The vision of the Internet of Things (IoT) relies on the provisioning of real-world services, which are provided
by smart objects that are directly related to the physical world. A structured, machine-processible approach to provision such
real-world services is needed to make heterogeneous physical objects accessible on a large scale and to integrate them with the
digital world. The incorporation of observation and measurement data obtained from the physical objects with the Web data, using
information processing and knowledge engineering methods, enables the construction of ?intelligent and interconnected things?.
The current research mostly focuses on the communication and networking aspects between the devices that are used for sensing
amd measurement of the real world objects. There is, however, relatively less effort concentrated on creating dynamic infrastructures
to support integration of the data into the Web and provide unified access to such data on service and application levels. This
paper presents a semantic modelling and linked data approach to create an information framework for IoT. The paper describes
a platform to publish instances of the IoT related resources and entities and to link them to existing resources on the Web. The
developed platform supports publication of extensible and interoperable descriptions in the form of linked data.
De S, Moessner K (2008) Method and apparatus for producing an ontology representing devices and services currently available to a device within a pervasive computing environment, USPTO 12/062,794
With universal usability geared towards user focused customisation, a
context reasoning engine can derive meaning from the various context elements and
facilitate decision-taking for applications and context delivery mechanisms. The
heterogeneity of available device capabilities means that the recommendation
algorithm must be in a formal, effective and extensible form. Moreover, user
preferences, capability context and media metadata must be considered
simultaneously to determine appropriate presentation format. Towards this aim, this
paper presents a reasoning mechanism that supports service presentation through a
rule-based mechanism. The validation of the approach is presented through
application use cases.
De S, Meissner S, Kernchen R, Moessner K (2008) A Semantic Device and Service Description Framework for Ubiquitous Environments, ICT-MobileSummit 2008 Conference Proceedings IIMC International Information Management Corporation
Wang W, De S, Cassar G, Moessner K (2015) An Experimental Study on Geospatial Indexing for Sensor Service Discovery, Expert Systems with Applications42(7)pp. 3528-3538 Elsevier
The Internet of Things enables human beings to better interact with and understand their surrounding environments by extending computational capabilities to the physical world. A critical driving force behind this is the rapid development and wide deployment of wireless sensor networks, which continuously produce a large amount of real-world data for many application domains. Similar to many other large-scale distributed technologies, interoperability and scalability are the prominent and persistent challenges. The proposal of sensor-as-a-service aims to address these challenges; however, to our knowledge, there are no concrete implementations of techniques to support the idea, in particular, large-scale, distributed sensor service discovery. Based on the distinctive characteristics of the sensor services, we develop a scalable discovery architecture using geospatial indexing techniques and semantic service technologies. We perform extensive experimental studies to verify the performance of the proposed method and its applicability to large-scale, distributed sensor service discovery.
Delivering individualized services that conform to the user?s current
situation will form the focus of ubiquitous environments. A description of the
networked environment at a semantic level will necessitate contextually
oriented knowledge acquisition methods. This then engenders unique
challenges for the crucial step of resource discovery. A number of service
discovery protocols exist to perform this role. In this paper, we identify the
requirements inherent for such an environment and investigate the suitability of
the available protocols against these. A suitable candidate solution is proposed
with an implementation with semantic extensions and reference points for
Zhou Y, De S, Wang W, Moessner K (2014) Enabling Query of Frequently Updated Data from Mobile Sensing Sources, IEEE
The Internet of Things (IoT) paradigm connects everyday objects to the Internet and enables a multitude of applications with the real world data collected from those objects. In the city environment, real world data sources include fixed installations of sensor networks by city authorities as well as mobile sources, such as citizens? smartphones,¬ taxis and buses equipped with sensors. This kind of data varies not only along the temporal but also the spatial axis. For handling such frequently updated, time-stamped and structured data from a large number of heterogeneous sources, this paper presents a data-centric framework that offers a structured substrate for abstracting heterogeneous sensing sources. More importantly, it enables the collection, storage and discovery of observation and measurement data from both static and mobile sensing sources.
