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Designing An IoT Cloud Solution for Aquaculture



Farming has been changing through multiple technological transformations in recent years, in parallel with the earth’s growing population and demands. The Internet of Things (IoT) provides very remarkable technological innovations on farming by bringing a new paradigm. Aquaculture in fresh or salt water is a rapidly growing food sector to produce aquatic plants or animals. IoT can also produce encouraging solutions for the problems in the implementation and maintenance of aquaculture systems. In this paper, we present a use case with an IoT cloud solution after summarizing the potential technologies and tools to use in accordance with the use case problem as identified in the Horizon2020 Project, the intelligent management system for integrated multi-trophic aquaculture (IMPAQT) in which we have been participating in.
Designing An IoT Cloud Solution for Aquaculture
gur Acar
Netas¸ Telekomunikasyon Anonim S¸irketi
Istanbul, Turkey
Frank Kane
Marine Institute
Galway, Ireland
Panagiotis Vlacheas
WINGS ICT Solutions
Athens, Greece
Vassilis Foteinos
WINGS ICT Solutions
Athens, Greece
Panagiotis Demestichas
WINGS ICT Solutions
Athens, Greece
uven Y¨
Netas¸ Telekomunikasyon Anonim S¸irketi
Istanbul, Turkey
Ioanna Drigkopoulou
INTRASOFT International
Aycan Varg¨
Netas¸ Telekomunikasyon Anonim S¸irketi
Istanbul, Turkey
Abstract—Farming has been changing through multiple
technological transformations in recent years, in parallel with
the earth’s growing population and demands. The Internet of
Things (IoT) provides very remarkable technological innova-
tions on farming by bringing a new paradigm. Aquaculture
in fresh or salt water is a rapidly growing food sector to
produce aquatic plants or animals. IoT can also produce
encouraging solutions for the problems in the implementation
and maintenance of aquaculture systems. In this paper, we
present a use case with an IoT cloud solution after summarizing
the potential technologies and tools to use in accordance with
the use case problem as identified in the Horizon2020 Project,
the intelligent management system for integrated multi-trophic
aquaculture (IMPAQT) in which we have been participating
Keywords-internet of things; smart farm; cloud; gateway
The internet of things is the network in which devices
working for different purposes are connected to exchange
data by extending internet connectivity from standard com-
puters to vehicles, electronics, software, sensors, actuators
etc. Home devices are also being controlled remotely by
the IoT technology [1]. The class of devices in an IoT
network can be named as consumer, commercial, industrial,
and infrastructure spaces [2]. IoT can also help us solve
industrial problems by bringing new solutions with different
approaches especially in manufacturing and agriculture. Op-
erating procedures in agriculture can now be automated can
be automated by digital control systems with the required
security and safety to be provisioned [5]. IoT can be used in
agriculture to automate the tools to collect different types of
data such as temperature, humidity, soil or water ingredients
etc. [6]. This data can be used to analyze and make predic-
tions to improve the product quality by minimizing the risk
and the waste [7].
An IoT network has three fundamental components such
as devices, gateways and the cloud [8]. Devices are sensors
and actuators. Gateways are utilized to connect devices
to the cloud via technologies like Zigbee, LoRa, Modbus
[8]. Through this connection, it can process data, provide
the connection security and it can make edge analytics
or fog computing [8]. The cloud may include applications
in the form of microservices architecture, databases and
their management system as a backend data storage system
[8] [9]. Based on this architecture schema, we present an
end to end solution by discussing proposed technologies in
IMPAQT project [10].
Aquaculture is the farming activity to produce fishes,
water plants and different aquatic organisms. As EU is
the one of the largest traders of aquaculture products in
the world, the amount of the trade flow with non-EU
countries and exchanges of export among EU members is
EUR 57 billion [12]. Salmon as the most produced and sold
product in the EU market has %14 volume and %22 value
terms [12]. In addition to salmon, the total value of EU
aquaculture production of Gilt-head seabream and European
seabass also is more than 20% [12]. Intelligent management
system for integrated multi-trophic aquaculture (IMPAQT)
is a Horizon2020 Project aiming to present a promising
solution for the sustainable development of aquaculture. The
solution should provide a new perspective to EU Industry by
its environmentally friendly and highly efficient approach.
