In the application dashboard, alert messages are created for people who are not involved
in the decision-making process. The main aim of this project is to regulate the behaviour of the
river basins and gather real-time data to alert when the limits have been exceeded. The control
unit must trigger the sirens and public address system for the people to leave the area in the case
of an alert.
The UNGRD (Unidad Nacional Para la Gestion de Riesgos de Desastres) and the munici-
pality of Salgar is quite satisfied with the system. They are thankful that it covers the first needs
to address the problem. The sensor nodes continuously compile the data every 5 minutes. The
system is also future proof as more sensors and monitoring points could be added to the sys-
tem. By this, the decision makers would be able to obtain more accurate information to take the
crucial decision of activating the protocols in the case of a disaster.
Recommended Readings
1. Towards Low-Cost, Real-Time, Distributed Signal and Data Processing for Artificial
Intelligence Applications at Edges of Large Industrial and Internet Networks
Link: https://ieeexplore.ieee.org (Accessed on 01 July 2019)
Abstract:
Digital Signal Processors (DSP) are vital system components in industrial Artificial Intelligence (AI)
applications. In this paper, FIR filters that could be used for industrial AI applications are designed from
the Spline Biorthogonal 1.5 (SB1.5) mother wavelet using a real-time, low-cost, generic industrial IoT
(IIoT) hardware: the C28x DSP and low-level, Embedded C, system software. Our contribution in this
paper is the first reported application of the C28x for SB1.5 wavelet construction. The significance of this
approach is to be able to repurpose low-cost, readily available hardware for distributed AI applications.
Our approach is different from the state of the art, in which specialized hardware are always manufac-
tured for implementing AI applications at large network edges. Our approach supports low-cost and fast
single-stage FIR implementation suitable for use in real-time, distributed AI application at network edges,
since in our case, successive recursion of FIR filters leading to a full implementation of Pyramid Algorithm
is not implemented. The designed FIR filter is evaluated and found capable of both low-pass and high pass
filtering operations. Results of this paper indicate that the C28x real-time DSP, which exists in many IoT
devices, could have improved scalability by being deployed for other important AI and IoT network edge
analytic applications, different from its present uses.
2. Application of artificial intelligence in the Internet of Things
Link: https://ieeexplore.ieee.org (Accessed on 01 July 2019)
Abstract:
Artificial intelligence is the best solution to manage huge data flows and storage in the IoT network. IoT
nowadays becoming more and more popular with the inventions of high speed internet networks and many
advanced sensors that can be integrated into a microcontroller. The data flows internets now will have sensors
data and user data that send and receive from the workstations. With the increase in the number of worksta-
tion and more and more sensors, some data may be facing problems on the storage, delay, channels limitation
and congestion in the networks. To avoid all these problems, there were many algorithms were proposed in
the past of 10 years. Among all the algorithms, Artificial Intelligence still being the best solution to the data
mining, manage and control of congestion in the network. The aim of this paper is to present the application of
Chapter 9 Artificial Intelligence for IoT 245
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artificial intelligence system in the IoT. The importance of data mining and management will be highlighted
in the paper. Also, the method used in the Artificial Intelligence like fuzzy logics and neural network also will
be discussed in this paper in conjunction with IoT network. The self-optimizing network and software defined
network are parts of the important parameters in the Artificial Intelligence IoT System.
3. IoT Solutions for Precision Farming and Food Manufacturing: Artificial Intelligence
Applications in Digital Food
Link: https://ieeexplore.ieee.org (Accessed on 01 July 2019)
Abstract:
In many respects, farming and food processing have lagged other industries when it comes to adoption of
innovative technology. Whilst bioengineering has brought about seeds with much higher yield and less need
for water and nutrients, it is only now with IoT that farmer can work on the intensive use of natural resources
to increase the sustainability of their operations. In the last ten years, high-end machinery has evolved pri-
marily to the benefit of larger corporations, with the introduction of satellite driven machines, sensors and all
components of precision farming. The most recent IoT advances bring about a level of simplification and cost
reduction that enable all farmers to benefit and a true adoption of prescription agriculture. In this conference
presentation, we will examine a practical case of a Malthouse, where careful modeling of how CO
2
, Tempera-
ture, Humidity and PH vary in the three steps of the malting process, enabled an artificial intelligence system
to prescribe different setting and schedules. The end result is malt with higher content of starch and proteins,
which in turn means higher alcohol in the downstream process. A second example on the cultivation of Medical
Marijuana, where similarly but in a more complex fashion (138 variables) the artificial intelligence supported
the tuning of many settings and schedules, is presented only in the conference.