Li N, Attou A, De S, Moessner K (2008) Device and service descriptions for ontology-based ubiquitous multimedia services, Proceedings of The 6th International Conference on Advances in Mobile Computing and Multimediapp. 370-375
Multimedia services are becoming increasingly popular among mobile users. Ontology and related technologies have been introduced into the multimedia domain as a means to provide declarative formal representations of the domain knowledge and thus to enable intelligent multimedia processing, such as media format adaptation. The range of devices available to access media content becomes increasingly heterogeneous and at the same time ubiquitous. Users expect to access their services and content without restrictions in time or location. Users have many and different gadgets/devices with network connectivity at their disposal to receive content, ranging from their smart phones, car audio systems to laptops, or office PCs, etc. Hence there is a need to link the discovery and the description of these ambient device with multimedia domain knowledge representations in order to facilitate a ubiquitous multimedia experience. The contribution of this work is an approach for mapping device descriptions, which are leveraged on the resource discovery protocol UPnP to OWL ontology instances. The ontology instances chosen are compliant with the MPEG-21 DIA OWL-formatted ontology. This approach bridges the gap between non-semantic description mechanisms of the legacy device/services discovery protocol with the semantic multimedia domain knowledge representation.
The heterogeneous, dynamic nature of current communication environments necessitates that all system components that form part of a personalisation framework should be context aware. To ensure context enabled interoperation, a shared, formalised specification of devices and services in the ambient environment is a must. With this aim, this paper presents an ontology model that captures the semantics of the multimodal devices and services in the mobile ad-hoc environment. The approach is validated using available metrics and compared to existing approaches, both through subjective feature-based evaluation and metrics? calculations. This paper also extends the metrics? usability by extending the analysis to interoperability with application logic and domain capture.
The Internet of Things envisions a multitude of heterogeneous objects and interactions with the physical environment. The functionalities provided by these objects can be termed as `real-world services' as they provide a near real-time state of the physical world. A structured, machine-processible approach to provision such real-world services is needed to make heterogeneous physical objects accessible on a large scale and to integrate them with the digital world. This paper presents a semantic modeling approach for different components in an IoT framework. It is also discussed how the model can be integrated into the IoT framework by using automated association mechanisms with physical entities and how the data can be discovered using semantic search and reasoning mechanisms.
-The VISIon of service personalization for mobile communication environments entails context sensitive service
provisioning. The realization of such customizable smart spaces necessitates acquisition and processing of modality context information from a variety of devices in the ambient
environment. The heterogeneity of available device capabilities and description formats brings new challenges for a context reasoning engine that formulates content delivery decisions. Specifically, to ensure interoperability with existing application logic, the enabling components should support semantic queries.
Secondly, situations where variously formatted context input may not provide enough information to answer queries, should be intelligently handled. Towards this aim, this paper discusses a context reasoning and query interface component as part of a Service Context Manager (SCM) framework that supports semantic querying and handles incomplete context information through a rule-based mechanism. The validation of the approach is provided by showing the mapping of disparate UAProf and UPnP descriptions into the framework and querying of supported modality services.
The current research on context-aware systems in
ubiquitous environments opens a number of interlinked research challenges. On the lowest level of such systems, discovery mechanisms and flexible semantic descriptions of available devices and services form the basis for end-user service personalization. The challenge is to design a description model that leverages implicit semantic information obtained, while being cognizant of resource constraints of a
device. To encounter the different device
characteristics and personalization challenges, this paper proposes a device and service description approach that provides a high level contextual view of device information. The work has been performed as part of the Personal Distributed Environment concept, also described in the paper. Further, a user-centric view of multiple user interface devices to access
services in a heterogeneous and dynamic networked
environment has been implemented by extending UPnP device discovery. A comparison with existing state of the art approaches concludes the work.
The heterogeneous, dynamic nature of current mobile environments necessitates that all system components that form part of a personalization framework should be context aware. This necessitates the development of a framework of
distributed context handlers to derive meaning from context and combine it with application logic. Towards this aim, this paper presents a Service Context Manager (SCM) framework that
handles all the stages of context gathering, processing and reasoning to enable personalized service presentation. The validation of the approach is shown through application use cases.
Poncela J, Vlacheas P, Giaffreda R, De S, Vecchio M, Nechifor S, Barco R, Aguayo-Torres MC, Stavroulaki V, Moessner K, Demestichas P (2014) Smart Cities via Data Aggregation,Wireless Personal Communications76(2)pp. 149-168
Cities have an ever increasing wealth of sensing capabilities, recently including also internet of things (IoT) systems. However, to fully exploit such sensing capabilities with the aim of offering effective city-sensing-driven applications still presents certain obstacles. Indeed, at present, the main limitation in this respect consists of the vast majority of data sources being served on a ?best effort? basis. To overcome this limitation, we propose a ?resilient and adaptive IoT and social sensing platform?. Resilience guarantees the accurate, timely and dependable delivery of the complete/related data required by smart-city applications, while adaptability is introduced to ensure optimal handling of the changing requirements during application provision. The associated middleware consists of two main sets of functionalities: (a) formulation of sensing requests: selection and discovery of the appropriate data sources; and (b) establishment and control of the necessary resources (e.g., smart objects, networks, computing/storage points) on the delivery path from sensing devices to the requesting applications. The middleware has the intrinsic feature of producing sensing information at a certain level of detail (geographical scope/timeliness/accuracy/completeness/dependability) as requested by the applications in a given domain. The middleware is assessed and validated at a proof-of-concept level through innovative, dependable and real-time applications expected to be highly reproducible across different cities.
