Internet Of Things Device Management Research Articles

Secure smart contract-based digital twins for the Internet of Things

The proliferation of Internet of Things (IoT) devices that operate unattended providing a multitude of important and often sensitive services highlights the need for seamless interoperability and increased security. We argue that digital twins of IoT devices, with the right design, can enhance the security, reliability, auditability, and interoperability of IoT systems. The salient features of digital twins have made them key elements for the IoT and Industry 4.0. In this paper, we leverage advances in W3C's Web of Things (WoT) standards and distributed ledger technologies (DLTs) to present a novel design of the smart contract-based digital twins with enhanced security, transparency, interoperability, and reliability. We provide two different variations of that general design using two different blockchains (one public and one private, permissioned blockchain), and we present design trade-offs. Furthermore, we introduce an architecture for accessing and controlling IoT devices securely and reliably, providing full auditability, while at the same time using the proposed digital twins as an indirection mechanism (proxy). The proposed architecture leverages the blockchain to offer notable properties, namely,decentralization, immutability, auditability, non-repudiation, availability, and reliability. Moreover, it introduces mass actuation, easier management of IoT devices, and enhanced security to the IoT gateways, enables new business models, and makes consumer devices (vendor-)agnostic.

Enhancing intelligent IoT services development by integrated multi-token code completion

The Internet of Things (IoT) is a revolutionary network of interconnected devices embedded with sensors and software that enables seamless communication, data sharing, and intelligent decision-making in the form of IoT services. To facilitate the efficient development of IoT services, code completion technique provides a promising solution by providing suggestions for missing code snippets. The development trend of IoT services is to support more mobile device terminals. Mobile devices are portable and easy to use, allowing IoT device operation and management anytime and anywhere. However, the current multi-token completion methods struggle to guarantee code generation quality under the constraints of low resources and low latency, making it difficult to fully support IoT service development. We propose a multi-token code completion framework, S2RCC, which completes code from skeleton to refinement with dual encoder and dual decoder. The framework consists of two phases: first, the code skeleton, which is the simplification of code containing structure-sensitive tokens, is predicted based on the semantics of the code context; second, the broken context is repaired with the predicted skeleton, and then parsed into the code structure so that the specific tokens can be generated combining the semantics and structure of context. Furthermore, we then provide an implementation of the framework, representing the repaired code as an improved Heterogeneous code graph and fusing the semantics and structure of code context by the three-layer stacked attention. We conducted experiments on multi-token completion datasets, showing that our model has achieved the state-of-the-art with the smallest possible scale and the fastest generation speed.

Enabling Efficient IoT Device Connectivity and Dynamic Network Management through SDN: A Weighted Sum Method Approach

SDN-Enabled IoT Networks bring about a transformative shift in conventional network models by integrating the core principles of Software-Defined Networking (SDN) into the realm of the Internet of Things (IoT). This integration empowers the agile and effective management of IoT devices, facilitating smooth connectivity, optimized distribution of resources, and flexible network setups. Through the consolidation of control and the utilization of virtualization methods, SDN-Enabled IoT Networks amplify scalability, security, and real-time responsiveness. This addresses the obstacles presented by the extensive proliferation of IoT devices. This paradigm transition heralds a fresh era of interconnectedness, where SDN assumes a central role in harmonizing the intricate interplay of IoT devices and services. The rapid expansion of Internet of Things (IoT) devices has introduced unparalleled complexities in overseeing networks and establishing connections. This compels the need for inventive strategies to effectively manage the substantial surge of IoT devices and their ever-changing connectivity prerequisites. Software-Defined Networking (SDN) emerges as a promising approach to tackle these issues by enabling the flexible management of networks and allocation of resources. This study investigates the amalgamation of SDN within the realm of IoT, aiming to streamline device connections, optimize data transmission efficiency, and accommodate adaptable network setups. Introducing an innovative weighted sum technique for resource allocation optimization, this work lays the foundation for a comprehensive framework that bolsters IoT network performance and expandability. Four different SDN implementations are examined, including the Conventional IoT Network, SDN-enabled IoT utilizing Centralized Control, SDN-enabled IoT employing Distributed Control, SDN-enabled IoT with Hierarchical Control, and SDN-enabled IoT utilizing Hybrid Control. The assessment considers various aspects such as Enhanced Scalability, Enhanced Traffic Engineering, Heightened Security, Implementation Complexity, Difficulty of Migration, and Reliance on Vendors. The Conventional IoT Network secures a moderate 3rd position with a Preference Score of 0.56030, while the SDN-enabled IoT with Centralized Control holds the 5th rank at 0.49732, despite excelling in specific domains. The SDN-enabled IoT with Distributed Control achieves the top rank with a notable Preference Score of 0.79414 due to comprehensive performance, followed by the SDN-enabled IoT with Hierarchical Control securing the 2nd spot (Preference Score: 0.57022), and the SDN-enabled IoT with Hybrid Control taking the 4th position (Preference Score: 0.51300), particularly excelling in Traffic Engineering.

