Large-scale Internet Of Things Research Articles

Modeling the Greedy Behavior Attack and Analyzing its Impact on IoT Networks

One of the major security issues in the Internet of Things (IoT) is maintaining the network availability against attacks and traffic congestion. In practice, the greedy behavioral attack is considered as an intelligent Denial of Service (DoS), which aims to compromise the availability of the network by consuming as much as possible the bandwidth of the deployed network. This attack is achieved by tuning the CSMA-CA network communication parameters that plays at the physical layer. In this paper, we propose an efficient modeling technique of attacks proper to the behavior of the greedy node in IoT networks while respecting unslotted IEEE 802.15.4. In fact, our developed greedy nodes algorithm relies on CSMA-CA protocol. This fashioned way of attack representation helped us to easily detect greedy nodes on large-scale IoT networks through simulations. Indeed, the obtained numerical results of different scenarios allow us to validate our approach and showed that the greedy nodes can monopolize the transmission channel during a significant period of time. Various relevant parameters (the number of sent/lost packets, the collision rate, and energy consumption) are considered to analyze and evaluate the impact of selfish nodes on the IoT networks.

Unification of Broadband and Broadcast Mobile Services using Cognitive Radio

The networks in future generation uses the confluence of multi-media, broadband, and broadcast services, Cognitive Radio (CR) networks are located as a preferred paradigm to bring up with spectrum functionality traumatic conditions. CRS addresses the ones troubles via dynamic spectrum access. However, the precept traumatic conditions faced through manner of manner of the CR pertain to accomplishing spectrum overall performance. At the end, spectrum overall performance improvement models based on spectrum sensing and sharing models have attracted quite a few research hobby in modern-day years, which incorporates CR mastering models, network densification architectures, and Massive Multiple Input Multiple Output (MIMO), and beamforming techniques. This paper deals with a survey of modern CR spectrum overall improvement performance models and techniques which helps ultra-high reliability with low latency communications which might be resilient to surges in web page site visitors and competition for spectrum. These models and techniques, mainly speaks about permit a big form of functionality beginning from extra superb mobiliary broadband to large-scale Internet of Things (IoT) type communications. It also provides a research correlation for many of the regular periods of a spectrum block, as well as the realistic statistics rate, the models which are used in this paper are applicable in an ultra-high frequency band. This study provides a super compare of CRs and direction for future investigations into newly identified 5G research areas, such as in business enterprise and academia.

Fog-based distributed trust and reputation management system for internet of things

Internet of things (IoT) provides connectivity between different smart devices. IoT systems aim to make data collection, and processing easier. Studies show that we can expect over 75 Billion IoT devices to be active by 2025. There is an increase in the interest in IoT systems, due to their ability to provide a better quality of services to end users. However, many critical challenges arise when deployed in the various applications. Among these issues are security, bandwidth, scalability, and network latency. Security is one of the most critical issues in IoT applications. Moreover, IoT devices can have different computational capabilities, and might be as simple as sensors nodes, or as complex as smart devices. As a result, it is not feasible to adopt standard security methods into IoT devices. In this paper, we propose a new solution that is suitable to IoT systems. The proposed solution is a Fog computing based trust and reputation system. Using Fog nodes, each IoT device evaluates trust towards other IoT devices and only proceed with an interaction with a device, if it meets a certain threshold trust value. This evaluation is necessary to eliminate any malicious devices from affecting the system and quality of service. It will also help protect the system from many attacks such as Bad Mouthing, On Off, and Self Promoting attacks. Simulation results are provided to highlight the behavior of the system under these attacks. Moreover, the proposed solution is suitable for large-scale IoT systems. A comparison between the proposed model and the related works show that the proposed model outperforms the previous works in terms of suitability to IoT systems, and security.

Protocol-Based and Hybrid Access Control for the IoT: Approaches and Research Opportunities.

