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Challenges and opportunities of internet of things


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To date, most Internet applications focus on providing information, interaction, and entertainment for humans. However, with the widespread deployment of networked, intelligent sensor technologies, an Internet of Things (IoT) is steadily evolving, much like the Internet decades ago. In the future, hundreds of billions of smart sensors and devices will interact with one another without human intervention, on a Machine-to-Machine (M2M) basis. They will generate an enormous amount of data at an unprecedented scale and resolution, providing humans with information and control of events and objects even in remote physical environments. The scale of the M2M Internet will be several orders of magnitude larger than the existing Internet, posing serious research challenges. This paper will provide an overview of challenges and opportunities presented by this new paradigm.
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Challenges and Opportunities of Internet of Things
Abstract - To date, most Internet applications focus on providing
information, interaction, and entertainment for humans.
However, with the widespread deployment of networked,
intelligent sensor technologies, an Internet of Things (IoT) is
steadily evolving, much like the Internet decades ago. In the
future, hundreds of billions of smart sensors and devices will
interact with one another without human intervention, on a
Machine-to-Machine (M2M) basis. They will generate an
enormous amount of data at an unprecedented scale and
resolution, providing humans with information and control of
events and objects even in remote physical environments. The
scale of the M2M Internet will be several orders of magnitude
larger than the existing Internet, posing serious research
challenges. This paper will provide an overview of challenges
and opportunities presented by this new paradigm.
I. Introduction
Technologies change our life. Out of many emerging
technologies, Internet of Things (IoT), also known as
machine-to-machine (M2M) (where smart devices that collect
data, relay information to one another, process the information
collaboratively, and take action automatically) is a new
paradigm offering both challenges and opportunities.
Over last twenty years innovative information technologies
have wrought significant change in human civilization. For
example, imagine that you were to give a speech in another
city which is normally one hour away from where you live.
Unfortunately, there was a traffic jam on the highway and you
were expected to be late. If this happened 20 years ago,
there was literally no way you can communicate the news to
your audience. Nowadays, if you get stuck on the highway,
you can simply use your cell phone to tell your audience.
This is because cell phones and telecommunication service are
affordable and available to almost everyone. Question: can
technologies do better?
More game-changing capabilities are emerging. First,
new types of sensors enhance our perceptual abilities by
detecting information that humans cannot detect and
collecting such information anytime and anywhere. Second,
robots similarly enhance our ability to act, overcoming our
physical limitations. They can be given greater-than-human
strength and may go where humans cannot. After the recent
earthquake and nuclear disaster in Japan, robots were used to
explore the damaged nuclear plants due to radiation fears [5].
Third, wireless communication and broadband internet
technologies enhance our communication capabilities, a power
that will only grow when 4G wireless [42] and greater internet
bandwidth become available. Fourth, emerging cloud
computing and machine intelligence will enhance our
analytical capabilities. With massive computation
capabilities and more mature machine learning techniques, we
may begin analyzing information that could not be processed
in the past.
Further, humans keep demanding technologies to improve
their lives. Fundamentally, people want to be happier. How
can people be happier? First, humans want more time, money,
and quality of life. Technologies can help humans save
money, enhance their appearance, eat better, and sleep better.
Second, most people want to avoid being in threatening or
troublesome situations. Technologies such as weather
forecasting or fire warning systems help predict future events.
Third, they want to be healthier. Technologies help humans
to provide better and safer environments for healing, to take
care of aging and sick people, and to avoid accident and injury.
Fourth, most people desire companionship. Email, internet
phones and social networks connect people. Finally, people
want to feel special or be respected. Personalized services
and products have become very popular.
Many of the above human desires can be further fulfilled by
emerging M2M and related technologies [12][32][36]. For
the same example given earlier, imagine that you were to give
a speech in another city and you got stuck on the highway.
With the improvement in M2M technology, your calendar and
your car can communicate. If you are expected to be late,
your smart phone will send a message to your audience
automatically telling them approximately how much time they
have to wait. Or even better, the calendar planner can look
up the traffic condition in advance and suggests what time you
should leave. Sensors can monitor the traffic conditions along
the routes to your destination so that you are able to select the
best route to get to the venue on time. There are many
similar examples. The nexus of human needs and emerging
computing and sensor technologies is bringing about a new
digital revolution.
Section II will give an overview of the new paradigm.
Section III will describe the challenges of the new paradigm.
