Heterogeneous Internet Of Things Devices Research Articles

Uncertainty-Aware Federated Reinforcement Learning for Optimizing Accuracy and Energy in Heterogeneous Industrial IoT

The Internet of Things (IoT) technology has revolutionized various industries by allowing data collection, analysis, and decision-making in real time through interconnected devices. However, challenges arise in implementing Federated Learning (FL) in heterogeneous industrial IoT environments, such as maintaining model accuracy with non-Independent and Identically Distributed (non-IID) datasets and straggler IoT devices, ensuring computation and communication efficiency, and addressing weight aggregation issues. In this study, we propose an Uncertainty-Aware Federated Reinforcement Learning (UA-FedRL) method that dynamically selects epochs of individual clients to effectively manage heterogeneous industrial IoT devices and improve accuracy, computation, and communication efficiency. Additionally, we introduce the Predictive Weighted Average Aggregation (PWA) method to tackle weight aggregation issues in heterogeneous industrial IoT scenarios by adjusting the weights of individual models based on their quality. The UA-FedRL addresses the inherent complexities and challenges of implementing FL in heterogeneous industrial IoT environments. Extensive simulations in complex IoT environments demonstrate the superior performance of UA-FedRL on both MNIST and CIFAR-10 datasets compared to other existing approaches in terms of accuracy, communication efficiency, and computation efficiency. The UA-FedRL algorithm attain an accuracy of 96.83% on the MNIST dataset and 62.75% on the CIFAR-10 dataset, despite the presence of 90% straggler IoT devices, attesting to its robust performance and adaptability in different datasets.

Towards Collaborative Edge Intelligence: Blockchain-Based Data Valuation and Scheduling for Improved Quality of Service

Collaborative edge intelligence, a distributed computing paradigm, refers to a system where multiple edge devices work together to process data and perform distributed machine learning (DML) tasks locally. Decentralized Internet of Things (IoT) devices share knowledge and resources to improve the quality of service (QoS) of the system with reduced reliance on centralized cloud infrastructure. However, the paradigm is vulnerable to free-riding attacks, where some devices benefit from the collective intelligence without contributing their fair share, potentially disincentivizing collaboration and undermining the system’s effectiveness. Moreover, data collected from heterogeneous IoT devices may contain biased information that decreases the prediction accuracy of DML models. To address these challenges, we propose a novel incentive mechanism that relies on time-dependent blockchain records and multi-access edge computing (MEC). We formulate the QoS problem as an unbounded multiple knapsack problem at the network edge. Furthermore, a decentralized valuation protocol is introduced atop blockchain to incentivize contributors and disincentivize free-riders. To improve model prediction accuracy within latency requirements, a data scheduling algorithm is given based on a curriculum learning framework. Based on our computer simulations using heterogeneous datasets, we identify two critical factors for enhancing the QoS in collaborative edge intelligence systems: (1) mitigating the impact of information loss and free-riders via decentralized data valuation and (2) optimizing the marginal utility of individual data samples by adaptive data scheduling.

Asynchronous Federated Learning with Grey Wolf Optimization for the Heterogeneity IoT Devices

The Internet of Things (IoT) creates new options for real-time data collection and Machine Learning (ML) model development. Nonetheless, it's feasible that a particular IoT device does not have sufficient computational power to train and implement a complete learning model. However, there are significant communication costs and data security and privacy concerns associated with sending actual data to a centralised server with a lot of computational power. Federated Learning (FL) is a potential way to train ML models using low-powered devices and Edge Servers (ES) since it is a distributed ML architecture. However, the vast majority of the works in existence make the unsustainable assumption of a synchronized parameters update manner with similar IoT nodes and reliable communications networks. To increase training efficiency and accelerate the speed for heterogeneous IoT devices in an unreliable network environment, we designed an Asynchronous Federated Learning strategy with Grey Wolf Optimization (AFL-GWO) in this research. In particular, we develop a Lightweight Node Selection (LNS) technique and propose an AFL-GWO model to efficiently complete learning tasks. To ensure that diverse IoT nodes with varying computational capabilities and network connectivity are represented in the global learning aggregate, the proposed technique makes node selections on an iterative basis. We show through extensive experiments that our suggested AFL-GWO system outperforms the state-of-the-art techniques on identically and independently distributed (IID) and non-IID data distribution in a variety of contexts.

