OpenPubSub: Supporting Large Semantic Content Spaces in Peer-to-Peer Publish/Subscribe Systems for the Internet of Multimedia Things

H-Pastry: An inter-domain topology aware overlay for the support of name-resolution services in the future Internet

The Internet of Things (IoT) concept is evolving rapidly and influencing new developments in various application domains, such as the Internet of Mobile Things (IoMT), Autonomous Internet of Things (A-IoT), Autonomous System of Things (ASoT), Internet of Autonomous Things (IoAT), Internet of Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc. that are progressing/advancing by using IoT technology. The IoT influence represents new development and deployment challenges in different areas such as seamless platform integration, context based cognitive network integration, new mobile sensor/actuator network paradigms, things identification (addressing, naming in IoT) and dynamic things discoverability and many others. The IoRT represents new convergence challenges and their need 98to be addressed, in one side the programmability and the communication of multiple heterogeneous mobile/autonomous/robotic things for cooperating, their coordination, configuration, exchange of information, security, safety and protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrency require new ideas for integrating the intelligent “devices”, collaborative robots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing, self-repair of resources, changing resource state, (re-) configuration and context based IoT systems for service implementation and integration with IoT network service composition are of paramount importance when new “cognitive devices” are becoming active participants in IoT applications. This chapter aims to be an overview of the IoRT concept, technologies, architectures and applications and to provide a comprehensive coverage of future challenges, developments and applications.

A challenging aspect of The Internet of Things (IoT) is to provide an architecture that can handle the range of IoT elements ranging from Cloud-based applications to constrained nodes in Wireless Sensor Networks (WSNs). Such an architecture must be scalable, allow seamless operation across networks and devices with little human intervention. This paper describes a set of abstractions and an architecture for the flow of data from sensors to applications supported by a Distributed Hash Table (DHT) and our novel Holistic Peer to Peer (HPP) Application Layer protocol to handle node ids, capabilities, services and sensor data. We show that this architecture can operate in a constrained node by presenting a ‘C’ implementation running on the Contiki3.0 OS and consider the effectiveness of its use of a DHT and its abstractions.

As any other communication system, the Internet of Things (IoT) requires a functional control plane. However developing such control plane in a centralized way presents a number of challenges given the multiple stakeholders, the huge number of devices distributed worldwide, their limited connectivity, and specially that most IoT devices are battery-powered and thus must be sleeping most of the time. This paper explores the possibility of employing a distributed control plane for the IoT that leverages the intrinsic scalability and flexibility of peer-to-peer Distributed Hash Tables (DHTs). In particular, it proposes using a so-called “command mailbox” resource to remotely control sleeping sensors and actuators in an asynchronous way, while also solving important issues such as device bootstrapping and security.

As a distributed hash table (DHT), a content addressable network (CAN) provides efficient routing and object location in a decentralized manner while offering fault tolerance and dynamic peer operations. However, as opposed to other DHTs that use a flat ID space, CAN uses a multi-dimensional logical space. DHTs usually require O(logN) routing information per peer and provide routing in O(logN) hops, where N is the number of peers in the system. In CAN, on the other hand, each peer keeps only constant amount of routing information and the routing takes O(dN/sup 1/d/) hops, where d is the dimensionality of the logical space. Hence the routing performance of CAN is worse than other DHTs especially when d is small. In this paper, we describe and evaluate several schemes for efficient routing in CAN by keeping additional routing information at the peers. Furthermore, due to the underlying multidimensional ID space, CAN is used by applications that require content-based mapping of data objects onto the ID space. Since uniform hashing is not used, such mappings introduce skewed object distributions among the peers. Thus we also describe load balancing schemes for CAN and investigate their efficiency.

Network coordinates (NCs) construct a logical space which enables efficient and accurate estimation of network latency. Although many researchers have proposed NCbased strategies to reduce the lookup latency of distributed hash tables (DHTs), these strategies are limited in the improvement of the lookup latency; the nearest node to which a query should be forwarded is not always included in the consideration scope of a node. This is because conventional DHTs assign node IDs independent of the underlying physical network. In this paper, we propose an NC-based method of constructing a topology-aware DHT by Proximity Identifier Selection strategy (PIS/NC). PIS/NC assigns an ID to each node based on NC of the node. This paper presents Canary, a PIS/NC-based CAN whose d-dimensional logical space corresponds to that of Vivaldi. Our simulation results suggest that PIS/NC has the possibility of dramatically improving the lookup latency of DHTs. Whereas DHash++ is only able to reduce the median lookup latency by 15% of the original Chord, Canary reduces it by 70% of the original CAN.