Section IV will discuss the opportunities created by these
Yen-Kuang Chen
Intel Labs
Intel Corporation
Santa Clara, CA, USA
978-1-4673-0772-7/12/$31.00 ©2012 IEEE
II. New Paradigm: Connected Context Computing
Recently, sensor networks, cyber physical systems, and
internet of things have become more common as sensing,
communication, and analytics technologies have matured. In
the future, digital sensing, communication, and processing
capabilities will be ubiquitously embedded into everyday
objects, turning them into the Internet of Things (IoT, or
machine-to-machine, M2M). In this new paradigm, smart
devices will collect data, relay the information or context to
each another, and process the information collaboratively
using cloud computing and similar technologies. Finally,
either humans will be prompted to take action, or the
machines themselves will act automatically.
The world is on the edge of revolutions in both quantity and
quality. Today more things than humans are connected to the
internet [9]. In the near future, the number of connected
devices will be tens or hundreds of times larger than the
number of connected people. It is estimated that 20
households in 2012 years will generate more internet traffic
than the entire internet in 2008 [10]. Furthermore, the
computation capabilities per device are also increasing. For
example, today’s smart phone is about 100,000 times faster
than the ENIAC I computer, built in the 1940s [4].
There are many applications of this new paradigm
[14][16][17], as shown in Figure 1. Some examples are
highlighted below:
1. Smart home: At home, embedded sensors can
understand the human activities and properly adjust
the air temperatures or lighting to reduce our energy
usage without sacrificing human comforts.
2. Economical agriculture: In a farm field, remote bug
traps can detect the outbreak of pests and initiate
spreading the right amount of pesticide. This will
reduce the chance of overspreading and potential
damage to the crops.
3. Vehicle safety: Sensors on a car can help drivers
understand the potential risk of running into each
other; in particular, sensors and inter-vehicle
communication can help us see what we cannot see.
With timely and proper warning, we can reduce the
vehicle collision rate.
4. Assisted living: The population is aging. There is
an increasing need to take care of more elders.
Sensors can help us monitor the health condition of
elders and properly provide help (e.g., reminders of
missing a dose, warning of high blood pressure,
requesting medical emergency).
In short, connected embedded sensors help humans
“hear/see” things that they could not hear or see in the past
and do something that we could not do in the past. This
paradigm shift creates numerous challenges and opportunities
for engineering.
III. Challenges
M2M can be broken down into four major layers as shown
in Figure 2. Sensors collect data, communication units relay
the information collected, computing units analyze the
information, and service layers take action.
In the future, enormous numbers of sensors will be
deployed. The costs of servicing such sensors will be a
major concern. Hence, one challenge is sensor technology
that requires minimal or even zero effort to deploy and
maintain. According to [3], many domestic ubiquitous
computing projects have failed because the complexity of
sensor deployment. Additionally, one important sensor
service cost is battery replacement. It is often almost
impossible to replace sensor batteries once they are in the field.
Therefore, another challenge is low power sensor design, or
designs which do not require a battery change over the
lifetime of the sensor. For example, if a sensor is deployed
on an animal for tracking purposes, the battery of the sensor
Figure 1: M2M application examples. Different rows reflect different human wishes. Different columns reflect who pay for the
Intelligent signa ge & shopping recommendation
lli i & h
Smart entertainment
Personal Enterprise Government
Save money
and time
Feel special
Chronic disease management
Food & drug tracing and au thentication
Health moni toring
Assisted living
Environmental conservation
Factory safetyVehicle safety
Building safety & security
Emergency response system
Natural disaster warning
Infrastructure monitoring
Homeland security
Unmanned defense
Efficient energy & water generation & consumption
Smart metering a nd billing
ti & ti
i& i
Efficient natural resource mini ng & transportation
Traffic managemen t
Intelligent building
Economical agriculture & breeding
Supply chain automation
Fleet management
Factory automation
Good/product/shipment tracking
Smart home
Smart route planning
should outlive the animal.
After the sensors collect the data, the next step is to
communicate the information collected. Even today the
number of devices connected to the internet exceeds the
number of humans; in the future this gap will only increase.
Many of the sensors will be connected wirelessly through
systems like Bluetooth, WiFi, or 3G/4G cellular networks.
Connecting the growing number of devices is a huge
challenge. Most base stations are designed to provide a
certain quality of service up to a given number of users.
When there are too many simultaneous users, some users will
not receive service. Since the number of devices will be
orders of magnitude larger than the number of human users,
this problem will become even more serious. Too, as the
number of devices connected to the internet grows, so do
security and privacy issues [6].
Connected devices (sensors) can produce oceans of data.
According to Cisco, the number of objects on the internet
exceeded the number of humans in 2008 or 2009 [9], a trend
that accelerates every year. Thus, in the future, the amount
of data generated by machines will be orders of magnitude
greater than that generated by humans. However, we need
layers of intelligence to transform this data into wisdom
(Figure 3). In this new computing era, analysis of data and
its context will play a key role [2].
