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italic xmlns:mml="" xmlns:xlink="">Edge Intelligence (EI) is an emerging computing and communication paradigm that enables Artificial Intelligence (AI) functionality at the network edge. In this article, we highlight EI as an emerging and important field of research, discuss the state of research, analyze research gaps and highlight important research challenges with the objective of serving as a catalyst for research and innovation in this emerging area. We take a multidisciplinary view to reflect on the current research in AI, edge computing, and communication technologies, and we analyze how EI reflects on existing research in these fields. We also introduce representative examples of application areas that benefit from, or even demand the use of EI.
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Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2017.DOI
The Many Faces of Edge Intelligence
Ella Peltonen1, Ijaz Ahmad2, Atakan Aral3, Michele Capobianco4, Aaron Yi Ding5, Felipe
Gil-Castiñeira6, Ekaterina Gilman1, Erkki Harjula1, Marko Jurmu2, Teemu Karvonen1,
Markus Kelanti1, Teemu Leppänen7, Lauri Lovén1, Tommi Mikkonen8, Nitinder Mohan9,
Petteri Nurmi8, Susanna Pirttikangas1, Paweł Sroka10, Sasu Tarkoma1,8, Tingting Yang11
1University of Oulu, Finland
2VTT Technical Research Centre, Finland
3University of Vienna, Austria
4Business Innovation Manager, Capobianco, Italy
5TU Delft, Netherlands
6University of Vigo, Spain
7Oulu University of Applied Sciences, Finland
8University of Helsinki, Finland
9Technical University of Munich, Germany
10Poznan University of Technology, Poland
11Pengcheng Laboratory, China
Corresponding author: Ella Peltonen (e-mail:
Edge Intelligence (EI) is an emerging computing and communication paradigm that enables
Artificial Intelligence (AI) functionality at the network edge. In this article, we highlight EI as an emerging
and important field of research, discuss the state of research, analyze research gaps and highlight important
research challenges with the objective of serving as a catalyst for research and innovation in this emerging
area. We take a multidisciplinary view to reflect on the current research in AI, edge computing, and
communication technologies, and we analyze how EI reflects on existing research in these fields. We also
introduce representative examples of application areas that benefit from, or even demand the use of EI.
INDEX TERMS Edge Intelligence, Edge Computing, 5G, 6G
Edge Intelligence (EI) is an emerging computing paradigm
that enables AI functionalities at the network edge to better
serve the needs of increasingly intelligent and autonomous
connected objects,connected systems, and connected ser-
vices [1]–[4]. EI builds on the development of powerful AI
solutions and the emergence of edge computing as a paradigm
that augments computer networks by bringing storage,
computing, and other functionality close to the devices that
need them. The combination of these developments, as sought
by EI, is challenging the dominant centralized cloud-based
view of AI by allowing intelligence or at least some parts
of it to be placed close to the services, applications, and
data sources that would require or benefit from it, and by
overcoming the limitations of the cloud for many critical
applications [5].
Besides challenging the current cloud-based view on
AI, EI brings additional benefits that enable new types of
applications, a new generation of services, and opportunities
for other innovations [6], [7]. Having intelligence at the
edge minimizes processing latency, which is critical for
applications with short response-time requirements, such
as augmented reality [8] or autonomous vehicles [9], and
applications that are characterized by high data velocity,
such as real-time visual analytics and medical imaging [10]–
[12]. Bringing intelligence directly on the network edge can
enhance privacy by limiting the scope of data disclosure,
particularly when distributed models such as federated
learning are adopted [13]. Finally, edge computing implies
the decoupling and distribution of application state and
application logic across multiple computing resources, mark-
ing a significant shift from today’s cloud-centric application
development. This, in turn, requires reconsidering software
development practises, principles and processes to deal with
new forms of architectures and enhances flexibility [14].
Prior literature has failed to examine interactions between
different components of EI and the challenges these in-
teractions pose. Instead, previous research has examined
EI solely from a narrow viewpoint where the focus is on
specific AI or edge challenges, on challenges emerging from
specific application areas [2], [15], on the implementation
in embedded devices or edge platforms [16]–[19], or on
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2022.3210584
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
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FIGURE 1. Edge Intelligence enables various novel applications.
comparing the cloud and the edge for AI applications
[20], [21]. Thus, this article argues for a more holistic
understanding of EI, highlighting EI as an emerging new
field, discussing the state of research, and identifying its
key challenges. We examine EI in a holistic light with the
aim of serving as a catalyst for research in EI. We take a
multidisciplinary view to reflect on the existing research in
AI and edge computing, analyze how EI extends on this
research, and identify gaps to establish a research roadmap
for the path forward. To summarize, the contributions of this
paper are:
Synthesis of key challenges
to realize EI, including
critical reflection on what there is already implemented
in the field of edge computing, and intelligent systems,
and how EI goes beyond the state-of-the-art.
Research roadmap
of EI, including a critical analysis
of what the EI should and could really provide to
complement the existing systems, and more critically,
how it can enable completely novel applications.
Practicality of EI
by identifying representative exam-
ples of EI verticals that have already been practically
demonstrated and implemented beyond those simply
existing as visions.
Large-scale uptake of EI requires application scenarios
that have sufficient business potential to drive deployment
while also posing unique scientific challenges to engage the
academic community. Thus far AI scenarios have largely
been driven by cloud computing scenarios, such as natural
language processing, and computer vision , among others
[20]. In contrast, edge computing has mostly operated on
scenarios that are characterized by large-volume data streams
and the need for low latency, such as real-time video analytics
or cognitive assistance [22], [23]. EI seeks to merge these
strands and to harness AI algorithms that are migrated to the
edge (instead of the cloud) while offering high bandwidth and
low-latency processing and communications [5], [20]. This
enables novel applications that involve massive data streams
that need to be analyzed and processed in a time-critical,
secure, and latency-bounded manner [1]–[4]. Representative
examples of applications that have already been realized
are illustrated in Figure 1 and include managing robotics
and vehicles in a spatio-temporally critical environment,
distributed manufacturing and logistics, serving users of
privacy-critical systems with highly personal data, and so
on. Note that these are not intended as an exhaustive list
of domains where EI is relevant, rather as diverse examples
of applications that have already been deployed in smart
factories and in emerging Internet of Things solutions
even if some of the deployments remain highly specific,
customized, or rudimentary. Indeed, the use cases for
EI stretch beyond these examples, covering societal (e.g.,
environmental monitoring), commercial (e.g., entertainment,
logistics, manufacturing) and governmental use cases (e.g.,
defense and healthcare) [24]. The envisaged key industry
benefit of EI ultimately pertains to all parts of the application
chain, covering the algorithms, protocols, enablers, and
platform and software engineering methodologies that enable
the deployment of data-intensive and low-latency applications
across the entire edge-cloud environment. Besides offering
increased capabilities for intelligence, EI provides oppor-
tunities for innovative applications and services that are
impossible to realize without EI. Below we briefly discuss
some of these domains, focusing specifically on ones where
academic or commercial demonstrations have already been
realized. Practical use cases are later presented in Section V.
Manufacturing, smart hospitals, and related data-
intensive domains
produce large data volumes from a high
number of sensors (e.g. manufacturing process monitoring)
and data-intensive instrumentation (e.g. PET scanners in
hospitals) [25], [26]. The processing of this data requires a
high computational capacity and EI can provide the necessary
capacity. EI also benefits these domains by driving down
hardware costs and the setup complexity.
AR assistance
for the person operating the equipment
to complete elaborate tasks is a paradigmatic example
of domains that benefit from EI. In this domain, the EI
application is capable of receiving a real-time video stream
containing the environment (set of pieces and tools, their
orientation, state, etc.) and the interactions performed by the
operator [27]. The application should understand the actions
completed by the operator and next steps, providing a tactile
visual guidance in real time. Nowadays, it is not possible
to equip operators with the required computing power, but
EI can offer the necessary intelligence to support this task
without violating response-time requirements.