In recent years, the Internet of Things (IoT) and enterprise management systems (EMS) have been rapidly growing and applied in advanced Industries. It provides better big data analytics and the most promising computing platforms. Moreover, IoT is transforming into the augmented intelligence of things (AIoT), developing a human-oriented paradigm for enterprises with AI. Still, smart enterprises and industries have additional requirements, such as device and data trust, robust decision-making, communication latency, and secure data storage. However, previous emerging paradigms and approaches did not fully address all of the aforementioned requirements. Therefore, this article proposes a blockchain-based trust management scheme for smart enterprises with augmented intelligence. The blockchain-based device trust authentication mechanism is used at the device connection layer for device authentication in clusters of IoT devices (smart enterprises branch-SEB). Furthermore, the blockchain-based augmented intelligence enabled approach is leveraged for data authentication at the authentication layer. Finally, smart enterprise data are stored in the distributed hash table (DHTs) and decentralized cloud layer with distributed hash table. We evaluated the proposed scheme using qualitative and quantitative analysis and compared it to the existing studies, showing better performance as 40.887-ms computational cost and 1872-bits transactional cost.

The ability to quickly recognize and learn new visual concepts from limited samples enables humans to quickly adapt to new tasks and environments. This ability is enabled by semantic association of novel concepts with those that have already been learned and stored in memory. Computers can start to ascertain similar abilities by utilizing a semantic concept space. A concept space is a high-dimensional semantic space in which similar abstract concepts appear close and dissimilar ones far apart. In this paper, we propose a novel approach to one-shot learning that builds on this core idea. Our approach learns to map a novel sample instance to a concept, relates that concept to the existing ones in the concept space and, using these relationships, generates new instances, by interpolating among the concepts, to help learning. Instead of synthesizing new image instance, we propose to directly synthesize instance features by leveraging semantics using a novel auto-encoder network we call dual TriNet. The encoder part of the TriNet learns to map multi-layer visual features from CNN to a semantic vector. In semantic space, we search for related concepts, which are then projected back into the image feature spaces by the decoder portion of the TriNet. Two strategies in the semantic space are explored. Notably, this seemingly simple strategy results in complex augmented feature distributions in the image feature space, leading to substantially better performance. The codes and models are released in the github: https://github.com/tankche1/ Semantic-Feature-Augmentation-in-Few-shot-Learning.

On the modern era of Internet of Things (IoT) and Industry 4.0 there is a growing need for reliable wireless long range communications. LoRa is an emerging technology for effective long range communications which can be directly applied to IoT applications. Wireless sensor networks (WSNs) are by far an efficient infrastructure where sensors act as nodes and exchange information among themselves. Distributed applications such as Peer-to-Peer (P2P) networks are inextricably linked with Distributed Hash Tables (DHTs) whereabouts DHTs offer effective and speedy data indexing. A Distributed Hash Table structure known as the Chord algorithm enables the lookup operation of nodes which is a major algorithmic function of P2P networks. In the context of this paper, the inner workings of Chord protocol are highlighted along with an introduced modified version of it for WSNs. Additionally, we adapt the proposed method on LoRa networks where sensors function as nodes. The outcomes of the proposed method are encouraging as per complexity and usability and future directions of this work include the deployment of the proposed method in a large scale environment, security enhancements and distributed join, leave and lookup operations.

Massive diffusion of constrained devices communicating through low-power wireless technologies in the future Internet of Things (IoT) will require in many scenarios the deployment of IoT gateways to allow applications to discover and access IoT resources. In this context, Fog/Edge computing will represent an opportunity to deploy IoT gateways in proximity of IoT domains, meeting the requirements of applications needing low-latency interactions with devices. In this paper, we present an edge-centric distributed architecture to provide resource discovery and access services to IoT applications. The proposed approach leverages the CoRE Resource Directory interface and the Constrained Application Protocol (CoAP) to expose a standard interface for global discovery and access. Multiple IoT gateways are federated through a peer-to-peer overlay implemented by a distributed hash table, which is exploited to share the information on IoT resources available across multiple domains. The proposed solution is validated by means of a small-scale prototype, and extensively evaluated through emulation in large scale deployment in comparison to a centralized Cloud-based approach. Experimental results demonstrated that the proposed approach guarantees lower latencies than a centralized solution and can ensure scalability for small to medium sized deployments.