Sensor generated data are different from human generated
1. There are often real-time requirements for data
processing, e.g., natural disaster warning. Stream
data processing or mining is a critical component in
the analysis.
2. There is often a huge amount of temporal and spatial
redundancy in the information. It will be more
efficient if the analytical algorithm can take advantage
of such redundancy. However, synchronization of
data from different sensors may be inaccurate.
3. Reliability or accuracy of the data may be
unpredictable. Separating in reliable from unreliable
signals will be a critical component of the analysis.
4. The goal for the sensor data processing is either
machine or human action, meaning that the
requirement for data processing is more stringent.
Generating accurate and timely answers is a tremendous
challenge. Further, many analytic approaches assume that all
of the data is present on the server. However, it takes both
power and bandwidth to communicate the data to the server.
As M2M devices become more powerful, intelligent
computation must be distributed across both the devices and
the cloud.
Finally, after understanding the contexts, machines should
either take proper action or prompt humans for proper action.
Ideally, machines should work for people. Today, when
using search engine, a significant portion of the work is done
by the users instead of the machines. The machines give us a
list of possible results and ask users to refine the search results.
As the users search through the list of results, the search
engine uses the results to refine future search results. Users
actually work for the machines in this case. Ultimately, we
want the machines to work for users, not vice versa.
Furthermore, to build a successful M2M ecosystem, it is
crucial to have unified standards for everyone to follow.
However, major standards are still under development
[1][7][13][18]-[29][38][41] and many emerging applications
are using their own standards. For example, EPC Global and
Ubiquitous ID are two different, non-compatible ways of
identifying objects [37]. Existing solutions are highly
fragmented and technology is typically dedicated to a single
application. Since the solutions are being designed from
scratch, such projects take longer than conventional IT
projects [11]. Diverse technical solutions and standards are
slowing the development of the global M2M market [39].
IV. Opportunities
While there are many challenges, they are also many
research and development opportunities. The following
subsections will discuss these opportunities.
A. Low-power wireless sensors
One of the challenges is to design low-power sensors which
do not need battery replacement over their lifetimes. This
creates a demand for energy-efficient designs. A typical
wireless sensor has 4 major components: a sensing unit, a
processing unit, a transmitting/receiving unit, and a power unit,
as shown in Figure 4.
One of the opportunities is to design low-power sensing
unit. Highly accurate sensor modules often consume great
amounts of power. One alternative is to use an array of
low-accuracy modules with lower power consumption, and
then use data fusion to create high-accuracy information.
Another opportunity is to design a new video processing
Figure 2: Major components in M2M and their challenges
Figure 3: Refinement process of transforming sea of raw data
into information, knowledge, and finally useful wisdom
Machines work for people frictionlessly& robustly
Standard interface to foster innovation i n the eco system
• Answers are compu ted ahead of the questions
• Optimum distribution of device & cloud intelligence
• Zero effort to connect large, dense populations of
stationary and moving devices with high energy efficiency
Complete data security a nd priva cy
• Low-power so that no need to change battery
• “Zero-touch” to deploy and manage devices
Know led ge
Useful service
and encoding algorithm. Traditionally, video encoders have
higher complexity than video decoders. This is because
encoders must analyze the redundancy in the video in order to
compress the video efficiently. It is acceptable
conventionally because usually the video is encoded only once
while it is decoded multiple times, e.g., DVD or video
on-demand. However, in the new paradigm, due to low
power budgets, the redundancy cannot be analyzed completely
at the video sensor node. A good video encoding algorithm
must shift the computational complexity from encoders to
decoders [8].
After the collected data is processed, the information must
be transmitted to the gateway or even to the backend server.
While the power of digital circuits scales reasonably with
Moore’s Law, the power of analog circuits does not scale well.
Therefore, analog circuits in wireless communication will
consume relatively larger amounts of power than digital
circuits. For low-power sensor node, design of low-power
transmitter circuits should also be considered. One solution
is to use digitally intensive circuits to replace analog circuits
in wireless communication.
To prolong the battery life of the sensor, we can harvest
energy from the ambient environment from sources such as
lights, heat, vibration, or radio frequency. The efficiency of
today’s RF energy harvesting solutions is around 16.3% [35].
More efficient solution are thus an urgent need. Further, the
amount of energy that can be harvested from RF is at the uW
level, still to low to power a wireless sensor. We also need a
high-efficiency energy harvesting circuit adaptable to different
B. Better connectivity
Another challenge in M2M is autonomous networking to
connect large, dense populations of stationary and moving
devices with efficient energy usage.
