture / Infrastructure • Increased production
• Costs • Wider experiences
• Pollution / Prevention disasters • Better decision making
• Innovation Management • Resource optimization
• Energy / Power Consumption • Human error removal
• Growth Data and analysis • More services

• The research area required to obtain such IoT vision requires a
significant study into many directions.
Research
A. Massive scaling
D. Robustness
E. Openness
F. Security
G. Privacy
H. Humans in the Loop

• Recent studies [2] have estimated that ~50 billion devices will be
connected to the internet by 2020.
A. Massive Scaling

Massive Scaling (cont.)
• Some of the many questions that we ask are:
• Will be IPV6 enough?
• Will emerge new standards and protocols?
• How will the devices (including mobile devices) to be
discovered?
• How will the real-time and reliability issues to be supported?
• etc.
It’s almost unlikely that any solution will be immediately
become the norm!

Simulation of large-scale IoT systems (using particular operating system
[3][4]) for the evaluation and optimization of specific aspects:
• Application Layer
• Energy consumption
• Code reuse
• Network connection
• etc.
Among the different existing approaches, a hybrid simulation
environment based on a combination of framework with simulators at the
system-level:
• It would reduce the gap between the research and the
implementations;
• It also would strengthen the simulation studies in the IoT field.
Massive Scale Simulation

• Since billions of devices are connected to the internet, it’s necessary
to provide an appropriate architecture that easily allow: connectivity,
communication, control and useful applications.
MOBILE APPROACH
Advantages (+) Disadvantages (-)
• Unlimited application development. • Interference problems due the sharing of
sensors and systems implementation
among multiple applications.
• Automatic controls for the execution of an
application on a specific platform.
• Dependence of IT methodologies based
on the assumption about the environment,
the hardware platform, naming, control
and various semantic devices.
• How all these “objects” will interact with each other?

Let’s suppose the integration of different systems responsible for the electricity
management (control thermostats, windows, doors, etc.) and the health care
(ECG measurement, temperature, sleep, activities, etc.).
Advantages
• Information sharing: this would allow the electricity management systems to
regulate the temperature of the rooms according to the physiological status of
the residents.
• Integration between systems: this would avoid unpleasant situations; for
instance the system will deactivate medical devices to save power while they
are used as suggested by the health care system.
• Sharing sensors and actuators: this would reduce the costs of
implementing/distribution, improving the aesthetics of the rooms and reducing
the flow of content.
Example: Architecture and Dependencies

Challenges
• Each system has its own assumptions and strategies for its control.
• A lack of knowledge about the other systems would cause many conflicts when
they are integrated without particular attention.
Example of interference
• The health care system could detect depression and decide to turn on all the
lights. On the other hand, the electricity management system may decide to
turn off the lights due to the absence of motion.
Example (cont.)

Research topics to be addressed
• Development of new approaches for the detection and
resolution of dependencies between applications.
• Autonomous decentralized architecture inspired by the
connection of sensor nodes.
• Introduction of cloud structures, architectures guided by
events, disconnected operation and synchronization.
IoT: Architecture and Dependencies

• IoT is the next technological revolution that is estimated to produce
over 44 Zettabyte of data (4.4e+10 Terabyte) by 2020 [5].
• Almost all the “objects” or devices will have an IP address, and will
be connected to each other.
• Given the incredible amount of data generated, new mechanisms will
be needed for:
• Collecting and selecting data
• Data pre-processing
• Turn raw data into useful information
• Extracting information
• Data interpretation

The main Big Data challenges in the IoT context are:
• Reliability
• Data security
• Big Data collection
• Identification of redundant data
• Data interpretation and creation of useful information that
includes noise
• Development of inference techniques that do not suffer of the
Bayesian and Dempster limitation
• Correct data binding
• Ect.
IoT & Big Data: Main challanges

• Reliability is one of the most important aspects of the use of Big
Data.
Development of new calibration techniques and transport protocols:
• Ensure that following inference do not operate on incorrect data
or data with too many missing data!
• Ensure “safe” operations of the actuators
• Draw up good decisions.
Possible solutions
• Associate a confidence level (in the form of probability) to the
information derived from the date.
• Using Fuzzy Logic [4].
• Minimizing the number of false negatives and false positives.
IoT & Big Data: Reliability

