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A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities


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The proliferation of IoT systems has received much attention from the research community, and it has brought many innovations to smart cities, particularly through the Internet of Moving Things (IoMT). The dynamic geographic distribution of IoMT devices enables the devices to sense themselves and their surroundings at multiple spatio-temporal scales, interact with each other across a vast geographical area, and perform automated analytical tasks everywhere and anytime. Currently, most of the geospatial applications of IoMT systems are developed for abnormal detection and control monitoring. However, it is expected that, in the near future, optimization and prediction tasks will have a larger impact on the way citizens interact with smart cities. This paper examines the state-of-the-art of IoMT systems and discusses their crucial for supporting anticipatory learning. The maximum potential of IoMT systems in our future smart cities can be fully exploited in terms of proactive decision making and decision delivery via an anticipatory action/feedback loop. We also examine the challenges and opportunities of anticipatory learning for IoMT systems in contrast to GIS. The holistic overview provided in this paper highlights the guidelines and directions for future research on this emerging topic.
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A Holistic Overview of Anticipatory Learning for the
Internet of Moving Things: Research Challenges and
Hung Cao * and Monica Wachowicz
People in Motion Lab, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Version April 3, 2020 submitted to ISPRS Int. J. Geo-Inf.
The proliferation of IoT systems has received much attention from the research community,
and it has brought many innovations to smart cities, particularly through the Internet of Moving
Things (IoMT). The dynamic geographic distribution of IoMT devices enables the devices to sense
themselves and their surroundings at multiple spatio-temporal scales, interact with each other across
a vast geographical area, and perform automated analytical tasks everywhere and anytime. Currently,
most of the geospatial applications of IoMT systems are developed for abnormal detection and
control monitoring. However, it is expected that, in the near future, optimization and prediction
tasks will have a larger impact on the way citizens interact with smart cities. This paper examines
the state-of-the-art of IoMT systems and discusses their crucial for supporting anticipatory learning.
The maximum potential of IoMT systems in our future smart cities can be fully exploited in terms of
proactive decision making and decision delivery via an anticipatory action/feedback loop. We also
examine the challenges and opportunities of anticipatory learning for IoMT systems in contrast to
GIS. The holistic overview provided in this paper highlights the guidelines and directions for future
research on this emerging topic.
Keywords: IoT; Internet of Moving Things; Anticipatory Learning; GIS; Smart Cities
1. Introduction
The Internet of Things (IoT) has received a significant attention from the research community
since its first introduction by Kevin Ashton in 1999 [
]. The basic concept of IoT is that every physical
thing in a smart city is connected, and can function as a sensor embedded in tiny computers, which
are then geographically distributed over a vast area of a smart city. An IoT device is always connected
through a communication network, ranging from short range networks (e.g. Bluetooth, Zigbee, NFD),
to medium range networks (e.g. Wi-Fi, Digi Mesh), to large range networks (e.g. LoRaWan, cellular,
WiMax). Today, IoT devices are usually expected to collect sensor data, communicate with each other,
and make decisions without human intervention [
]. Some examples of IoT devices include smart
traffic lights, smart parking meters, smart home meters, smartphones, and wearable devices [813].
The IoT market in smart cities has not really taken off yet due to a number of technical, political
and financial barriers; however, previous survey papers have already shown different points of view
on the role of IoT in smart cities. These are mainly related to IoT architecture concerns such as elements,
facilities, protocols, and standards for IoT[
], as well as the development of new IoT applications
such as smart factories [20], smart homes [21], and smart hospitals[22].
Taking it a step further is the Internet of Moving Things (IoMT), which can be defined by extending
the concept of the IoT to moving things which are essentially any IoT device that moves. Instead of
having a fixed location in a smart city, an IoMT device can be anything people wear or carry around,
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such as clothes, smartphones, and wearables; or things used for transportation, such as cars, trucks,
trains, bikes, and planes. When these IoMT devices are connected to each other, not only can they
sense themselves (e.g. speed, acceleration, and direction) and their surrounding environment (e.g.
temperature, noise and air pollution), they can also exploit the resources made available by edge, fog
and cloud computing.