First, many existing wireless standards are optimized for
human-to-human applications, but may not be able to support
large numbers of devices in a limited spectrum. Moreover,
in the future, the rate of increase in the available spectrum will
be slower than the rate of increase in the number of wireless
sensors. Fortunately, machine-to-machine communication
has several unique characteristics: the data rate is often lower,
the information from different sensors or at different time
steps may have strong correlations, and some messages do not
require real-time delivery. Therefore, one approach to these
problems is to form clusters of machines. Instead of
communicates with the base station directly, machines talk to
nearby cluster head, which in turn pass on to the base station.
This will reduce the machine transmission power demand and
increase spatial reuse of the spectrum. Another possibility is
to fuse information or remove redundancy of the information,
e.g., via distributed coding, to further reduce bandwidth usage.
Second, many connected devices are mobile, such as
sensors install on vehicles. These sensors not only need to
communicate with other sensors via intra-vehicle networks,
but also inter-vehicle networks. Existing radios most likely
underperform in on-road wireless channels. To provide
reliable vehicle-to-vehicle communication, we should first
study the vehicle mobility model and then develop the optimal
communication protocols based on the channel model.
Finally, wireless communication consumes large amounts
of bandwidth. A recent study shows that up to 70% of the
power used when a person is playing an on-line game on a
mobile device goes to wireless communication [33]. As
Moore’s Law continues to scale down the power consumption
of the computation circuits, the power consumption of the
communication circuits will become even more dominant.
Many connected devices are battery-powered, and current
human-to-human communication designs do not consider
energy efficiency as the first priority. Yet, energy-efficient
machine-to-machine communication is crucial. In particular,
signaling costs are high. Self-organizing hybrid distributed
and centralized competition and cooperation framework may
be one approach to reducing signaling overhead.
C. “Zero-touch” management & analysis
Today, the application development infrastructure of the
M2M is highly fragmented and proprietary [30]. Lack of
common tool kits for application developers actually hinders
the growth of M2M. Development of a flexible, scalable,
robust, and optimized applications development framework
for data analytics, data security, and sensor management is
also important.
Turning the sea of data into useful contexts or wisdom is
extremely critical. Massive multi-modality, heterogeneous
sensors create great complexity in analyzing data. Since data
may be collected by hundreds or thousands of different
sensors, the incoming data rate may be billions or trillions of
bytes per day, with the information noisy and unreliable in
nature. An API should be defined so that (1) common
machine learning and data mining techniques can be easily
used and (2) application specific problems can be effectively
addressed. To increase the reliability of the information, the
temporal and spatial redundancy should be leveraged to detect
anomalies. To reduce the computation time of network
inferences, fast hashing techniques may be used, as the
majority (95%) of the data may not be useful.
Another great opportunity is to design a context-aware,
decentralized analysis algorithm. Many analytic algorithms
assume that the system has all the data on the server.
However, it takes power and bandwidth to communicate the
data to the server. Moreover, not all the data are important or
useful. Architecture-wise, it is natural for sensor data to be
processed in a hierarchical and distributed fashion. Data
may be analyzed and fused in sensors or gateways before
arriving at the data center to save energy and bandwidth. If
the system can first understand what context is important, it
Figure 4: Schematic of a typical wireless sensor
Sensing unit
Processing unit
only needs to transmit the relevant information to the backend
server or the cloud. As devices become more
computationally capable, intelligent computation may easily
be distributed between the sensors and the backend servers.
Furthermore, it will help if the computation can easily migrate
from one piece of hardware to another piece of hardware.
This is because the computational capabilities of the devices
and the bandwidth availability and energy consumption of the
communication will improve over time. However, we need
to explore programming techniques to improve software
development processes for distributed heterogeneous
computing. Furthermore, open challenges in this direction
remain. These include (1) network protocols, and data
formats are not yet compatible across different devices,
applications, and servers; (2) sharing devices resources across
multiple applications is not a common practice; and (3)
infrastructure will become incompatible over time.
Data security and privacy is always a major concern, even
more important in M2M, which touches many aspects of
human life. Some low-cost devices have a limited budget to
implement strong security or cryptography features. These
lightweight devices can become the weakest links in the
system. Conventional firewalls that provide network
security by blocking malicious traffic can no longer work in
M2M because of its decentralized nature. If the lightweight
devices are not properly secured, the data they produce cannot
be fully trusted. Attackers may provide false information
that alters the behaviors of the system. Designing low-cost
and scalable crypto algorithms and hardware accelerators is
A system-level security analytics and self-adaptive security
policy framework are also needed. Security policies specify
trust relationships between entities, information creation and
protection rules, and access control rules to any target asset in
the system. Security policies also define requirements of the
actual security mechanism that enforces security policies. In
the current common practice, the security policies are either
administered by IT departments or statically configured with
fairly naïve rules. However, often these policies do not
satisfy the requirements of the applications. This leads to an
incomplete system. Further, unique challenges are faced in
creating suitable security policies for M2M. First, the
expected behaviors of a M2M system may evolve over time
due to environmental and/or policy changes. It is thus
necessary to develop solutions for detecting system behaviors
outside original specifications while adapting to changes yet
be able to detect deviations from expected system
communications in real-time. Finally, distributed
peer-to-peer machine interactions and wireless
communications demand attack-defense systems that collect
and analyze incoming attack information in a distributed
fashion. However, usually the amount of available data is
limited and sometimes only a partial view of the entire system
is possible.