A lightweight authentication and management method for Internet of Things

Internet of Things (IoT) devices are vulnerable due to their limited resources and inadequate security mechanisms. The number of IoT devices has increased day by day, so the number of devices that are connected to the Internet has also increased. Devices may be deployed anywhere and made available to anyone, making the management of numerous devices problematic. Device management is crucial, as the compromise of IoT devices might cause serious consequences and already there are examples of such a compromise caused widespread DDoS attacks. This paper aims to propose a mechanism that ensures the security of IoT devices and presents a management model and lightweight authentication mechanism simultaneously. The proposed mechanism is novel, computationally lightweight, financially low-cost, remotely usable, and requires no special hardware. This mechanism includes a key management phase to generate ephemeral keys for every session and a trivial and cost-efficient data transfer phase. The proposed mechanism provides an IoT device management model to achieve a scalable IoT environment. Data is transferred cost-effectively via XORs and hash functions. The security of the authentication mechanism is formally verified using AVISPA model checker.

DRMT: A Decentralized IoT Device Recognition and Management Technology in Smart Cities

The Internet of Things (IoT) faces many security and privacy risks due to the large number of devices accessing the network while providing convenience to the current society. At present, the management of IoT devices is centralized. All terminal devices are managed in a centralized manner, and the data accessing IoT devices can only be connected to the network with the permission of the centralized nodes. In this scenario, the increase in the amount of equipment can cause central node overload or single node failure. Moreover, flexible expansion requirements are difficult to achieve in such a centralized architecture. This paper proposes a safe and efficient IoT device recognition and management scheme based on blockchain and edge computing, DRMT. This scheme realizes feature extraction, clustering, and learning of IoT devices’ traffic based on the characteristics of distributed deployment of edge servers. In this process, the recognition model can be continuously updated. The scheme also has an advantage that it is possible to enhance the management efficiency and scalability of the system by establishing a blockchain network between edge servers to share identification information and automate deployment security. This paper also verifies the scheme’s effectiveness by building an experimental network and a blockchain system. The results showed that DRMT would help realize the effective management of large-scale IoT devices in smart cities.

Internet of Things for Digital Forensics Application in Saudi Arabia

Despite the extensive empirical literature relating to the Internet of Things (IoT), surprisingly few attempts have sought to establish the ways in which digital forensics can be applied to undertake detailed examinations regarding IoT frameworks. The existing digital forensic applications have effectively held back efforts to align the IoT with digital forensic strategies. This is because the forensic applications are ill-suited to the highly complex IoT frameworks and would, therefore, struggle to amass, analyze and test the necessary evidence that would be required by a court. As such, there is a need to develop a suitable forensic framework to facilitate forensic investigations in IoT settings. Nor has considerable progress been made in terms of collecting and saving network and server logs from IoT settings to enable examinations. Consequently, this study sets out to develop and test the FB system which is a lightweight forensic framework capable of improving the scope of investigations in IoT environments. The FB system can organize the management of various IoT devices found in a smart apartment, all of which is controlled by the owner’s smart watch. This will help to perform useful functions, automate the decision-making process, and ensure that the system remains secure. A Java app is utilized to simulate the FB system, learning the user’s requirements and security expectations when installed and employing the MySQL server as a means of logging the communications of the various IoT devices.