Therefore, IoMT devices generate unbounded data streams from a vast amount of indoor and
outdoor locations that will require a low-latency database for storing and exploring data in space. Time
is an important dimension because different time windows used to handle IoMT data streams will
have an impact on pre-processing, analytical and visualization tasks. Some examples include landmark
windows [
], sliding windows [
], damped widows [
], and tilted windows [
]. Different time
windows have been proposed to cope with transporting data streams where the data rate could
overwhelm the processing power of the computation resources at the edge, fog and cloud. In contrast,
the space dimension has been overlooked until now, despite the fact that the data streams are being
generated by IoMT devices moving over large geographical areas and having fine spatial granularity.
There is now a growing interest and demand for developing IoT-GIS platforms that can handle data
streams generated by IoMT devices. This paper is one step in this direction, mainly because IoMT is
paving the way for anticipatory learning.
As indicated in [
], anticipatory learning is an often-misused term. Rosen defined it as “a system
whose current state is determined by a (predicted) future state", while Nadin has defined it as “a system whose
current state is determined not only by a past state, but also by possible future states" [
]. Nevertheless,
both authors agree that prediction and anticipation are not interchangeable concepts. The consensus is
that an anticipatory system makes a decision to impact the future in order to benefit a user; meanwhile
a predictive system uses a predictive model that can foresee the future state of the system itself.
In this paper, anticipatory learning for IoMT is defined as “a system where the current state is
determined by the past and future behaviour of IoMT devices that is represented by the dynamic geographical
distribution of IoMT devices over time". This is critical for building context intelligence for anticipatory
learning models. Mainly because IoMT devices are equipped with different sensors, which generate
data streams of spatio-temporal information used to infer contextual intelligence on what is happening,
where and why it is happening, and what should be done about it. In other words, contextual
intelligence requires that anticipatory learning models have (1) a context sensing strategy of relevant past
events detected or monitored by IoMT devices; (2) spatio-temporal awareness of present contextual variables
being continuously used for gathered IoMT data; and (3) user-driven awareness of the preferred future so the
system can exert influence and help a user to make appropriate decisions.
Current edge-fog-cloud computing is the technology allowing us to run machine learning
algorithms and build anticipatory learning models [
]. In contrast, our current GIS technology
has been primarily developed for supporting predictive systems. Recent attempts in designing
IoMT-GIS have shown the main limitations of GIS in processing IoMT data streams [
]. Adding
the functionalities of an anticipatory learning model to GIS will only create more barriers to using GIS
for running streaming machine learning for building anticipatory learning models.
Since a fairly systematic overview of IoT systems has been recently published elsewhere [
], our
paper focuses on IoMT systems. Our purpose is not only to give a holistic overview of IoMT research
that is relevant to each stage of an anticipatory learning model but also to provide some guidelines
and future research directions for building anticipatory learning models for IoMT systems.
The rest of the paper is organized as follows. Section 2introduces the main concepts of IoMT
systems and compares the data collection strategies currently being used in research projects. Section
3describes the main steps involved in building Anticipatory Models for IoMT systems. Section 4
describes the research being carried out on context sensing at the edge of a network, while Section
5introduces context intelligence using fog computing. Section 6delineates the prediction and
intelligent actions for anticipatory learning. Section 7gives a a holistic overview of the challenges and
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opportunities for building anticipatory learning for IoMT systems. Finally, conclusions and future
research are given in Section 8.
2. Internet of Moving Things
In general, IoMT devices are equipped with many types of sensors, from accelerometers and
gyroscopes to proximity, light, and ambient sensors, as well as microphones, and cameras. They also
have the capability of computing by using a wide range of communication interfaces, such as Wi-Fi,
Bluetooth, or NFC (Near-Field Communication). The ability to sense themselves and their surrounding
environments is key to generating “small data streams” over space and time in such a way that they
share many characteristics of big data, including the five V’s: Variety, Velocity, Volume, Veracity, and
Value [3741].
The nature of IoMT data streams is multi-model, diverse, heterogeneous, and voluminous; often
supplied at high speed, and with a degree of uncertainty. In general, these data streams also have
distinctive characteristics that make obsolete the traditional storage, management, and processing of
current GIS. [42]. These characteristics can be described as one of the following:
Data in Motion: the IoMT devices not only have the ability to sense themselves using context
variables such as velocity, acceleration, and direction at a specific location and time, but they
can also sense their surrounding environments using context variables such as temperature,
noise and air pollution, and depending on the type of sensor deployed inside an IoMT device,
these variables might have a variety of spatial ranges (e.g. from 1 and 10m to 100m and 1km) as
well time granularities (e.g. from milliseconds and seconds to hours and days). Overall context
sensing data is constantly moving from the IoMT devices to edge and fog nodes, up to the cloud
depending on the processing power and storage resources available.
