Intelligent and context-aware mobile services require users and applications to share information and utilize services from remote locations. Thus, context information from the users must be structured and be accessible to applications running in end-devices. In response to this challenge, we present a shared object-oriented meta model for a persistent agent environment. The approach enables agents to be context aware facilitating the creation of ambient intelligence demonstrated by a sensor-based scenario. The agents are context-aware as agent actions are based upon sensor information, social information, and the behavior of co-agents.

With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.

The introduction of distributed-reasoning through ubiquitous instrumentation within the distributed Internet of Things (IoT) leads to outstanding improvements in real-time monitoring, optimization, fault-tolerance, traffic, healthcare, so on. Using a ubiquitous controller to interconnect devices in the IoT, however monumental, is still in its embryonic stage, it has the potential to create distributed-intelligent IoT solutions that are more eclient and safer than centric intelligence. It is essential to step in a new direction for designing a distributed intelligent controller for task scheduling as a means to, first, dynamically interact with a smart environment in eclient real-time data processing and, second, react to flexible changes. To cope with these issues, we outline a two-level intelligence schema, using edge computing to enhance distributed IoT. The edge schema pushes the streaming processing capability from cloud to edge devices to better support timely and reliable streaming analytics to improve the performance of smart IoT applications. In this paper, in order to provide better, reliable, and flexible streaming analytics and overcome the data uncertainties, we proposed an IoT gateway controller to provide low-level intelligence by employing a fuzzy abductive reasoner. Numerical simulations support the feasibility of our proposed approaches.

The development of the Internet of Things over the last decade has led to large amounts of data being generated at the network edge. This highlights the importance of local data processing and reasoning. Machine learning is most commonly used to automate tasks and perform complex data processing and reasoning. Collecting such data in a centralized location has become increasingly problematic in recent years due to network bandwidth and data privacy concerns. The easy-to-change behavior of edge infrastructure enabled by software-defined networking (SDN) allows IoT data to be gathered on edge servers and gateways, where federated learning (FL) can be performed: creating a centralized model without uploading data to the cloud. In this paper, we analyze the use of edge computing and federated learning, a decentralized machine learning methodology that increases the amount and variety of data used to train deep learning models. To the best of our knowledge, this paper reports the first use of federated learning to help the Microgrid Energy Management System (EMS) predict load and obtain promising results. Simulations were performed using TensorFlow Federated with data from a modified version of the Dataport site

With recent advancements in communications and sensor technologies, the Internet of Things (IoT) has been experiencing rapid growth. It is estimated that billions of objects will be connected, which would create a vast amount of data. Cloud computing has been the predominant choice for monitoring connected objects and delivering data-based intelligence, but high response time and network load of cloud-based solutions are limiting factors for IoT deployment. In order to cope with this challenge, this paper proposes a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge

This article analyzes the challenges of supporting continual changes of context information in Internet-of-Things applications. These applications require a constant flow of continuously changing information from sensor based sources in order to ensure a high quality-of-experience. However, an uncontrolled flow between sources and sinks on a global scale wastes resources, such as computational power, communication bandwidth, and battery time. In response to these challenges we present a general approach which focuses on four layers where we provide a proposed solution to each layer. We have realized the general model into a proof-of-concept implementation running on devices with limited resources, where we can moderate the information exchange based on relevance and sought after quality-of-experience by the applications. In conclusion, we evaluate our solution and present a summary of our experiences regarding the impact of continuously changing information on the Internet-of-Things.

Ökningen av sensor-assisterade tillämpningar såsom sensorkampanjer och interaktioner i storskaliga befolkningar ställer nya krav på smidig delning av sensor-information i global skala. Befintligt stöd är begränsat på grund av förenklade informationsmodeller återspeglar smala applikationsscenarier med dåligt skalbarhet i arkitekturer. Som svar på detta presenterar vi en tillämpningsarkitektur för allstädes Internet-av-saker applikationer som kringgår dessa begränsningar. Vår arkitektur gör att program kan använda information från sensorer och trådlösa sensornätverk via en peer-to-peer-overlay. Den överlagrade delade sensorinformation i en generisk informationsmodell, som är utbyggbar och därmed möjliggör intelligent tillämpningsbeteende. Vår utvärdering av arkitekturen visar att det stöder delning av sensorns information i en global skala i sensor-assisterafe tillämpningar med låga svarstider. Slutligen utvärderar vi möjligheten att köra sådana ansökningar i slutet enheter med begränsade resurser i en sensor-assisterad tjänsteprototyp.