Integrated multi-torphic aquaculture (IMTA) is a concept in
which output such as byproduct and waste from one species
is the input for another species. IMTA allows to sustain
aquaculture like an environmentally compatible ecosystem
by aiming economic stability [11]. It can be managed by
IMPAQT’s intelligent management platform by end of the
IMPAQT project.
An IoT project has three fundamental parts such as
devices, gateways and the cloud mentioned previously. To
that respect, this paper aims to introduce and analyze these
cloud components, applied in project use case. A solution for
a use case will be presented by selecting proper technologies
which are proposed for components on the cloud due to the
IMPAQT project. The contributions brought by this paper
to bring a logical schema for IoT cloud architecture in
which the fundamental components are defined,
to propose the most recent technologies for IoT cloud
architecture component after seperating IoT cloud ar-
chitecture into layers.
The layout of this paper is that in section 2, we present
a use case about fish farming. In section 3, we present the
logical diagram of the cloud architecture in which the use
case implemented. In section 4, we present new technologies
and standards. We conclude this paper with conclusions with
section 5.
Achieving the optimal growth from a fed farm animal is
a key priority for any farmer, and likewise, is a key con-
sideration for an aquaculture producer. One of the primary
concerns is to get the maximum benefit of the food fed to
the animals to ensure optimal use of resources, maximise
growth, minimise waste and minimise environmental impact.
Food represents the bulk of the total cost of marine farm
exploitation, so it is desirable to reduce wastage. The fish
farmer is keen to know the status of the environment and
the condition of the fish prior to beginning feeding to ensure
appropriate and optimal feeding amounts and times.
The status of the environment in which the animals are
living influences and affects the welfare of the animals,
their behaviour and appetite. Environmental conditions such
as water temperature, dissolved oxygen, water turbidity,
brightness, current speed, weather conditions, etc. all have
an influence on how receptive to food the fish will be. The
health status, their well-being and any stressors of the fish
that affect the appetite of the animal, are also important for
the farmer to be aware of. The farmer must know the number
and biomass of the stock in the pen that is to be fed.
The system must also take account of the fish welfare pa-
rameters and put values on these, to quantify to the farmers
the state of welfare of the fish. These include parameters
based on the fish behaviour and stress indicator from the
stock of fish. These parameters must be reported in real time
and used to access the acceptability of the fish to being
fed. They can be used to calculate the suggested feeding
quantities and rate of feeding of the stock, to ensure the fish
are fed to satiation while still minimizing any wastage. The
system must inform the farmer on the optimal quantity of
food to give the stock of fish, the optimal feeding duration
and the best times to provide the food. It must consider day-
light hours and brightness in suggesting feeding times. The
system should provide a warning to the farmer if there is a
combination of parameters that is unsatisfactory and is likely
to cause sub-optimal feeding, or to result in a sub-optimal
environment for the fish, causing stress to the stock of fish.
The farmer should be alerted if any individual parameter, or
any combination of parameters, deviates from the expected
in a way that is likely to stress the stock of fish, so that
the farmer can be aware of the risk and consider mitigation
As we mentioned before, an IoT contains three main parts
such as devices, gateways and the cloud. In our use case, the
main source of input data which will flow through the system
is coming from sensors to be handled on the cloud. A logical
aspect of the cloud is illustrated in Figure 1, presenting the
components and their connections. Logical diagram includes
two main parts such as Integrated Management System
(IMS) and Data Management System. Each component has
parts to classify the tasks that they will fulfil. Briefly, IMS
Applications are responsible for the user and the device
management. In addition, it includes IMS user interface. In
Data Management System, the data from different sources
such as sensors, satellites and the crowd are handled. IMS
Smart Services are using these data for their management
or analytical purposes.
A. Data Management System
The data management layer handles, stores and delivers
data to IMS and application layers. In the data brokering
sublayer, the data is acquired by consumer applications by
using various protocols. In the data handling sublayer, the
data from different sources is consumed according to their
purposes by API’s and delivered to related component such
as IMS applications Layer and IMS smart services layer.
The data storing sublayer contains databases and data storing
tools with their API’s. In this sublayer, data is divided and
located according to their specific purposes.
