Service vs Protection: A Bayesian Learning Approach for Trust Provisioning in Edge of Things Environment

Edge of Things (EoT) technology enables end-users participation with smart-sensors and mobile devices (such as smartphones, wearable devices) to the smart devices across the smart city. Trust management is the main challenge in EoT infrastructure to consider the trusted participants. The Quality of Service (QoS) is highly affected by malicious users with fake or altered data. In this paper, a Robust Trust Management (RTM) scheme is designed based on Bayesian learning and collaboration filtering. The proposed RTM model is regularly updated after a specific interval with the significant decay value to the current calculated scores to update the behavior changes quickly. The dynamic characteristics of edge nodes are analyzed with the new probability score mechanism from recent services’ behavior. The performance of the proposed trust management scheme is evaluated in a simulated environment. The percentage of collaboration devices are tuned as 10%, 50% and 100%. The maximum accuracy of 99.8% is achieved from the proposed RTM scheme. The experimental results demonstrate that the RTM scheme shows better performance than the existing techniques in filtering malicious behavior and accuracy

Named data networking for efficient IoT-based disaster management in a smart campus

Disasters are uncertain occasions that can impose a drastic impact on human life and building infrastructures. Information and Communication Technology (ICT) plays a vital role in coping with such situations by enabling and integrating multiple technological resources to develop Disaster Management Systems (DMSs). In this context, a majority of the existing DMSs use networking architectures based upon the Internet Protocol (IP) focusing on location-dependent communications. However, IP-based communications face the limitations of inefficient bandwidth utilization, high processing, data security, and excessive memory intake. To address these issues, Named Data Networking (NDN) has emerged as a promising communication paradigm, which is based on the Information-Centric Networking (ICN) architecture. An NDN is among the self-organizing communication networks that reduces the complexity of networking systems in addition to provide content security. Given this, many NDN-based DMSs have been proposed. The problem with the existing NDN-based DMS is that they use a PULL-based mechanism that ultimately results in higher delay and more energy consumption. In order to cater for time-critical scenarios, emergence-driven network engineering communication and computation models are required. In this paper, a novel DMS is proposed, i.e., Named Data Networking Disaster Management (NDN-DM), where a producer forwards a fire alert message to neighbouring consumers. This makes the nodes converge according to the disaster situation in a more efficient and secure way. Furthermore, we consider a fire scenario in a university campus and mobile nodes in the campus collaborate with each other to manage the fire situation. The proposed framework has been mathematically modeled and formally proved using timed automata-based transition systems and a real-time model checker, respectively. Additionally, the evaluation of the proposed NDM-DM has been performed using NS2. The results prove that the proposed scheme has reduced the end-to-end delay up from 2% to 10% and minimized up to 20% energy consumption, as energy improved from 3% to 20% compared with a state-of-the-art NDN-based DMS

Deep Q-Learning on Internet of Things System for Trust Management in Multi-Agent Environments for Smart City

Smart Cities are vital to improving urban efficiency and citizen quality of life due to the fast rise of the Internet of Things (IoT) and its integration into varied applications. Smart Cities are dynamic and complicated, making trust management in multi-agent systems difficult. Trust helps IoT devices and agents in smart ecosystems connect and cooperate. This study suggests using Deep Q-Learning and Bidirectional Long Short-Term Memory (Bi-LSTM) to manage trust in multi-agent Smart City settings. Deep Q-Learning and Bi-LSTM represent long-term relationships and temporal dynamics in the IoT network, enabling intelligent trust-related judgments. The architecture supports real-time trust assessment, decision-making, and response to smart city changes. The suggested solution improves dependability, security, and trustworthiness in the IoT system's networked agents. A complete collection of studies utilizing real-world IoT data from a simulated Smart City evaluates the system's performance. The Deep Q-Learning and Bi-LSTM technique surpasses existing trust management approaches in dynamic, complicated multi-agent environments. The system's capacity to adapt to changing situations and improve decision-making make IoT device interactions more dependable and trustworthy, helping Smart Cities expand sustainably and efficiently