Dudycz H, Dyczkowski M, Korczak J, Abramowicz W, Alt R, Andres F, Chmielarz W, Cypryjanski J, Czarnacka C, Damiani E, De S, Dufourd JF, Kannan R, Kersten G, Kowalczyk R, Ligeza A, Magoni D, Owoc M, Pankowska M, Sikorski M, Stanek S, Teufel S, Ziemba E (2013) 11th conference on advanced information technologies for management, 2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013
De S, Carrez F, Reetz E, Toenjes R, Wang W (2013) Test-Enabled Architecture for IoT Service Creation and Provisioning, In: Galis A, Gavras A (eds.), The Future Internet. Future Internet Assembly 2013: Validated Results and New HorizonsLNCS7858pp. 233-245 Springer Berlin Heidelberg
The information generated from the Internet of Things (IoT) potentially enables a better understanding of the physical world for humans and supports creation of ambient intelligence for a wide range of applications in different domains. A semantics-enabled service layer is a promising approach to facilitate seamless access and management of the information from the large, distributed and heterogeneous sources. This paper presents the efforts of the IoT.est project towards developing a framework for service creation and testing in an IoT environment. The architecture design extends the existing IoT reference architecture and enables a test-driven, semantics-based management of the entire service lifecycle. The validation of the architecture is shown though a dynamic test case generation and execution scenario.
Semantic modeling for the Internet of Things has
become fundamental to resolve the problem of
interoperability given the distributed and heterogeneous
nature of the ?Things?. Most of the current research has
primarily focused on devices and resources modeling while
paid less attention on access and utilisation of the
information generated by the things. The idea that things are
able to expose standard service interfaces coincides with the
service oriented computing and more importantly,
represents a scalable means for business services and
applications that need context awareness and intelligence to
access and consume the physical world information. We
present the design of a comprehensive description ontology
for knowledge representation in the domain of Internet of
Things and discuss how it can be used to support tasks such
as service discovery, testing and dynamic composition.
Cassar G, Barnaghi P, Wang W, De S, Moessner K (2013) Composition of Services in Pervasive Environments: A Divide and Conquer Approach,
Data searching and retrieval is one of the fundamental functionalities in many Web of
Things applications, which need to collect, process and analyze huge amounts of sensor stream data.
The problem in fact has been well studied for data generated by sensors that are installed at fixed
locations; however, challenges emerge along with the popularity of opportunistic sensing applications
in which mobile sensors keep reporting observation and measurement data at variable intervals and
changing geographical locations. To address these challenges, we develop the Geohash-Grid Tree,
a spatial indexing technique specially designed for searching data integrated from heterogeneous
sources in a mobile sensing environment. Results of the experiments on a real-world dataset collected
from the SmartSantander smart city testbed show that the index structure allows efficient search
based on spatial distance, range and time windows in a large time series database.
The heterogeneous, dynamic nature of ubiquitous environments necessitates that all system components that form part of a personalisation framework should be context aware. Personalised service delivery requires that the system must detect and interpret device modality contexts in real time and provide automated adaptation on behalf of the user. Towards this aim, this paper presents the design and implementation of a demonstrator that offers personalised, context sensitive, service and content delivery.
Semantic modelling provides a potential basis for interoperating among different systems and applications in
the Internet of Things (IoT). However, current work has mostly focused on IoT resource management while not
on the access and utilisation of information generated by the ?Things?. We present the design of a comprehensive
and lightweight semantic description model for knowledge representation in the IoT domain. The design follows
the widely recognised best practices in knowledge engineering and ontology modelling. Users are allowed to
extend the model by linking to external ontologies, knowledge bases or existing linked data. Scalable access to IoT
services and resources is achieved through a distributed, semantic storage design. The usefulness of the model is
also illustrated through an IoT service discovery method.