Ensemble averaging deep neural network for botnet detection in heterogeneous Internet of Things devices

The botnet attack is one of the coordinated attack types that can infect Internet of Things (IoT) devices and cause them to malfunction. Botnets can steal sensitive information from IoT devices and control them to launch another attack, such as a Distributed Denial-of-Service (DDoS) attack or email spam. This attack is commonly detected using a network-based Intrusion Detection System (NIDS) that monitors the network device’s activity. However, IoT network is dynamic and IoT devices have many types with different configurations and vendors in IoT environments. Therefore, this research proposes an Intrusion Detection System (IDS) by ensemble-ing traffic from heterogeneous IoT devices. This research proposes Deep Neural Network (DNN) to create a training model from each heterogeneous IoT device. After that, each training model from each heterogeneous IoT device is used to predict the traffic. The prediction results from each training model are averaged using the ensemble averaging method to determine the final result. This research used the N-BaIoT dataset to validate the proposed IDS model. Based on experimental results, ensemble averaging DNN can detect botnet attacks in heterogeneous IoT devices with an average accuracy of 97.21, precision of 91.41, recall of 87.31, and F1-score 88.48.

AN ANALYSIS OF THE HETEROGENEOUS IOT DEVICE NETWORK INTERACTION IN A CYBER-PHYSICAL SYSTEM

The article is devoted to the study of existing technologies regarding Internet of Things (IoT) device interaction in a heterogeneous network. Since each smart home appliance can be controlled by a customer who aims to find a cost-effective and easy-to-connect product for their own connected home, there are certain functional limitations for devices from distinct manufacturers that may decrease the intention to merge them all into a single network. A variety of proprietary protocols and communication standards embedded by vendors make their products unable to interact with other vendor devices if the connection standard used is not identical. Also, an IoT product design refers to its own functionality, mainly excluding the possibility of integration into other existing infrastructure. As IoT equipment emerges on the market, the complexity of its connection to a heterogeneous network corresponds to the firmware and the standard unification according to modern demands. It means that potential users might face the necessity of overcoming these issues to achieve high performance in terms of network interoperability. In general, an IoT gateway operating as a middleware might have the potential to enable a network with distinct communication models to operate without failure or data loss. This task requires the received data to be converted into the format in which the data is intended. This paper includes a comparative analysis of existing IoT device interaction standards, connection protocols, and data transfer technologies, evaluating their features for an effective adoption of the proposed network architecture, which can be used to improve the interoperability of heterogeneous IoT devices.

MagSign: Harnessing Dynamic Magnetism for User Authentication on IoT Devices

User authentication is a critical module to achieve security and privacy protections, especially for pervasive Internet of Things (IoT) deployments. However, existing methods on IoT devices are significantly short of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">implementability</i> thanks to the lack of device uniformity and protocol openness. For instance, password becomes useless for devices void of text entry interfaces. Biometrics may not scale well as they require both non-trivial sensors and cumbersome user involvement. Proximity-based methods exploiting shared ambient contexts are vulnerable to co-located malicious attacks. Therefore, a low-cost authentication scheme widely implementable on heterogeneous IoT devices is urgently demanded. To this end, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MagSign</i> that leverages two fundamental capabilities owned by common IoT devices: the ubiquity of magnetic induction sensors and the power of screens to change magnetic field. Essentially, MagSign controls screen contents of an authorized device (possessed by a user) to generate specific currents in its electronic components that in turn induce a magnetic signature. This signature, sensed by a nearby device, allows the user to be authenticated and hence to unlock that device. In designing MagSign, we explore critical parameters employable to magnetic signature generation by analyzing electronic components' workflow. Moreover, we innovatively encode binary sequences into magnetic intensity transitions, so that a sequence issued from a trusted server can be converted into a magnetic signature. Different from existing proximity-based approaches relying on shared static environment information, magnetic signature is directly derived from a server-issued sequence, allowing for dynamic signature generation that effectively thwarts potential attacks. The comprehensive experiments show MagSign has a false acceptance rate (FAR) of 0.38% and a false rejection rate (FRR) of 3.13%.