The sensor data is handled by the data brokering sublayer
when it arrived to the cloud. For each type of data, topics
are created to be published to subscribers. The data handling
sublayer uses these topics to store data to proper databases
in the data storing sublayer.
B. IMS Applications
This layer is purposed to manage the users and the devices
of the platform by ensuring the security of user/device
access and the security in the integration of components.
At the user management sublayer, the user access to the
platform and their registrations are managed. At the device
management sublayer, the devices that are connected to the
Figure 1. Logical Diagram
platform such as gateways, sensors, cameras, actuators are
managed. At the user interface sublayer, there are different
types of interfaces which interact with end users such as
monitoring visualization interfaces and email/SMS alarming
system activated by alarms etc. The user interface sublayer
takes the output generated by the analytics sublayer and
presents it to end-users or actuators or alarm systems. At
the security sublayer, the access of users (such as admins,
end users etc.) and devices which are connected directly
to the system is handled in addition to the security of the
integration between the components. As a concrete example,
when a broker is connected to the system, the authentication
for its REST API is controlled here.
Integrated management system (IMS) requests and tracks
user, device and security data from the data brokering
sublayer to take some actions like adding, removing or
Security in an IoT solution is an essential requirement.
IoT devices, the network, and micro-services running on
the cloud must be secured to ensure end-to-end security
[13]. Even, naively forgetting to change default passwords
or having them hardcoded might cause big problems. There
are many security frameworks for IoT, however, there is no
single standard accepted by the IoT industry. The security
must be a whole solution from manufacturer to end-user
and it varies from domains to domains. For high security
demanding applications starting from semiconductor compa-
nies, the hardware must be built in a way that IoT devices
are tamper-proof, receives patches and updates, and allow
dynamic testing. Afterward, the developers should keep in
mind the security starting from the design phase till to the
end. Security should be enabled by default.
In an IoT project, each node (sensor, device, user) should
have an identity and there should be a central platform
to manage authentications and role-based authorizations.
Therefore, every node can be tracked on how it behaves
and how it interacts with other devices using NAC (network
access control). The data at rest and in transition should be
secured by strong encryption algorithms. For network secu-
rity, disabling port forwarding and never opening an unused
port, using firewalls, blocking unauthorized IP addresses are
just the first steps to ensure port and network security.
Finally, we are using micro-service architecture for IM-
PAQT on the cloud. We are using two widely used API
security standards [14]:
1. OAuth2 is an access authorization protocol. The client
authenticates with the authorization server (API in our case)
and gets an token”. The token has a reference to user
information which only can be retrieved by authorization
API and it is safe to use even on the internet.
2. Open ID Connect behaves like AOuth2 but it is not safe
to use on the internet since it contains information about
the user. In addition to the access token authorization server
issues an ID token which contains information about the
user. The tokens often implemented as JWT (Jason Web
token) and signed by the authorization server.
The key steps implementing a solution for client-
microservice communication securely are in Figure 2:
1) Authorization server authenticates a client using
OAuth and OpenID Connect who has right to connect
that microservices and sends the acces token to API
gateway along with the request.
2) API gateway sends the access token to the server and
receive back the JWT and then passes this file with
request to the micro-services.
3) JWT has the necessary information to store user ses-
sion so that the client and micro-services communicate
C. IMS Smart Services
IMS smart services communicate both with end users
and the other two main layers (IMS applications layer
and data management layer). In analytics sublayer, decision
and assessment algorithms will run by using processed and
filtered data from Data Management System.
The main scope of the application layer is to provide
smart and optimal services to give the opportunity to users to
take optimized operational decisions regarding farm manage-
ment. The IMS smart services layer will contain functional-
ities such as data and predictive analytics to enable several
operational capabilities; one of these capabilities is water
quality management related to complex event processing and
predictive capabilities for the identification of quality prob-
lems in water. Another decision making aspect is based on a
control system, which takes into account cost functions and
decides either to activate actuators (if exist in the production
sites) or to issue an alarm notification to the operator of the
farm through the alert system, either through dashboards
or through a smartphone application. Other functionalities
include behavior monitoring to deal with monitoring and
mapping of behavioral traits in fish; disease diagnosis that
performs early identification of warning signs that could
indicate diseases; Intelligent feeding, to provide precised
feeding, while feed waste management to minimize the
production of waste. Furthermore, IMS comprises analytics
for fast and accurate screening of food quality and safety,
specialized to the kind of sensor used. The IMS would also
provide an interface to the IMTA model, which comprises
ecosystem scale modelling of both the whole aquaculture
system (ecosystem scale hydrodynamics, coupled to water
quality processes, ecological processes) and processes within
farms (e.g. to optimize the design in terms of stocking
densities and configuration).