In upcoming smart urban environments, various things can be interconnected, and the Internet of Things (IoT) can be used to construct a safer and more convenient urban environment. Things in the IoT need an addressing system that can uniquely identify each one; internet protocol (IP) addresses can be used for this purpose. The IP address the two roles of an identifier and a locator. However, this binding has problems related to mobility and multihoming, and it is hard to deploy on a legacy IP system because of some limitations of sensor devices. To solve the problem, we propose a design for software-defined networking (SDN)-based identifier–locator separation architecture on IoT networks. In the proposed scheme, Internet Protocol version 6(IPv6)-based addresses are used for the identifiers and locators. The network is partitioned into a host identity domain for local routing and an IP domain for global routing. The host identity domain operates as an overlaid network over the IP domain, and it makes the unrouteable identifiers routable with a distributed hash table (DHT)-based routing strategy. For the evaluation of the proposed scheme, a packet forwarding cost and signaling cost model is calculated, and the results show that the proposed scheme is conjugable to an IoT network environment.

The generated real-time data on the Internet of Things (IoT) and the ability to gather and manipulate them are positively affecting various fields. One of the main concerns in IoT is how to provide trustworthy data. The data validation network ensures that the generated data by data sources in the IoT are trustworthy. However, the existing data validation network depends on a centralized entity for the selection of data validators. In this paper, a decentralized validator selection model is proposed. The proposed model creates multiple clusters using the distributed hash table (DHT) technique. The selection process of data validators from different clusters in the model is done randomly in a decentralized scheme. It provides a global method of assignment, selection, and verification of the selected validators in the network.

The rising amount of data in Internet of Things (IoT) and Wireless Sensor Network (WSN) scenarios motivates new computing paradigms like fog or edge computing. To reduce the amount of data sent upstream, in-network (pre-)processing is widely used, which demands for both compute and distributed storage capacities in highly constrained environments. This paper introduces a new way of using Distributed Hash Tables (DHTs) to create a distributed storage in P2P-networks. The main design goals are to introduce the lowest overheads possible and allowing for fair load balancing, even if nodes contributing storage capacities of arbitrary/different sizes form the network. A combination of an optimized bootstrap mechanism and a virtual node scheme that deploys a variable number of virtual nodes depending on a node's storage capacity yields success. An evaluation and comparison with state of the art work shows that the new method performs well in terms of load balancing while minimizing overheads introduced by newly introduced virtual nodes.

To achieve a fully connected network in Internet of Things (IoT) there are number of challenges that have to be overcome. Among those, a big challenge is how to keep all of the devices accessible everywhere and every time. In the IoT network, the assumption is that each IoT device can be reached by any client at any given time. In practice, this is not always possible and without a proper mechanism the nodes behind a NAT are unable to communicate with each other directly, and their addresses have to be shared through a trusted third party. This challenge becomes harder by taking into consideration that most NAT traversal approaches have been developed prior to rising of the IoT, without taking into account the constrained nature of the participating devices and mostly depend on a centralized entity. In this paper we proposed the Distributed Address Table (DAT), a decentralized, secure and lightweight address distribution model that allows any two nodes to get the addresses of the other end without relying on a trusted third party. Structured Peer-to-Peer (P2P) overlay by utilizing Distributed Hash Table (DHT) technique is generated as its underlying communication scheme to ensure that all participating devices are accessible at any given time. This is achieved through simple, yet secure and efficient decentralized model. The DAT adopts the edge/fog computing paradigms to ensure a decentralized address distribution. The results showed that the proposed model is efficient. In addition, the security properties of the proposed model have been defined and proved.

The resources in the Internet of Things (IoT) network are distributed among different parts of the network. Considering huge number of IoT resources, the task of discovering them is challenging. While registering them in a centralized server such as a cloud data center is one possible solution, but due to billions of IoT resources and their limited computation power, the centralized approach leads to some efficiency and security issues. In this paper we proposed a location aware and decentralized multi layer model of resource discovery (LaMRD) in IoT. It allows a resource to be registered publicly or privately, and to be discovered in a decentralized scheme in the IoT network. LaMRD is based on structured peer-to-peer (p2p) scheme and follows the general system trend of fog computing. Our proposed model utilizes Distributed Hash Table (DHT) technology to create a p2p scheme of communication among fog nodes. The resources are registered in LaMRD based on their locations which results in a low added overhead in the registration and discovery processes. LaMRD generates a single overlay and it can be generated without specific organizing entity or location based devices. LaMRD guarantees some important security properties and it showed a lower latency comparing to the cloud based and decentralized resource discovery.