Most current wireless sensor networks require great human
effort to configure and deploy applications. Thus, human
beings are often the bottleneck in large-scale deployment and
long-term sustainability of systems in the field. One of the
key components that will increase the adoption rate and
breadth of M2M across multiple vertical markets is a
configurable middleware that supports intelligent devices
management with self-configuration, self-optimization,
self-healing, and self-protection capabilities. For example,
gateway devices should be able to detect, diagnose, decide,
and defuse faulty end nodes. Master devices should be able
to auto-configure devices by deploying in-situ program
updates based on context changes or user commands. In this
case, the whole system can be reloaded and upgraded remotely.
Further, if the middleware is designed well, it is not only easy
for application developers to reprogram devices after
deployment, but also easy to develop the applications/services
in a distributed heterogeneous computing environment before
D. Smart service for People
Diverse and fragmented standards and interfaces between
layers of the systems hinder the innovation capabilities of
M2M application developers and service providers. If we
want the M2M industry to grow as reliably and powerfully as
Moore’s Law, we will need to develop the same kind of
interdependent, mutually advantageous industry structure that
causes the PC software and hardware industry spiral to
function. Standard interfaces are the key to the spiral.
When each individual component provider has a fixed
boundary condition, it is easier and faster to create innovative
solutions. It is not easy for one single component or solution
provider to define a generic system architecture and the
interface. The best way is to bring community efforts
together, as shown in Figure 5. While some works on
across-the-broad sensor, communication, and context
computing technologies, some should develop a few key
vertical applications and interact deeply with the horizontal
components. After understanding the requirements from
multiple verticals, we may be able to create standardized
platforms to better serve human beings. The broader the
diverse set of applications is (use fixed and mobile sensors to
monitor both human and non-human objects and perform
energy saving, vehicle safety, health/wellness monitoring, and
environmental monitoring functions), the easier we can define
the generic architecture and standard interfaces. After that,
we can focus on building innovative M2M services to server
V. Summary
As growing numbers of devices are added to the internet,
Figure 5: An inter-discipline research effort to quest for
unified system architecture and standard interface, which
can increase the rate and breadth of M2M adoption
Sensor research
Communication research
Context computing research
Services and applications research
M2M will transform the way we live, play, and work. An
exciting area for innovation, it offers numerous challenges and
opportunities, from scaling applications and services from
billions to trillions of connected devices, and from tera to zeta
bytes of data.
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... Unlike conventional computing platforms that are directly accessible by users, such as desktop computers or smartphones, IoT devices are often deployed in physical environments that are hard to reach, a problem that heightens the importance of low power consumption [11] [25]. To illustrate, long lifetime water quality sensors in reservoirs, forest fire detectors, and chemical leakage detectors in rivers all require low-power designs. ...
... To illustrate, long lifetime water quality sensors in reservoirs, forest fire detectors, and chemical leakage detectors in rivers all require low-power designs. To be able to deploy them in large and noisy physical environments, these devices must be robust, and the cost per device must be very low [25]. ...
Stochastic computing (SC) is an unconventional computing approach that processes data represented by pseudo-random bit-streams called stochastic numbers (SNs). It enables arithmetic functions to be implemented by tiny, low-power logic circuits, and is highly error-tolerant. These properties make SC practical for applications that need massive parallelism or operate in noisy environments where conventional binary designs are too costly or too unreliable. SC has recently come to be seen as an attractive choice for tasks such as biomedical image processing and decoding complex error-correcting codes. Despite its desirable properties, SC has features that limit its usefulness, including insufficient accuracy and an inadequate design theory. Accuracy is especially vulnerable to correlation among interacting SNs and to the random fluctuations inherent in SC’s data representation. This dissertation examines the major factors affecting accuracy using analytical and experimental approaches based on probability theory and circuit simulation, respectively. We devise methods to quantify the error effects in stochastic circuits by means of probabilistic transfer matrices and Bernouilli processes. These methods make it possible to compare the impact of errors on conventional and stochastic circuits under various conditions. We then analyze correlation in detail and show that correlation-induced errors can be reduced by the careful insertion of delay elements, a de-correlation technique called isolation. Noting that different logic functions can have the same stochastic behavior when constant SNs are applied to their inputs, we show how to partition logic functions into stochastic equivalence classes (SECs). We derive a procedure for identifying SECs, and apply SEC concepts to the synthesis and optimization of stochastic circuits. While addition, subtraction and multiplication have well-known and simple SC implementations, this is not true for division. We study stochastic division methods and propose a new type of stochastic divider that combines low cost with high accuracy. Finally, we turn to the design of general stochastic circuits and investigate a desirable property of SNs called monotonic progressive precision (MPP) whereby accuracy increases steadily with bit-stream length. We develop an SC design technique which produces results that are accurate and have good MPP. The dissertation concludes with some ideas for future research.