Adaptive fuzzy convolutional neural network for medical image classification

Due to resource constraints and the diverse nature of the devices involved, energy efficiency and scalability enhancement are important challenges in the Internet of Things (IoT) ecosystem. It is difficult to manage the edge resources in a consistent way that promotes cooperation and sharing of resources across the devices because of the heterogeneity of the Internet of Things devices and the dynamic nature of the surroundings in which edge computing takes place. In this research, we offer Intelligent techniques for resource optimization for Internet of Things devices. This is a full-stack system architecture to support across heterogeneous Internet of Things devices that have limited resources. The paradigm that is being suggested is made up of several edge servers, and Internet of Things (IoT) devices have the qualities of being heterogeneity-compatible, high performing, and intelligently adaptable. In order to do this, a clustered environment is generated in heterogeneous Internet of Things devices, and a routing method called Search and Rescue Optimization is used to set up connections between the CH nodes. After that, the edge nodes that are closest to the source of the data are chosen for transmission. Overall, what was suggested Multi-Edge-IoT accomplishes superior efficiency in terms of consumption of energy, latency, communication overhead, and packet loss rate than existing approaches to attaining energy efficiency in the Internet of Things.

Exploring the IoT Data Analytics Landscape: An Extensive Study

Abstract: Since the past decade, the data production and consumption has been increasing exponentially. Ever since the emergence of cheap computer chips, wireless networks, and technological advancements in the cloud, there has been a tremendous increase in the number of IoT devices being developed. The data collected by connected heterogeneous IoT devices are highly diverse and unstructured and need to be analyzed to gain valuable insights therefrom. Data analysis can effectively help Internet of Things (IoT) businesses to identify trends, enhance growth and productivity, and empower employees. To date, immense opportunities are being discovered by analyzing massive amounts of data generated by IoT devices. Through an indepth analysis of existing surveys, we explore IoT, data analytics, and their unification. In this survey, we investigated various applications with their extensive usage and significant challenges of IoT data analytics. By examining the plethora of advantages of data analysis in the IoT sector, one can conclude that field has numerous applications and the true potential of data analysis in IoT sector is yet to be realized.

Intelligent VNF Placement to Mitigate DDoS Attacks on Industrial IoT

The Internet of Things (IoT) has undergone rapid popularization, reaching a wide range of application domains, such as manufactures. Hence, more and more heterogeneous IoT devices have been deployed in a variety of industrial environments, progressively becoming common objects to the supply chain. The physical infrastructure of manufacturing systems has become complex and requires efficient and dynamic solutions for managing network performance and security. Network Function Virtualization (NFV) has attracted attention when the intention is to respond to security threats on Industrial IoT (IIoT). Few works use NFV to detect and mitigate security threats on IIoT networks, but even less consider performance indicators of the network context when placing the Virtual Network Functions (VNFs). Thus, this work introduces a Machine Learning (ML) approach to place security VNFs based on NFV performance to mitigate Distributed Denial of Service (DDoS) attacks on IIoT. Experiments considering a new composed data set and diverse ML techniques show ML classification as an alternative for IIoT scenarios, achieving, according to the best-performing technique, 99.40% of accuracy in relation to the ideal placement. To facilitate the reproduction of the work, all the code and data produced are publicly available.

Partition Placement and Resource Allocation for Multiple DNN-Based Applications in Heterogeneous IoT Environments

The evolution of the Internet of Things (IoT) has been driving the explosive growth of deep neural network (DNN)-based applications and processing demands. Hence, edge computing has emerged as a potential solution to meet these processing requirements. However, emerging IoT applications have increasingly demanded to run multiple DNNs to extract multifaceted knowledge, requiring more computational resources and increasing response time. Consequently, edge nodes cannot act as a complete substitute for the previous cloud paradigm, owing to their relatively limited resources. To address this problem, we propose to incorporate nearby IoT devices when allocating resources to multiple DNN models. Furthermore, the optimization of resource allocation can be hindered by the heterogeneity of IoT devices, which affects the delay performance of DNN-based computing. In this context, we propose a DNN partition placement and resource allocation strategy that considers different processing powers, memory, and battery levels for heterogeneous IoT devices. We evaluate the performance of the proposed strategy through extensive simulations. Simulation results reveal that the proposed strategy outperforms other existing solutions in terms of end-to-end delay, service probability, and energy consumption. The proposed solution was further simulated in a Kubernetes testbed consisting of actual devices to assess its feasibility.

