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

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.

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

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.

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.

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.

Information-Centric Networking (ICN) ¹ is a significant networking paradigm for the Internet of Things, which is an information-centric network in essence. The ICN paradigm owns inherently some security features, but also brings several new vulnerabilities. The most significant one among them is Interest flooding, which is a new type of Denial of Service (DoS) attack, and has even more serious effects to the whole network in the ICN paradigm than in the traditional IP paradigm. In this paper, we suggest a new mechanism to mitigate Interest flooding attack. The detection of Interest flooding and the corresponding mitigation measures are implemented on the edge routers, which are directly connected with the attackers. By using statistics of Interest satisfaction rate on the incoming interface of some edge routers, malicious name-prefixes or interfaces can be discovered, and then dropped or slowed down accordingly. With the help of the network information, the detected malicious name-prefixes and interfaces can also be distributed to the whole network quickly, and the attack can be mitigated quickly. The simulation results show that the suggested mechanism can reduce the influence of the Interest flooding quickly, and the network performance can recover automatically to the normal state without hurting the legitimate users.

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.