Future traffic systems and connected vehicles
are foreseen
to take advantage of EI. Examples include applications of
extended sensors and remote or fully autonomous driving,
that require highly reliable and low-latency data processing
and analysis [28]. While some degree of automation can
be achieved with in-vehicle processing, more advanced
algorithms require computational power and resources that
are not available locally. Sensor data collected from cars
and passengers is also an essential element of smart traffic
2VOLUME 4, 2016
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management. Excessive network dynamics, latency and
reliability constraints hinder efficient management using a
centralized approach, whereas using distributed reasoning
with EI can accommodate and adapt to these challenges.
Generative Internet
is, in our view, a candidate for being the
killer application for EI. Indeed, the main impact of EI results
from multi-edge and multi-cloud support. In a generative
Internet, the application logic is generated and provisioned
across the communications, computing, and AI infrastructure.
Dynamic self-management of the communication network
itself is one of the core examples of such a vision for EI
as an automatic and intelligent adaptation of the network
is fundamental for developing an intelligent Internet that
integrate AI across the Internet. However, such a intelligent,
self-aware Internet is still far from our current state of art,
even if early applications of EI move towards directions of
self-aware, dynamic applications as discussed above.
Common to all of the application areas discussed above
are certain enablers from the edge computing and artificial
intelligence. However, simply applying AI into edge or
edge into AI is not sufficient enough to harness the full
presumed capabilities of EI. Indeed, as will be discussed
through several objectives in Section IV, EI is more than
the sum of its parts (i.e., the combination of edge and AI.)
Before discussing the key research challenges and reflecting
on the state of the research with respect to these challenges,
we briefly describe what we mean when we talk about edge
computing, intelligence, and edge intelligence.
Edge Computing:
Networking consortia such as ETSI,
OpenEdge, and Industrial Internet Consortium (IIC), view
the edge as a way to bring additional capabilities closer to
devices with some differences in the definitions related to
the clients and the networking infrastructure that is expected
to be available. For example, ETSI refers to the availability
of cloud computing capability at the Radio Access Network
of cellular operators [29] whereas IIC sees the edge as
the boundary between digital and physical entities that is
delineated by IoT devices. Academic definitions in turn,
consider the edge as a generic entity that can be seen as a
ubiquitous platform, which is not necessarily restricted to
specific resource type capability, deployment location, or
other characterizing parameters (such as storage, network,
and computing capacity).
Intelligence is complex to define in a general
way and as a result there are hundreds of definitions in the
literature. For our purposes, we follow Legg&Hutter [30]
who collected different definitions for intelligence and found
three common features that characterize intelligence: (i) it
belongs to a subject and measures the subject’s ability to
interact with its environment; (ii) it measures the capability
to set and reach objectives; and (iii) it characterizes the
ability to adapt behaviour in response to the environment.
Edge Intelligence
refers to the amalgam of edge computing
FIGURE 2. EI as a network of intelligent operations and services.
and intelligence. The definitions for edge computing highlight
that EI is supported through a ubiquitous platform that is not
restricted by specific resource type constraints while being
able to support applications and services, whereas the defini-
tions for intelligence define this platform to be intelligent by
being able to optimize its behavior and react to changes in its
environment. This definition highlights that EI goes beyond
deploying (artificial) intelligence tools on a platform that is
used to support applications (intelligence on the edge) and
requires that the platform is capable of optimizing behaviour
and reacting to changes in its operational environment. It also
goes beyond the traditional platforms deployed in predefined
locations and evolves towards new distributed architectures
in which all the involved components collaborate to build
ubiquitous intelligence and to provide composed services to
users, other devices and applications; see Figure 2.
Edge intelligence adds specialized intelligence and special-
ized services to leverage the current and emerging cloud and
local intelligence into a network of intelligent operations
and services. In this section, we reflect on prior research to
identify challenges, summarized in Table 1, that need to be
addressed to fully realize the potential of EI. The analysis
was completed by exploring previous publications to identify
the challenges networks face in providing new specialized
services. Then, we studied how such challenges are being
addressed by edge architectures and AI developments. Thus,
our vision aims towards generalized dynamic EI solutions
over the Internet and in our summary we also incorporate
what is already implemented in the edge or AI fields (middle
columns in Table 1 ), and highlight what is further needed
to fully realise the potential and capabilities of EI (far right
column in Table 1). Naturally, these challenges are not
exhaustive and we have prioritized challenges that we have
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TABLE 1. Key objectives for EI and comparison to edge computing and intelligent systems.
Objective Currently on Edge Currently on Intelligence EI should provide
Self-recovery of edge servers [31],
Automatic fault-recovery and fault-
prediction [33]
Device and network outage resiliency; new KPIs to ensure
emerging application QoS and QoE
Latency of
Latency as communication
speed [34]
Performance as model-building
time [35]
KPIs for full operational loop; low-latency network reconfigu-
ration; latency of milliseconds
Localized in-
First steps toward edge-capable AI,
federated learning; no generaliza-
tion over multiple problems [36]
Cloud-centric AI/ML capabilities;
lack in real-time demands [1], [37]
Distributed model building, sharing, and cooperation between
different application verticals
Where is the
Local computational resources;
tasks distribution over the network
Cloud computing paradigm [20]
Dynamic collaboration and resource sharing between end-
user devices, specific application-domain devices, and cloud
Sensing for context-awareness and
localized actions [38], [39]
Application composition solutions,
ML and reasoning to support the
end-users [40], [41]
Enabling self-management properties (e.g. migration, service
continuity, application self-healing)
Virtualization, CI/CD & DevOps,
microservices [14], [42], [43]
ML-specific edge frameworks;
Cloud-based APIs for ML/AI [39]
Assisting methodologies with analytics and ML/AI for edge
development. Dynamic allocation of intelligent components
and interop-
First attempts in references, data
models, protocols and APIs for
resource-constrained and distributed
architectures [14]
Centralized solutions for manage-
ment of distributed architectures
Aligning/handling various edge platform solutions to enable
platform portability
Privacy, secu-
rity, and reli-
Decentralized security and failure
prevention mechanisms [2], [44],
Decentralized trust management
and decision making [44], [46]
Dynamic adaptation into changing situations; intelligent secu-
rity and privacy prevention; locally adjusted trust management
encountered in our development of practical EI solutions
and applications.
Autonomous systems are one of the primary use cases for
EI [47], [48].In many domains, such as smart cities, medical
monitoring, industrial control systems, or defense, these sys-
tems also cannot be shut down but must operate continuously.
Ensuring these systems can operate consistently requires
access to sufficient resources, and meeting highly dynamic
resource demands. Edge solutions that can intelligently adapt
functionality to meet these changing resource requirements,
and software solutions that can scale up to ever-increasing
amounts of devices are critical to achieve this. It is also
important to keep in mind that as the solutions and underlying
compute platforms become more sophisticated and widely
available, the requirements for quality of service along with
expectations for these applications also increase. Within the
smart city context, the problem has evolved from a "city that
never sleeps" paradigm to enabling "connected megacities"
with an increasingly connected life, with more connected
objects and increasingly autonomous systems [49]. The
advantage of the new communication technologies, and the
intelligent platforms they provide, is that we allow even small
cities to access added value services and function exactly
like those “megacities" with highly scalable infrastructure
investments and limited additional costs.
From infrastructure and operator’s point-of-view, systems
that never sleep require the application of Quality-of-Service
(QoS) and Quality-of-Experience (QoE) to become part of
the operational and provisioning decisions [50]. A smart
city, in particular, can be viewed as a “multiagent cyber-
physical system” presenting a synergy between human agents
and intelligent agents encompassing infrastructure, trans-
portation systems, waste collection, smart energy systems,
surveillance, security, etc. Not only should the human agent
seamlessly interact with other cyber-physical agents in the
environments, but there is also a tight integration between
the different intelligent agents to achieve a holistic operation.