This paper proposes an extension to Session Initiation Protocol (SIP) for contextualized service delivery in a service delivery platform (SDP) that enables device specific multimedia delivery. SIP separates between session establishment and description and is thus, amenable to be extended for advanced implementations which make it an ideal platform for service creation. Device specific multimedia delivery needs rich and flexible device descriptions, and our approach proposes advanced device descriptions through semantic technologies. The proposed SIP extensions have been implemented on a SIP Application Server which functions as SDP in IP Multimedia Subsystem (IMS). The validation of the proposed extensions is shown through an Android SIP client application that acts as a device browser and recommender for different multimedia services to users. An example device user agent (UA) application has also been implemented on a laptop.
The Web of Things (WoT) paradigm enables access to physical world things and their data through standard Web protocols. This provides interoperability at the hardware and communication protocol level, but does not add intelligence to the things or facilitate unambiguous interpretation of their data. The evolution of the WoT towards the semantic WoT offers the promise of meeting the interoperability challenge through the use of semantic Web technologies. The W3C Web of Things initiative encourages the use of common vocabularies to ensure interoperability and a common understanding of the domain knowledge. Ontologies provide a structured, common formalism to the disparate elements of the WoT and can form the basis of a common knowledge base. The research community and standardisation bodies have developed numerous ontologies describing the elements of the WoT and associated domains. A comprehensive review of the various proposed ontologies is needed to facilitate the adoption and reuse of the available models. This survey reviews the current state-of-the-art in WoT ontologies, which are presented from two perspectives: cross-domain ontologies which are classified into device, service, data and localisation models, and domain ontologies, which are presented from an environmental and user-oriented perspective.
The immediacy of social media messages means that it can act as a rich and timely source of real world event information. The detected events can provide a context to observations made by other city information sources such as fixed sensor installations and contribute to building ?city intelligence?. In this work, we propose a novel unsupervised method to extract real world events that may impact city services such as traffic, public transport, public safety etc., from Twitter streams. We also develop a named entity recognition model to obtain the precise location of the related events and provide a qualitative estimation of the impact of the detected events. We apply our developed approach to a real world dataset of tweets collected from the city of London.
With technologies developed in the Internet of
Things, embedded devices can be built into every fabric of urban
environments and connected to each other; and data continuously
produced by these devices can be processed, integrated at different
levels, and made available in standard formats through open
services. The data, obviously f a form of ?big data?, is now seen as
the most valuable asset in developing intelligent applications. As
the sizes of the IoT data continue to grow, it becomes inefficient
to transfer all the raw data to a centralised, cloud-based data
centre and to perform efficient analytics even with the state-ofthe-
art big data processing technologies. To address the problem,
this article demonstrates the idea of ?distributed intelligence? for
sensor data computing, which disperses intelligent computation
to the much smaller while autonomous units, e.g., sensor network
gateways, smart phones or edge clouds in order to reduce
data sizes and to provide high quality data for data centres.
As these autonomous units are usually in close proximity to
data consumers, they also provide potential for reduced latency
and improved quality of services. We present our research on
designing methods and apparatus for distributed computing on
sensor data, e.g., acquisition, discovery, and estimation, and
provide a case study on urban air pollution monitoring and
The concept of sensing-as-a-service is proposed to enable a unified way of accessing and controlling sensing devices for many Internet of Things based applications. Existing techniques for Web service computing are not sufficient for this class of services that are exposed by resource-constrained devices. The vast number of distributed and redundantly deployed sensors necessitate specialised techniques for their discovery and ranking. Current research in this line mostly focuses on discovery, e.g., designing efficient searching methods by exploiting the geographical properties of sensing devices. The problem of ranking, which aims to prioritise semantically equivalent sensor services returned by the discovery process, has not been adequately studied. Existing methods mostly leverage the information directly associated with sensor services, such as detailed service descriptions or quality of service information. However, assuming the availability of such information for sensor services is often unrealistic. We propose a ranking strategy by estimating the cost of accessing sensor services. The computation is based on properties of the sensor nodes as well as the relevant contextual information extracted from the service access process. The evaluation results demonstrate not only the superior performance of the proposed method in terms of ranking quality measure, but also the potential for preserving the energy of the sensor nodes.
The integration of things? data on the Web and Web linking for things? description and discovery is leading the way towards smart Cyber?Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber?Physical?Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human?machine intelligence.