Transparent and Tamper-Proof Event Ordering in the Internet of Things Platforms

Today, the audit and diagnosis of the causal relationships between the events in a trigger-action-based event chain (e.g., why is a light turned on in a smart home?) in the Internet of Things (IoT) platforms are untrustworthy and unreliable. The current IoT platforms lack techniques for transparent and tamper-proof ordering of events due to their device-centric logging mechanism. In this article, we develop a framework that facilitates tamper-proof transparency and event order in an IoT platform by proposing a Blockchain protocol and adopting the vector clock system, both tailored for the resource-constrained heterogeneous IoT devices, respectively. To cope with the unsuited storage (e.g., ledger) and computing power (e.g., proof-of-work puzzle) requirements of the Blockchain in the commercial off-the-shelf IoT devices, we propose a partial consistent cut protocol and engineer a modular arithmetic-based lightweight proof of work puzzle, respectively. To the best of our knowledge, this is the first Blockchain designed for resource-constrained heterogeneous IoT platforms. Our event ordering protocol based on the vector clock system is also novel for the IoT platforms. We implement our framework using an IoT gateway and 30 IoT devices. We experiment with ten concurrent trigger-action-based event chains while each chain involves 20 devices, and each device participates in five different chains. The results show that our framework may order these events in 2.5 s while consuming approximately 140 mJ of energy per device. The results, hence, demonstrate the proposed platform as a practical choice for many IoT applications, such as smart home, traffic monitoring, and crime investigation.

Edge–IoT Computing and Networking Resource Allocation for Decomposable Deep Learning Inference

Deep learning (DL) applications have attracted significant attention with the rapidly growing demand for Internet of Things (IoT) systems. However, performing the inference tasks for DL applications on IoT devices is challenging due to the large computational demands of DL models. Recently, edge computing has offered us a solution by deploying resources near the end users. However, resources at the edge are still limited; thus, management issues, such as allocating the networking resources as well as the computing capabilities and configuring the devices appropriately for different applications, become essential. For knobs in such edge management, we consider multiple application tasks with different options of DL models and different hyperparameter settings, along with possible decomposition points that utilize the split DL concept to design the configuration tables. Layer-level decomposition in split DL provides greater flexibility by splitting a single DL inference model into parts on different computing devices, and each part consists of several consecutive layers. We then propose the SplitDL-Image and the SplitDL-Video algorithms based on the Vickrey–Clarke–Groves (VCG) mechanism by considering model performance and frames per second (FPS) requirements with the preferences of the heterogeneous IoT devices. The proposed method allocates networking and edge server computing resources according to the designed configuration tables by assigning the appropriate configuration to each IoT device. Simulation results based on real-world applications show that the proposed method indeed allocates more resources to IoT devices with more urgent/important tasks, preference for better accuracy, or higher local computational cost. In addition, other desired properties, such as truthful bidding, individual rationality, and weakly budget balance, are also guaranteed.

Hieraledger: Towards malicious gateways in appendable-block blockchain constructions for IoT

Appendable-block blockchain was established in an effort to standardize a blockchain architecture for heterogeneous Internet of Things devices. In current constructions, the main disadvantage lies in the full trust of gateways, which can launch coordinated and individual eclipse attacks against IoT sensing devices without being caught. This paper introduces Hieraledger, a novel commitment protocol for exposing malicious gateways in appendable-block blockchain constructions. To minimize the likelihood of eclipse attacks, each sensing device selects more than one gateway based on a novel reputation scoring model and the self-valuation of its privacy. To expose malicious gateways, our protocol employs fail-stop signatures thwarting signature forging, cryptographic hash functions, and a novel Merkle appendable tree. For each sensing device, we generate a resource-aware repudiable cryptographic proof of origin evidence. For gateway nodes, we generate a chain of commitment-based non-repudiable cryptographic proofs of delivery evidence. To demonstrate the security and robustness of our scheme, we analyze it under a solid theoretical security model. Based on our findings, our protocol enables sensing devices to seek arbitration and expose malicious gateways to the network. The present study is a further addition to the growing body of research into privacy preserving blockchain solutions for the Internet of Things