Internet of Things (IoT) applications and services are becoming more prevalent in our everyday life. However, such an interconnected network of intelligent physical entities needs appropriate security to sensitive information. That said, the need for proper authentication and authorization is paramount. Access control is in the front line of such mechanisms. Access control determines the use of resources only to the specified and authorized users based on appropriate policy enforcement. IoT demands more sophisticated access control in terms of its usability and efficiency in protecting sensitive information. This conveys the need for access control to serve system-specific requirements and be flexibly combined with other access control approaches. In this paper, we discuss the potential for employing protocol-based and hybrid access control for IoT systems and examine how that can overcome the limitations of traditional access control mechanisms. We also focus on the key benefits and constraints of this integration. Our work further enhances the need to build hierarchical access control for large-scale IoT systems (e.g., Industrial IoT (IIoT) settings) with protocol-based and hybrid access control approaches. We, moreover, list the associated open issues to make such approaches efficient for access control in large-scale IoT systems.

On-chain is not enough: Ensuring pre-data on the chain credibility for blockchain-based source-tracing systems

The blockchain provides a reliable and scalable method for enabling source-tracing functionality in large-scale Internet of Things (IoT) systems. Traditional blockchain-based source tracing applications are generally based on the hypothesis that the raw data collected by each IoT node are credible and consistent, which however may not always be the truth. As no mechanism ensures the reliability of the original data collected from the IoT devices, these data may be accidently screwed up or maliciously tampered with before they are uploaded on-chain. To address this issue, we propose the Multi-dimensional Certificates of Origin (MCO) method to filter out the potentially incredible data-till all the data uploaded to the chain are credible. To achieve this, we devise the Multi-dimensional Information Cross-Verification (MICV) and Multi-source Data Matching Calculation (MDMC) methods. MICV verifies whether a to-be-uploaded datum is consistent or credible, and MDMC determines which data should be discarded and which data should be kept to retain the most likely credible/untampered ones in the circumstance when data inconsistency appears. Large-scale experiments show that our scheme ensures on the credibility of data and off the chain with an affordable overhead.

A Fast Hierarchical Physical Topology Update Scheme for Edge-Cloud Collaborative IoT Systems

The awareness of physical network topology in a large-scale Internet of Things (IoT) system is critical to enable location-based service provisioning and performance optimization. However, due to the dynamics and complexity of IoT networks, it is usually very difficult to discover and update the physical topology of the large-scale IoT systems in real-time. Considering the stringent latency requirements in IoT systems, while the initial processing time for topology discovery can be tolerated, latency due to real-time topology update constitutes an even higher level of challenge. In this paper, a novel fast hierarchical topology update scheme is proposed for the large-scale IoT systems enabled by using the edge-cloud collaborative architecture. Specifically, an event-driven neighbor update algorithm, termed as TriggerOn, is firstly developed to update the local neighbor table of the end devices when device association or disassociation occurs. Based on the updated neighbor tables, the physical topology update of the subnet is conducted at the coordinated edge device, where a hybrid multidimensional scaling (MDS) based 3D localization algorithm is developed to locate the newly associated devices. Simulation results have indicated that as compared to the benchmark methods, the neighbor discovery latency has been reduced dramatically, and the 3D localization accuracy has been improved. Furthermore, the overall latency incurred by the proposed hierarchical physical topology update scheme is significantly lower than the distributed consensus-based update scheme, especially for the large-scale IoT subnets.

IoT Big Data provenance scheme using blockchain on Hadoop ecosystem

The diversity and sheer increase in the number of connected Internet of Things (IoT) devices have brought significant concerns associated with storing and protecting a large volume of IoT data. Storage volume requirements and computational costs are continuously rising in the conventional cloud-centric IoT structures. Besides, dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. In this paper, a layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system are proposed. It has been developed to mitigate the above-mentioned challenges by using the Hyperledger Fabric (HLF) platform for distributed ledger solutions. The need for a centralized server and a third-party auditor was eliminated by leveraging HLF peers performing transaction verifications and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata are stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Additionally, a prototype has been implemented on embedded hardware showing the feasibility of deploying the proposed solution in IoT edge computing and big data ecosystems. Finally, experiments have been conducted to evaluate the performance of the proposed scheme in terms of its throughput, latency, communication, and computation costs. The obtained results have indicated the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the Big Data ecosystem using the HLF blockchain. The experimental results show the throughput of about 600 transactions, 500 ms average response time, about 2–3% of the CPU consumption at the peer process and approximately 10–20% at the client node. The minimum latency remained below 1 s however, there is an increase in the maximum latency when the sending rate reached around 200 transactions per second (TPS).