Research in Internet-of-Things infrastructures has so far mainly been focused on connecting sensors and actuators to the Internet, while associating these devices to applications via web services. This has contributed to making the technology accessible in areas such as smart-grid, transport, health, etc. These early successes have hidden the lack of support for sensor-based applications to share information and limitations in support for applications to access sensors and actuators globally. We address these limitations in a novel open-source platform, MediaSense. MediaSense offers scalable, seamless, real-time access to global sensors and actuators via hetero- geneous network infrastructure. This paper presents a set of requirements for Internet-of-Things applications support, an overview of our architecture, and application prototypes created in order to verify the approach in a test bed with users connected from heterogeneous networks.

Mobile devices and virtualized appliances in the Internet of Things can be end nodes on varying networks owned by different parties over time, while still seamlessly participating in licit or illicit activities. Digital Forensics and Incident Response (DFIR) tools today struggle to perform digital investigations in such loosely controlled networked environments as they face several challenges including: scarcity of resources, availability, trust, privacy, data volumes, velocity and variety. In this paper we analyze the state of research in DFIR in networked environments, identifying the challenges facing DFIR tools particularly in loosely controlled network environments. We present the requirements for a system to address these challenges at the various steps of the typical digital investigation methodology. From this we identify the need for support from Peer to Peer (P2P) overlays and discuss their relative merits and drawbacks in order to identify those that would best support DFIR in loosely controlled networked environments. Finally we incorporate both structured and unstructured P2P overlays in various capacities in our architecture in order to organize devices in loosely controlled networks, using context information, thus enabling efficient capture, analysis and reporting of artifacts of use in digital investigations.

The center of gravity of our private and work lives has shifted to the Internet, facilitated by technological advances such as the Internet of Things. This Internet now allows us, ideas, and things to interact and collaborate in new ways and on a large scale. This shift has also accelerated the search for untethered ways for people and things to interact in real and virtual places. However, at least one lesson learned from using the Internet in our daily lives is that – while it is very good at enabling people to interact with other people – there is much room for improvement in the technology to allow people to interact with things to improve their lives.

In this publication we present Immersive Networking as a novel framework for connecting people and places and things in virtual environments with augmented reality to be used in eg. Virtual training environments. The research is mandated by technology advances in internetworking underpinned by 5G networks and Internet-of-Things. These advances present new possibilities and challenges to integrate people, places and things in virtual environments. Existing frameworks such as MPEG-V possess representational capabilities but have insufficient support for integrating entities from the real world via heterogeneous infrastructure. MPEG-V for instance makes no statements about distributed control. Seamless experiences in virtual environment require self-organization of connectivity between people, places and things via heterogeneous 5G and Internet-of-Things infrastructures. A second important aspect of the quality of our experience is the immediacy of responses. Both aspects of seamless and self-organizing connections between entities require that we push control to the end-devices co-located with the entities themselves. These end-devices may incorporate sensing gateways and interaction devices, which include both local and non-local information from the virtual environment in the interaction. Thus delegation of control to end-devices requires means for the organizing or relations and clustering by relevance. This capability is particularly important as the projected number of devices and sensors to be connected via the Internet-of-Things is projected to be in the order of 50 billion by 2020. Immersive Networking supported by MediaSense constitutes a scalable self-organizing means for connecting people and places and things in virtual environments with augmented reality. MediaSense moves control to the edge enabling immediacy in experiences based on seamless self-organization through clustering of entities in relations organized by relevance. We conclude by validating our approach in several scenarios evaluating the relevance and application in human decision-making..

Users require applications and services to be available everywhere, enabling users to focus on what is important to them. Therefore, context information from users (e.g., spatial data, preferences, available connectivity and devices, etc.) needs to be accessible to systems that deliver services via a heterogeneous infrastructure. We present a novel approach to support ubiquitous sensing and availability of context to services and applications. This approach offers a scalable, distributed storage of context derived from sensor networks wirelessly attached to mobile phones and other devices. The support handles frequent updates of sensor information and is interoperable with presence services in 3G mobile systems, thus enabling ubiquitous sensing applications. We demonstrate these concepts and the principle operation in a sample ubiquitous mobile awareness service. The importance of this contribution, in comparison to earlier work, lies in the availability of real-time ubiquitous sensing to both applications on the Internet as well as applications in mobile systems.