A verifiable and privacy-preserving cloud mining pool selection scheme in blockchain of things

Blockchain of Things (BCoT), as a new paradigm that incorporates blockchain with the Internet of Things (IoT), can address the privacy and extensibility issues in IoT systems and effectively enhance the data management of IoT devices. Asmost of theIoT devices are resource-constrained in terms of computing power, storage capacity, and energy consumption. Meanwhile,the enormous computational consumption in the consensus process of the blockchain makes it difficult to integrate with IoT closely and exert the great potential of BCoT. latterly, the ”cloud mining” mechanism has been proposed to address these issues, however,some potential problems still exist, such as data privacy and dishonest cloud mining pools, etc. In this paper, we propose a verifiable and privacy-preserving cloud mining pool selection scheme in BCoT (namely, VPP-CMPS). To obtain a better solution for cloud mining pool selection, we implement an improved time-locked puzzles technique to identify dishonest mining pools and perform the authenticity for mining pool selection.Afterwards,we obtain the privacy protection of the data during the entire selection process by adopting additively homomorphic ElGamal cryptosystem. Meanwhile, considering the resource-constrained case in IoT devices, we use a semi-honest cloud server to cooperate in dealing with the selection procedure, thereby reducing the computational overhead of IoT devices. Experimental results indicate that our scheme is more efficient and provides less computational cost.

Secure Edge-Based Energy Management Protocol in Smart Grid Environments with Correlation Analysis

For the monitoring and processing of network data, wireless systems are widely used in many industrial applications. With the assistance of wireless sensor networks (WSNs) and the Internet of Things (IoT), smart grids are being explored in many distributed communication systems. They collect data from the surrounding environment and transmit it with the support of a multi-hop system. However, there is still a significant research gap in energy management for IoT devices and smart sensors. Many solutions have been proposed by researchers to cope with efficient routing schemes in smart grid applications. But, reducing energy holes and offering intelligent decisions for forwarding data are remain major problems. Moreover, the management of network traffic on grid nodes while balancing the communication overhead on the routing paths is an also demanding challenge. In this research work, we propose a secure edge-based energy management protocol for a smart grid environment with the support of multi-route management. It strengthens the ability to predict the data forwarding process and improves the management of IoT devices by utilizing a technique of correlation analysis. Moreover, the proposed protocol increases the system's reliability and achieves security goals by employing lightweight authentication with sink coordination. To demonstrate the superiority of our proposed protocol over the chosen existing work, extensive experiments were performed on various network parameters.

DroneTalk: An Internet-of-Things-Based Drone System for Last-Mile Drone Delivery

At present, drone delivery systems usually function only in global positioning system (GPS)-friendly environments and thus cannot deliver goods into customers’ houses, which is an element of urban last-mile delivery. In this study, we investigated solutions for enabling drones to fly autonomously in mixed indoor–outdoor environments. We propose a novel Internet of Things (IoT)-based drone delivery system, namely DroneTalk, for mail delivery. DroneTalk combines a GPS, an inertial measurement unit, and visual information to achieve mixed indoor–outdoor autopilot operation. Furthermore, DroneTalk integrates an autonomous drone control system with an IoT device management platform to enable a drone to achieve automatic online weather awareness. Most importantly, DroneTalk is a novel work considering communication, processing, and control latencies simultaneously during autopilot operation to avoid problems related to drone collisions. These problems have always existed but become more serious when drones are flying close to buildings. Simulation results indicate that the proposed system can attain a high user-defined flight success rate (i.e., 99% in our simulations) without any collisions; thus, the proposed system can be feasibly used in real-world environments.

PPDMIT: a lightweight architecture for privacy-preserving data aggregation in the Internet of Things

Data is generated over time by each device in the Internet of Things (IoT) ecosphere. Recent years have seen a resurgence in interest in the IoT due to its positive impact on society. However, due to the automatic management of IoT devices, the possibility of disclosing sensitive information without user consent is high. A situation in which information should not be unintentionally disclosed to outside parties we do not trust, i.e., privacy-preservation. Additionally, IoT devices should share their data with others to perform data aggregation and provide high-level services. There is a trade-off between the amount of data utility and the amount of disclosure of data. This trade-off has been causing a big challenge in this field. To improve the efficiency of this trade-off rather than current studies, in this study, we propose a Privacy-Preserving Data Aggregation architecture, PPDMIT, that leverages Homomorphic Paillier Encryption (HPE), K-means, a One-way hash chain, and the Chinese Remainder Theorem (CRT). We have found that the proposed privacy-preserving architecture achieves more efficient data aggregation than current studies and improves privacy preservation by utilizing extensive simulations. Moreover, we found that our proposed architecture is highly applicable to IoT environments while preventing unauthorized data disclosure. Specifically, our solution depicted an 8.096% improvement over LPDA and 6.508% over PPIOT.