An Efficient On-Demand Hardware Replacement Platform for Metamorphic Functional Processing in Edge-Centric IoT Applications

The paradigm of Internet-of-things (IoT) systems is changing from a cloud-based system to an edge-based system. These changes were able to solve the delay caused by the rapid concentration of data in the communication network, the delay caused by the lack of server computing capacity, and the security issues that occur in the data communication process. However, edge-based IoT systems performance was insufficient to process large numbers of data due to limited power supply, fixed hardware functions, and limited hardware resources. To improve their performance, application-specific hardware can be installed in edge devices, but performance cannot be improved except for specific applications due to a fixed function of an application-specific hardware. This paper introduces an edge-centric metamorphic IoT (mIoT) platform that can use various hardware modules through on-demand partial reconfiguration, despite the limited hardware resources of edge devices. In addition, this paper introduces an RISC-V based metamorphic IoT processor (mIoTP) with reconfigurable peripheral modules. We experimented to prove that the proposed structure can reduce the server access of edges and can be applied to a large-scale IoT system. Experiments were conducted in a single-edge environment and a large-scale environment combining one physical edge and 99 virtual edges. According to the experimental results, the edge-centric mIoT platform that executes the reconfiguration prediction algorithm at the edge was able to reduce the number of server accesses by up to 82.2% compared to our previous study in which the prediction process was executed at the server. Furthermore, we confirmed that there is no additional reconfiguration time overhead even for the large IoT systems.

A Parallel Event System for Large-Scale Cloud Simulations in DISSECT-CF

Discrete Event Simulation (DES) frameworks gained significant popularity to support and evaluate cloud computing environments. They support decision-making for complex scenarios, saving time and effort. The majority of these frameworks lack parallel execution. In spite being a sequential framework, DISSECT-CF introduced significant performance improvements when simulating Infrastructure as a Service (IaaS) clouds. Even with these improvements over the state of the art sequential simulators, there are several scenarios (e.g., large scale Internet of Things or serverless computing systems) which DISSECT-CF would not simulate in a timely fashion. To remedy such scenarios this paper introduces parallel execution to its most abstract subsystem: the event system. The new event subsystem detects when multiple events occur at a specific time instance of the simulation and decides to execute them either on a parallel or a sequential fashion. This decision is mainly based on the number of independent events and the expected workload of a particular event. In our evaluation, we focused exclusively on time management scenarios. While we did so, we ensured the behaviour of the events should be equivalent to realistic, larger-scale simulation scenarios. This allowed us to understand the effects of parallelism on the whole framework, while we also shown the gains of the new system compared to the old sequential one. With regards to scaling, we observed it to be proportional to the number of cores in the utilised SMP host.

Entitlement-Based Access Control for Smart Cities Using Blockchain.

Smart cities use the Internet of Things (IoT) devices such as connected sensors, lights, and meters to collect and analyze data to improve infrastructure, public utilities, and services. However, the true potential of smart cities cannot be leveraged without addressing many security concerns. In particular, there is a significant challenge for provisioning a reliable access control solution to share IoT data among various users across organizations. We present a novel entitlement-based blockchain-enabled access control architecture that can be used for smart cities (and for any ap-plication domains that require large-scale IoT deployments). Our proposed entitlement-based access control model is flexible as it facilitates a resource owner to safely delegate access rights to any entities beyond the trust boundary of an organization. The detailed design and implementation on Ethereum blockchain along with a qualitative evaluation of the security and access control aspects of the proposed scheme are presented in the paper. The experimental results from private Ethereum test networks demonstrate that our proposal can be easily implemented with low latency. This validates that our proposal is applicable to use in the real world IoT environments.

Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things

In this paper, we propose a lightweight physical layer aided authentication and key agreement (PL-AKA) protocol in the Internet of Things (IoT). The conventional evolved packet system AKA (EPS-AKA) used in long-term evolution (LTE) systems may suffer from congestion in core networks by the large signaling overhead as the number of IoT devices increases. Thus, in order to alleviate the overhead, we consider cross-layer authentication by integrating physical layer approaches to cryptography-based schemes. To demonstrate the feasibility of the PL-AKA, universal software radio peripheral (USRP) based tests are conducted as well as numerical simulations. The proposed scheme shows a significant reduction in the signaling overhead, compared to the conventional EPS-AKA in both the simulation and experiment. Therefore, the proposed lightweight PL-AKA has the potential for practical and efficient implementation of large-scale IoT networks.

VIoLET

Internet of Things (IoT) deployments have been growing manifold, encompassing sensors, networks, edge, fog, and cloud resources. Despite the intense interest from researchers and practitioners, most do not have access to large-scale IoT testbeds for validation. Simulation environments that allow analytical modeling are a poor substitute for evaluating software platforms or application workloads in realistic computing environments. Here, we propose a virtual environment for validating Internet of Things at large scales (VIoLET), an emulator for defining and launching large-scale IoT deployments within cloud VMs. It allows users to declaratively specify container-based compute resources that match the performance of native IoT compute devices using Docker. These can be inter-connected by complex topologies on which bandwidth and latency rules are enforced. Users can configure synthetic sensors for data generation as well. We also incorporate models for CPU resource dynamism, and for failure and recovery of the underlying devices. We offer a detailed comparison of VIoLET’s compute and network performance between the virtual and physical deployments, evaluate its scaling with deployments with up to 1, 000 devices and 4, 000 device-cores, and validate its ability to model resource dynamism. Our extensive experiments show that the performance of the virtual IoT environment accurately matches the expected behavior, with deviations levels within what is seen in actual physical devices. It also scales to 1, 000s of devices and at a modest cloud computing costs of under 0.15% of the actual hardware cost, per hour of use, with minimal management effort. This IoT emulation environment fills an essential gap between IoT simulators and real deployments.

A fuzzy based Co-Operative Blackmailing Attack detection scheme for Edge Computing nodes in MANET-IOT environment

Smart cities have been developed with the help of large scale Internet of Things (IoT) device deployment. Under the combination of IoT and traditional Mobile ad-hoc network, the developers could design the communication model for smart city. The increased deployment of IoT devices in smart environment also raised the security and Quality of Service (QoS) related issues. The proposed, Secure approach for Smart city Environment by Accusation based List management (Secure SEAL) primarily explores the various chances of disruption in IoT network caused by co-operative attacks at the edge nodes. The internal co-operative attack is a type of attack which is jointly performed by a group of nodes in IoT network. There is no fine-grained technique to defend against co-operative attacks. To this end, a fuzzy based trustworthy environment is established for reducing the security threats in smart cities with the help of Edge Computing. The trustworthy environment ensures the timely identification of malicious entities and the detection of co-operative attacks. In Edge Computing, the computations required to perform at the end devices can be placed at Edge servers. This will reduce the expected latency and bandwidth utilization required for traditional cloud access. Secure SEAL uses a fuzzy based approach to avoid and isolate the presence of malicious nodes in IoT network. The suspected nodes are re-analyzed based on the reputation value obtained by reaction based trust evaluation. Eventually, the performance of the proposed trustworthy environment has been validated. The proposed work’s efficacy in minimizing the impact of co operative attacks (more than 90%) is comparatively high.