4. Nonvolatile Circuits-Devices Interaction for Memory, Logic and Artificial Intelligence
Link: https://ieeexplore.ieee.org (Accessed on 01 July 2019)
Abstract:
Emerging nonvolatile memory (eNVM) has aroused extensive attention due to their low power and
high speed. Recent advances have further moved eNVM to the forefront as key enablers of nonvolatile logics
(nvLogics) for IoT devices and computing-in-memory (CIM) for AI chips. In this paper, we firstly examine
the circuit-device-interaction (CDI) issues to implement high-performance memory macro. Then we review
examples of emerging eNVM-based nvLogics for nonvolatile processors and CIM macro for AI chips with an
emphasis on the challenges required CDI.
5. In-Situ AI: Towards Autonomous and Incremental Deep Learning for IoT Systems
Link: https://ieeexplore.ieee.org (Accessed on 01 July 2019)
Abstract:
Recent years have seen an exploration of data volumes from a myriad of IoT devices, such as various sensors
and ubiquitous cameras. The deluge of IoT data creates enormous opportunities for us to explore the physical
world, especially with the help of deep learning techniques. Traditionally, the Cloud is the option for deploying
deep learning based applications. However, the challenges of Cloud-centric IoT systems are increasing due
to significant data movement overhead, escalating energy needs, and privacy issues. Rather than constantly
moving a tremendous amount of raw data to the Cloud, it would be beneficial to leverage the emerging powerful
IoT devices to perform the inference task. Nevertheless, the statically trained model could not efficiently handle
the dynamic data in the real in-situ environments, which leads to low accuracy. Moreover, the big raw IoT data
246 Internet of Things
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challenges the traditional supervised training method in the Cloud. To tackle the above challenges, we propose
In-situ AI, the first Autonomous and Incremental computing framework and architecture for deep learning based
IoT applications. We equip deep learning based IoT system with autonomous IoT data diagnosis (minimize data
movement), and incremental and unsupervised training method (tackle the big raw IoT data generated in ever-
changing in-situ environments). To provide efficient architectural support for this new computing paradigm,
we first characterize the two In-situ AI tasks (i.e. inference and diagnosis tasks) on two popular IoT devices
(i.e. mobile GPU and FPGA) and explore the design space and tradeoffs. Based on the characterization results,
we propose two working modes for the In-situ AI tasks, including Single-running and Co-running modes.
Moreover, we craft analytical models for these two modes to guide the best configuration selection. We also
develop a novel two-level weight shared In-situ AI architecture to efficiently deploy In-situ tasks to IoT node.
Compared with traditional IoT systems, our In-situ AI can reduce data movement by 28-71%, which further
yields 1.4X-3.3X speedup on model update and contributes to 30-70% energy saving.
6. IoT Object Authentication for Cyber Security : Securing Internet with Artificial intelligence
Link: https://ieeexplore.ieee.org (Accessed on 01 July 2019)
Abstract:
Cyber Security is a big concern which needs to be discussed loquaciously. IoT introduces comfort as well as
more insecurities but this idea elaborates how IoT itself can provide Security and facilitate the convenience with
which we can use the internet. It broadens our perspective of how simple things in our daily life can be intercon-
nected through internet and ease out our lives. We can also say that making internet an artificially intelligent
system is actually an effortless way of living life.
7. Readiness for Artificial Intelligence
Link: https://ieeexplore.ieee.org (Accessed on 01 July 2019)
Abstract:
In the age of the fourth industrial revolution, where collaborative systems are the real innovation people
are still not prepared for the results of the third revolution, namely for artificial intelligence. While every change
has its life cycle with innovators, early adopters, early majority before it can reach its fruition, the age of robots
has too fast been followed by the internet of things (IoT) of the fourth industrial revolution. Hence, people
didn’t have enough time to adapt to the change. In present paper a primary research is presented, that aimed to
explore the attitude of young adolescents towards artificial intelligence. Based on the result, trust is clearly one
of the main issues regarding change in general and readiness in particular. People are not ready for robotic peers
within their workplace yet. Psychological and emotive needs shall be addressed for the people to accept artificial
intelligence in their workplace and surrounding.
8. Semiconductor Industry Driven by Applications: Artificial Intelligence and Internet-of-
Things
Link: https://ieeexplore.ieee.org (Accessed on 01 July 2019)
Abstract:
We present a historical view on semiconductor device scaling and how challenges were overcome. Recently,
the semiconductor industry is increasingly driven by new applications such as Artificial Intelligence and
Internet-of-Things. We will review the factors that accelerate artificial intelligence and their recent advance-
ment. We will then provide a few AI application examples. Data acquisition and dedicated AI hardware are
keys for project success. We will also present a cost-effective IP implementation of the NB-IoT.
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