... [11] Some definitions however compartmentalize IoT only in relation to physical objects as opposed to the inter-relativity between animate and inanimate entities. For example, Al-Fagaha, et al argue that IoT is a technology that allows physical objects to perceive, hear, see, analyse and undertake tasks by having them interact to exchange data and, 'relay information to one another, process the information collaboratively, and take action automatically.' [13] This attempt however limits the operationality of IoT to the Internet thereby disregarding the workability of offline digital platforms and their functionalities. In another attempt that overlooks the (human) users of IoT, Whitmore, et al however view IoT as 'a paradigm where everyday objects can be equipped with identifying, sensing, networking and processing capabilities that will allow them to communicate with one another and with other devices and services over the internet to accomplish some objectives.' [14] In creating a basis for researchers and academics to further expound on the definitions of IoT, one of the European Commission's intervention initiatives defined the concept as a universally connected network of infrastructure 'linking physical and virtual objects through the exploitation of data capture and communication capabilities.' ...
Conference Paper
Internet of Things (IoT) refers to the seamless communication and interconnectivity of multiple devices within a certain network enabled by sensors and other technologies facilitating unusual processing of personal data for the performance of a certain goal. This article examines the various definitions of the IoT from technical and socio-technical perspectives and goes ahead to describe some practical examples of IoT by demonstrating their functionalities vis a vis the anticipated privacy and information security implications. Predominantly, the article discusses the information security and privacy risks posed by the operationality of IoT as envisaged under the EU GDPR and makes a few recommendations on how to address the risks.
... e development of micro-electro-mechanical system technology provides sophisticated applications that make the sensors relatively better and complex in technology advancement [2]. e cost of servicing and maintaining the IoT and handling with a larger number of sensors deployment play the major role [3], and replacing batteries in already deployed location is difficult based on the specific application. ...
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The advent of the automated technological revolution has enabled the Internet of Things to rejuvenate, revolutionize, and redeem the services of sensors. The recent development of microsensor devices is distributed in a real-world terrestrial environment to sense various environmental changes. The energy consumption of the remotely deployed microsystems depends on its utilization efficiency. Improper utilization of sensor nodes’ heterogeneity could lead to uneven energy consumption and load imbalance across the network, which will degrade the performance of the network. The proposed heterogeneous energy and traffic aware (HETA) considers the key parameters such as delay, throughput, traffic load, energy consumption, and life span. The residual energy and a minimum distance between the base station and cluster members are taken into consideration for the cluster head selection. The probability of hitting data traffic has been utilized to analyse energy and traffic towards the base station. The role of the sensor node has been realized and priority-based data forwarding are also proposed. As a result, the heterogeneous energy and traffic aware perform well in balancing traffic towards the base station, which is analysed in terms of maximum throughput and increase in a lifetime of heterogeneous energy networks more than 5000 rounds, and the algorithm outperforms 34.5% of nodes are alive with transmissible energy. The proposed research also endorses unequal clustering and minimum energy consumption. We have modeled our proposed research using various p-type junctionless nanowire FET without doping injunctions. The materials used in this analysis were silicon (Si), germanium (Ge), indium phosphide (InP), gallium arsenide (GaAs), and Al(x)Ga(1−x)As. The dimensions of the p-type cylindrical nanowire channel were 25 nm long and 10 nm in diameter.
The internet of things is a cutting-edge technology that is vulnerable to all sorts of fictitious solutions. As a new phase of computing emerges in the digital world, it intends to produce a huge number of smart gadgets that can host a wide range of applications and operations. IoT gadgets are a perfect target for cyber assaults because of their wide dispersion, availability/accessibility, and top-notch computing power. Furthermore, as numerous IoT devices gather and investigate private data, they become a gold mine for hostile actors. Hence, the matter of fact is that security, particularly the potential to diagnose compromised nodes, as well as the collection and preservation of testimony of an attack or illegal activity, have become top priorities. This chapter delves into the timeline and the most challenging security and privacy issues that exist in the present scenario. In addition to this, some open issues and future research directions are also discussed.