An example can be provided as the control function of
traffic lights in such a city. For a truly smart operation, the
traffic light not only must keep track of vehicle density
on road but also presence of pedestrians, mobility patterns
derived by external factors such as school or office hours,
and weather changes. A smart city is a formidable example
of a situation where the high level of interaction and ever-
growing requirements offer new opportunities for a high level
of interaction and more challenging requirements. Moreover,
a sophisticated operation of smart city applications envision
some level of service sentience, i.e. the applications should
operate as per predefined service level agreements (SLAs)
regardless of the time of day. From a practical standpoint,
"never sleeping" refers to ensuring high SLA requirements for
different operations of the city, such as energy, surveillance,
and security.
The digital transformation processes for worldwide cities
are accelerating, with a focus on sustainability and improving
citizen’s quality of life [51]. Such processes are progressing
together with a constantly growing introduction of sensors
and connected objects, and together with an explosion of data
production. D’Amico et al. [32] explore the main challenges
related to sensors in cities, emphasizing the opportunities
and critical issues of this growing digitalization of urban
context. A city that never sleeps needs to communicate mores,
at more levels, and more frequently. However, a city that
communicates more, at more levels, and more frequently
becomes progressively a smarter city that never sleeps [33].
With more strict requirements in terms of service levels of
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the basic connectivity functions and of the basic functions
that are expected from the communication networks.
Serving such networks is not only a question of low latency
and high bandwidth but also presents several other challenges.
Just from software engineering perspective, such systems
must endure and remain resilient towards disconnections and
outages of individual connected devices. Therefore, edge
intelligence must seamlessly support condition monitoring,
fault detection, network reliability, and essential resilience
functions within its control decisions to not just be reliable
to state changes, but also be reactive.
The efficacy of Edge Intelligence is significantly driven
by the context and requirement of the application that
incorporates it. While edge computing, by nature, can help
application developers leverage resources closer to the users,
the needs and demands for optimal user experience can differ
significantly for different applications. Mohan et al. [52] find
that three strict human vestibular thresholds guide the latency
requirements of edge-driven applications. Immersive appli-
cations, such as AR/VR, must abide by motion-to-photon
(MTP) latency of
20 ms - which requires the sensory input
and interactions to be completely synchronized [53], [54].
Interactive applications, such as gaming or video streaming
must operate within perceivable latency (PL) of around
100 ms for optimal QoE [55]. Finally, applications that
require active user inputs and engagements, e.g. teleoperated
surgery, are highly dependent on the human reaction time
(HRT) threshold of 250 ms. Vital applications within the
smart healthcare and smart city domain, like remote surgery,
also fall in this category [22].
The latencies for optimal quality of experience of edge
applications described above do not just include network
or processing latency but latency for the entire end-to-
end process. For example, out of the 20 ms latency quota
of AR/VR applications,
13 ms is reserved for display
technology [53] due to refresh rate, pixel switching, and
other functionality. Therefore, the application processing
pipeline only has the remaining 7 ms to accommodate all
communication, processing, modeling, and output formula-
tion. Similarly, a typical perceived maximum communication
and processing latency for autonomous vehicles is estimated
to be below 10 ms and for remote surgery to be below 150 ms.
This period does not include requirements to perform the
data fusion, processing, and ML necessary for guiding the
efficacy of the application.
The integration of AI with edge can introduce or exac-
erbate the existing latency challenges in many use-cases of
EI. For example, industrial control systems harnessing EI
have extremely strict latency requirements [34]. Arjevani and
Shamir [35] conclude that many communication rounds will
be required in AI processing in the edge and still provide
the worst-case optimum in minimum assumption situations.
The raw data acquisition, data analysis and training, and
the continuous feedback loop in ML will introduce much
higher delay simply considering the increased number of
communication rounds.
The dynamic nature of many IoT environments and an
increasing number of connected devices make flexibility and
self-organization are among the most important capabilities
that EI must offer. The main challenge is to design the
optimized EI pipelines, faster and reliable on-the-air commu-
nications, and transparent symbiosis between the edge and
end-user devices. The EI infrastructure should be scalable
and capable of maintaining latency constraints, including
the time required for data processing, model building, and
AI/ML. Network virtualization likely becomes a key element
where dedicated software can be distributed among nodes to
make the best use of the available resources. Sharing fractions
of available data or trained AI/ML models can significantly
reduce the latency for highly dynamic situations and allow
rapid reconfiguration without dropping users’ QoE.
In smart cities, latency requirements are again strongly
tied to application characteristics. While some applications,
such as smart parking or air quality management, can
endure an increased level of latency, there also exist safety-
critical applications, such as smart traffic management, that
cannot accommodate higher latency. Considering the brake
reaction time of drivers, pedestrian monitoring and driver
notifications systems have to complete their execution under
100 ms, which includes the acquisition of video streams from
the pedestrian crossing, analyzing them to detect potential
accidents, and transmitting a warning signal to the mobile
devices of the affected drivers. A proof-of-concept for such
a system has been successfully deployed on the smart traffic
lights in Vienna urban area and shown to satisfy the latency
quota mentioned above provided that pre-trained lightweight
models are used, and 5G connectivity is available [56].
However, it is an open question whether more dynamic
(and consequently computationally complex) AI models
(e.g., online, active, or transfer learning) can achieve similar
The edge’s size and boundaries are essentially dependant
on the used definitions and on the application domain. In a
simplistic view, we could consider the edge to be exactly
where the devices are connected. However, dealing with
EI, this view might be different for the model training and
inference phases and can vary in time and space in relation
to what is connected to the network and the exact operational
conditions. In practice, the network edge consists of a broad
range of devices, including base stations, servers, IoT sensors
and actuators and personal devices. Their computing capacity,
memory, and storage are limited to some extent, in contrast
to the cloud, where multiple services can operate in tandem.
The connectivity and communication among edge devices
are mainly enabled via the underlying wireless networks
that are highly dynamic and diverse due to their inherited
mobility and spatiotemporal characteristics. In addition, edge
devices utilize several different software stacks ranging from
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almost bare metal to sophisticated container systems [14]
with varying adaptation capacity.
In wireless networks, the geographical locations of the
devices and their surroundings affect the communication
reliability, latency, and capacity, and the resources available
for a particular application may not be easily predictable. In
this view, the immediate adoption of training and inference
architectures from cloud to edge can be highly inefficient,
neglecting on-device and communication constraints as
well as the dynamics of the operating environment. Hence,
optimizing distributed AI/ML algorithms and developing
new mechanisms accounting for channel dynamics and
communication overhead is of paramount importance. These
challenges include the generalization to unmodeled phenom-
ena under limited heterogeneous local data and straggling
devices in the training process. The scale of the edge can
vary from a few to thousands depending on time, users,
and service providers. A small indoor environment can e.g.
consist of a few cooperative devices with low-to-no dynamics
within the duration of hours, in which enabling intelligence
can be based on conventional architectures and algorithms. In
contrast, automated vehicles in an intelligent transport system
are highly susceptible to network dynamics, heterogeneity
and spatiotemporal availability of resources, calling for novel
AI/ML designs.
Thus, edge architectures and platforms must satisfy a
challenging combination of scenarios and applications with
potentially conflicting requirements. On the one hand, from
an economic point of view, operators will be interested in
minimizing the number of edge locations. On the other hand,
edge and fog applications may require many edge instances
or even to distribute the edge among a large set of devices
(including embedded devices towards what is called mist
computing). According to Lan et al. [57] applications can
be classified according to their requirements:
Latency-sensitive applications: Applications with strict
latency requirements can only be achieved by executing
the services at edge locations physically near the source
of the data.