Data in Many Forms: Depending on the context intelligence envisaged for an Anticipatory
Learning model, each IoMT device can perform different sensing functions for collecting
time-series and event triggered data. This leads to different data types including structured,
semi-structured, unstructured, and mixed data streams.
Data at Rest: It is indisputable that IoMT devices produce a large amount of data streams that
are always tied with a location over time. This poses a challenge to capturing, processing, and
managing the data within an appropriate spatio-temporal scale that is needed to be a-priori
known when developing Anticipatory Learning models.
Data in Suspicion: The uncertainty refers to the biases, noise and abnormalities in the data streams
for reasons such as data inconsistency and incompleteness, latency, ambiguity, deception, and
Data of Many Values: the potential context hidden deeply in the IoMT data streams is significant
and it has not yet been fully exploited. By processing, computing, analyzing and making
decisions based on this context could help us support decision making actions. Anticipatory
computing is considered in this paper as a key approach to exploiting that potential.
Table 1compares some selected research projects where the data from IoMT devices was collected
using several different sensors, such as GPS, RFIDs tags, and cameras. They have been categorized
into four common types: Structured, Unstructured, Semi-structured, and Mixed. Structured data is
the information that complies with a formal schema and data models; meanwhile unstructured data
does not follow any pre-defined data model. Semi-structured data does not reside in a data model,
but it does have some organizational structures that make it easier to analyze (e.g.: CSV, XML, JSON
File). Mixed data is the combination of many types of data together. It is argued that a large part of
IoMT data produced today is either semi-structured or unstructured data [
]. Our literature review of
selected projects confirms this hypothesis, and it also reveals the following main issues in GIS:
Uniqueness: The IoMT data streams are an unique type of spatio-temporal data because they
represent an immense cloud of location points over time in such a way that current spatial
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representations (e.g. trajectories, time geography and layers) can not handle the volume of these
data points and their assigned semi-structured and unstructured data.
Propagation: We consider propagation as a discrete-time process starting from one data point
to another data point that is able to accumulate context information and is governed by the
progress speed between the two or more data points. Spatio-temporal progress matrices have
been used in the past, but they can not handle non-structured and unstructured data streams.
More research work is needed in this domain.
Multiprocessing: It is easy to see from Table 1 that accumulated data streams can arrive and
require processing at various speeds from batch to near-real time, or real-time processing. Most
of the research projects have used batch processing to analyze their data. The development of
streaming GIS is needed for analyzing the data streams as they arrive.
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Table 1. Overview of IoMT research projects.
Data in Many
Data at Rest Goal Sensors/IoMT devices Reference
Moving Object Map
Analytics (MOMA) for
connected vehicles
GPS, Camera, Environmental
Location Prediction
GSM traces, Cellular calls,
survey data
Mobility-aware trustworthy
crowdsourcing (MATCS)
Crowdsourced data [45]
Urban Trajectory Data
Analytics System
GPS, Rain Gauge Data,
Road Incident Report, Social
Semi-structured Real-time Smart Object framework Sensors [47]
Traffic Monitoring Traffic lights [48]
Structured Batch
Clustering of IoT devices UAVs [49]
CityPulse framework Bus [50]
IoT-Based Smart Parking Ultrasonic [51]
Analyzing people’s activities
RFID tags [52]
Ambient intelligence with
adaptive decisions
Internet Packet [53]
Ambient intelligence with
adaptive decisions
Internet Packet [54]
Media-aware security RFID tags, IPTV, VoIP, VoD [55]
Locationing phone
Wifi Scanner, Bluetooth
UBICON (Anticipatory
Ubiquitous Computing)
RFID tags, Bluetooth Signal [57]
Traffic Congestion Prediction
GPS [58]
Complex Event Processing RFID, GPS [59]
Mode Transportation
Crowdsourced data [60,61]
Mobility Prediction Smart Card [62]
Mining the semantics of
origin-destination flows
GPS, Mobile Phone [63]
Optimizing the mobility
models and communication
GPS [64]
CarStream Services
driving data including
vehicle status, driver
activity, and passenger-trip
Traffic monitoring and alert
Geo-location and speed data [66]