• IoT has launched new security challenges that cannot be addressed
by using traditional security mechanism.
• Addressing the security concerns of the IoT requires a paradigm
shift.
For example, how do you deal with the situation where the fridge and coffee
maker are equipped with hidden Wi-Fi access and spammers?
According to the experts, the problem of data security has two major
aspects:
1. Confidentiality
2. Protection
IoT & Big Data: Security

Companies need only to collect data related to their business, and this
requires:
• Filtering techniques on redundant data and protection data
techniques.
• Efficient mechanisms that include software and protocols.
Solutions
• One approach is to use devices with sensors for data collections, in
combination with transport protocols such as: Message Queue Telemetry
Transport (MQTT) and Data Distribution Service (DDS).
Example
One of the most important transportation companies in the world, UPS, uses
sensors in its vehicle to improve delivery performance and reduce coast.
• These data help UPS out in reducing fuel consumption, harmful emissions
and waste time.
IoT & Big Data: Big Data Collection

Problems in the collection of Big Data
By the statistic coming from [6], it’s shown the various challenges that
companies have faced with the data collection activities.
19%
13%
9%
22%
25%
12%
Big Data challenges Problems in data collection
Unreliable data collection
Slowness in data collection
Cumbersome amout of data to
analyze properly
Techniques for analysis and
data processing premature
Existing business processes
not flexible for the efficient
collection of data

• Not all device provide useful information.
• It’s not easy to filter data in real-time.
53%
47%
Rationale: Methods of inefficient
data collection.
Do not track their projects with quantifiable metrics
They do not measure the progress of their IoT
projects
13%
87%
96% of organizations have difficulties
to filter large amounts of redundant
data.
Able to collecting data efficiently
Recognize the benefits for their business
Survey conducted by Parstream [6]
IoT & Big Data: Identifying redundant data

IoT offers an exciting prospect from the point of view of Big Data, but
it’s necessary to:
• Optimize the entire setup for managing the IoT impact in the
context of the Big Data.
• Solve the problems concerning the security & the privacy and
the efficient data collection.
There’s hope that these issues may be efficiently solved since both IoT and the
infrastructure for its management are in the early stages.
IoT & Big Data: Summary

IoT vision: Application based on sensors, actuators and communication
distributed platforms connected to a network of “objects”.
In such distributions it is common for devices to know:
• their positions
• nearby devices cooperating having
• synchronized clocks
• Consistent parameter setting such as: sleep, wake, battery levels and security
key pairs.
Challenges
The deterioration of these conditions cause the break up of the clocks that:
• Causes the nodes to have different time shift, that leads to failures
within the system.
Solutions
Synchronization techniques are used in this context [7].
D. Robustness

• Although it is recognized that the synchronization of the clock is
necessary, this simple concept is too general.
• More serious problem is the physical relocation of the sensor nodes.
• (e.g., random-temporal displacement of one or more nodes)
The higher the entropy, the greater the disorder and less energy is available in the
system to do useful things.
Robustness (cont.)

- Goal: Create a robust system that can cope with changes in its
operations without suffering serious damage or loss of functions.
How can it possible to keep a persistent, dynamic and mobile IoT
system?
- Method: The consistency services required (entropy) must to be
combined with other approaches for producing a robust system,
ensuring:
• Persistence: Automatic operations for organization, monitoring,
diagnostic and repair.
• Dynamism and mobility: Methods for the development of
reliable code, online tolerance errors and locally debbuging.
Robustness: consideration

The majority of sensor-based systems were closed systems.
• (e.g., cars, planes, and ships have had network sensor systems
operating largely within the vehicle itself).
The capabilities of these systems are increasing rapidly.
Some example are:
• Cars transmitting information assistance.
• Airplanes sending technical real-time information.
• Cars communicating with each other and control each other to avoid
collisions.
• Doctors who interact in real-time with their patients analyzing
physiological data, automatically loaded.
In order to achieve such benefits, the systems requires openness, but…
What does it mean the Openness of such systems?
E. Openness

Research problems to the openness support
• Rethinking of the current composition techniques, analysis and the respective
tools.
• New communication interface to enable the efficient exchange on information
between the systems.
• Difficulties with security and privacy.
It’s necessary to provide a “fair balance” between:
Functionality, Security and Ease of Use.
Openness (cont.)