Wireless Sensor Network Optimization Using Genetic Algorithm

Wireless Sensor Network (WSN) is a high potential technology used in many fields (agriculture, earth, environmental monitoring, resources union, health, security, military, and transport, IoT technology). The band width of each cluster head is specific, thus, the number of sensors connected to each cluster head is restricted to a maximum limit and exceeding it will weaken the connection service between each sensor and its corresponding cluster head. This will achieve the research objective which refers to reaching the state where the proposed system energy is stable and not consuming further more cost. The main challenge is how to distribute the cluster heads regularly on a specified area, that’s why a solution was supposed in this research implies finding the best distribution of the cluster heads using a genetic algorithm. Where using an optimization algorithm, keeping in mind the cluster heads positions restrictions, is an important scientific contribution in the research field of interest. The novel idea in this paper is the crossover of two-dimensional integer encoded individuals that replacing an opposite region in the parents to produce the children of new generation. The mutation occurs with probability of 0.001, it changes the type of 0.05 sensors found in handled individual. After producing more than 1000 generations, the achieved results showed lower value of fitness function with stable behavior. This indicates the correct path of computations and the accuracy of the obtained results. The genetic algorithm operated well and directed the process towards improving the genes to be the best possible at the last generation. The behavior of the objective function started to be regular gradually throughout the produced generations until reaching the best product in the last generation where it is shown that all the sensors are connected to the nearest cluster head. As a conclusion, the genetic algorithm developed the sensors’ distribution in the WSN model, which confirms the validity of applying of genetic algorithms and the accuracy of the results

DITrust Chain: Towards Blockchain-Based Trust Models for Sustainable Healthcare IoT Systems

© 2013 IEEE. Today, internet and device ubiquity are paramount in individual, formal and societal considerations. Next generation communication technologies, such as Blockchains (BC), Internet of Things (IoT), cloud computing, etc. offer limitless capabilities for different applications and scenarios including industries, cities, healthcare systems, etc. Sustainable integration of healthcare nodes (i.e. devices, users, providers, etc.) resulting in healthcare IoT (or simply IoHT) provides a platform for efficient service delivery for the benefit of care givers (doctors, nurses, etc.) and patients. Whereas confidentiality, accessibility and reliability of medical data are accorded high premium in IoHT, semantic gaps and lack of appropriate assets or properties remain impediments to reliable information exchange in federated trust management frameworks. Consequently, We propose a Blockchain Decentralised Interoperable Trust framework (DIT) for IoT zones where a smart contract guarantees authentication of budgets and Indirect Trust Inference System (ITIS) reduces semantic gaps and enhances trustworthy factor (TF) estimation via the network nodes and edges. Our DIT IoHT makes use of a private Blockchain ripple chain to establish trustworthy communication by validating nodes based on their inter-operable structure so that controlled communication required to solve fusion and integration issues are facilitated via different zones of the IoHT infrastructure. Further, text{C}mathrm {sharp } implementation using Ethereum and ripple Blockchain are introduced as frameworks to associate and aggregate requests over trusted zones

Trust Management for Internet of Things: A Systematic Literature Review

Internet of Things (IoT) is a network of devices that communicate with each other through the internet and provides intelligence to industry and people. These devices are running in potentially hostile environments, so the need for security is critical. Trust Management aims to ensure the reliability of the network by assigning a trust value in every node indicating its trust level. This paper presents an exhaustive survey of the current Trust Management techniques for IoT, a classification based on the methods used in every work and a discussion of the open challenges and future research directions.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Individual, technological, organizational, and environmental factors impact of the internet of things on e-learning adoption in higher education institutions in Jordan