Transportation Network
GIS and the Internet of
Emissions and traffic-related
Crowdsourced data [68]
Multi Access Physical
Monitoring System
wearable smart-log data [69]
Wearable health monitoring
RFID, ECG Sensor, Body
Temperature Sensor, Blood
Pressure Sensor
Early detection of Alzheimer
Motion Sensor data [70]
Near real-time Transportation Planning Bluetooth Signal [71]
Real-time Pedestrian Safety Detection Phone Camera [72]
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3. Anticipatory Learning Model
“Anticipation pertains to change, that is, to a sense of the future" [
]. From an IoMT perspective, we
need to be able to acquire data streams that can be used to sense a comprehensive context in space
and time, and infer anticipatory actions based on predictions of the future state of this context. To that
end, Figure 1illustrates four main steps in building anticipatory learning models which are (1) context
sensing,(2) context intelligence,(3) context prediction, and (4) anticipatory action/feedback loop, as previously
proposed in [
]. Most state-of-the-art research is currently limited to the first three steps. Pejovic
and Musolesi [
] stated that the main barrier to further proliferation of anticipatory computing is
the inability of IoMT devices (and IoT in general) to seamlessly interact with humans and generate
feedback, which is vital to guiding an anticipatory learning process. The literature review described
in this paper also reveals another barrier to the proliferation of anticipatory learning models that is
the lack of approaches to represent a-priori spatio-temporal knowledge of a particular context. This is
crucial for avoiding an Internet of “Useless" Mobile Things in guiding anticipatory learning processes
in the near future.
Body Sensor
Edge Node
Fog Node
Rack Servers
Big Data Analytics
Data Mining
Context Sensing
Context Intelligence
Context Prediction
Anticipatory Action/Feedback Loop
Anticipatory Learning
Key Steps
IoMT Systems
Figure 1.
Overview of the main steps involved in building Anticipatory Learning Models using IoMT
4. Context Sensing at the Edge of a Network
For an anticipatory learning model, sensing plays an important role in delivering the data used
to generate context intelligence. Context may be divided into various categories ( location, identity,
activity, time) [
] and may have numerous aspects, such as geographical, physical, social, and
temporal [
]. Contextual sensing aims to provide an interface between IoMT devices (things) in the
physical world and a person or a group of people.
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In-vehicular context sensing, IoMT devices in a vehicle can detect important aspects of driver
behavior and the surrounding environment over time. On-board sensors on the vehicle, as well as
sensors built into mobile devices carried by the driver, can also be used to gather IoMT data streams.
Furthermore, IoMT data streams from different cars can provide increased spatial coverage to better
understand the context, and can also help to reduce disambiguation. Context sensing can provide
information on drivers changing lanes, stop signs, obstructions and potholes. These features can the
be further used to infer a context that will be used within an anticipatory learning model to improve
driver safety and engine efficiency.
In order to achieve that, data pre-processing is necessary to extract features from IoMT data
streams and use those features to provide context intelligence. The availability of edge computing
power promisingly allows us to run many pre-processing techniques near to an IoMT device, rather
than having all IoMT data streams sent to a data center [
]. The correct choice of pre-processing
techniques will be vital in the later steps of building an anticipatory learning model. A brief description
of each pre-processing step is presented as follows:
Dealing with missing data: For a large accumulated data streams, deleting observations based on
missing values is usually not considered as to be a problem, but for a continuous data stream, it
may affect our later steps in anticipatory learning. Therefore, missing values could be replaced
based on predictive models [82,83].
Filtering: IoMT devices usually produce noise data streams. In order to minimize the impact on
succeeding steps, a clear set of automated tasks are needed to define, detecting, and correcting
errors. Some new approaches can be found in [84,85].
Summarization and Aggregation: For some applications, the summary form of accumulated data
streams might be enough for statistical analysis [
]; other applications may require data
aggregation to diminish the bandwidth consumption as well as the data latency [88].