Thus, the application layer is in communication with
the data management system. Data coming from sensors,
satellites or crowd-sourcing are requested by the application
layer. The analytics sublayer creates outputs by using the
use case input in its decision mechanism which has complex
event processing and predictive capabilities.
Message Queuing Telemetry Transport (MQTT) is an ISO
standard using publish/subscribe schema on application layer
over TCP/IP although a variant of MQTT (MQTT-SN) is
used over UDP [15], [16]. Thanks to the broker (clients’
communication server), publisher clients can publish data
to subscriber clients. Publishers use topics to define their
communication channels. Subscribers subscribe to these
topics to get published data. Clients cannot communicate to
each other directly. Instead, communications are managed
only by the broker. Clients can communicate to each other
in one-to-one, one-to-many, many-to-many format [17]. For
the sensor data at the data brokering sublayer, MQTT can
be used to publish and subscribe after creating related topics
API Gateway
Rest API Analytics
Rest API Data Storage
Figure 2. Micro-service security of IMPAQT [14]
as well as The File Transfer Protocol (FTP) can be used
to deliver large data to data management layer. As its
name defines, FTP is a file transferring protocol based on
client-server model to share different types and size of data
[18]. Some devices may not be able to support MQTT. In
this case Advanced Message Queuing Protocol (AMQP) or
Hypertext Transfer Protocol (HTTP) can be used. AMQP
is an application layer protocol which enables message-
oriented communication including queuing, routing with
message-delivery authentication by guaranteeing encryption
[19]. HTTP is also an application layer protocol which is
using hyperlinks to tie resources that the user can easily
access [20].
In data management layer, Queuing tools are simple data
brokers to publish/subscribe data. For each type of data, a
topic can be created and then it can be sent to databases
by using queueing tools such as brokers. RabbitMQ, Kafka,
Orion and ActiveMQ are some examples. Kafka is a good
solution at big scaled messaging and streaming applications
because it is like a persistent buffer on you disk to en-
able you to re-read messages. But it doesn’t guarantee the
messages to be delivered [21]. RabbitMQ is also a smart
broker like Kafka, but it guarantees the messages to deliver
and it manages the message states thoroughly. However, it
doesn’t allow filtering or updating the data in queues directly
[21]. Orion Context Broker is a part of FIWARE platform
implementing an NGSIv2 server. It allows to create context
elements and manage them by updates, queries, registrations
[22]. ActiveMQ is a message broker providing enterprise
features supporting the communication from more than one
client or server [23].
To store data, relational databases such as PostgreSQL or
non-relational databases such as MongoDB, InfluxDB are
some candidates. Relational databases are storing data in
tables and rows with their relations [24] while non-relational
databases are including a mechanism to store and retrieve
data like collections of JSON documents [25]. The data is
not nested or related strictly. For example, raw sensor data
coming from IADAS are not related to each other. They
can be stored on a non-relational database like InfluxDB.
However, if some devices are related to some users in an
IoT system, it is better to use a relational database to store
them with their relations.
HTTP is the most common standard application protocol
for component communication. From the data management
system to the application layer (IMS/Analytics/User Inter-
face), it can be used by REST APIs’ of component tools.
For IMS applications layer as the main part of the platform,
FIWARE [26], ION [27], Kapua [28], Wings Cataract [29],
AquaTracker [30] are some suggested platforms to manage
users and devices.
As an open source, Grafana and its API are a widely
used tools to monitor data from different sources such as
PostgreSQL, MongoDB, MySQL, InfluxDB. Grafana is pre-
senting some plugins to display data which have coordinates
on the world map [31].
IoT technology has become a highly adaptive solution
to different use cases ranging from smart cities to smart
aqua farming industry, providing us the ability to construct
advanced solutions that make life easier. The enhancement in
the IoT and the cloud technologies give rise to new possibili-
ties of building a brand-new farming approach. In this paper,
we present an IoT cloud integration schema together with the
most recent technologies and widely used tool. By picking
the right technology and applying it in an IoT system, we can
construct our cloud in a more concrete and easier manner.