TD-RA policy-enforcement framework for an SDN-based IoT architecture

Internet of Things (IoT) has been suffering from increasing security threats since many years which compromise the whole network security. Automating the management of IoT devices helps in implementing security measures within communication systems. Software Defined Networking (SDN) has been introduced as a new networking approach that enables this automation. Many approaches were developed to mitigate IoT attacks in SDN-based IoT networks. Some studies investigated the prevention of flooding attacks, while others tried to cover broader attack surfaces. However, their proposed methods are time consuming and resource-exhausting as they use complex algorithms. In this paper, we propose a lightweight secure Threat Detection (TD) and Rule Automation (RA) framework namely “TD-RA” to effectively detect and mitigate different cyber-security threats in an SDN-based IoT environment. The proposed solution is composed of a Binary and Multi-class Classification Modules (BCM/MCM) for IoT threat detection and a Policy-Enforcement Module (PEM) for attack mitigation. Different machine learning methods have been implemented and compared to solve the classification problems. It is shown that for binary classification, the Decision Tree method achieves the highest accuracy which is around 98.7%, while for multi-class classification, Random Forest achieves the highest accuracy which is around 91.1%. The experimental results show that the proposed framework can successfully detect abnormal traffic and prevent IoT threats through SDN with smaller network overhead and high performance. Moreover, the overall processing time of our security modules is significantly smaller than that of existing solutions by reaching a mean value of 6 ms. This paper also introduces a large-scale architecture that comprises clusters of controllers to maintain high availability of network services. Such an integrated security approach, including detection and mitigation techniques, provides IT industries with reliable security measures that can be implemented to increase SDN-based IoT system responsiveness to different IoT attacks.

A Lightweight Flow Feature-Based IoT Device Identification Scheme

Internet of Things (IoT) device identification is a key step in the management of IoT devices. The devices connected to the network must be controlled by the manager. For this purpose, many schemes are proposed to identify IoT devices, especially the schemes working on the gateway. However, almost all researchers do not pay close attention to the cost. Thus, considering the gateway’s limited storage and computational resources, a new lightweight IoT device identification scheme is proposed. First, the DFI (deep/dynamic flow inspection) technology is utilized to efficiently extract flow-related statistical features based on in-depth studies. Then, combined with symmetric uncertainty and correlation coefficient, we proposed a novel filter feature selection method based on NSGA-III to select effective features for IoT device identification. We evaluate our proposed method by using a real smart home IoT data set and three different ML algorithms. The experimental results showed that our proposed method is lightweight and the feature selection algorithm is also effective, only using 6 features can achieve 99.5% accuracy with a 3-minute time interval.

Clustering of IoT Devices Using Device Profiling and Behavioral Analysis to Build Efficient Network Policies

The Internet of Things (IoT) has emerged as a new paradigm, and billions of devices are connected with the internet. IoT is being penetrated in major domains of daily life like health care, agriculture, industry, smart homes and monitoring of the environment. The operator of such complex, huge and diverse heterogeneous networks may not even be fully aware of their IoT devices working, activity, behavior and resource utilization etc. The efficient management of IoT devices becomes a challenge for network managers to ensure smooth network operation. Network traffic analysis of IoT devices is a necessary and rudimentary tool to understand the behavior of devices. In this paper firstly, we identify insights of device network traffic, discuss the activity patterns of some IoT devices and present a visual description of the pattern of IoT devices. Secondly, after analyzing the device's behavior, we build and demonstrate a profile of each device based on its activity cycle and traffic patterns information. Thirdly, the K-Means clustering algorithm is used to make clusters of IoT devices using their profile information. The clustering algorithm groups similar devices in a single group. The obtained results clearly describe the patterns of devices which help the network managers to make appropriate network policies for efficient secure network management.