Cleaning: IoMT data streams sometimes originate irrelevant or inaccurate data. Cleaning
techniques are needed to reduce computational time and complexity, and to improve the
performance of the predictive model, due to fewer data features [83,89].
Transforming: to deal with the complexity of the IoMT data streams, Principal Component
Analysis (PCA) is a commonly used technique to reduce the number of the data features [
Another technique, Latent Dirichlet Allocation (LDA) is used to find a linear combination of
features that characterizes or separates two or more classes [
]. Recently, Pattern Reduction
(PR) was presented in [93] for reducing the number of patterns.
It is of paramount importance that IoMT data streams are pre-processed before passing to the
next step (i.e. context intelligence). Should we, therefore, stream all of our IoMT data to the cloud
(data centers)? Our answer to this question is no. The closer to the data source that pre-processing is
performed, the more advantages the IoMT system has. With the huge volume of IoMT data streams
produced by a variety of sensors, it is highly possible to flood and overwhelm the networks and data
centers (i.e. cloud). In addition, some pre-processing tasks can be implemented using a specific set of
IoMT devices which can help to improve the interactions between devices and improve the efficiency
of the whole system.
5. Context Intelligence at the Fog Layer of a Network
Context intelligence requires inductive reasoning to infer higher-level concepts from pre-processed
IoMT data streams. With academic references from as early as the 1980s, this is not a new theory;
however, IoMT systems have revealed that context intelligence requires anticipatory learning models
which understand the limitations of our algorithms in generating new knowledge, and are able to
adapt this knowledge to an environment different from the one in which the learning model was
trained. Contextual intelligence requires moving far beyond an analysis of economic, urban, rural and
many others spaces. It is common to rely on simple explanations for complex high-level concepts (i.e.
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complex phenomena as human behavior). The most difficult task in this step is adjusting our persistent
mental models and learning to differentiate between universal beliefs and their specific patterns and
Our vision of context intelligence is to distribute streaming analytics into a hierarchical order,
starting with descriptive analytics, which can be processed on edge nodes themselves (i.e. gateways),
and performing more complex diagnostic analytics on fog nodes. Bonomi et al. [
] have previously
proposed a hierarchical distributed architecture based on fog computing to process IoT data with low
latency, location awareness, and mobility support. We have extended this distributed architecture with
the following elements:
Scalability: By distributing automated analytical tasks, context intelligence depends on the
scalability of IoMT devices. Many context models will require simple machine learning
algorithms such as Linear Spanish Inquisition Protocol (L-SIP) which has been applied to reduce
data transmission; Filtered State Classification (ClassAct) as a human posture/activity classifier
based on decision tree; and Time-Discounted Histogram Encoding (Bare Necessities) which is
used for summarizing the relative time spent in given contexts [94].
Mobility and geographic distribution: These are indispensable requirements for context intelligence,
however an anticipatory learning system also requires a rich scenario of communication and
interaction between all available computational resources. To achieve this, a-priori data pipelines
must be designed that will support an analytics everywhere framework [9597].
Heterogeneity and Interoperability: Obviously, terminal devices in the IoMT system can collect data
with different timestamps, formats, and locations. Additionally, the edge network computing
devices which deploy the IoT gateways could seamlessly support the interoperability between
terminal devices. For example, an array of devices including an armband sensor, a Bluetooth
headset, a smartphone, an external antenna for GPS receiver, and a light laptop with transceiver
in [
] were combined together to collect human activity data, then processed to predict the
context around them.
6. Context Prediction and Anticipatory Actions
Context Prediction and Anticipatory Action are the two important steps for anticipatory learning
models. Anticipatory action is referring to the act (behavior), including actual decision making; internal
preparatory mechanisms; or learning that is dependent on predictions, expectations, aims, or beliefs
about future states. According to [
], anticipation focuses on the impact of a prediction or expectation
of current behavior. Stated in another way, anticipatory actions are not only about predicting the future
or expecting a future event but also about changing behavior (or behavioral biases and predispositions)
according to this prediction or expectation. For anticipatory learning models to assist citizens in
changing their behaviour, context prediction and intelligent-driven actions must play a major role.