In our future works, the analytics sublayer and the IMS user
interface layer can be improved.
This work has been realized in context of the IMPAQT
project. This project has received funding from the European
Union’s Horizon 2020 research and innovation programme
under grant agreement No 774109.
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... IoT has three fundamental parts: devices, gateways, and the cloud. Devices refer to sensors and actuators, gateways connect devices to the cloud, and the cloud manages applications in the microservices and database management [3]. IoT has been integrated to perform various functions such as water quality monitoring [4][5][6], smart farming [7][8][9], climate, and environment monitoring [10][11][12], healthcare [13][14][15], smart homes [16,17] and a lot more. ...
... In aquaculture, AIoT gave a brand-new farming approach that eased the farming industry's burden in monitoring [3]. Computer vision, machine learning, and deep learning models have been widely integrated into cloud-based infrastructures as part of AI services. ...
... The work of Acar et al. [3] focused on designing an IoT solution for aquaculture composed of three major components: data management system, integrated management system (IMS) applications, and IMS services. The data management system handles, stores, and delivers data to IMS and application layers. ...
... Cloud computing services enable the collection and storage of big data for processing using AI methodologies capable of predictive analysis to provide informed decision-making mechanisms for precise aquaculture. It enables a brand-new farming approach [8] that eases the burden of the farming industry in terms of monitoring. ...
... Similarly, the corresponding 3D point set [ , , , , , , , ]can be computed based on the four pixels in the stereo images using the disparity computing algorithm mentioned above. Equation(8) can now be written as ...
Full-text available
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Full-text available
One of the current concerns in aquaculture is the management of the culture environment due to the impact that this generates, in the culture at production level, to the environment due to uncontrolled residues and in food safety, this concern is increasing due to the intensification of this activity in recent years which generates a high demand for food. Currently, many of the aquaculture processes are carried out manually, which requires cost in both time and resources, so this research, making use of technology, proposes a framework for full real-time temperature monitoring , configurable, scalable and low-cost by applying IOT, with the aim of optimizing the harvesting time and analysis of the crop status, which allows to support decision-making before the crop is affected.
With the increase in the world population, the demands on the food supply are rising globally. Thus there is a need for increased supply of food from both plant and animal sources which has a direct need for increased agricultural productivity globally. Challenges to farming and increased productivity are manifold including the monitoring of individual plant health, environmental, soil, pathogens disease conditions for crops and monitoring of animal health, growth and well-being, animal behavior with relation to its alteration in response to disease onset or changed environmental conditions. This is done by taking pictures, videos, from satellites, drones and modeling the data by application of algorithms and software. This particularly becomes challenging when maintaining big animal herds by farmers. Precision agriculture/animal farming is an interdisciplinary science incorporating the inputs from informatics, algorithms, engineering, animal and veterinary sciences, physiologists in order to monitor the health, behavior, growth and production of animals in real time so that immediate decisions in tackling problems can be taken up by the farmers. We discuss in this chapter briefly applications of precision agriculture in (1) plant production, (2) animal production, (3) the big data and livestock application, (4) precision animal breeding, (5) precision livestock management.
Aquaculture based on the Internet of Things (IoT) is a growing field of interest in the fishing industry. The IoT technology is advancing the agriculture 4.0 era, and yet, Aquaculture 4.0 is a lagging field in many countries. This article presents results obtained so far from ongoing research of published work highlighting water quality monitoring in fishponds. This analysis was performed extensively from May to December 2020 by meticulously selecting a total of 30 internationally published research papers. This review is divided into five categories: (1) recent research (2011–2020), (2) aquaculture environments, (3) research approaches, (4) most common water quality parameters and (5) forms of the solution provided. Most of the published research concentrated on inland aquaculture (81%), while research articles on marine aquaculture species accounted for 19% of papers reviewed so far. The framework and architecture approach (48%) was the most widely practised research approach in IoT‐based aquaculture for water quality monitoring. There is a need for long‐term experimental research to identify the challenges and suggest appropriate solutions. With regards to water quality parameters, temperature (20%), dissolved oxygen (18%) and pH (17%) are the topmost prioritised water quality parameters considered in the IoT‐based aquaculture. Finally, real‐time monitoring (50%) is offered generally as a form of a solution while autonomous (3%) monitoring can be a unique solution. The findings from this study are expected to support the aquaculture industry, researchers, practitioners and decision‐makers in the long run.