A survey on IoT platforms: Communication, security, and privacy perspectives

The Internet of Things (IoT) redefines the way how commodity and industrial tasks are performed every day. The integration of sensors, lightweight computation, and the proliferation of different wireless technologies on IoT platforms enable human beings to easily interact with their surrounding physical world thoroughly. With the recent rise of IoT, several different IoT platforms have been introduced for researchers and developers to ease the management and control of various IoT devices. In general, the IoT platforms act as a bridge between core IoT functionalities and users by providing APIs. Due to their wide variety of applications, IoT platforms are mostly unique in their architectures and designs. Thus, IoT administrators, developers, and researchers (i.e., IoT users) are challenged with substantial configuration differences in the proper configuration, implementation, and protection of the IoT solutions. In this survey, we conduct an in-depth analysis of popular IoT platforms from different application domains. More specifically, we define a comprehensive evaluation framework that considers seven different technical comparison criteria: (1) topology design, (2) programming languages, (3) third-party support, (4) extended protocol support, (5) event handling, (6) security, and (7) privacy. Then, we use the framework to evaluate the different IoT platforms highlighting their distinguishing attributes on communications, security, and privacy. First, we describe the communication protocols supported by the different IoT platforms surveyed. Then, rather than uncovering novel threats affecting IoT, we aim to analyze how the different IoT platforms handle security and privacy vulnerabilities affecting the most common security services of confidentiality, integrity, availability, and access control. Further, we present possible solutions that these platforms could implement to strengthen security and privacy within the IoT solution. Finally, we discuss the advantages and disadvantages of every IoT platform, so IoT administrators, developers, and researchers (i.e., IoT users) can make an informed decision on the use of specific platforms to implement their IoT solutions. To the best of our knowledge, this is the first comprehensive survey to evaluate different IoT platforms using the criteria defined in this work.

Data Privacy Based on IoT Device Behavior Control Using Blockchain

The Internet of Things (IoT) is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT context is a non-trivial task due to the low capability of these connected devices. Generally, the IoT device management is based on a centralized entity that validates communication and connection rights. Therefore, this centralized entity can be considered as a single point of failure. Yet, in the case of distributed approaches, it is difficult to delegate the right validation to IoT devices themselves in untrustworthy IoT environments. Fortunately, the blockchain may provide decentralization of overcoming the trust problem while designing a privacy-preserving system. To this end, we propose a novel privacy-preserving IoT device management framework based on the blockchain technology. In the proposed system, the IoT devices are controlled by several smart contracts that validate the connection rights according to the privacy permission settings predefined by the data owners and the stored record array of detected misbehavior of each IoT device. In fact, smart contracts can immediately detect the devices that have vulnerabilities and have been hacked or pose a threat to the IoT network. Therefore, the data owner’s privacy is preserved by enforcing the control over the own devices. For validation purposes, we deploy the proposed solution on a private Ethereum blockchain and give the performance evaluation.

FusionTalk: An IoT-Based Reconfigurable Object Identification System

Multisensor data fusion combines various information sources to produce a more accurate or complete description of the environment. This article studies an object identification (OID) system using multiple distributed cameras and Internet-of-Things (IoT) devices for better visualizability and reconfigurability. We first propose a data processing and fusing method to merge the detection results of different IoT devices and video cameras, in order to locate, identify, and track target objects in the monitored area. Then, we develop the FusionTalk system by integrating the data fusion techniques with IoTtalk, an IoT device management platform. FusionTalk is designed with flexibility, modularity, and expansibility, where cameras, IoT devices, and network applications are modularized and can be conveniently plugged in/out, reconfigured, and reused through graphical user interfaces. In FusionTalk, the scope and the target of surveillance can be flexibly configured and associated, and administrators can be warned and easily visualize the movement and behavior of specific objects. Our experimental evaluation of the data fusion algorithm in various scenarios shows an identification accuracy above 95%. Finally, theoretical and numerical analyses on the failure probability of pairing IoT devices with video objects by FusionTalk are presented. Extensive experiments are performed to demonstrate the pairing effectiveness in real-world scenarios with failure probability less than 0.01%.

Autonomous IoT Device Management Systems: Structured Review and Generalized Cognitive Model

Research on autonomous management for large-scale deployments of constrained devices is still a maturing field in the Internet of Things (IoT). Although much research has been conducted on how to achieve autonomous management in specific cases, there is a need for literature investigating which mechanisms can achieve such behavior in a generalized way. In this review, we present a comprehensive and structured study of the mechanisms for autonomous device management of constrained IoT devices in the light of management tasks, operational environment, network topology, resource constraints, scalability and management categories. Data extracted from 32 relevant cases are first organized and analyzed according to a synthesized taxonomy of observed adaptation mechanisms, and then combined with the state-of-the-art models of autonomous operations, identifying common patterns for autonomous management. Based on our findings we substantiate best practices for designing and implementing solutions around adaptation mechanisms. We then present a generalized model for autonomous device management that describes and explains the processes required for autonomous operation, unifying the insights from previous works as one cohesive archetype.