Autonomous applications: Some applications are de-
ployed in areas with poor connectivity, so they can not
take advantage of the cloud paradigm.
Privacy and security applications: Some applications
have to address privacy concerns (e.g. if they manage
health-related information), and they have to store and
manage the data locally.
Context-awareness applications: Distributed applications
that have to use information such as the location or
other local information related to each IoT device.
Data processing and computation are conducted on
small datasets that can be processed locally to avoid
overloading the network.
In this scenario, the edge can not be static. Its functionality
has to be distributed among instances in different locations
and even taking advantage of the computing capabilities of
MEC Data Center
Local IoT
Fog Cloud
service Local
Low resources
Low latency
High resources
High latency
Computational & communication architecture
Service architecture
FIGURE 3. Where is the Edge.
all the devices participating in the network.
Thus, in edge-assisted cloud computing, applications can
take advantage of all the available infrastructure. Cloud
systems can better serve applications requiring low latency
while saving computational and networking resources at core
networks and data centers. The parts of services that require
low latency or provide functions for reducing data, such as
filtering, fusion or other processing, are beneficial to deploy
at MEC hosts residing at access networks near base stations.
The main benefits are the low end-to-end latency between
the local node and MEC node and the reduced amount of
data that needs to be delivered to data centers. As can be
seen, IoT and smart environments can significantly benefit
from MEC residing at the mobile access networks.
However, the current model where MEC hosts are de-
ployed at servers located within or near the access network
base stations also has its limitations [38]. In many smart
space and IoT applications, to deal with possible connectivity
problems and limit the propagation of sensitive data outside
the domain, at least some degree of processing of the sensor
data and the decision-making/control logic is beneficial to
be managed locally on-site [17], [20]. Therefore, in many
scenarios it is beneficial to bring EC capacity within local
IoT clusters, as illustrated in Figure 3. Since it cannot be
expected that local IoT/IoE clusters include devices with
sufficient stability and hardware capacity to accommodate
full-functional MEC host, alternative decentralized solutions
fitting better to the IoT/IoE environments need to be studied.
A vital thrust towards utilizing the full potential of the cloud-
edge continuum is the three-tier edge architecture as proposed
in the present literature [38].
Ubiquitous computing and IoT introduce physical environ-
ments as opportunistic playgrounds for distributed applica-
tions. In such environments, EI has a crucial role in providing
context-aware services and maintaining QoE for users. Key
factors for orchestration of the service deployment and access
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include user location, computational and communication
resources, and application data. In essence, edge resources
must be placed [58]–[60] and their resources allocated [61]
in a way that considers such factors and their trade-offs.
Edge intelligence can provide tools for such orchestration,
considering and predicting user activities and the resulting
fluctuating requirements in terms of multi-tenant resources:
locations, migrating application contexts, providing connec-
tivity, redirecting network traffic and maintenance. With
intelligence, as self-capabilities, the applications become
aware of the edge environment and can continuously ne-
gotiate their reliable and robust execution with the help of
system services. Such developments lead to distributed EI
where user, application, and system components intelligently
adapt, offload, relocate, negotiate, and collaborate without a
central authority to become the de-facto architectural model.
Nevertheless, orchestration is a system-wide collaborative
effort [40], where resource management and control func-
tionality is often separated from application functionality,
e.g., data flows, at architectural level [41]. It is clear that the
resource management functionality relies on AI solutions
at large. The AI is to be distributed across the systems,
where we believe EI, in particular, has a role in reducing
the gap between separated functionalities. Therefore, an
extensive set of edge services would be introduced [39],
such as service discovery, on-demand logical topology and
support for self-configuration, self-optimization, self-healing,
and self-protection. Interoperability issues, such as shared
functionality, interaction protocols, and portability should be
supported on a technical level. The resulting distributed
operation across the edge platform calls for distributed
lightweight service provisioning and control mechanisms
among the applications and systems components [38].
Taking a step further, virtual resource pools, including
micro-operator resources, autonomous vehicles, network
infrastructure components, mobile user devices, and everyday
appliances, call for intelligent resource sharing solutions as
exemplified by 3C-L and Tactile internet.
The discussed services require standardization, such as
reference architectures and APIs, edge-specific software, and
service modelling practises that support immersed intelli-
gence. The starting point here would be the ETSI MEC refer-
ence architecture [62], currently under standardization. The
ETSI standards provide the overall edge system architecture,
required system components and their outlined functionality,
set of APIs for system operation, information dissemination
and third-party integration, guidelines and best practises,
and set of Proof-of-concept (PoC) applications currently
under consideration. Such efforts provide a solid base to
realized edge systems, where some of the MEC system
components (e.g., MEC orchestrator) and PoC applications
largely are seen as relying on AI. However, the realization of
AI functionalities is open, and EI has not yet been considered
as a built-in capability of the edge system.
Moreover, Frameworks such as Fog05 [63], MobileFog
[64], Distributed data flow (DDF) [65], or FogFlow [66] are
being developed to manage the resources and to simplify the
programming. Ubiquitous applications can take advantage of
these frameworks to handle the different parts of their life-
cycle (such as the development, deployment, execution, and
management) transparently to be deployed in a distributed
edge architecture without requiring operators and third party
developers to worry about managing the reservation and
orchestration of computing, networking or storage resources,
while satisfying the application requirements in terms of
latency, mobility, heterogeneity, scalability or quality of
The characteristics of EI solutions depend on a number
of factors. First, the quality of data, in terms of volume,
velocity, and variety; and the availability and location of
processing, communication and storage resources restrict
the potential functionality of the EI solution. Further, the
functions, devices, and users to be served by the EI solution
at a particular location set their requirements on, for example,
the degree of autonomy needed. The key questions are: where
should intelligence be deployed,how does the deployed
intelligence adapt to the local environment, and how do the
localized intelligence interact.
For example, a smart city needs to consider phenomena
such as weather, air quality, and traffic. The corresponding
data generating processes contain prominent spatio-temporal
dependencies, which are reflected in the collected data. In
some cases, the structure emerging from such spatial depen-
dencies may be significant enough to affect the resulting
model. On the positive side, such dependency structures can
offer a way to distribute the model. For example, Lovén et
al. [36] propose a distributed interpolation method that takes
advantage of spatio-temporal dependencies and partitions
data for local model learning along boundaries projected on
the spatial dimension.
Localization introduces several challenges. Massive-scale
data analysis requires time for data delivery and process-
ing, let alone building complex ML/AI models. Further,
geographically distributed data, even if extensive, does
not always improve the accuracy of the learned models,
especially if local models learn only from local data. Such
local models may be easy and lightweight to implement
whenever they fit the application profile. Still, they can
suffer in quality and generality due to a lack of variety in
the local data sets. To overcome this problem, more delicate
model communication standards and protocols need to be
studied. Local models should exchange information and learn
from each other to improve model quality and generality. It
is imperative to determine what models can effectively be
distributed and trained with highly localized data and how
the life-cycle management of such distributed models can be
arranged. Current promising approaches aim to identify the
most significant updates and minimize communication while
maximizing the knowledge and experience transfer [37]. EI
solutions based on federated learning, such as In-Edge AI
VOLUME 4, 2016 7
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[1], solve the problem by periodically replacing local models
with a global one, but consequently lose out any localized
The ETSI MEC Application Development Community and
PoCs already provide demonstrations in the realization of
edge benefits and practical development aspects, such as
feasibility, interoperability and testing, through a set of
use cases. In addition, available tools for edge software
development are well-established, including DevOps and
MLOps practises with automatized continuous integration
and delivery (CI/CD) on top of virtualization technolo-
gies and based on microservices and serverless computing
paradigms. Platforms for managing and deploying ML/AI
solutions on edge have been proposed (e.g. KubeFlow and
MLFlow). Also, more fine-grained platforms (e.g. Function-
as-a-Service, FaaS) and runtimes for elastic on-demand
serverless computing have appeared, omitting the need to
also focus on infrastructure/platform by application/service
developers [42].