Previous research has described different prediction models used to predict the behavior of people
or IoMT devices. Tsai, Chun-Wei, et al. [
] gives a brief review of data mining techniques for IoT
systems. Figure 2illustrates the state-of-the-art research for context prediction using different analytical
algorithms and a variety of data sources, while Table 2below summarizes the approaches used for
building a prediction model based on supervised and unsupervised prediction techniques [
Supervised techniques rely on labeled data and training to find a model that can afterwards be applied
to a new data set. Unsupervised techniques, in contrast, use unlabeled data and attempt to predict
common patterns.
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Data Source
(iii) Spatial-Temporal Data
(xx) Camera
(xxi) Internet Packet
(xv) Survey data
(xiii) RFID tags
(viii) Sensors
(xvii) Traffic lights
(v) Crowdsourced data
(xvi) Rain Gauge Data
(xxii) Road Incident
(xii) Smart Card
(xi) Mobile Phone
(vi) Ultra-Wide Band
(x) Online location-based
(xix) social networks
(ii) Bluetooth and WLAN
(i) WiFi and GSM radio
(vii) Community well-
being census data
(xviii) Public transport
mobility data
(xiv) GPS data
(ix) Passenger-side-facing
ultrasonic rangefinder
(iv) Trace of the mobility
(2) Bayesian coalition game based on
the concepts of game theory and
Learning Automata (LA)
(12) Emperical Study, Bayesian
(5) Support Vector Machine (SVM)
(6) Deep Restricted Boltzmann
(8) Markov Chain Monte Carlo in
Bayesian Model Averaging
(4) Kernel Regression
(3) Gradient Boosting Trees
(10) Mobile probability tree
(13) Decision Tree Algorithms
(14) Multivariate nonlinear time
series prediction techniques
(15) Naïve Bayes
(17) Regression
(18) Bayesian Network
(19) Pearson correlation coefficient
(20) Conditional Random Fields
(22) Nonlinear time series analysis
(21) Markov-based and compression-
based predictors (LZ-based
(23) T-pattern Tree
(11) Artificial Neural Networks (ANN)
(16) Dynamical Bayesian Network
(1) Linear Discriminant Analysis (LDA)
(9) Recurrent Neural Network
(7) Gaussian Mixture Models with
Expectation Maximization
Figure 2. Overview of different approaches developed for prediction models.
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Table 2. State of the art projects using approaches in Figure 2.
Analytical Algorithms References Data Sources References
(1) [103](i) [48,49,104107]
(2) [53](ii) [48,57,108,109]
(3) [60,109](iii) [43,110,111]
(4) [46](iv) [112]
(5) [46,48,61,113](v) [60,61,110,113116]
(6) [58](vi) [104,117]
(7) [71](vii) [118]
(8) [71,111,117](viii) [103,111,119]
(9) [58,112](ix) [120]
(10) [121](x) [122]
(11) [63,109](xi) [63,108,109,121124]
(12) [49](xii) [62]
(13) [57,115,119](xiii) [57,104]
(14) [108](xiv) [43,46,58,63,103,106,108,114,117,125127]
(15) [48,103](xv) [49]
(16) [109](xvi) [46]
(17) [124,128](xvii) [43]
(18) [123](xviii) [118]
(19) [118](xix) [46,122]
(20) [62,126](xx) [43,117,129]
(21) [43,105,107,114](xxi) [53]
(22) [106](xxii) [46]
(23) [127]
7. Research Challenges and Opportunities
While the principles of anticipatory learning modelling have been studied for several decades
], IoMT is actually in its infancy. Although recently, researchers attempted to integrate an
anticipatory process into artificial learning systems [
], few attempts can be found on research
applications that apply the theory of anticipatory computing to building context intelligence in IoMT
devices [
]. We advocate that the proliferation of IoMT devices has created a unique opportunity
to explore anticipatory learning models using the vast amount of IoMT data streams. This section
discusses the research challenges in applying anticipatory computing for IoMT systems.
7.1. Research Challenges
Anticipatory learning for IoMT systems is reliant on multi-disciplinary research fields such as
Internet of Things; Big Data Analytics; GeoSpatial Data Science; Cloud Computing; Edge Computing;
Machine Learning; and Data Mining. Inherent challenges to this are discussed below.