Internet-scale Insecurity of Consumer Internet of Things

The number of Internet-of-Things (IoT) devices actively communicating across the Internet is continually increasing, as these devices are deployed across a variety of sectors, constantly transferring private data across the Internet. Due to the extensive deployment of such devices, the continuous discovery and persistence of IoT-centric vulnerabilities in protocols, applications, hardware, and the improper management of such IoT devices has resulted in the rampant, uncontrolled spread of malware threatening consumer IoT devices. To this end, this work adopts a novel, macroscopic methodology for fingerprinting Internet-scale compromised IoT devices, revealing crucial cyber threat intelligence on the insecurity of consumer IoT devices. By developing data-driven techniques rooted in machine learning methods and analyzing 3.6 TB of network traffic data, we discover 855,916 compromised IP addresses, with 310,164 fingerprinted as IoT. Further analysis reveals China and Brazil to be hosting the most significant population of compromised IoT devices (100,000 and 55,000, respectively). Additionally, we provide a longitudinal analysis on data from one year ago against this work, revealing the evolving trends of IoT exploitation, such as the increased number of vendors targeted by malware, rising from 50 to 131. Moreover, countries such as China (420% increased infected IoT count) and Indonesia (177% increased infected IoT count) have seen notably high increases in infection rates. Last, we compare our geographic results against Global Cybersecurity Index (GCI) ratings, verifying that countries with high GCI ratings, such as the Netherlands and Germany, had relatively low infection rates. However, upon further inspection, we find that the GCI rate does not accurately represent the consumer IoT market, with countries such as China and Russia being rated with “high” CGI scores, yet hosting a large population of infected consumer IoT devices.

Characterizing and Mining Traffic Patterns of IoT Devices in Edge Networks

As connected Internet-of-things (IoT) devices in smart homes, smart cities, and smart industries continue to grow in size and complexity, managing and securing them in distributed edge networks have become daunting but crucial tasks. The recent spate of cyber attacks exploiting the vulnerabilities and insufficient security management of IoT devices have highlighted the urgency and challenges for securing billions of IoT devices and applications. As a first step towards understanding and mitigating diverse security threats of IoT devices, this paper develops an IoT traffic measurement framework on programmable and intelligent edge routers to automatically collect incoming, outgoing, and internal network traffic of IoT devices in edge networks, and to build multidimensional behavioral profiles which characterize who, when, what, and why on the behavioral patterns of IoT devices based on continuously collected traffic data. To the best of our knowledge, this paper is the first effort to shed light on the IP-spatial, temporal, entropy, and cloud service patterns of IoT devices in edge networks, and to explore these multidimensional behavioral fingerprints for IoT device classification, anomaly traffic detection, and network security monitoring for vulnerable and resource-constrained IoT devices on the Internet.

IoTShare: A Blockchain-Enabled IoT Resource Sharing On-Demand Protocol for Smart City Situation-Awareness Applications

We propose a blockchain-based, distributed protocol for enabling the deployment of Internet-of-Things (IoT) networks on-demand on top of IoT devices. Specifically, the proposed protocol leverages blockchain technology to: 1) enable distributed and secure authentication, registration, and management of participatory IoT devices; 2) provide fast discovery of IoT resources and scalable and secure instantiation of IoT networks-on-demand; and 3) manage payment operations and ensure reliable fund transfers among the network entities. The proposed protocol relies on a peer-to-peer network communication infrastructure to allow communications among the IoT devices in a distributed manner and uses a self-recovery/self-healing mechanism to ensure robustness against device failure and maliciousness. The protocol also introduces and uses a reputation system to monitor registered devices to keep track of their service delivery quality so that their service delivery reputations could be leveraged for future device selection and mapping. We implemented and evaluated the proposed protocol intensively using simulations and showed that it scales well with network parameters, is resilient to faulty devices, and is robust to 51% attack.