Fog Computing Based on Machine Learning: A Review

<p>Internet of Things (IoT) systems usually produce massive amounts of data, while the number of devices connected to the internet might reach billions by now. Sending all this data over the internet will overhead the cloud and consume bandwidth. Fog computing's (FC) promising technology can solve the issue of computing and networking bottlenecks in large-scale IoT applications. This technology complements the cloud computing by providing processing power and storage to the edge of the network. However, it still suffers from performance and security issues. Thus, machine learning (ML) attracts attention for enabling FC to settle its issues. Lately, there has been a growing trend in utilizing ML to improve FC applications, like resource management, security, lessen latency and power usage. Also, intelligent FC was studied to address issues in industry 4.0, bioinformatics, blockchain and vehicular communication system. Due to the ML vital role in the FC paradigm, this work will shed light on recent studies utilized ML in a FC environment. Background knowledge about ML and FC also presented. This paper categorized the surveyed studies into three groups according to the aim of ML implementation. These studies were thoroughly reviewed and compared using sum-up tables. The results showed that not all studies used the same performance metric except those worked on security issues. In conclusion, the simulations of proposed ML models are not sufficient due to the heterogeneous nature of the FC paradigm.</p>

An optimal cluster-based routing algorithm for lifetime maximization of Internet of Things

Edge computing for Internet of Things (IoT) is a promising framework that can help small devices such as low-powered sensor nodes to accomplish complex computational tasks. The limited power supply of sensor nodes is one of their major limitations. A successful approach to improve the network lifetime and the overall scalability of the IoT supported wireless sensor networks (WSNs) is clustering. However, in a clustered IoT supported WSNs, some of the Cluster Heads (CHs) bear more traffic load than the others and therefore die sooner leading to decrease the network lifetime. To overcome this problem and maximize the network lifetime, the load of the CHs must be balanced. This research work suggests a new clustering method to balance the traffic load imposed on the cluster heads in IoT supported WSNs. The proposed clustering method uses a 1.2-approximation algorithm. In addition, we introduce an energy-aware routing algorithm for transmitting data packets from the CHs to their destination. The proposed routing algorithm distributes the communication load of the data packets among more nodes near the destination by a proper segmentation of the area. The simulation results show that the proposed clustering and routing algorithms in addition to being practical for large-scale IoT supported WSNs, cause the network to have a better performance compared to other similar algorithms.

BlockAIM: A Neural Network-Based Intelligent Middleware For Large-Scale IoT Data Placement Decisions

Current Internet of Things (IoT) infrastructures rely on cloud storage however, relying on a single cloud provider puts limitations on the IoT applications and Service Level Agreement (SLA) requirements. Recently, multiple decentralized storage solutions (e.g., based on blockchains) have entered the market with distinct architecture, Quality of Service (QoS) parameters and at lower price compared to the cloud storage. In this work, we introduce BAM: a neural network-based middleware designed for intelligent selection of storage technology for IoT applications. We first propose a blockchain-based data placement protocol and theoretically model a decision optimization problem, which jointly considers cloud, multi-cloud and decentralized storage technologies to select the appropriate medium to store large-scale IoT data, while ensuring data integrity, traceability, auditability and decision verifiability. We then propose a neural network-based maintenance reconfiguration, which aims to optimize the computational complexity of the middleware design along with the blockchain transaction and storage overhead by learning and predicting the applications parameters. We also propose the aggregation rate feedback functionality in our design and model it as a linear optimization problem to improve data quality and precision. Finally, we provide a reference implementation and perform extensive experiments, which demonstrate the effectiveness of the proposed design.