Privacy: One of the main concerns about deploying IoMT devices around a smart city is how
to generate anticipatory actions from IoMT data streams without violating user privacy. Some
examples of sensitive information gathered by IoMT devices include locations, activities, and
emotions, For example, anticipatory computing can be misused to predict the future user
locations or activities for an individual. Preserving privacy becomes even more complex when
it comes to considering the inconsistent privacy policies among multiple users. One example
includes the case of one user who may only want to donate one type of data (i.e. Bluetooth
data), while another one donates two types (e.g. Bluetooth and Wi-Fi usage data). When these
data are combined and co-location patterns are found, the information of the first user can be
unintentionally exposed.
Security: The diversity of IoMT devices that we expect in smart cities poses a significant challenge
to ensuring the security of the entire anticipatory learning process, especially, when it comes to
particularly with wearable devices, body sensor networks, or carried items (such as smartphones).
IoMT devices may pose a threat to users, due to susceptibility to hacking. Although there is
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currently some attention on the issue of security for the IoMT systems [
], there is no
common standard, protocol, or security framework for IoMT devices. Therefore, addressing
security issues for IoMT is now an urgent concern in our research work.
Connection: One of the key factors to making IoMT devices to work effectively is the
communication networks used by them. Mobility poses a challenge for always keeping to
maintaining a stable connection among IoMT devices in a smart city. In the future, new
networking technology is expected to be used to keep IoMT devices collecting data seamlessly
and independently of their location over a short and long periods of time [141145].
Turbulence: Different from the fixed-location based IoT devices, the mobility of the devices
usually creates chaotic and unstable interactions between these devices. For example, IoT
devices deployed at a fixed-location always know to which neighbors they are communicating.
In contrast, IoMT devices do not know a-priori about their close neighbors. The first Law
of Geography needs to be further explored in terms of potential impact of geographical
proximity on the interoperability, power usage, automation of analytical tasks, data pipelines
and communication protocols of IoMT devices.
Management: Selecting the right type of an IoMT device to support a specific anticipatory task is
not an easy choice. If we choose many IoMT devices it may cause many problems such as power
drains, noises, and data latency, to mention a few. Alternatively, if fewer devices, edge nodes
and fog nodes are deployed over a large geographical area, there may be gaps in data collection.
Another challenge is how to efficiently manage the energy usage patterns of IoMT devices as
they move.
Information Loss: Processing data streams at the edge of a network brings potential information
loss, a risk that must be balanced between the efficiency of the system and the value of contextual
information lost. It also raises the an important question about possible geographical divide,
where regions of a smart city will determine which data streams should be processed at the
edge nodes, and which data streams should be processed in a cloud computing environment.
Determining which types of data streams and mobility behaviour of IoMT devices and where
they should be used for data processing remains an interesting research challenge.
Streaming Geospatial Analytics: the spatial relationship among the locations of the measured
contextual variables using a sequence of accumulated data streams is demanding new methods
that do not rely on density and proximity, but on the connectivity of a massive cloud of data
points. The research challenge is three-fold: (1) how to develop new spatial interpolation processes for
determining which data points from the current data streams should be used to estimate values at other
unknown points; (2) how to select the type of time windows that should be used for streaming geospatial
analytics; (3) geospatial summarization where the connectivity of the IoMT devices is used to summarize
accumulated data streams over space and time.
Analytics Everywhere Frameworks: From our literature review, there are over 400 architectures
that were developed to handle the incoming IoT data streams using different strategies such
as streaming, micro-batch, and batch processing. These strategies have been designed to work
towards an asynchronous approach for static IoT devices. For developing anticipatory learning
models using IoMT systems, we identified the need for Analytics Everywhere frameworks
that are capable of breaking down the processing and analytical capabilities into a network
of streaming tasks and distributing them into different compute nodes in an edge-fog-cloud
continuum. The research challenge is to develop location aware analytical capabilities to support
streaming descriptive, diagnostic and predictive analytics.
7.2. Opportunities
Along with the above-mentioned challenges, there are always some opportunities. We illustrate
some of these toward the anticipatory computing for IoMT systems.
Locations offer many opportunities to geospatial research: The context sensing ability of an
IoMT system usually produces data streams that bring the opportunity for developing new
Version April 3, 2020 submitted to ISPRS Int. J. Geo-Inf. 12 of 20
location-aware applications. The mobility of these devices can also be examined using different
spatial and temporal scales. New location prediction and mobility prediction models are needed
to support anticipatory learning models, especially in the case for smart cities.