This paper describes research issues and work-in-progress concerning ubiquitous sensing. We present scenarios where the current approaches are deficient in addressing the needs for ubiquitous sensing in services and applications on the Future Internet, involving the massive sharing of information from sensors via heterogeneous networks. We propose an information-centric architecture for real-time ubiquitous sensing which capitalizes on the proposed locator/identifier split, thus extending the Network of Information (NetInf) approach. From this we identify the challenges for which we present work-in-progress within the framework of the EU-funded MediaSense project. Firstly, we integrate sensors as addressable objects, exposed by means of sensor gateways and relocatable abstract interfaces. Sensor information is thus made available to applications solely based on identity. Secondly, sensor information is made available in a distributed data model towards searching and browsing. Finally, we evaluate the effectiveness of the architecture in proof-of-concept applications for intelligent commuting, environmental monitoring and seamless media transfer, utilizing two different sensor platforms.

A rapidly increasing and vast number of “things” will be connected to the Internet, in all sectors of society (people, places, sensors, homes, industry, government services, etc.). The urgency of finding sustainable solutions requires “things” and services of the overall system to display autonomic intelligent behavior. The ability of cloud infrastructure to orchestrate the fine-grained and agile control of “things” is limited. This mandates an alternative approach intelligently moving control to the “things”. Thereby we minimize the reliance on cloud infrastructure, and are able to build more agile, intelligent and effective solutions in various application areas, such as Health, Transport and through Automation. We provide examples of such novel solutions tested in “Urban ICT Arena”, a largescale smart city testbed in Stockholm.

This paper presents a system and mechanisms enabling real-time awareness and interaction among vehicles connected via heterogeneous mobile networks. Information obtained by vehicles is considered as the centre in our system. Vehicles are organized dynamically in overlaid clusters. In each cluster, vehicle-related information is pushed in time. As a network node, each vehicle has the function of content abstraction and distribution. Through processing and abstracting the sensed data, various vehicle-related information are organized and denoted in hierarchical names at each node. The data are transmitted and forwarded using protocols accordant with the characteristics of the content. In this way, large-scale and real-time information exchanges among vehicles are realized. Part of our system has been implemented and tested. An open source platform providing standard sensor and actuator API can be provided.

A novel conceptual framework is presented in this paper with an aim to standardize and virtualize Internet of Things’ (IoT) infrastructure through deploying OpenFlow technology. The framework can receive e-services based on context information leaving the current infrastructure unchanged. This framework allows the active collaboration of heterogeneous devices and protocols. Moreover it is capable to model placement of physical objects, manage the system and to collect information for services deployed on an IoT infrastructure. Our proposed IoT virtualization is applicable to a random topology scenario which makes it possible to 1) share flow-sensors’ resources, 2) establish multi-operational sensor networks, and 3) extend reachability within the framework without establishing any further physical networks. Flow-sensors achieve better results comparable to the typi-cal-sensors with respect to packet generation, reacha-bility, simulation time, throughput, energy consump-tion point of view. Even better results are possible through utilizing multicast groups in large scale net-works.

This article investigates new forms for creating and enabling massive and scalable participatory immersive experiences in live cultural events, characterized by processes, involving pervasive objects, places and people. The multi-disciplinary research outlines a new paradigm for collaborative creation and participation towards technological and social innovation, tapping into crowd-sensing. The approach promotes user-driven content-creation and offsets economic models thereby rewarding creators and performers. In response to these challenges, we propose a framework for bringing about massive and real-time presence and awareness on the Internet through an Internet-of-Things infrastructure to connect artifacts, performers, participants and places. Equally importantly, we enable the in-situ creation of collaborative experiences building on relevant existing and stored content, based on decisions leveraging multi-criteria clustering and proximity of pervasive information, objects, people and places. Finally, we investigate some new ways for immersive experiences via distributed computing but pointing forward to the necessity to do more with regard to collaborative creation.

Mobile telecommunication is evolving rapidly. People no longer only communicate with each other regardless of time and place, but also share other information that is important for the tasks with which they are involved. In response to this growing trend, the Media Sense framework addresses the intelligent delivery of any information to any host, anywhere, based on context-aware information regarding personal preferences, presence information, and sensor values. The focus of this paper is the overall framework, the handling of user profiles, management of context information, real-time distribution and proof of concepts.