However, already IoT software engineering (SE) as such
is complicated because tools, techniques, and skills in nearly
all areas of modern software development are needed for
developing end-to-end (E2E) systems [14]. A new layer
of complexity appears with EI [20], which influences the
design methodologies, architectures, tools, best practices,
and the overall software life-cycle. The platforms supporting
EI should provide elastic host service support and tools and
means for straightforward deployment of on-demand software
components that can exploit reliable, near real-time runtimes
and execution environments with fast access to data and
computing resources. EI can also mean another architectural
layer to introduce complexity in system maintenance and
resource sharing across the device-edge-cloud continuum.
Hence, the SE discipline must increasingly address EI
as a building block towards autonomous, adaptive, and
intelligent applications in an opportunistic and elastic online
environment. Such SE discipline could be located in the
intersection of APIs, distributed heterogeneous execution
environments, distributed computing platforms, and AI.
Here, the MLOps practises facilitating automatization of the
management, operation and life-cycle of all types of ML/AI
models for the edge applications atop the edge infrastructure.
As these somewhat different fields require different com-
petencies, a risk is that the developer’s role will become
more complex, as has happened in the context of full-stack
web development [43]. Still, systematic and consolidated
methodologies for software development are needed with EI
integrated from distinct perspectives. A novel EI software de-
velopment process integrates applied ML/AI techniques and
software modeling practises in the initial design phases [39].
ML/AI helps identify and assess the inherent opportunistic
elements in parallel with domain expertise and provide
feedback during the initial stages of the process. At the
system development and deployment stages, EI is already
aware of these aspects and could address them in operation.
In contrast to the centralized data center setting, EI needs to
address challenges originating from hardware heterogeneity
and resource management in a highly dynamic and decen-
tralized environment. Therefore, interoperability becomes a
key issue for portability, communication protocols, and data
models. Portability enables EI applications and services to
be deployed to different vendor solutions. Heterogeneous
hardware and low-level communication should be beneath
the provided standardized abstraction levels, e.g. with in-
frastructures and APIs suggested for the application and
service developers. Requirements assessment, authentication,
resource discovery, system configuration and deployment,
and life-cycle management provided by the IE platform
should have unified interfaces that enable more rapid
adoption of IE technologies by vendors and industry. Here,
the ETSI standardization provides well-defined architecture
with a set of functionalities, APIs and development practices
as the background to realize edge systems. Currently, it is
unclear how the EI capabilities can be built into the edge
systems, requiring specifications for further APIs, different
software constructs and system services for integration, with
support from the underlying edge infrastructure. Commonly
accepted practices should be established, considering the
existing standardization and frameworks.
At the extreme, this may lead us to isomorphic IoT
architectures in which the devices, gateways, and cloud are
able to run the same applications and services, allowing
flexible migration of code between any element in the
overall system [14]. Instead of learning different incompatible
software development methods, one base technology will
suffice and cover all aspects of E2E development. Although
fully isomorphic IoT systems are still years away, their
arrival may ultimately dilute or even dissolve the boundaries
between the cloud and edge. Isomorphic systems will allow
computations to be transferred dynamically and performed
on any level of the cloud-edge architecture that provides the
optimal performance, storage, latency, and energy-efficiency
In parallel with the edge system standardization, data
specifications, models, interfaces, and representations should
be agreed to ensure that concepts and their relationships
are interpreted in the same way in the edge platforms,
services, and applications. While this is impossible to achieve
generally, the data integration and management tasks between
the system, applications and application-specific services can
be standardized, providing common mechanisms for data
integration, discovery and management.
Edge intelligence inherits the existing security, reliability,
and privacy challenges of edge computing. In addition,
8VOLUME 4, 2016
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2022.3210584
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Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
massively interconnected and high-speed communication
networks introduce amplified security and privacy problems.
Increasingly autonomous systems can attract large-scale
attacks and introduce a wealth of vulnerabilities at different
parts of the interconnected systems. A new category of
risks emerges from possible malicious intelligence. Edge
servers can be considered as aggregating points for all
sensors in a local, possibly unprotected area, providing a
single entry point for malicious entities that can access feeds
from multiple sensors but target attention towards a single
server responsible for handling the operation. For mature
and trusted services, EI needs to be self-learning in all the
layers of communication-related to end-to-end security [2],
[44]. This calls for the careful design of centralization versus
decentralized security protocols.
The use of AI also introduces security vulnerabilities [67]
which, if exposed or exploited, can have severe consequences
for EI and connected and derived functions. Computing and
storage resources in the edge will be limited, and deploying
complex AI procedures require higher resources, which can
cause resource exhaustion attacks easier, as discussed in the
case of IoT in [34]. Furthermore, mixing data from diverse
sources can lead to unpredictable entanglements and hidden
feedback loops [67]. Therefore, security validation of AI
procedures and techniques and consequential analysis of the
deployment of AI techniques in edge platforms must be
carried out before enabling the automated EI infrastructure.
Modular and hierarchical distribution of AI tasks can also
minimize security risks.
Trust mechanisms are needed to guarantee the validity
and trustworthiness of the EI devices and data providers.
Distributed Ledger Technologies (DLT), such as Blockchain,
has emerged as a potential solution to provide distributed
and decentralized trust through mutual consensus mechanism
among various actors [44]. Safeguards on user privacy are
presently governed by the General Data Protection Regulation
(GDPR) and by the Cybersecurity Regulation in the EU,
and the restrictions of the data usage under the directives
will affect the AI/ML paradigms in EI [2]. These represent
fundamental legal milestones ensuring that privacy and
security are reinforced.
Failure proneness of edge servers is another important
issue that might endanger the overall reliability. Being
deployed in exposed locations without data center-level
advanced support systems increases the potential impact
of hardware failure at the edge, which may risk the
EI integrity. Existing reliability mechanisms such as re-
execution or check-pointing might be infeasible, particularly
for real-time EI [45]. While EI promises more resilience by
edge flexibility within critical and transient failures due to
network fluctuations, transparent control and reconfiguration
mechanisms must be designed and implemented. Currently,
there exist fault tolerance solutions for edge computing
infrastructure [45], and neural network architecture [46];
however, joint consideration of the two aspects is missing.
The European Union Agency for Cybersecurity ENISA,
has recently defined key research directions and innovation
topics in cybersecurity. Indeed, it is clear that the more
connectivity and the more intelligence is available at the
edge, the more the balance between security and utility,
privacy enhancement, and failure proneness need to be
studied and adequately considered. Solutions will emerge
from more intelligent security threat prevention, dynamically
changeable privacy prevention acts, and locally adjusted trust
management. The question about privacy that will remain
open in the future, for instance in 6G, will be that: how
personal can the information be in the time of shared storage,
processing and data economy [68]?
A fundamental enabler for EI is the underlying edge
infrastructure based on medium- or small-scale edge servers.
Due to the data and computation requirements for AI/ML
and required system services in orchestration and sharing
resources with real-time responsiveness, the placement of
edge components is a crucial concern. However, the resulting
architectures are typically fixed based on mobile networks
and capabilities dictated by many factors, such as core
network topology, capacity and traffic, operator policies,
and user mobility. Therefore, intelligent approaches are
needed for scalable edge infrastructure placement in different
scenarios. To maintain QoE, extensive sets of real-world
parameters need to be considered for online intelligence.