Heterogeneous IoT/LTE ProSe virtual infrastructure for disaster situations

Natural disasters of any kind can have catastrophic consequences for properties, infrastructure, and human lives. During large-scale calamities, two common problems are faced: (i) the partial or even complete destruction of communications infrastructure, and (ii) the difficulty of collecting accurate information on the disaster area and trapped victims. These two problems prevent a quick and accurate assessment of damage, leading to inefficient rescue operations and putting the lives of rescuers and victims in great danger. The LTE Proximity Services (ProSe) and the Internet of Things (IoT) are promising technologies that can offer an efficient solution to the aforementioned problems in the form of a rapidly established emergency network for post-disaster management. In this paper, we propose a solution for the establishment of an efficient emergency communications network for post-disaster situations based on the LTE Device-to-Device ProSe (D2D ProSe) technology and IoT devices. It facilitates fast and efficient service discovery that allows querying heterogeneous IoT devices such as health sensors, wireless cameras, smartwatches, or any other relevant devices or sensors. The goal is to allow rescuers to have access to information produced by various things available during the rescue operation. We take advantage of LTE ProSe to create a distributed D2D broadcast backbone that enables efficient and reliable message dissemination while reducing energy consumption and achieving high coverage. We then add a multipath forwarding mechanism, in which each service type of IoT device is ensured an optimal path for its traffic. Unlike previous works, we fully implemented our solution in the NS3 simulator and relied on realistic models (wireless channels, energy, and mobility) to evaluate its performance compared to previous solutions, which we also implemented on NS3. The obtained results show that our solution achieves significant improvement compared to those proposed in the literature. The entire code used in this project is freely available to the community through GitHub.11https://github.com/iorisam/NS3_ED2D_IoT.

Deep Model Training and Deployment in Heterogeneous IoT Networks

As a typical form of machines learning, deep learning has attracted much attention from researchers. It can independently construct (train) basic rules according to the sample data in the learning process. Especially in the field of machine vision, neural networks are usually trained by supervised learning, that is, by example data and predefined results of example data. In this paper, we firstly overview the current research progress on the deep model training and deployment on the heterogeneous Internet of Things (IoT) networks, by taking into account both the latency and energy consumption from various devices in the system. We then summarize the existing challenges on the model training and model deployment on the heterogeneous IoT devices. We further give some feasible solutions to solve the challenges on the model training and model deployment on the heterogeneous IoT devices. The study in this paper can serve as an important reference for the development of deep model training and model deployment for heterogeneous IoT networks.

Design and implementation of a framework for blockchain based security using IoT

The Internet of Things (IoT) has altered the world in the last few years due to its capacity to impact almost every part of life. However, IoT raises concerns about data security and privacy because it collects data from devices via the cloud, increasing its vulnerability to hacking. IoT security is a serious issue that has delayed its widespread adoption. Several security and privacy solutions have been proposed for IoT contexts that meet prevalent security criteria such as authentication, integrity, and secrecy. However, due to resource restrictions and heterogeneous IoT devices, present solutions are unable to address the security requirements of the approaching large-scale IoT paradigm. Blockchain, well known for bitcoin and Ethereum, provides an intriguing approach for IoT security. The IoT and blockchain technologies may be combined and significant improvements in distributed systems have been made as a result of the widespread use of IoT technology. A novel framework with a unique design was proposed to improve security in bitcoin transaction by combining blockchain and SHA-256 hash algorithm. Additionally, the performance of proposed framework is compared with the state-of-the-art algorithms like MD5 and SHA1 in term of encryption time, power consumption, latency, speed and security. It is observed that the proposed framework takes 12 ms lesser latency than MD5 and consumes 2.7Wh lesser power consumption than SHA1 and provides better security than both the techniques.

Enhanced Interoperating Mechanism Between OneM2M and OCF Platform Based on Rules Engine and Interworking Proxy in Heterogeneous IoT Networks

In recent years, the Internet of Things (IoT) is growing rapidly and is being applied in a variety of industries including healthcare, smart homes, and smart cities. Many standard IoT platforms are proposed to connect and communicate with IoT devices easily and securely such as oneM2M, GoogleWeave and Apple HomeKit. However, this makes IoT application development difficult as it requires IoT devices and applications to support multiple protocols to connect different IoT platforms. Therefore, it is necessary to provide a consistent schema to support interoperability in heterogeneous IoT networks. In this paper, we propose how to design and implement interoperating schema between two edge servers oneM2M and Open Connectivity Foundation (OCF) with heterogeneous IoT devices. Specifically, we build proxies for bridging oneM2M Mobius edge server and OCF IoTivity edge server. The sensor data is collected from various IoT devices and sent to an edge server with a compatible platform. Next, the data stored in each server will be exchanged with the other server through a proposed interworking proxy. We also use a rules engine to automatically identify registered devices to support edge server interaction within the same domain and across domains. In addition, we build a web application in each edge server to provide friendly IoT services (data visualization) to clients from different environments. In order to evaluate our system, we collect the delay time of each process in the edge servers. The results show that our proposal is completely applicable in practice.