The features of information dissemination in vehicular networks make it necessary to introduce Information-Centric Networking (ICN) technique to vehicular networks. However, some design principles of ICN must be implemented and extended in order to realize efficient data dissemination in vehicular networks. This paper proposes an approach for realizing ICN-based data dissemination in vehicular networks. An architecture for organizing and disseminating information in a vehicle is suggested. The concept of cluster of common interest is proposed, with which groups of vehicles can be established dynamically according to the information they are interested in. Within each cluster, information are disseminated to the vehicles in a way decided by the features of the information. To show the feasibility of our approach, the approach has been implemented and tested in the simulation environment.

Context information can add more meaning and value to the sensor data in Internet of Things. However, due to the large amount of sources of contexts and sensor data, the exchange of contexts through the networks may cause more and more data traffic. This paper proposes a novel method for maintaining and exchanging contexts through the Internet more efficiently. Contexts are classified into different levels. Each level of contexts is maintained and exchanged using different methods. A new protocol for exchanging the contexts is designed. Through the independent transmitting of contexts and sensor data, the context-awareness in Internet of Things can be realized, and the network bandwidth usage can be greatly reduced and thus the energy of node can also be saved.

The Internet of Things (IoT) architecture is expected to evolve into a horizontal model containing various open systems, integrated environments, and platforms. However, not much research effort has been devoted to developing architectures for horizontal IoT solutions so far. This paper presents an IoT architecture based on Software-Defined Networking (SDN). In this architecture, devices, gateways, and data are open and programmable to IoT application developers and service operators. Moreover, IoT data provision and interoperability are supported at different levels. We present an implementation of the proposed architecture. Our implementation shows that the proposed architecture enables rapid creation of IoT applications by reusing ready applications and data. The measurement and evaluation results demonstrate the feasibility of the proposed architecture.

A novel idea presented in this paper is to deploy the Open Flow technology in wireless sensor networks that can lead to a significant achievement in Internet of things and cloud computing arena through network virtualization. Two new abstract layers namely Common platform layer and virtualization layer can be added at the top and bottom of a preset Infrastructure as a Service architecture. Our proposed IOT virtualization can be applicable in a random topology scenario which makes possible of the flow-sensors' resources to be shared, establishment of multi operational sensor networks and escalation of the reach ability under the same platform without establishing any further physical networks. Flow-sensor achieved 39% more reach ability than the typical sensors in an ideal scenario and even better results are possible from the amount of packets generation and simulation time viewpoint for larger scale networks.

This paper studied communication between sensor groups-access points and a networking & communication model and packet transmission algorithm such that networks are divided into several multicast domains of stationary flow-sensors and received several packet-flows from mobile flow-sensors. The proposed approach to Internet of Things' virtualization is suitable for invoking and multi-tasking sensor networks within random network scenarios ranging from large single operational networks to distinct service networks within the involvement of multiple operational networks. The result implies larger multicast groups perform better than smaller ones whereas large flows of packets decrease the performances from the whole network viewpoint.

With the rise of the Internet of Things (IoT), this means recognizing the need for architectures to handle billions of devices and their interactions. A virtual world engine at the massively multiplayer scale is a massively multiplayer online world (MMOW); one thing virtual world engines realized when going into the scale of MMOs, is the cost of maintaining a potentially quadratic number of interactions between a massive number of objects, laid out in a spatial dimension. Research into IoT was fueled by research in wireless sensor networks, but rather than start from a device perspective, this article looks at how architectures deal with interacting entities at large scale. The domain of MMOWs is examined for properties that are affected by scale. Thereafter the domain of IoT is evaluated to see if each of those properties are found and how each is handled. By comparing the current state of the art of MMOWs and IoT, with respect to scalability, the problem of scaling IoT is explicated, as well as the problem of incorporating an MMOW with IoT into a pervasive platform. Three case studies of a MMOW interfacing with IoT are presented in closing.

A virtual world engine at the massively multiplayer scale is a massively multiplayer online world (MMOW); one thing virtual world engines realized when going into the scale of MMOs, is the cost of maintaining a potentially quadratic number of interactions between a massive number of objects, laid out in a spatial dimension. With the rise of the Internet of Things (IoT), this means recognizing the need for architectures to handle billions of devices and their interactions. Research into IoT was fueled by research in wireless sensor networks, but rather than start from a device perspective, this article looks at how architectures deal with interacting entities at large scale. The domain of MMOWs is examined for properties that are affected by scale. Thereafter the domain of IoT is evaluated to see if each of those properties are found and how each is handled. By comparing the current state of the art of MMOWs and IoT, with respect to scalability, it is discussed how research from one domain can possibly be exapted to the other domain and vice versa. A case study of a MMOW interfacing with IoT is presented in closing.