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The Internet undergoes a fundamental transformation as billions of connected "things" surround us and embed themselves into the fabric of our everyday lives. However, this is only the beginning of true convergence between the realm of humans and that of machines, which materializes with the advent of connected machines worn by humans, or wearables. The resulting shift from the Internet of Things to the Internet of Wearable Things (IoWT) brings along a truly personalized user experience by capitalizing on the rich contextual information, which wearables produce more than any other today's technology. The abundance of personally identifiable information handled by wearables creates an unprecedented risk of its unauthorized exposure by the IoWT devices, which fuels novel privacy challenges. In this paper, after reviewing the relevant contemporary background, we propose efficient means for the delegation of use applicable to a wide variety of constrained wearable devices, so that to guarantee privacy and integrity of their data. Our efficient solutions facilitate contexts when one would like to offer their personal device for temporary use (delegate it) to another person in a secure and reliable manner. In connection to the proposed protocol suite for the delegation of use, we also review the possible attack surfaces related to advanced wearables.
Technical Report
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This document discusses use cases concerning the management of networks in which constrained devices are involved. A problem statement, deployment options, and the requirements on the networks with constrained devices can be found in the companion document on "Management of Networks with Constrained Devices: Problem Statement and Requirements" (RFC 7547).
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The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organizations making their way into the market. In this paper, we survey over 100 IoT smart solutions in the marketplace and examine them closely in order to identify their applications and the technologies they use. More importantly, we identify the trends, opportunities, and open challenges in the industry-based IoT solutions. Based on the application domain, we classify and discuss these solutions under five different categories: 1) smart wearable; 2) smart home; 3) smart city; 4) smart environment; and 5) smart enterprise. This survey is intended to serve as a guideline and a conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions. 14 INDEX TERMS Internet of Things, industry solutions, IoT marketplace.
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Definition of the SubjectFulfilling aquaculture’s growth potential requires responsible technologies and practices. Sustainable aquaculture should be ecologically efficient, environmentally benign, product-diversified, profitable, and societally beneficial. Integrated multi-trophic aquaculture (IMTA) has the potential to achieve these objectives by cultivating fed species (e.g., finfish or shrimps fed sustainable commercial diets) with extractive species, which utilize the inorganic (e.g., seaweeds or other aquatic vegetation) and organic (e.g., suspension- and deposit-feeders) excess nutrients from fed aquaculture for their growth. Thus, extractive aquaculture produces valuable biomass, while simultaneously rendering biomitigative services for the surrounding ecosystem and humans. Through IMTA, some of the uneaten feed and wastes, nutrients, and by-products, considered “lost” from the fed ...
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The Internet of Things (IoT) is expected to substantially support sustainable development of future smart cities. This article identifies the main issues that may prevent IoT from playing this crucial role, such as the heterogeneity among connected objects and the unreliable nature of associated services. To solve these issues, a cognitive management framework for IoT is proposed, in which dynamically changing real-world objects are represented in a virtualized environment, and where cognition and proximity are used to select the most relevant objects for the purpose of an application in an intelligent and autonomic way. Part of the framework is instantiated in terms of building blocks and demonstrated through a smart city scenario that horizontally spans several application domains. This preliminary proof of concept reveals the high potential that self-reconfigurable IoT can achieve in the context of smart cities.
In this chapter, the authors present a review of security requirements for IoT and provide an analysis of the possible attacks, security issues, and major security threats from the perspective of layers that comprise IoT. To overcome these limitations, the authors describe a security implementation challenges in IoT security. This chapter serves as a manual of security threats and issues of the IoT and proposes possible solutions and recommendations for improving security in the IoT environment.
Conference Paper
This document discusses the use cases concerning the management of networks, where constrained devices are involved. A problem statement, deployment options and the requirements on the networks with constrained devices can be found in the companion document on "Management of Networks with Constrained Devices: Problem Statement and Requirements".