• Key issue which will become more and more intense every day.
• The greater the volume of sensitive data (Big Data’s V) that
moves in IoT, the higher is the risk of data theft and identity,
manipulation of devices, data faking, IP theft, etc.
F. Security
Problems
• IoT products are often sold with old
embedded operating systems and software
products.
• Many of these systems/software (designed
to be safe) are still vulnerable.

The vulnerabilities found in the IoT have drawn attention to the topic
of “security”. Some types of threats are:
• Failures of devices (disrupt services)
• Violation of the privacy (steal information)
• Control of devices (take control)
IoT: Vulnerability

Given the particular nature of these devices, IoT has the following
challenges to face:
1. Critical functions
2. Replication
3. Difficulty in repairing
4. Security assumptions
5. Long life cycle
6. etc.
What are the challenges to be addressed to ensure the security of
embedded devices and to secure IoT?
Security: IoT challenges
Solutions used for standard computer do not solve the IoT security
issues of embedded devices, due to the their heterogeneity.

Security features Implementation on embedded devices
Secure boot Security standard that ensures that the device boots using only
secure software (software signature check).
Secure code updates Safe methods for updating the code, bugging fixes, security
patches, etc. (use the signed code).
Data security Prevent unauthorized access to the device, encrypting the data
storage and / or communication.
Authentication All communications with devices should be authenticated with
strong passwords.
Secure communication Communication to and from the devices must be secured using
encrypted protocols (SSH, SSL, etc.).
Protection against cyber attacks Integrated firewall to restrict communications to only trusted host,
blocking hackers before they can launch attacks.
Intrusion detection and security
monitoring
Detection and communication of disabled login attempts and other
potential malicious activity.
Tamper detection on device New processors / cards that have capacity of device tampering
detection.
Self Healing Methods for "healing" from cyber attacks (e.g., code updates).
Security requirements

• Significant hardware supports [8] encryption, authentication and key
tampering.
• Ensure the connection between the gateway devices and the
corporate network or factory.
• Greater effort to protect the IoT data, to ensure consumer privacy
and functionality of the company.
• Solid action plan for the installation of security updates.
What is left to do?

IoT may become the term most in vogue
of the day at the workplace.
87% of the customers do not know what
the IoT is [9], but they are aware of:
1. Granting of their personal (to retailers)
data by means of devices.
2. Annoyed in sharing their data.
Requirement for companies to develop
better criteria for communicating the
utilization and sharing data of its
customers.
G. Privacy

Solutions
1. Specific privacy policies for each domain / system and
2. Privacy reinforcement in the IoT infrastructure of the IoT
applications.
IoT paradigm
1. Expression of users' requests for accessing to the data and the
policies they have adopted.
2. Assessment of the requests to ensure whether or not their
permission.
• Advantages: The ubiquity and interaction involved in IoT will provide
many useful benefits and services.
• Disadvantages: It will create many circumstances for privacy
violation.
Privacy (cont.)

The current legislation does not properly express the privacy policies for
the following requirements:
1. Expression of different types of contexts (time, space, etc.).
2. Representation of different types of data owners (human and
non-human entities).
3. Representation of high-level aggregation requests by anonymous
functions.
4. The need to support not only adherence to privacy for queries of
data, but also privacy on request to set a system’s parameters.
5. Allow dynamic changes in policies
One of the main problem concerns the interaction between different
systems (each with its own privacy policies), which could give rise to
many inconsistencies.
Privacy problems