Use of context has been prevailing in distributed computing with the emergence of mobile computing. An entity is said to be context-aware if it responds to context changes. These distributed context-aware entities share context information in order to make intelligent decisions or carry out important tasks. MediaSense, an Internet-of-Things (IoT) platform, offers such intelligent delivery of context information to any host- anywhere. However, such an IoT platform is facing challenge of discovering context entities. In view of this, this paper particularly addresses the challenge of discovering distributed context entities by extending the distributed protocol, Distributed Context eXchange Protocol (DCXP), for MediaSense platform. In particular, a publish/subscribe approach has been employed to overcome the challenge which enables fast context entity discovery.

One of the challenges for Autonomic Management in Future Internet is to bring about self-organization in a rapidly changing environment and enable participating nodes to be aware and respond to changes. The massive number of participating nodes in Internet-of-Things calls for a new approach in regard of Autonomic Management with dynamic self-organization and enabling awareness to context information changes in the nodes themselves. To this end, we present new algorithms to enable self-organization with logical-clustering, the goal of which is to ensure that logical-clustering evolves correctly in the dynamic environment. The focus of these algorithms is to structure logical-clustering topology in an organized way with minimal intervention from outside sources. The correctness of the proposed algorithm is demonstrated on a scalable IoT platform, MediaSense. Our algorithms sanction 10 nodes to organize themselves per second and high accuracy of nodes discovery. Finally, we outline future research challenges towards autonomic management of IoT.

Crowdsourcing was initially devised as a method for solving problems through soliciting contributions from a large online community. Crowdsourcing is facing new challenges to handle the increase of information in real-time from a vast number of sources in Internet-of-Things (IoT) scenarios. Thus we seek to leverage the power of social web, smart-devices, sensors, etc., fusing these heterogeneous sources into distributed context information in order to enable novel crowdsourcing scenarios. This mandates research in efficient management of heterogeneous and distributed context information through logical-clustering. Logical-clustering can efficiently filter out similar context information obtained from distributed sources based on context similarity. However, the efficiency of logical-clustering is challenged by the distribution of context information in crowdsourcing scenarios. Publish/Subscribe mechanism can counter this challenge. To this end, we propose a scalable publish/subscribe model, MediaSense, which is based on p2p technologies. This paper presents our approach to a scalable logical-clustering concept. The evaluation of our approach applied to MediaSense can achieve a rate of approximately 3530 messages/sec for publish/subscribe events. Moreover, this approach further achieves 99% increase for subscription matching and 163% improvement in memory requirements in comparison with other approaches.

The Internet of Things (IoT) paradigm has so far been investigating into designing and developing protocols and architectures to provide connectivity anytime and anywhere for anything. IoT is currently fast forwarding towards embracing a paradigm shift namely Internet of Everything (IoE) where making intelligent decisions and providing services remains a challenge. Context plays an integral role in reasoning the collected data and to provide context-aware services and is gaining growing attention in the IoT paradigm. To this end, a Context-Aware reasoNer (CAN) has been proposed and designed in this chapter. The proposed CAN is a generic enabler and is designed to provide services based on context reasoning. Discovering and filtering entities, i.e. entity configuration, become pivotal in analysing context reasoning to provide right services to right context entities in right time. This chapter leverages the concept of entity configuration and CAN towards enabling an IoT controller. The chapter further demonstrates use cases and future research directions towards generic CAN development and facilitating context-aware services to IoE.

Future Internet is expected to be driven by prevalence of the Internet of Things (IoT). This prevalence of IoT promises to impact every aspect of human life in the foreseeable future where computing paradigm would witness huge influx of IoT data. Context is gaining growing attention to make sense of the data and it is envisaged that context-aware computing would act as an indispensable enabler for IoT. Contextualizing the collected IoT data enables to reap value from the data and to harvest the knowledge. Reasoning the contextualized data, that is, context information is imperative to the vision of harvesting knowledge. Edge computing is also expected to play a vital role in IoT to reduce dependency on cloud based solution, to achieve faster response, and to provide intelligence closer to the IoT things. The combination of context-awareness and edge solution would be inseparable in the future IoT. Furthermore, IoT vision comprises of different IoT applications controlled by a capable controller at the edge, an edge controller necessitates to counter the challenge of providing knowledge for each of the IoT applications. Therefore, such a controller requires to offer different context-aware reasoning to alleviate the intelligence-of-things. In view of this, this paper proposes a multi-modal context-aware reasoner the aim of which is to provide knowledge at the edge for each IoT application. The context-aware reasoning has been verified with rules-based and Bayesian reasoning for three IoT applications and initial results suggest that it is promising to realize such multimodal reasoning at the edge with low latency.