Furthermore, architecture that dynamically supports such
placement is an important requirement for the hosting
software infrastructure. Below we discuss some application
use cases for such deployments. As noted in Section II, EI
is relevant for a wide range of domains and the application
scenarios are not meant as exhaustive list of all possible
solutions, rather as examples that demonstrate the practical
benefits and have potential for uptake. Specifically, we require
the application scenarios to meet two criteria. First, we
require the application use cases to be novel representative
and concrete examples of already implemented use cases
rather than application domains that have thus far only been
envisioned. Second, the case studies were chosen to cover
three promising application areas for EI as has been identified
in prior surveys [2] [15]: Smart Cities, Industrial IoT, and
Environmental Monitoring.
Intelligent traffic light
solution utilizing EI is implemented
in Vienna, Austria, in order to improve traffic safety [56].
The road sections around dangerous intersections with low
visibility and their surroundings are continuously monitored
with video cameras deployed at traffic lights, where the
streaming video is processed locally. Relevant events such
as pedestrians or cyclists entering the road segments are
detected in real-time and nearby drivers are alerted via a
mobile application. The local processing of data not only
reduces response time by avoiding long-distance transfer of
big streaming data but also enables continuous delivery of
the service even if the remote infrastructure is not accessible.
VOLUME 4, 2016 9
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2022.3210584
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Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
FIGURE 4. Edge nodes setup on Vienna’s chosen intersection and the
integration into the traffic-signal chambers [56]
Moreover, the privacy of pedestrians is preserved since the
video recordings never leave the traffic light.
In this case study, single-board Raspberry Pi edge devices
extended with Google’s Coral Edge TPU accelerators have
been integrated into traffic signal chambers (Figure 4). These
nodes run pre-trained TensorFlow Lite models to detect
pedestrians or cyclists and send alerts to nearby drivers’
mobile devices via MQTT protocol over 5G. Real-world
evaluation shows that affected drivers can be notified in
around 100 ms, 18 ms of which is the processing time of a
frame and the rest is for communication with the guaranteed
delivery of the alert [56].
EI application for the automotive scenario is
studied at Poznan, Poland, where dynamic management of
autonomous car platooning is supported using rich context
information stored in databases. The study focuses on
improving the reliability of intra-platoon wireless com-
munications that suffer from channel congestion in the
5.9 GHz frequency band. The utilization of alternative
frequency bands is proposed, such as the TV white spaces
or mmWave, that are dynamically selected based on the
additional information from databases (e.g. the observed TV
signal power at a specific location). A hierarchical structure
of edge intelligence support is considered, where, depending
on the origin of information and its scope, it can be stored
in regional or local databases or even in distributed form.
The initial findings indicate that it is possible to improve
communication reliability for platooning with EI significantly
[69], [70].
Environmental sensing
The EDISON project, studied in
Oulu, Finland, proposes an edge-native method and archi-
tecture for distributed interpolation [36]. EDISON assumes
a large fleet of mobile sensors, collecting environmental
data. The mobile nodes are calibrated upon rendezvous with
sparse high-quality fixed sensors, transmitting their data for
learning and inferring with a distributed interpolation model
running at edge nodes. Early simulation studies promise an
improvement over baseline distributed methods as well as a
global interpolation model, assuming data is generated by
relatively independent, spatially distributed processes.
Urban-scale air quality sensing
is an example of city-scale
application domains that can benefit from edge intelligence.
The MegaSense programme at the University of Helsinki
explores how to extend the scale of air quality monitoring
to support dense and high-resolution information [71]. Air
quality is traditionally monitored using professional-grade
measurement stations that are highly expensive to both
deploy and operate. Increasing the monitoring scale requires
integrating sensors of different types, such as low-cost
sensors carried by citizens to industrial-grade sensors located
at industrial sites and in the urban infrastructure with the
professional-grade monitoring stations. Low-cost sensors
tend to suffer from lower accuracy, which can be mitigated
using machine learning-based calibration [72]. The idea in
calibration is to learn a model that can compensate for
the errors in the low-cost sensors. Air quality information
tends to have strong spatial correlations, and thus the
calibration models of sensors in the same spatial area can
share information instead of learning a separate model for
each sensor. Edge deployments are essential for ensuring the
calibration can operate efficiently, e.g., recent work at the
University of Helsinki has demonstrated how deploying the
calibration on edge can reduce latency and minimize overall
communication bandwidth in city-scale deployments [73].
Cloudification has so far helped to promote the adoption
of AI/ML methods and develop intelligent applications and
services. Local use of these techniques on edge is now
progressively growing. In the near future, we envisage
AI being pervasive and supporting many aspects of the
operations of future communication networks and their edge.
We took a broad view on emerging EI solutions, discussing
the motivations and applications that will not simply benefit
from EI but will be enabled by EI, and presented some early
case studies in emerging application areas. We also identified
aspects that we consider priorities for the likely R&D and
innovation activities.
Recent large-scale cyber-security incidents and attacks
rely on a traditional communication network and relatively
non-autonomous interconnected systems and devices. What
could happen with an increasingly intelligent and autonomous
network if not properly instructed to support privacy, security,
reliability, and resilience by design. Considering the expe-
rience of 5G and 5G acceptance, a proper consideration
of these aspects since the early phases of any beyond
5G development is an absolute necessity. As a regulatory
example, under the EU Commission’s new digital strategy,
additional regulatory actions have been planned, including
creating a specific AI framework addressing safety and
ethical challenges, and the adaptation of existing safety
and liability frameworks to possible new technologies. A
dedicated extension to future intelligent communication
networks is essential, with certification schemes for privacy,
security, reliability, and resilience to boost the development of
secure and robust networking environments, while ensuring
10 VOLUME 4, 2016
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2022.3210584
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
that the relevant legislation, initiatives, and policies are fully
This paper has been written by an international expert
group, led by the 6G Flagship at the University of Oulu,
Finland (AoF grants 318927, 326291, 323630; Infotech
Oulu grants B-TEA, TrustedMaaS). This work is partially
supported by the European Union’s Horizon 2020 research
and innovation programme under the grant agreement No.
101021808 and Marie Skłodowska-Curie grant agreement
No. 956090, National Science Centre in Poland (grant
2018/29/B/ST7/01241), Austrian Science Fund (FWF grants
Y 904-N31, I 5201-N), CHIST-ERA (grant CHIST-ERA-19-
CES-005) and the City of Vienna (5G Use Case Challenge
InTraSafEd 5G).
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This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
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12 VOLUME 4, 2016
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2022.3210584
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
received her Ph.D. at the Uni-
versity of Helsinki, Finland. She did her postdoc-
toral period at University College Cork, Ireland,
and is currently working as an Assistant Professor
(Tenure Track) at the University of Oulu, Finland.
Her research focuses on pervasive intelligent sys-
tems and services in the 5G/6G era.
received his M.Sc. and Ph.D. in
Wireless Communications from the University of
Oulu, Finland in 2012 and 2018, respectively. Cur-
rently, he is working with VTT Technical Re-
search Centre of Finland, and is an adjunct profes-
sor at the University of Oulu, Finland. His research
interests include cybersecurity, security of 5G/6G,
healthcare technologies, and the applications of
machine learning in wireless networks.
received dual M.Sc. degrees in
Computer Science & Engineering from Politec-
nico di Milano in 2011 and Istanbul Technical
University (ITU) in 2012, and a Ph.D. degree in
Computer Engineering from ITU in 2016. He is a
project leader and research fellow at the University
of Vienna. His research interests center around
resource management for geo-distributed and vir-
tualized computing systems such as intercloud and
edge computing, as well as edge intelligence.