DEF: Human-in-the-loop (HITL) is defined as a model that requires
human interaction.
• Many IoT applications involves the interaction between objects and
humans.
Advantages: HITL offers exciting opportunities to a wide range of
applications (i.e. energy management, health care, etc.).
Disadvantages: Hard modeling of human behavior because of its
complex physiological, psychological and behavioral aspects.
New research is needed to:
1. Increase the control of HITL in designing systems;
2. Solve the following three challenges.
H. Human in the loop

First challenge
• The need for a comprehensive understanding of the complete
spectrum of types of human-in-the-loop controls..
The wide heterogeneity and the subtle differences of HITL generates
various problems in the creation of a single system.
The HITL applications can be classified into four categories:
1. Global system control by human being (e.g., surveillance
monitoring).
2. Passive monitoring of the human system, adopting appropriate
measures (e.g., eating habits).
3. Modeling of human physiological parameters.
4. Hybrids of 1, 2 and 3.
Main challenges of HITL (1)

Second challenge
• The need for extensions to system identification or other techniques to
derive models of human behaviors..
Advantages: The identification systems are very powerful tools for
creating models.
Disadvantages: Very difficult to apply to human behavior.
Example:
• If we were to use "identification techniques" to shape a human being who is
suffering from depressive illness, there would be many questions about:
• Type of inputs;
• Possible states;
• Change states according to different physiological, psychological and
environmental states.
The existence of a formal or estimated model of the "human behavior"
could help us to close the loop!

Third challenge
• Determining how to incorporate human behavior models into the
formal methodology of feedback control.
Example:
• Optimization of human behavior based on reports of accidents and incidents
that occur during operation of an electric power system:
• Components Models of Emotion (CME) for observing, recording, and
analyzing the emotional component of the operator's behavior.
• Simulation of the dynamic behavior of an operator in performing
operations in a context that leads to an error.
If we could incorporate into the system such a behavioral model, we
would be able to analyze various security features of the entire system.

• IoT is a chance to make our world smarter.
• IoT is a very profitable field of application for companies.
• IoT has a good implications in different application fields (health,
transport, etc.).
• The ongoing research needs to intensify to benefit from the above
mentioned points and to solve new problems that arise due to:
• Growing number of connected devices;
• Openness of the systems;
• Continuous privacy and security problems.
Expectations
It is hoped that there is more cooperation between the research
communities in order to:
• Solve the myriad of problems as soon as possible;
• Avoid reinventing the wheel when a particular community solves a problem.
Conclusion

[1] Gartner Says the Internet of Things Installed Base Will Grow to 26 Billion Units By
2020 (http://www.gartner.com/newsroom/id/2970017).
[2] http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov IoT_IBSG_0411FINAL.pdf
[3] P. Levis, N. Lee, M. Welsh, and D. Culler. TOSSIM: Accurate and scalable simulation
of entire TinyOS applications. In SenSys'03: Proceedings of the First International
Conference on Embedded Networked Sensor Systems.
[4] K. Shang, Z. Hossen. Applying fuzzy logic to risk assessment and decision-making
[5] https://www.emc.com/leadership/digital-universe/2014iview/index.htm
[6] Internet of things (Iot) Meets Big Data anD analytIcs: a survey of Iot stakeholDers.
Sponsored by Parstream. December 12, 2013.
[7] M. Maroti, B. Kusy, G. Simon, and A. Ledeczi, “The flooding time synchronization
protocol,” in Proc. 2nd Int. Conf. Embedded Netw. Sens. Syst. (ACM SenSys’04), Nov.
2004.
[8] S. Ravi, A. Raghunathan, and S. Chakradhar, “Tamper resistance mechanisms for
secure, embedded systems,” in Proc. 17th Int. Conf. VLSI Des., 2004.
[9] http://go.pardot.com/l/69102/2015-07-12/pxzlm.
[10] http://www.globaltelecomsbusiness.com/article/2985699/Connected-devices-will-be-
worth-45t.html
[11] F. Osterlind, A. Dunkels, J. Eriksson, N. Finne, and T. Voigt. Cross-level sensor
network simulation with cooja. In Local Computer Networks, Proceedings 2006 31st IEEE
Conference.
References

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