The upsurge of social networks, mobile devices, Internet or Web-enabled services have enabled unprecedented level of human participation in pervasive computing which is coined as crowdsourcing. The pervasiveness of computing devices leads to a fast varying computing where it is imperative to have a model for catering the dynamic environment. The challenge of efficiently distributing context information in logical-clustering in crowdsourcing scenarios can be countered by the scalable MediaSense PubSub model. MeidaSense is a proven scalable PubSub model for static environment. However, the scalability of MediaSense as PubSub model is further challenged by its viability to adjust to the dynamic nature of crowdsourcing. Crowdsourcing does not only involve fast varying pervasive devices but also dynamic distributed and heterogeneous context information. In light of this, the paper extends the current MediaSense PubSub model which can handle dynamic logical-clustering in crowdsourcing. The results suggest that the extended MediaSense is viable for catering the dynamism nature of crowdsourcing, moreover, it is possible to predict the near-optimal subscription matching time and predict the time it takes to update (insert or delete) context-IDs along with existing published context-IDs. Furthermore, it is possible to foretell the memory usage in MediaSense PubSub model.

Internet-of-Things (IoT) promises to impact every aspect of our daily life by connecting and automating everyday objects which bring the notion of SmartLiving. While it is certain that the trend will grow at a rapid speed, at the same time, challenge to alleviate intelligence of things by reaping value from the data requires to be addressed. The intelligence further cannot depend only on the existing cloud-based solutions which edge computing is expected to mitigate by integrating distributed intelligence. An IoT application necessitates applying knowledge with low latency. However, to comply with the vision of autonomic IoT and real-time intelligence, extracting and applying knowledge are necessitated for which this chapter proposes to exploit mobile edge computing (MEC) to further assist distributed intelligence. Therefore, the problem that this chapter addresses is feasibility investigation of MEC to provide intelligence by reasoning contextualised data and, thereby, the role of MEC in distributed intelligence.

The popular concept of SmartHome means that the appliances such as lighting, heating and door locks are controllable remotely through for example remote controls or mobile phones. The concept is becoming more and more realizable due to recent advancements in Internet-enabled technologies. SmartHomes can become even more intelligent and automated by exploiting such intelligent and affordable Internet-enabled technologies. However, this necessitates a context-aware system that provides services to respond to the context changes to enable such SmartHome automation at the edge of today’s context-centric networks. To this end, this paper designs and develops a context-aware reasoning service for home automation which provides a novel way to connect SmartHomes through the use of a distributed context exchange network overlay. It enables mobility service application to communicate with and control SmartHomes remotely.

There is increasing demand for adaptive control of multi-robot and as well distributing large amount of content to cluster of UAV on operation. In recent years several large-scale accidents have been happened. To facilitate rescue operations and gather information, technology that can access and map inaccessible areas is needed. This paper presents a disruptive approach for address the issues with communication, data collection and data sharing for UAV units in inaccessible or dead zones and We demonstrated feasibility of the approach and evaluate its advantages over the Ad Hoc architecture involving autonomous gateways.

This paper presents a disruptive approach "Immersive Networking" enabling massively distributed IoT nodes to participate in autonomous and cooperative decision-making. The approach is mandated by perceived limitations in 5G networking architecture maintaining control in the edge gateway. In our approach, control may be delegated to clusters of IoT nodes beyond the edge gateway. The communication is event-based involving publish-subscribe between related nodes. Clusters are identified in an autonomic fashion based on multi-criteria proximity. Local decisions can combine global and local context information to establish network slices in a decentralized fashion based on application demands. Moreover, such decisions may be part of a collaborative effort (map-reduce) based on either local or global context. Application demands expressed as such are modeled compatible with Open Data initiatives. We demonstrated feasibility of the approach and evaluate its advantages over the 5G architecture involving an edge gateway.

Development of Internet-of-Services will be hampered by heterogeneous Internet-of-Things infrastructures, such as inconsistency in communicating with participating objects, connectivity between them, topology definition & data transfer, access via cloud computing for data storage etc. Our proposed solutions are applicable to a random topology scenario that allow establishing of multi-operational sensor networks out of single networks and/or single service networks with the participation of multiple networks; thus allowing virtual links to be created and resources to be shared. The designed layers are context-aware, application-oriented, and capable of representing physical objects to a management system, along with discovery of services. The reliability issue is addressed by deploying IETF supported IEEE 802.15.4 network model for low-rate wireless personal networks. Flow- sensor succeeded better results in comparison to the typical - sensor from reachability, throughput, energy consumption and diversity gain viewpoint and through allowing the multicast groups into maximum number, performances can be improved.