The Internet of Things (IoT) paradigm aims to realize heterogeneous physical world objects interacting with each other and with the surrounding environment. In this prospect, the automatic provisioning of the varied possible interactions and bridging them with the digital world is a key pertinent issue for enabling novel IoT applications. The introduction of description logic-based semantics to provide homogeneous descriptions of object capabilities enables lowering the heterogeneity and a limited set of interactions (such as those with stationary objects with fixed availability) to be deduced using classical reasoning systems. However, the inability of such semantics to capture the dynamics of an IoT system as well as the scalability issues that reasoning systems encounter if too many descriptions have to be processed, necessitate that such approaches should be used in conjunction with others. Towards this aim, this paper proposes an automated rule-based association mechanism for integrating the digital IoT components with physical entities along temporal-spatial-thematic axes. To address the scalability issue, this mechanism is distributed over a federated network of nodes, each embodying a set of objects located in the same geographical area. Nodes covering nearby geographical areas can share their object descriptions while all nodes are capable of deducing interactions between the descriptions that they are aware of.© 2013 Elsevier B.V.
Investigation of the state-of-the-art in Cyber-Physical-Social System (CPSS) reveals that significant work has been done in interpreting data from different sources in isolation, performing correlations for numerical observations across one or two domains, or to provide simple textual explanations from social networks content for analysed physical world data. Existing works also work with ideal sets or already cleaned data that does not provide the same real-world insight into working with big data in cities. Thus, there is a need for integrated solutions that address the complete data flow; from data acquisition to processing and knowledge representation.
This thesis presents a data-centric framework for CPSS that contains management and processing capabilities for knowledge discovery from mobile sensing data and social networks content, by mainly addressing challenges from mobile sensing scenarios in CPSS, including: 1) interoperability issues caused by the vast amount of heterogeneous data sources; 2) thematic-spatial-temporal information retrieval of opportunistic mobile sensing; 3) incomplete datasets generated from noisy data sources of mobile sensing techniques; 4) different scales/types of data and information, which cannot be correlated directly.
The contributions of the thesis include 1) a data retrieval method that addresses the issue of searching for both current and historical sensor measurement values from the heterogeneous data sources; 2) a novel spatio-temporal model for regression analysis that can perform missing data estimation in the incomplete datasets; 3) a knowledge discovery mechanism that merges and correlates physical and social sensing data, enabling links between different scales/types of data: numeric values of sensor observation data and textual content of social networks? messages.
The above contributions were evaluated through experimentation on real smart city datasets and data collected from the Twitter social network to prove their accuracy and reliability, as well as to show the applicability of the proposed approaches to existing smart cities.
In pervasive environments, availability and reliability of a service cannot always be guaranteed. In such environments, automatic and dynamic mechanisms are required to compose services or compensate for a service that becomes unavailable during the runtime. Most of the existing works on services composition do not provide sufficient support for automatic service provisioning in pervasive environments. We propose a Divide and Conquer algorithm that can be used at the service runtime to repeatedly divide a service composition request into several simpler sub-requests. The algorithm repeats until for each sub-request we find at least one atomic service that meets the requirements of that sub-request. The identified atomic services can then be used to create a composite service. We discuss the technical details of our approach and show evaluation results based on a set of composite service requests. The results show that our proposed method performs effectively in decomposing a composite service requests to a number of sub-requests and finding and matching service components that can fulfill the service composition request.
Mobile sensing techniques have been increasingly deployed in many Internet of Things based
applications because of their cost efficiency, wide coverage and flexibility. However, these techniques are
unreliable in many situations due to noise of different kinds, loss of communication, or insufficient energy.
As such, datasets created from mobile sensing scenarios are likely to contain large amount of missing data,
which makes further data analysis difficult, inaccurate, or even impossible. We find that the existing
estimation models and techniques developed for static sensing do not work well in the mobile sensing
scenarios. To address the problem, we propose a spatio-temporal method, which is specifically designed for
answering queries in such applications. Experiments on a real-world, incomplete mobile sensing dataset
show that the proposed method outperforms the state-of-the-art noticeably in terms of estimation errors.
More importantly, the proposed model is tolerant to datasets with extremely high missing data rates.
Training with the proposed model is also efficient, which makes it suitable for deployment on
computationally constrained devices and platforms that need to process massive amounts of data in real
The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the Air Quality Index (AQI), using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel Non-linear Autoregressive neural network with exogenous input (NARX) model, especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.
In this paper, we present a method that facilitates Internet of Things (IoT) for
building a product passport and data exchange enabling the next stage of the circular economy.