In this article, the proposed feedback-based resource management approach provides data processing, huge computation, large storage, and networking services between Internet of Things (IoT)-based Cloud data centers and the end-users. The real-time applications of IoT, such as smart city, smart home, health care management systems, traffic management systems, and transportation management systems, require less response time and latency to process the huge amount of data. The proposed feedback-based resource management plan provides a novel resource management technique, consisting of an integrated architecture and maintains the service-level agreement (SLA). It can optimize energy consumption, response time, network bandwidth, security, and reduce latency. The experimental results are tested with the IFogSim tool kit and have proved that the proposed approach is effective and suitable for smart communication in IoT-based cloud.
In this era of technological revolution, we are familiar with many scientific terminologies and gadgets. Today, internet is the backbone of the whole world as internet connectivity plays a vital role in our routine life and makes our life much easier. Wireless sensors are implemented in many applications like agricultural sector, military, home automation and health care sector. These wireless sensors are easy to operate and handle. Their performance varies according to the application. By connecting internet with these smart wireless sensors they act like Internet of Things. In the present scenario, whole world is suffering from Covid-19 pandemic. This is very strenuous situation for mankind. It is enigmatic to recognize a person with Covid-19 symptoms. For the identification of affected patient, some models are coined with the aid of wireless sensors and internet of things. The principle goal of this survey is to demonstrate the critical role of wireless sensor networks with internet of things for Covid-19 health care purposes.
The idea of the Internet of Things (IoT) has emerged in recent years and is growing rapidly. The main aim is to connect real‐world things over the cloud to the Internet. The various real‐time applications such as farming, weather forecasting uses rain, temperature, moisture, and loam sensors which are connected to an Internet. Thus, various kinds of information such as temperature, moisture, humidity, and rain can be processed later by using data analytics method to identify these and make effective decisions and approaches for monitoring smart environment. Similarly, traders of online shopping uses online data collected through various online shopping clouds through servers to analyze which product is to be more in demand, etc. The reflective transformation in economical system shall be possible in coming years through cloud‐based data analytics which can be said as reliable, sustainable, and robust system. The transformation of modernization can only be achieved with persistent usage of information technologies and communication technologies to manage as well as integrate this complete system. Therefore, by providing parallel processing of data and distributed data storage, cloud computing has been envisaged as an emerging technology of making possible this integration. Due to rapid up‐gradations in IoT, the Industry 4.0 grows and to handle the issues of large amount of data storage and its processing, cloud came up with variations of data storage and management strategies. Along with support of combination of cloud and IoT, the working styles in many fields become easier. The various challenges and issues have discussed in this chapter. The communication technologies that vary according to the application requirements have been depicted in this chapter. The today's working scenario and life style have been conquered by combination of IoT and cloud. It has been reached to every little part of the human life right from the monitoring of health, farming, smart industries, smart home, metering, video surveillance, etc. This chapter is designed for readers who intend to begin cloud‐based data analytics research with detailed knowledge and compile challenges, issues, organize, research avenues, and summarize using cloud. This chapter discusses the overview of the potential applications of cloud computing in smart working systems and case studies. It also describes the main technologies and innovations that will support the smart environment. The organization of the chapter is followed by subsections including introduction, challenges and issues, data models and applications, etc.
Smart cities are not any more waves of the future, they are more being actualities we live and witness every day. Smart cities are erected by the integration of Smart components into our systems and the way we promote this integration to produce the desired outcome of the efficiency and stability of these practicalities, the closer we are to form Smart cities. The intelligent amalgamation and interaction between Smart transportation systems and Smart people are discussed thoughtfully in this chapter, where we propose a promoting mutual version of an innovative Smart transportation system to act as an intelligent learning object-based transportation system in quest of the real time. The interacting proposal meets the real-time needs and requirements of the Smart people. The chapter proposes a mutual design of the Me-Online mobile application that promotes a simple planned trip into the fourth dimension of gratification where Smart people request a fulfillment journey supported with all logistics support they desire to have while taking the ride, such as choosing a partner, picking a coffee from close stay-at-home/small businesses along with being online interacting and updated with social network-based recommending systems while ongoing. The application is expected to increase the joyful of the ride, make decisions, raise, and refresh the stand-alone businesses available close to the chosen route. All this while still taking the financial side into consideration that keeps the smartness of having a journey with minimum fees. The successful trip is considered a template (transportation learning object) that can be reused, updated, and customized by others who like the content.
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This presentation discusses various issues of standardization in Internet of Things. It particularly focuses on specific challenges in designing protocols for Internet of Things with particular attention to specific applications.
