Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas

A Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

The In-Network Computing (COIN) paradigm is a promising solution that leverages unused network resources to perform some tasks to meet up with computation-demanding applications, such as metaverse. In this vein, we consider the metaverse partial computation offloading problem for multiple subtasks in a COIN environment to minimise energy consumption and delay while dynamically adjusting the offloading policy based on the changing computation resources status. We prove that the problem is NP and thus transformed it into two subproblems: task splitting problem (TSP) on the user side and task offloading problem (TOP) on the COIN side. We modelled the TSP as an ordinal potential game (OPG) and proposed a decentralised algorithm to obtain its Nash Equilibrium (NE). Then, we model the TOP as Markov Decision Process (MDP) proposed double deep Q-network (DDQN) to solve for the optimal offloading policy. Unlike the conventional DDQN algorithm, where intelligent agents sample offloading decisions randomly within a certain probability, our COIN agent explores the NE of the TSP and the deep neural network. Finally, simulation results show that our proposed model approach allows the COIN agent to update its policies and make more informed decisions, leading to improved performance over time compared to the traditional baseline.Comment: 14 pages, 9 figure

Blockchain-based Perfect Sharing Project Platform based on the Proof of Atomicity Consensus Algorithm

The Korean government funded 12.8 billion USD to 652 research and development (R&D) projects supported by 20 ministries in 2019. Every year, various organizations are supported to conduct R&D projects focusing on selected core technologies by evaluating emerging technologies which industries are planning to develop. To manage the whole cycle of national R&D projects, information sharing on national R&D projects is very essential. The blockchain technology is considered as a core solution to share information reliably and prevent forgery in various fields. For efficient management of national R&D projects, we enhance and analyse the Perfect Sharing Project (PSP)-Platform based on a new blockchain-based platform for information sharing and forgery prevention. It is a shared platform for national ICT R&D projects management with excellent performance in preventing counterfeiting. As a consensus algorithm is very important to prevent forgery in blockchain, we survey not only architectural aspects and examples of the platform but also the consensus algorithms. Considering characteristics of the PSP-Platform, we adopt an atomic proof (POA) consensus algorithm as a new consensus algorithm in this paper. To prove the validity of the POA consensus algorithm, we have conducted experiments. The experiment results show the outstanding performance of the POA consensus algorithm used in the PSP-Platform in terms of block generation delay and block propagation time

SDN-Based Active Content Networking

This paper proposes a Software Defined Networking- (SDN-) based active content networking architecture for future media environments. The proposed architecture aims to provide customized delivery of various types of media content in order to satisfy users' demand and service requirements. To this end, we have developed an active content processing model which provides in-network content processing through service objects that are integral parts of active content. The main benefits provided by the proposed model are high flexibility and creativity to meet the evolving future media environments

Priority-based duplicate burst transmission mechanism in optical burst switching networks

This paper proposes a priority-based duplicate burst transmission mechanism in an optical burst switching network to enhance lhe probability of successful reception of bursts. The performance of the proposed mechanism is evaluated by NS2 simulations. Our results show that the burst loss rate is improved especially under light traffic loads

Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

Olive cultivation over the past few years has spread across Mediterranean countries with Spain being the world’s largest olive producer among them. Because olives are a major part of the economy for such countries keeping records of their tree count and crop yield is of high significance. Manual counting of trees over such large areas is humanly infeasible. To address this problem, we propose an automatic method for the detection and enumeration of olive trees. The algorithm is a multi-step classification system comprising pre-processing, image segmentation, feature extraction, and classification. RGB satellite images were acquired from the Spanish territory and pre-processed to suppress the additive noise. The region of interest was then segmented from the pre-processed images using K-Means segmentation, through which statistical features were extracted and classified. Promising results were achieved for all classifiers, namely Naive Bayesian, Support Vector Machines (SVMs), Random Forest and Multi-Layer Perceptrons (MLPs), at various division ratios of data samples. In a comparison of all the classification algorithms, Random Forest outperformed the rest by an overall accuracy of 97.5% at the division ratio of 70 to 30 for training to testing

Soft-State Bandwidth Reservation Mechanism for Slotted Optical Burst Switching Networks

This paper proposes a novel transport network architecture for the next generation network (NGN) based on the optical burst switching technology. The proposed architecture aims to provide efficient delivery of various types of network traffic by satisfying their quality-ofservice constraints. To this end, we have developed a softstate bandwidth reservation mechanism, which enables NGN transport nodes to dynamically reserve bandwidth needed for active data burst flows. The performance of the proposed mechanism is evaluated by means of numerical analysis and NS2 simulation. Our results show that the packet delay is kept within the constraint for each traffic flow and the burst loss rate is remarkably improved

Trust Management for Artificial Intelligence: A Standardization Perspective

With the continuous increase in the development and use of artificial intelligence systems and applications, problems due to unexpected operations and errors of artificial intelligence systems have emerged. In particular, the importance of trust analysis and management technology for artificial intelligence systems is continuously growing so that users who desire to apply and use artificial intelligence systems can predict and safely use services. This study proposes trust management requirements for artificial intelligence and a trust management framework based on it. Furthermore, we present challenges for standardization so that trust management technology can be applied and spread to actual artificial intelligence systems. In this paper, we aim to stimulate related standardization activities to develop globally acceptable methodology in order to support trust management for artificial intelligence while emphasizing challenges to be addressed in the future from a standardization perspective

Design and Implementation of a Trust Information Management Platform for Social Internet of Things Environments

As the vast amount of data in social Internet of Things (IoT) environments considering interactions between IoT and people is accumulated and processed through cloud and big data technologies, the services that utilize them are applied in various fields. The trust between IoT devices and their data is recognized as the core of IoT ecosystem creation and growth. Connection with suspicious IoT devices may pose a risk to services and system operation. Therefore, it is essential to analyze and manage trust information for devices, services, and people, as well as to provide the trust information to the other devices or users that need it. This paper presents a trust information management framework which contains a generic IoT reference model with trust capabilities to achieve the goal of converged trust information management. Additionally, a trust information management platform (TIMP) consisting of trust agents, trust information brokers, and trust information management systems has been proposed, which aims to provide trustworthy and safe interactions among people, virtual objects, and physical things. Implementing and deploying a TIMP enables a trustworthy ecosystem to be built while activating social IoT businesses by reducing transaction costs, as well as by eliminating the uncertainties in the use of social IoT services and data transactions

A novel fountain code-based mobile IPTV multicast system architecture over WiMAX network

In this paper, we present a novel fountain code-based mobile IPTV multicast system architecture over WiMAX network. In the proposed system, the transmission algorithm at a base station determines the control parameters of a fountain-encoded IPTV multicast stream adaptively to the wireless link states of subscribers in order to provide a stable IPTV service with minimum resource usage on WiMAX network, and the channel grouping algorithm at a server makes near-optimal channel grouping based on channel selection preferences to pursue an effective tradeoff between the channel zapping time and the processing complexity of a subscriber. Finally, experimental results are provided to show the performance of the proposed system. (C) 2011 Elsevier Inc. All rights reserved.X1145sciescopu