SmartTags based on printed sensors (i.e., using functional ink) and a modified GS1 barcode standard
enable unique identification of objects on a per item-level (including Fast-Moving Consumer
Goods?FMCG), collecting, sensing, and reading of parameters from environment as well as tracking
a products? lifecycle. The developed ontology is the first effort to define a semantic model for
dynamic sensors, including datamatrix and QR codes. The evaluation of decoding and readability
of identifiers (QR codes) showed good performance for detection of sensor state printed over and
outside the QR code data matrix, i.e., the recognition ability with image vision algorithm was possible.
The evaluation of the decoding performance of the QR code data matrix printed with sensors was
also efficient, i.e., the QR code ability to be decoded with the reader after reversible and irreversible
process of ink (dis)appearing was preserved, with slight drop in performance if ink density is low.
The Internet of Things (IoT) aims to connect everyday physical objects to the internet. These objects will produce a significant amount of data. The traditional cloud computing architecture aims to process data in the cloud. As a result, a significant amount of data needs to be communicated to the cloud. This creates a number of challenges, such as high communication latency between the devices and the cloud, increased energy consumption of devices during frequent data upload to the cloud, high bandwidth consumption, while making the network busy by sending the data continuously, and less privacy because of less control on the transmitted data to the server. Fog computing has been proposed to counter these weaknesses. Fog computing aims to process data at the edge and substantially eliminate the necessity of sending data to the cloud. However, combining the Service Oriented Architecture (SOA) with the fog computing architecture is still an open challenge. In this paper, we propose to decompose services to create linked-microservices (LMS). Linked-microservices are services that run on multiple nodes but closely linked to their linked-partners. Linked-microservices allow distributing the computation across different computing nodes in the IoT architecture. Using four different types of architectures namely cloud, fog, hybrid and fog+cloud, we explore and demonstrate the effectiveness of service decomposition by applying four experiments to three different type of datasets. Evaluation of the four architectures shows that decomposing services into nodes reduce the data consumption over the network by 10% - 70%. Overall, these results indicate that the importance of decomposing services in the context of fog computing for enhancing the quality of service.
Rapid urbanisation has brought about great challenges to our daily lives, such as traffic congestion, environmental pollution, energy
consumption, public safety and so on. Research on smart cities aims to address these issues with various technologies developed for
the Internet of Things. Very recently, the research focus has shifted towards processing of massive amount of data continuously generated
within a city environment, e.g., physical and participatory sensing data on traffic flow, air quality, and healthcare. Techniques from computational intelligence have been applied to process and analyse such data, and to extract useful knowledge that helps citizens better understand their surroundings and informs city authorities to provide better and more efficient public services. Deep learning, as a relatively new paradigm in computational intelligence, has attracted substantial attention of the research community and demonstrated greater potential over traditional techniques. This paper provides a survey of the latest research on the convergence of deep learning and smart city from two perspectives: while the technique-oriented review pays attention to the popular and extended deep learning models, the application-oriented review emphasises the representative application domains in smart cities. Our study showed that there are still many challenges ahead for this emerging area owing to the complex nature of deep learning and wide coverage of smart city applications. We pointed out a number of future directions related to deep learning efficiency, emergent deep learning paradigms, knowledge fusion and privacy preservation, and hope these would move the relevant research one step further in creating truly distributed intelligence for smart cities.
The Internet of Things is infiltrating many businesses. It provides simple means to collect and analyze technical system data to
identify and optimize the performance of many things in our private and work lives. This technical revolution is also revealing new
challenges and issues with our current IoT technologies. New solutions like artificial intelligence, blockchain, and 5G promise to
overcome these challenges. Within this article we discuss with leading experts the pros and cons of these technologies and what it
means for future IoT business.
The Internet of Things envisions the creation of an environment
where everyday objects (e.g., microwaves, fridges,
cars, coffee machines) are connected to the Internet and make
users? lives more productive, efficient, and convenient. During
this process, everyday objects capture a vast amount of data
that can be used to understand individuals and their behaviors.
In the current IoT ecosystems, such data is collected and used
only by the respective IoT solutions. There is no formal way to
share data with external entities. We believe this is very inefficient
and unfair for users. We believe that users, as data owners,
should be able to control, manage, and share data about
them in any way that they choose and make or gain value out
of them. To achieve this, we proposed the sensing as a service
(S2aaS) model. In this article, we discuss the (S2aaS) ecosystem
in terms of its architecture, components, and related user interaction
designs. This article aims to highlight the weaknesses of
the current IoT ecosystem and to explain how S2aaS would
eliminate those weaknesses. We also discuss how an everyday
user may engage with the S2aaS ecosystem as well as design