AirSync: Time Synchronization for Large-Scale IoT Networks Using Aircraft Signals

The prosperity of Internet of Things (IoT) brings forth the deployment of large-scale sensing systems such as smart cities. To enable the collaboration tasks among distributed devices, time synchronization is crucial. However, due to the long-range and device heterogeneity, accurate time synchronization for a large-scale IoT network is challenging. Existing GPS or NTP solutions either require an outdoor environment or only have low and unstable accuracy. In this paper, we propose AirSync, a novel synchronization method that leverages the widely existed aircraft signals, ADS-B, to synchronize large-scale IoT networks with nodes even in indoor environments. But ADS-B messages have no time stamp and cannot provide a reference time. We leverage the continuity of aircraft movements to estimate the aircraft traveling time. Then devices that observe common aircraft moving segments can calculate their time offset. To obtain the time skew, we propose a combined aircraft linear regression method. We also design a transitive synchronization for devices that cannot observe common aircraft. Besides, we also design a duty-cycled ADS-B message collection method for resource-limited IoT devices. We implement a prototype of AirSync and evaluate its performance in various real-world environments. The results show that AirSync can obtain the sub-ms accuracy.

Analysis of IoT-Based Load Altering Attacks Against Power Grids Using the Theory of Second-Order Dynamical Systems

Recent research has shown that large-scale Internet of Things (IoT)-based load altering attacks can have a serious impact on power grid operations such as causing unsafe frequency excursions and destabilizing the grid's control loops. In this work, we present an analytical framework to investigate the impact of IoT-based static/dynamic load altering attacks (S/DLAAs) on the power grid's dynamic response. Existing work on this topic has mainly relied on numerical simulations and, to date, there is no analytical framework to identify the victim nodes from which that attacker can launch the most impactful attacks. To address these shortcomings, we use results from second-order dynamical systems to analyze the power grid frequency control loop under S/DLAAs. We use parametric sensitivity of the system's eigensolutions to identify victim nodes that correspond to the least-effort destabilizing DLAAs. Further, to analyze the SLAAs, we present closed-form expression for the system's frequency response in terms of the attacker's inputs, helping us characterize the minimum load change required to cause unsafe frequency excursions. Using these results, we formulate the defense against S/DLAAs as a linear programming problem in which we determine the minimum amount of load that needs to be secured at the victim nodes to ensure system safety/stability. Extensive simulations conducted using benchmark IEEE-bus systems validate the accuracy and efficacy of our approach.

Brief Paper: Secret Key and Tag Generation for IIoT Systems Based on Edge Computing

Industry 4.0 is continuous automation by applying the latest smart technologies to traditional manufacturing industries. It means that large-scale M2M (Machine-to-Machine) communication and IoT (Internet of Things) technologies are well integrated to build efficient production systems by analyzing and diagnosing various issues without human intervention. Edge computing is widely used for M2M services that handle real-time interactions between devices at industrial machinery tool sites. Here, secure data transmission is required while interacting. Thus, this paper focused on a method of creating and maintaining secret key and security tag used for message authentication between end-devices and edge-device.

Utilizing Coherent Transmission in Cooperative Compressive Sensing in IoT

Utilizing the sparsity of observations, compressive sensing (CS) is a promising technique to observe physical quantities in the Internet of Things (IoT). The existing CS-based IoT networks require the distributed nodes to realize the CS measurements before transmission, which leads to the heavy computation burden at nodes and the low network performance, especially in large-scale IoT networks. To tackle this problem, in this article, we propose a multicluster cooperative CS (CCS) scheme for large-scale IoT networks to observe physical quantities efficiently, which utilizes the cooperative observation and coherent transmission to realize CS measurement. Specifically, CCS-based IoT first utilizes the intracluster cooperation to observe physical quantities and then exploits the intercluster coherent transmission to obtain CS measurements. To evaluate the performance of CCS-based IoT, the upper bound of average recovery error is derived, which is closely associated with the node clustering and the power allocation. Therefore, under the total power constraint, we jointly optimize the node clustering and power allocation to improve recovery performance. With the optimized result, the cooperation gain of the CCS scheme relative to others is derived, which indicates that the CCS scheme is superior as long as total power exceeds a certain threshold. Furthermore, the exact and asymptotic outage probability and average throughput of CCS-based IoT are derived. Finally, simulation results illustrate the correctness of the theoretical analysis.