Business Innovation
Manager, Capobianco, Italy. Michele Capobianco
received his M.Sc. in electronics engineering at the
University of Padova, Italy. He has over 30 years
of experience in R&D, product development, and
project management for a variety of technology
sectors. He is currently working as a consultant
in innovation management and technology transfer
is leading Cyber-Physical Intel-
ligence Lab as tenured faculty at TU Delft, Nether-
lands. He has over 15 years of R&D experience
across EU, UK and USA. His research focuses
on Edge AI solutions for cyber-physical systems
in smart health, mobility and energy domains. He
is an associate editor for ACM Transactions on
Internet of Things (TIOT), IEEE OJ-ITS and Mul-
timodal Technologies and Interaction.
received the M.Sc.
and Ph.D. degrees in telecommunication engineer-
ing from the University of Vigo in 2002 and
2007, respectively. He is currently an Associate
Professor with the Department of Telematics En-
gineering, University of Vigo. Between 2014 and
September 2016 he was the head of the iNetS area
in the Galician Research and Development Center
in Advanced Telecommunications. His interests
include wireless communication and core network
technologies, multimedia communications, embedded systems, ubiquitous
computing, and the Internet of things. He is the co-founder of a university
spin-off, Ancora.
D.Sc. (Tech.), is a post-
doctoral researcher supported by the Academy
of Finland at the Center for Ubiquitous Comput-
ing, University of Oulu, Finland. She has also
worked as a visiting researcher in Northern Fin-
land Biobank Borealis and Centre for Health and
Technology, Oulu, Finland. Her current research
interests include data analytics, context modeling
and reasoning, and machine learning in ubiquitous
computing, IoT, and data-intensive systems.
(Member, IEEE) received the
M.Sc. and D.Sc. degrees from the University of
Oulu, Finland. He works as an Assistant Professor
(tenure track) at CWC-NS Research Group, Uni-
versity of Oulu. He focuses on wireless systems
level architectures for future digital healthcare,
where his key research topics are wrapped around
intelligent trustworthy distributed IoT and edge
computing. Dr. Harjula has coauthored more than
70 international peer-reviewed articles. He is an
Associate Editor of Wireless Networks journal (Springer).
is a Senior Scientist in the
Cognitive Production Industry research area at
VTT Technical Research Centre Finland Ltd. He
received his M.Sc. (honors) and Dr. Tech. degrees
in computer science from University of Oulu in
2007 and 2014, respectively. He has been a vis-
iting research scientist at University of Maryland
in USA, Ludwig-Maximilians University in Ger-
many and Keio University in Japan. His research
interests include data analytics, machine learning,
distributed systems and human-computer interaction.
(Ph.D.) is currently work-
ing as a post-doctoral researcher at the University
of Oulu, in M3S research unit (Information Tech-
nology and Electrical Engineering). Before his cur-
rent position he worked as a post-doctoral research
associate in Agile Research Network at the Open
University, Faculty of Science Technology Engi-
neering and Mathematics (UK, Milton Keynes).
His current research is related to software-defined
vehicles and vehicular connectivity in 6G. Before
his current academic researcher career, he has worked in various industry
projects on IP-networks and mobile telecommunications systems and ser-
VOLUME 4, 2016 13
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2022.3210584
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
received his Ph.D. at the
University of Oulu, Finland. He is currently work-
ing as an postdoc researcher at the University of
Oulu, Finland. His research focuses on software
engineering, digital twins and intelligent systems
and services.
received his doctoral de-
gree in computer science and engineering at the
University of Oulu, Finland. Currently he serves
as principal lecturer at the Oulu University of
Applied Sciences. His current research interests
include edge computing for Internet of Things
systems. He is a senior member of IEEE.
received the M.Sc. (Stat) and
D.Sc. (Tech) degrees at the University of Oulu,
Finland, in 2016 and 2021. He’s currently affili-
ated as a faculty Universitätsassistent at the Dis-
tributed Systems Group, TU Wien, and as a post-
doctoral researcher at the Center for Ubiquitous
Computing, University of Oulu. His research fo-
cuses on edge intelligence, and in particular, learn-
ing and decision making in relation to orchestra-
tion in the computing continuum.
received his doctorate at
Tampere University of Technology, Finland. He
is a full professor at University of Helsinki and
University of Jyväskylä, both located in Finland.
His current research interests include IoT, software
engineering, and multi-device programming.
received his Ph.D. (as Marie
Curie ITN fellow) from the Department of Com-
puter Science at the University of Helsinki in
Finland and M.Tech. degre (honors) from In-
draprastha Institute of Information Technology
Delhi (IIIT-D) India in 2019 and 2015, respec-
tively. Currently, he is a senior researcher in the
Chair of Connected Mobility at Technical Univer-
sity of Munich, Germany. He has been awarded
“Outstanding Ph.D. Dissertation Award" by IEEE
Technical Committee on Scalable Computing (TCSC). His research interests
include edge computing, next-generation networked applications and large-
scale Internet measurements.
received the Ph.D. degree in
Computer Science from the Department of Com-
puter Science, University of Helsinki, Finland, in
2009. He is an Associate Professor of Distributed
Systems and Internet of Things at the Department
of Computer Science, University of Helsinki. His
research interests include distributed systems, per-
vasive data science, and sensing systems.
(mat.) and D.Sc. (tech) degrees at the University
of Oulu, Finland, in 1998 and 2004. She works
as the deputy director of Center for Ubiquitous
Computing, University of Oulu and as the direc-
tor of Interactive Edge research team developing
adaptive, realiable and trusted edge computing. Dr.
Pirttikangas also works as a freelance lead AI
scientist in a Finnish company Silo.AI.
received the M.Sc. and Ph.D.
(with honors) degrees in telecommunications at
Poznan University of Technology (PUT), Poland,
in 2004 and 2012, respectively. Currently he is
employed as an Assistant Professor at the Institute
of Radiocommunications, Faculty of Computing
and Telecommunications, PUT. His main research
interests include 5G systems, radio resource man-
agement, vehicular communications (V2X), cross-
layer optimization and MIMO systems.
(Senior Member, IEEE) is a
Professor of computer science with the University
of Helsinki. He is a visiting professor with the 6G
Flagship at the University of Oulu. He completed
his Ph.D. in Computer Science at the University of
Helsinki in 2006. He has authored four textbooks
and has published over 250 scientific articles. He
holds ten granted U.S. patents. His research inter-
ests include Internet technology, distributed sys-
tems, data analytics, and mobile and ubiquitous
(M’13) received the B.Sc. and
Ph.D. degrees from Dalian Maritime University,
China, in 2004 and 2010, respectively. She is
currently a Professor at Pengcheng Laboratory,
and also with Dalian Maritime University. Her
research interests are in the areas of, Network
AI, edge intelligence and Space-Air-Ground-Sea
integrated networks.
14 VOLUME 4, 2016
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2022.3210584
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
... As illustrated in Figure 1, this strategy effectively reduces the amount of data transmitted from the device monitoring a target phenomenon to a remote server, thus reducing the time and the communication that pass between the operations of sensing and actuation. However, the challenge of EI involves not only embedding the characteristics of massive computing systems into tiny, restricted devices, as if trying "to fit an elephant into a small box" [13], but indeed, a full-fledged infrastructure to allow EI, at its extreme, to transform Big Data into intelligent data and to enable real-time device management, system performance, decision-making, and operation monitoring, thus improving overall responsiveness, privacy, effectiveness, and productivity [12,[14][15][16]. Although EI holds the potential to rival the cloud, it cannot maximize its capabilities when employed alone. ...
... Although the term EI occasionally appears in older EI-related works such as [16,41], only in the last decade has the research established clearer boundaries of the domain, and only in the last three years (2019-2022) have more systematic studies been published in conference proceedings and journals, mostly. From a deep study of all the analyzed works, indeed, we were able to identify a clear classification of the studies under the general umbrella of EI: the majority of them are truly narrowed on specific techniques and domains (e.g., [18,19,[43][44][45]), while few horizontal studies seek to explore the fundamentals, perspectives, and trends of EI (whereas with coarse-grained [42,46] or fine-tuned [12,14,35] analysis). However, as previously pointed out, the specific aim of this survey was to methodically shed light on the state-of-the-art of EI by performing a systematic analysis (interesting and somewhat surprisingly, there is only a systematic literature review [7] and a systematic classification [35]) in the form of a tertiary study, thus centering our analysis on comprehensive reviews, surveys, roadmaps, etc. ...