To achieve self configuring of Active Queue Management based on Fuzzy Logic Controller (FLC), we propose a Fuzzy Adaptive Active Queue Management Controller (FAAQMC). FAAQMC eliminates buffer overflow by adapting the buffer size to the required queue length with a control cycle time shorter than the mean inter-arrival time of a burst. This makes FAAQMC suitable to manage invariant burstiness or self-similar network traffic.

Internet of Things (IoT) is becoming necessities of people’s daily life and establishing itself as an essential part of future Internet. One of the challenges for using IoT is the security of data collected by trillions of IoT devices and used by millions of services. Distributed ledger technology (DLT) provides a distributed security method which can benefit IoT. Yet challenges are put forward when integrating DLT with IoT, such as scalability and heterogeneous capability of IoT devices. In this paper, we propose a mechanism for integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on various proximity context information. A cluster head is used to bridge the IoT devices with the blockchain network where smart contract is deployed. Through this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate our mechanism and discuss issues that should be considered and implemented when using the proposed mechanism.

In the current era, access to information has become ubiquitous for anything and anyone, and the trend will continue in the foreseeable future as well. Sensor networks have been an integral part of pervasive computing and are expected to play a pivotal role in the future Networked Society. Context information is ubiquitous due to the deployment of sensors in Internet infrastructure and availability to services. This corresponds to the phenomena where any situation can be sensed and analyzed anywhere. Services can access heterogeneous context information anywhere through the distributed acquisition and dissemination of sensor data assembled from physical objects. The novel approach of logical clustering is beneficial for heterogeneous interoperability of physical objects, thereby, heterogeneous contexts. The idea enables resources (data, services) to be shared among physically distributed objects. The approach provides topological sensor networks with scalability, robustness, mobility, heterogeneity support, adaptability to different contexts, etc. A performance study demonstrates feasibility and scalability, adaptability, heterogeneity, and robustness of the proposed approach. Computational efficiency plays a significant role so that network traffic does not encounter abrupt and frequent fluctuations. In this paper, further computational efficiency analysis in terms of network traffic for logical clustering is highlighted.

In state-of-the-art Pervasive Computing, it is envisioned that unlimited access to information will be facilitated for anyone and anything. Wireless sensor networks will play a pivotal role in the stated vision. This reflects the phenomena where any situation can be sensed and analyzed anywhere. It makes heterogeneous context ubiquitous. Clustering context is one of the techniques to manage ubiquitous context information efficiently to maximize its potential. Logical-clustering is useful to share real-time context where sensors are physically distributed but logically clustered. This paper investigates the network performance of logical-clustering based on ns-3 simulations. In particular reliability, scalability, and reachability in terms of delay, jitter, and packet loss for the logically clustered network have been investigated. The performance study shows that jitter demonstrates 40 % and 44 % fluctuation for 200 % increase in the node per cluster and 100 % increase in the cluster size respectively. Packet loss exhibits only 18 % increase for 83 % increase in the packet flow-rate.

In the state-of-the-art sensor networks are becoming an integral part of ubiquitous computing. Context information is ubiquitous due to the deployment of sensors in Internet infrastructure and availability to services. This corresponds to the phenomena where any situation can be sensed and analyzed anywhere. Services can access heterogeneous context information anywhere through the distributed acquisition and dissemination of sensor data assembled from physical objects. A novel idea of clustering sensors based on context similarity is presented in this paper. The sensors are physically distributed but logically clustered based on similar context. This will enable resources (data, services) to be shared. The network is a two-tier hierarchical distributed hash tables (DHTs) system based on the HyperFlow platform. The approach provides topological sensor networks with scalability, robustness, mobility, heterogeneity support, adaptability to different contexts, etc. A performance study demonstrates feasibility and scalability, adaptability, heterogeneity, and robustness of the proposed approach.

Context-centric applications and services are premised on the ability to readily respond to changes in context. Centralized approaches to enabling this are undermined by their dependencies on DNS naming services while decentralized approaches using DHT variants have been centred on the provisioning of the underlying context information, creating information-centric rather than context-centric solutions. A dynamic Internet of Things mandates a new paradigm; approaches storing, discovering and associating context entities relevant to their context state. In this paper, we explore such a paradigm, and with the implementation of a prototype, show the advantages of moving towards the notion of context-state centricity on the Internet of Things.