The world of the Internet of Things (IoT), even though it is continuously morphing as a fresh paradigm at the intersection of technology and education, is still struggling with several difficulties that prevent its absorption into the e-learning platforms of higher education institutions (HEIs). The breadth of Internet of Things implementation in developing countries, particularly Jordan, Malaysia, Iran, Saudi Arabia, Iraq, and Bangladesh, remains behind, even though industrialized nations have made significant advancements in their utilization of IoT, with the United Kingdom, the United States of America, China, and Japan acting as prominent examples. In the realm of research that focuses on the progression of the IOT integration into the e-learning systems of economically challenged countries, there is a substantial disparity. In particular, the focus of this research is on Jordan to shed light on the primary variables that are either facilitating or hindering the adoption of the IoT within the e-learning sector of Jordan's HEIs. A comprehensive analysis of previous research has been undertaken as a first stage in this investigation. The goal of this analysis is to identify important factors that are involved in the process of IOT adoption. Following that, we used an inferential technique, collecting data from 306 respondents who were enrolled in Jordanian higher education institutions. During our investigation, we discovered that the rate of the IOT integration was significantly influenced by factors such as accessibility, usability, technical assistance, and individual capabilities. In addition, our findings suggest that factors such as attitude, behavior, financial preparedness, dependability, and training have a substantial impact on the adoption of the IOT. On the other hand, the study seemed to indicate that characteristics such as class capacity, awareness, system resources, and course design had a minor influence on the adoption rates inside HEIs. In conclusion, this study provides tangible suggestions to strengthen the integration of the IoT inside Jordanian HEIs. These recommendations provide significant insights that can be used by policy architects, government entities, and higher education institutions to overcome the challenges that relate to the deployment of IoT in the higher learning sector

StabTrust-A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks

Vehicular Ad-hoc Network (VANET) is a modern era of dynamic information distribution among societies. VANET provides an extensive diversity of applications in various domains, such as Intelligent Transport System (ITS) and other road safety applications. VANET supports direct communications between vehicles and infrastructure. These direct communications cause bandwidth problems, high power consumption, and other similar issues. To overcome these challenges, clustering methods have been proposed to limit the communication of vehicles with the infrastructure. In clustering, vehicles are grouped together to formulate a cluster based on certain rules. Every cluster consists of a limited number of vehicles/nodes and a cluster head (CH). However, the significant challenge for clustering is to preserve the stability of clusters. Furthermore, a secure mechanism is required to recognize malicious and compromised nodes to overcome the risk of invalid information sharing. In the proposed approach, we address these challenges using components of trust. A trust-based clustering mechanism allows clusters to determine a trustworthy CH. The novel features incorporated in the proposed algorithm includes trust-based CH selection that comprises of knowledge, reputation, and experience of a node. Also, a backup head is determined by analyzing the trust of every node in a cluster. The major significance of using trust in clustering is the identification of malicious and compromised nodes. The recognition of these nodes helps to eliminate the risk of invalid information. We have also evaluated the proposed mechanism with the existing approaches and the results illustrate that the mechanism is able to provide security and improve the stability by increasing the lifetime of CHs and by decreasing the computation overhead of the CH re-selection. The StabTrust also successfully identifies malicious and compromised vehicles and provides robust security against several potential attacks.This work was supported by the Deanship of Scientific Research, King Saud University through the Vice Deanship of Scientific Research Chairs. The authors are grateful to the Deanship of Scientific Research, King Saud University for funding through Vice Deanship of Scientific Research Chairs

Crypto-Stegno based model for securing medical information on IOMT platform

The integration of the Internet of Things in medical systems referred to as the Internet of Medical Things (IoMT), which supports medical events for instance real-time diagnosis, remote monitoring of patients, real-time drug prescriptions, among others. This aids the quality of services provided by the health workers thereby improve patients’ satisfaction. However, the integrity and confidentiality of medical information on the IoMT platform remain one of the contentions that causes problems in medical services. Another serious concern with achieving protection for medical records is information confidentiality for patient’s records over the IoMT environment. Therefore, this paper proposed a Crypto-Stegno model to secure medical information on the IoMT environment. The paper validates the system on healthcare information datasets and revealed extraordinary results in respect to the quality of perceptibility, extreme opposition to data loss, extreme embedding capability and security, which made the proposed system an authentic strategy for resourceful and efficient medical information on IoTM platform