Blockchain-Based Decentralized Authentication Model for IoT-Based E-Learning and Educational Environments

In recent times, technology has advanced significantly and is currently being integrated into educational environments to facilitate distance learning and interaction between learners. Integrating the Internet of Things (IoT) into education can facilitate the teaching and learning process and expand the context in which students learn. Nevertheless, learning data is very sensitive and must be protected when transmitted over the network or stored in data centers. Moreover, the identity and the authenticity of interacting students, instructors, and staff need to be verified to mitigate the impact of attacks. However, most of the current security and authentication schemes are centralized, relying on trusted third-party cloud servers, to facilitate continuous secure communication. In addition, most of these schemes are resource-intensive; thus, security and efficiency issues arise when heterogeneous and resource-limited IoT devices are being used. In this paper, we propose a blockchain-based architecture that accurately identifies and authenticates learners and their IoT devices in a decentralized manner and prevents the unauthorized modification of stored learning records in a distributed university network. It allows students and instructors to easily migrate to and join multiple universities within the network using their identity without the need for user re-authentication. The proposed architecture was tested using a simulation tool, and measured to evaluate its performance. The simulation results demonstrate the ability of the proposed architecture to significantly increase the throughput of learning transactions (40%), reduce the communication overhead and response time (26%), improve authentication efficiency (27%), and reduce the IoT power consumption (35%) compared to the centralized authentication mechanisms. In addition, the security analysis proves the effectiveness of the proposed architecture in resisting various attacks and ensuring the security requirements of learning data in the university network.

Transparent Access to Heterogeneous IoT Based on Virtual Resources

The Internet of Things (IoT) inspires industries to deploy a massive number of connected devices to provide smart and ubiquitous services to influence our daily life. Edge computing leverages sufficient computation and storage at the edge of the network to enable deploying complex functions closer to the environment using Internet-connected devices. According to the purpose of the environment including privacy level, domain functionality, network scale and service quality, various environment-specific services can be provided through heterogeneous applications with sensors and actuators based on edge computing. However, for providing user-friendly service scenarios based on the transparent access to heterogeneous devices in edge computing, a consistent interface shall be provided to deliver services from edge computing to clients. In this paper, we propose transparent computing based on virtual resources to access heterogeneous IoT devices without considering the underlying network configuration at the edge of the networks. For supporting transparent access to different edge computing environments through a consistent interface, the virtual resource of edge gateway is proposed to bridge the Internet and devices which are deployed on the edge of the network. The proposed edge gateway exposes the services of the Internet of Things devices to the Internet using virtual resources that represent the resources of physical devices. The virtual resources provide a consistent interface to enable clients to access devices in edge computing without considering underlying protocols. The virtual resource is generated by the resource directory in the edge gateway through the registration of a device. Based on the device registration, the device information is stored in the gateway to link virtual resources and devices for translating messages according to the destination protocols and identifying physical devices that are represented by virtual resources. Moreover, through collaboration with the service provider, the function of device discovery and monitoring is provided to clients.

Policy-aware Distributed and Dynamic Trust based Access Control Scheme for Internet of Things

The use of smart devices is driving the Internet of Things (IoT) trend today. Day by day IoT helps to support more services like car services, healthcare services, home automation, and security services, weather prediction services, etc, to ease user’s life. Integration of heterogeneous IoT devices and social resources sometimes creates many problems like the privacy of data. To avoid privacy issues, an appropriate access control mechanism is required to check authorized and trusted devices, so that only valid devices can access the data which is only required. In the sequel, this paper presents implementation of distributed and dynamic trust based access control mechanism (DDTAC) for secure machine to machine communication or distributed IoT environment. Novelty of this mechanism is that, it uses trust calculation and device classification for dynamic access control. The proposed scheme is implemented, tested and deployed on Node MCU and same mechanism is also simulated on NS-2 for large number of nodes. This access control model support Scalability, Heterogeneity, Privacy, Trust, Selective disclosure, Principle of least privileges, and lightweight calculation features. Results of this models proves that it gives good performance as compared to existing scheme in terms of scalability, throughput and delay. As number of devices increase it does not degrade performance. This mechanism is also protected against the Man-in-the-Middle attack, Sniffing attack, Session Hijacking attacks and Injection attacks. It required less time to detect and resist those attacks.