The creation of applications and services realising massive immersive participation require the provisioning of current, relevant and accurate context information. These applications benefit from access to this highly dynamic information in real time. Existing approaches to provisioning context information are limited by their interpretation of context relationships as address book solutions thus limiting the discovering of related entities. We introduce the context proximity query language (CPQL) for querying context related entities on distributed across collections of remote endpoints. As a declarative query language (CPQL) is similar in structure to SQL and describes the relationships between entities as distance functions between their associated context information. We simulate CPQL and show that it offers improvements over existing approaches while scaling well.

The provisioning of user context information between service endpoints is central to realizing massive immersive participation on an Internet of Things. This information must in turn be provisioned to endpoints with minimal overhead costs. Where this is achieved through centralized repositories of context information there arises issues of scalability and availability. Where distributed approaches have been proposed, information dissemination has been optimized relative to the underlying network properties. In this paper we extend the Distributed Context Protocol (DCXP) to support subscriptions relative to an entity-application-entity triple, minimizing the number of subscriptions required and through application specific optimization minimize the overall cost of delivering user context information to service endpoints.

Immersive Participationentails massive participatory activities in the Internetengaging people, places and objects. This ispremised on the existence of an Internet of Things infrastructure supporting applications and services with the same richness of experience as the World Wide Web. This in turn presupposes the existence of models for establishing and maintaining context relations. Where these models do exist, they impose a limited interpretation of context relations in the presence of the inherent heterogeneous and dynamic characteristics of the supporting information. In this paper we introduce an approach towards establishing context relations through the use of an improved context relational model permitting a wider, more complete range of application specific scenarios. Additionally, wederive a measure of context proximity that considers the situation, attributes, relations, accuracy and heterogeneity of both the underlying information and the vast array of requirements for metrics supporting application problem domains

Massive Immersive Participation is enriched through the use of context information describing the dynamic states and relations among people places and things. This in turn mandates the creation of methods and models for establishing and supporting these relationships. Previous approaches are undermined by their limited interpretation of context centric relations and subsequently do not offer support for multi-criteria relationships. In this paper, we extend on our previous work on establishing multi-criteria context relationships, to adding the support required for maintaining these relationships over heterogeneous and dynamic context information. We introduce a query language that supports an extended publish-subscribe approach and define solutions for maintaining, evaluating and adjusting these relationships while minimizing overall costs.

The same activity under different user situations in the elderly care system may lead to different intelligent system response, since user needs vary with the changing situations. Activity awareness (AA) emphasizes that systems intelligently respond to user needs by aware the user-performed activities which is denoted and comprehended according to various user situations. But most current AA technics use similar patterns retrieved from the growing historical data, neglecting the situation change, called dataset driven. Thus, an aptly approach is needed in support of AA systems to handle various activity denotations in different user situations. Responding to the problem, this paper proposes an Ontology-based Problem-logic driven approach (OPL) to enhance the AA system by denoting user activities with the problem logic according to user-oriented denoted problem domains, where the AA system can seamlessly integrate with inferring for intelligent system response. Specifically, denoted problem domain and logic are proposed to support the OPL concepts, while activity graph is formed to support the intelligent system response based on the annotated problem logic. With the OPL, systems can directly target at the changing situations with rule-based inferring. A case study is performed upon the scenario retrieved from a European elderly care project, where a proof-of-concept prototype is established to confirm the validity of the OPL approach.

Entities in Internet of Things (IoT) need intelligent associations to allow a flexible and dynamic system reaction towards varying user situations. Purely data-oriented association methods (e.g., data-mining, machine-learning, etc.) are limited in deduction from existing associations. Also such data-oriented methods are limited in accuracy for working with small-scale datasets (e.g., working with patterns retrieved from historical data), outputting associations based on statistics rather than logic. Moreover, existing semantic technologies (ontological-oriented or rule-oriented) are facing with either flexibility or dynamicity challenges to discover and maintain the associations. This paper proposes an alternative technique of semantically constructing context-centric data objects based on service logics for logical associations, which enables an event net based on association nets adapting for the changing situations (called context-centric). A proof-of-concept implementation is carried out based on a vehicle planning scenario to validate the data construction technique. Comparing to previous work, this technique possesses advantages of flexibility and dynamicity for entity associations based on service logics.