... • Evolutionary EI definitions, which "simply" mean EI as the next stage of current edge computing [14,[48][49][50], where edge nodes self-process their own data, being empowered through lightened AI algorithms (individually measured in terms of <accuracy, latency, energy, memory footprint> [48]) or pre-trained models (intended as "pluggable AI capabilities for edge computers" [51]); • Revolutionary EI definitions, which propose EI as a new paradigm combining ("the amalgam", "the marriage", "the confluence") both novel and existing approaches, techniques, and tools from different areas (mainly from edge computing and AI, but also approximate computing, cognitive science, etc.) and realizing a fully distributed intelligence among end devices, edge nodes, and cloud servers [12,15,35,47,[51][52][53][54]. ...
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The Edge Intelligence (EI) paradigm has recently emerged as a promising solution to overcome the inherent limitations of cloud computing (latency, autonomy, cost, etc.) in the development and provision of next-generation Internet of Things (IoT) services. Therefore, motivated by its increasing popularity, relevant research effort was expended in order to explore, from different perspectives and at different degrees of detail, the many facets of EI. In such a context, the aim of this paper was to analyze the wide landscape on EI by providing a systematic analysis of the state-of-the-art manuscripts in the form of a tertiary study (i.e., a review of literature reviews, surveys, and mapping studies) and according to the guidelines of the PRISMA methodology. A comparison framework is, hence, provided and sound research questions outlined, aimed at exploring (for the benefit of both experts and beginners) the past, present, and future directions of the EI paradigm and its relationships with the IoT and the cloud computing worlds.
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Edge AI accelerators have been emerging as a solution for near customers' applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, robotics, and remote sensing satellites. These applications require meeting performance targets and resilience constraints due to the limited device area and hostile environments for operation. Numerous research articles have proposed the edge AI accelerator for satisfying the applications, but not all include full specifications. Most of them tend to compare the architecture with other existing CPUs, GPUs, or other reference research, which implies that the performance exposé of the articles are not comprehensive. Thus, this work lists the essential specifications of prior art edge AI accelerators and the CGRA accelerators during the past few years to define and evaluate the low power ultra-small edge AI accelerators. The actual performance, implementation, and productized examples of edge AI accelerators are released in this paper. We introduce the evaluation results showing the edge AI accelerator design trend about key performance metrics to guide designers. Last but not least, we give out the prospect of developing edge AI's existing and future directions and trends, which will involve other technologies for future challenging constraints.
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The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence, environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: They suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.
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In a level-5 autonomous driving system, the autonomous driving vehicles (AVs) are expected to sense the surroundings via analyzing a large amount of data captured by a variety of onboard sensors in near-real-time. As a result, enormous computing costs will be introduced to the AVs for processing the tasks with the deployed machine learning (ML) model, while the inference accuracy may not be guaranteed. In this context, the advent of edge intelligence (EI) and sixth-generation (6G) wireless networking are expected to pave the way to more reliable and safer autonomous driving by providing multi-access edge computing (MEC) together with ML to AVs in close proximity. To realize this goal, we propose a two-tier EI-empowered autonomous driving framework. In the autonomous-vehicles tier, the autonomous vehicles are deployed with the shallow layers by splitting the trained deep neural network model. In the edge-intelligence tier, an edge server is implemented with the remaining layers (also deep layers) and an appropriately trained multi-task learning (MTL) model. In particular, obtaining the optimal offloading strategy (including the binary offloading decision and the computational resources allocation) can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved via MTL in near-real-time with high accuracy. On another note, an edge-vehicle joint inference is proposed through neural network segmentation to achieve efficient online inference with data privacy-preserving and less communication delay. Experiments demonstrate the effectiveness of the proposed framework, and open research topics are finally listed.
Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.
Conference Paper
The modern trend of moving artificial intelligence computation near to the origin of data sources has increased the demand for new hardware and software suitable for such environments. We carried out a scoping study to find the current resources used when developing Edge AI applications. Due to the nature of the topic, the research combined scientific sources with product information and software project sources. The paper is structured as follows. In the first part, Edge AI applications are briefly discussed followed by hardware options and finally, the software used to develop AI models is described. There are various hardware products available, and we found as many as possible for this research to identify the best-known manufacturers. We describe the devices in the following categories: artificial intelligence accelerators and processors, field-programmable gate arrays, system-on-a-chip devices, system-on-modules, and full computers from development boards to servers. There seem to be three trends in Edge AI software development: neural network optimization, mobile device software and microcontroller software. We discussed these emerging fields and how the special challenges of low power consumption and machine learning computation are being taken into account. Our findings suggest that the Edge AI ecosystem is currently developing, and it has its own challenges to which vendors and developers are responding.
The name Edge Intelligence , also known as Edge AI , is a recent term used in the last few years to refer to the confluence of Machine Learning, or broadly speaking Artificial Intelligence, with Edge Computing. In this manuscript, we revise the concepts regarding Edge Intelligence, such as Cloud, Edge and Fog Computing, the motivation to use Edge Intelligence, and compare current approaches and analyze application scenarios. To provide a complete review of this technology, previous frameworks and platforms for Edge Computing have been discusses in this manuscript in order to provide the general view of the basis for Edge AI. Similarly, the emerging techniques to deploy Deep Learning (DL) models at the network edge, as well as specialized platforms and frameworks to do so, are review in this manuscript. These devices, techniques and frameworks are analyzed based on relevant criteria at the network edge such as latency, energy consumption and accuracy of the models to determine the current state of the art as well as current limitations of the proposed technologies. Because of this, it is possible to understand what are the current possibilities to efficiently deploy state-of-the-art DL models at the network edge based on technologies such as AI accelerators, Tensor Processing Units and techniques that include Federated Learning and Gossip Training. Finally, the challenges of Edge AI are discusses in the manuscript as well as the Future directions that can be extracted from the evolution of the Edge Computing and Internet of Things (IoT) approaches.
The traditional definition of full stack development refers to a skill set that is required for writing software both for the frontend and backend of a web application or site. In recent years, the scope of full stack development has expanded significantly, though. Today, a full stack software developer is assumed to master various additional areas especially related to cloud infrastructure and deployment, message brokers and data analytics technologies. In addition, the emergence of Internet of Things (IoT) and the rapidly spreading use of AI/ML technologies are introducing additional skill set requirements. In this paper, we discuss the expectations for a modern full stack developer based on our industry observations, and argue that these expectations have significant implications for software and web engineering education.
Conference Paper
Despite advances in vehicle technology and road modernization, traffic accidents are a huge global issue, causing deaths and injuries, especially among pedestrians and cyclists. This often happens due to pedestrians and cyclists in drivers' blind spots or distractions delaying drivers' reactions. Therefore, timely warning drivers about critical situations is important to increase traffic safety. New edge computing and communication technologies have been proposed to reduce latency in critical IoT systems. However, state-of-the-art solutions either do not focus on traffic safety or do not consider low-latency requirements in this context. We propose InTraSafEd5G (Increasing Traffic Safety with Edge and 5G) to address these issues. InTraSafEd5G performs real-time edge analytics to detect critical situations and deliver early warnings to drivers. After describing our design choices, we provide a prototype implementation and evaluate its performance in a real-world setup. The evaluation shows that InTraSafEd5G can (i) detect critical situations in real-time and (ii) notify affected drivers in 108.73ms on average using 5G, which is within expected latency requirements of road safety IoT applications. Our solution shows a promising step towards increasing overall traffic safety and supporting decision-making in critical situations.