Real-time anticipatory actions: Having a learning engine close to an IoMT device, and combining
the knowledge and insight which is computed in a cloud environment, can anticipate the needs
of citizens in real-time. As delineated in [
], “if this real-time analytics is fed into some kind
of a predictive model and the results are used to take the user current decisions, then we have what is
defined as anticipatory computing. If the output of the predictive model is directly fed into an automated
decision-making process, it ensures a desired outcome. This is prescriptive analytics. This roadmap
essentially is shaping the future."
Integration with opportunistic computing: There is a concern for how users carrying IoMT devices
could interact with each other opportunistically [
]. IoMT could be an enabler by providing
more interaction between users through moving devices. Some typical applications might
include human-centric sensing, and data sharing.
Combination of different research fields to mimic human anticipatory actions: Recently, some digital
assistants such as Apple Siri, Google Now, Microsoft Cortana [
] are able to help people
do things such as sending a text, playing a song, adding a reminder, etc. None of these tasks
required anticipatory actions. Researchers are looking for a tool that can give instantaneous
delivery, understand surrounding context, and be able to analyze a huge amount of streaming
data [
]. To achieve this, anticipatory computing needs to combine many fields of research
such as geography, deep learning, humanoid robots, artificial general intelligence, and big data
8. Conclusions
This paper discusses anticipatory computing, which refers to systems that are focused on
anticipating what is most relevant to users and acting accordingly, rather than only reacting to user
commands. Anticipatory actions rely on different predictive models by combining processing levels
such as cloud, edge, and fog nodes deployed around a smart city. It is important to point out that
anticipatory computing and IoMT systems are continuously changing. In addition, the proliferation of
IoMT devices offers many related research challenges and opportunities as discussed in this paper.
The promising trend toward IoMT (and IoT in general) has already attracted researchers from
different industries, academic fields, research groups, government departments, etc., who are laying
the foundation for smart cities. We have identified a gap in this foundation: the anticipation actions,
which are expected to have a strong impact on the way smart cities will operate in the future. Hopefully,
the path laid out in this paper will give useful guidelines for further research in this emerging topic.
Author Contributions: These authors contributed equally to this work.
Funding: This research was supported by the NSERC/Cisco Industrial Research Chair [Grant IRCPJ 488403-14].
The authors would like to thank Ms. Alica Farnham for proofreading this paper. The authors
also appreciate the insightful comments and suggestions provided by three anonymous reviewers and the guest
editors on the previous version of this manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
The following abbreviations are used in this manuscript:
Version April 3, 2020 submitted to ISPRS Int. J. Geo-Inf. 13 of 20
ANN Artificial Neural Network
DBN Dynamical Bayesian Network
ECG Electrocardiogram
GIS Geographic Information System
GPS Global Positioning System
GSM Global System for Mobile communication
LDA Latent Dirichlet Allocation
IoMT Internet of Moving Things
IoT Internet of Things
IPTV Internet Protocol television
NFC Near-Field Communication
PCA Principal Component Analysis
PR Pattern Reduction
RFID Radio-frequency Identification
SVM Support Vector Machine
UAV Unmanned Aerial Vehicle
VoD Video on Demand
VoIP Voice over Internet Protocol
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The automatic generation of street networks is attracting the attention of research and industry communities in areas such as routable map generation. This paper presents a novel mechanism that focuses on the automatic detection of street elements such as traffic lights, street crossings and roundabouts which could be used to generate street maps and populate them with traffic influencing infrastructural elements such as traffic lights. In order to minimize the system requirements and simplify the data collection from many users with minimal impact for them, only traces of GPS data from a mobile device while driving are used. Speed and acceleration time series are derived from the GPS data. An outlier detection algorithm is used first in order to detect abnormal driving locations (which can be due to infrastructural elements or particular traffic conditions). Using deep learning, speed and acceleration patterns are automatically analyzed at each outlier in order to extract relevant features which are then classified into a traffic light, street crossing, urban roundabout or other element. The classification results are enhanced by adding the degree of atypicity for each point calculated as the percentage of times that a particular location is detected as an outlier in several drives. The proposed algorithm achieves a combined recall of 0.89 and a combined precision of 0.88 for classification.