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CoolSpots enable a wireless mobile device to automatically switch between multiple radio interfaces, such as WiFi and Bluetooth, in order to increase battery lifetime. The main contribution of this work is an exploration of the policies that enable a system to switch among these interfaces, each with diverse radio characteristics and different ranges, in order to save power – supported by detailed quantitative measurements. The system and policies do not require any changes to the mobile applications themselves, and changes required to existing infrastructure are minimal. Results are reported for a suite of commonly used applications, such as file transfer, web browsing, and streaming media, across a range of operating conditions. Experimental validation of the CoolSpot system on a mobile research platform shows substantial energy savings: more than a 50% reduction in energy consumption of the wireless subsystem is possible, with an associated increase in the effective battery lifetime.
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Machine-to-machine (M2M) communication is viewed as one of the next frontiers in wireless communications. M2M communication applications and scenarios are growing and lead the way to new business cases. Because of the nature of M2M scenarios, involving unguarded, distributed devices, new security threats emerge. The use case scenarios for M2M communication also address the new requirement on flexibility, because of deployment scenarios of the M2ME in the field. We believe that these new requirements require a paradigm shift. One important pillar of such a shift will be a new, more balanced mix of device-centric trust and traditional enforcement of security properties.
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We present an integrated vehicular system for the collection, management, and provision of context-aware information on traffic and driver location. This system uses an integrated vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication paradigm enriched with an information management system. The infrastructure manages vehicle-detected safety hazards and other relevant information, adapting them to the vehicle's context and driver's preferences. This vehicular integrated system resembles the concept of a smart road.
Conference Paper
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This paper describes two wireless power transfer systems. The Wireless Identification and Sensing Platform (WISP) is a platform for sensing and computation that is powered and read by a commercial off-the-shelf UHF (915 MHz) RFID reader. WISPs are small sensor devices that consume on the order of 2 uW to 2 mW, and can be operated at distances of up to several meters from the reader. The second system harvests VHF or UHF energy from TV towers, with power available depending on range and broadcast transmit power. We report on an experiment in which 60 uW is harvested at a range of about 4 km.
Conference Paper
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This paper proposes and describes a new software and application programming interface view of an RF transceiver as implemented in the first single-chip GSM radio in 90 nm CMOS. It demonstrates benefits of using programmable digital control logic in deep-submicron CMOS RF system. It also describes a micro-processor architecture design in digital RF processor (DRP) and how it controls compensation for process, temperature and voltage variations of the analog and RF circuits to meet the required RF performance
Low-power and low-cost distributed wireless video sensors play important roles for applications in machine-to-machine (M2M) and wireless sensor networks. Distributed video coding (DVC), an emerging coding technology based on Wyner-Ziv theory, seems to be a possible solution for implementing low-power video sensors since most of the computational complexity is moved from the encoder to the decoder. In this paper, existing works on DVC are discussed with rate-distortion and power consumption analyses compared with H.264/AVC-based approaches. We show that, since more transmission power is required for compensating the lower rate-distortion performance, the power consumption of sensor nodes using DVC is just similar to that of using H.264/AVC with zero motion vectors. Therefore, there is still a room for improvement to make DVC applicable for distributed wireless video sensors. Based on our analysis results, several possible research directions, such as studies on the tradeoff between hardware cost and system power consumption, are also addressed in this paper under a unified DVC framework.
There are many more machines - defined as things with mechanical, electrical, or electronic properties - in the world than people. And a growing number of machines are networked. Harbor Research, a technology consultancy and analysis firm, estimates that by 2010, at least 1.5 billion devices will be Internet-connected worldwide. The increasingly popular machine-to-machine technology plans to take advantage of these developments. M2M would leverage connectivity to enable machines - including manufacturing and telecommunications equipment, data centers, storage tanks, property-security products, industry-specific assets such as public-utility systems, and even vending machines - to communicate directly with one another. M2M is based on the idea that a machine has more value when it is networked and that the network becomes more valuable as more machines are connected. Sensors that gather the information that some M2M systems transmit are becoming more widely used and thus are driving demand for the technology. The biggest new trend is that vendors are expanding M2M into wireless technology, using radio chips or modules they can attach to almost any device or machine. Thus, M2M is gearing up for exponential growth.
Participatory sensing is the process whereby individuals and communities use ever more capable mobile phones and cloud services to collect and analyze systematic data for use in discovery. The convergence of technology and analytical innovation with a citizenry that is increasingly comfortable using mobile phones and online social networking sets the stage for this technology to dramatically impact many aspects of daily lives. Ubiquitous data capture, leveraged data processing, and personal data vault are the essential components for these emerging systems. The architecture, usage models,and application of participatory sensing were discussed in this paper.