Internet Of Things Nodes Research Articles

A Novel Approach for Classification in Resource-Constrained Environments

Internet of Things’ (IoT) deployments are increasingly dependent upon learning algorithms to analyse collected data, draw conclusions, and take decisions. The norm is to deploy such learning algorithms on the cloud and have IoT nodes interact with the cloud. While this is effective, it is rather wasteful in terms of energy expended and temporal latency. In this article, the endeavour is to develop a technique that facilitates classification, an important learning algorithm, within the extremely resource constrained environments of IoT nodes. The approach comprises selecting a small number of representative data points, called prototypes, from a large dataset and deploying these prototypes over IoT nodes. The prototypes are selected in a manner that they appropriately represent the complete dataset and are able to correctly classify new, incoming data. The novelty lies in the manner of prototype selection for a cluster that not only considers the location of datapoints of its own cluster but also that of datapoints in neighboring clusters. The efficacy of the approach is validated using standard datasets and compared with state-of-the-art classification techniques used in constrained environments. A real world deployment of the technique is done over an Arduino Uno-based IoT node and shown to be effective.

A Fuzzy-Based Method for Objects Selection in Blockchain-Enabled Edge-IoT Platforms Using a Hybrid Multi-Criteria Decision-Making Model

The broad availability of connected and intelligent devices has increased the demand for Internet of Things (IoT) applications that require more intense data storage and processing. However, cloud-based IoT systems are typically located far from end-users and face several issues, including high cloud server load, slow response times, and a lack of global mobility. Some of these flaws can be addressed with edge computing. In addition, node selection helps avoid common difficulties related to IoT, including network lifespan, allocation of resources, and trust in the acquired data by selecting the correct nodes at a suitable period. On the other hand, the IoT’s interconnection of edge and blockchain technologies gives a fresh perspective on access control framework design. This article provides a novel node selection approach for blockchain-enabled edge IoT that provides a quick and dependable node selection. Moreover, fuzzy logic to approximation logic was used to manage numerical and linguistic data simultaneously. In addition, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a powerful tool for examining Multi-Criteria Decision-Making (MCDM) problems, is used. The suggested fuzzy-based technique employs three input criteria to select the correct IoT node for a given mission in IoT-edge situations. The outcomes of the experiments indicate that the proposed framework enhances the parameters under consideration.

Double Layered Priority based Gray Wolf Algorithm (PrGWO-SK) for safety management in IoT network through anomaly detection

For mitigating and managing risk failures due to Internet of Things (IoT) attacks, many Machine Learning (ML) and Deep Learning (DL) solutions have been used to detect attacks but mostly suffer from the problem of high dimensionality. The problem is even more acute for resource starved IoT nodes to work with high dimension data. Motivated by this problem, in the present work a priority based Gray Wolf Optimizer is proposed for effectively reducing the input feature vector of the dataset. At each iteration all the wolves leverage the relative importance of their leader wolves’ position vector for updating their own positions. Also, a new inclusive fitness function is hereby proposed which incorporates all the important quality metrics along with the accuracy measure. In a first, SVM is used to initialize the proposed PrGWO population and kNN is used as the fitness wrapper technique. The proposed approach is tested on NSL-KDD, DS2OS and BoTIoT datasets and the best accuracies are found to be 99.60%, 99.71% and 99.97% with number of features as 12,6 and 9 respectively which are better than most of the existing algorithms.

Adaptive Multiuser Cooperative NOMA Scheme With Energy Buffer-Aided Near-Users for High Spectral and Energy Efficiency

In this article, a multiuser cooperative nonorthogonal multiple access (NOMA) network is considered. In order to improve their battery lifetime, the near-user (NU) Internet of Things (IoT) nodes relay information to the far-user (FU) IoT nodes using only the energy harvested from the ambience. The harvested energy at all the NU IoT nodes is stored in energy buffers. To improve the throughput performance, the best NU and best FU (BNBF) user selection scheme is employed. Moreover, unlike conventional orthogonal multiple access (OMA) networks, an adaptive NOMA network is considered in this article that switches between direct NOMA, NOMA with relaying, and OMA modes to maximize throughput. To improve accuracy and reduce the computational complexity, the energy buffer states at the NU IoT nodes are modeled using a continuous state-space Markov chain (CSMC) instead of discrete state-space Markov chain (DSMC). We derive the limiting distributions of the stored energy in energy buffers with either the best effort policy (BEP) or the on–off policy (OOP) applied for buffer energy management. Expressions for throughput are derived for both harvest-store-use (HSU) and harvest-use (HU) architectures. The Monte Carlo simulations are used to validate the derived analytical expressions.

Learning-Based UAV Path Planning for Data Collection With Integrated Collision Avoidance

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectory in multi-UAV noncooperative scenarios while collecting data from distributed Internet of Things (IoT) nodes is a challenging task. In this article, we consider a path-planning optimization problem to maximize the collected data from multiple IoT nodes under realistic constraints. The considered multi-UAV noncooperative scenarios involve a random number of other UAVs in addition to the typical UAV, and UAVs do not communicate or share information among each other. We translate the problem into a Markov decision process (MDP) with parameterized states, permissible actions, and detailed reward functions. Dueling double deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (D3QN) is proposed to learn the decision-making policy for the typical UAV, without any prior knowledge of the environment (e.g., channel propagation model and locations of the obstacles) and other UAVs (e.g., their missions, movements, and policies). The proposed algorithm can adapt to various missions in various scenarios, e.g., different numbers and positions of IoT nodes, different amount of data to be collected, and different numbers and positions of other UAVs. Numerical results demonstrate that real-time navigation can be efficiently performed with high success rate, high data collection rate, and low collision rate.

A Deep Autoencoder Trust Model for Mitigating Jamming Attack in IoT Assisted by Cognitive Radio

This article proposes deep learning (DL) framework constructed using deep autoencoder (DAE) to detect the malicious nodes in an Internet of Things (IoT) network assisted by the cognitive radio (CR) technology. In the IoT era, a plethora of nodes are connected to the network for the purpose of collecting and exchanging data. CR technology finds its role in IoT applications because of its ability to efficiently exploit the available spectrum. In this article, we consider IoT nodes as secondary users that perform cooperative spectrum sensing (CSS). Specifically, these IoT nodes sense the spectrum and send their reports to the fusion center (FC) to determine whether the spectrum is occupied by the primary user or not. In such a scenario, the existence of malicious IoT nodes might mislead the FC. To determine which of these IoT nodes are benevolent and which are malicious is crucial. We adopt a DAE-based DL framework, called DAE-TRUST, to detect malicious nodes in CR-assisted IoT. The proposed DAE-TRUST is able to identify the malicious nodes, whose reports can be excluded from the spectrum detection process. Simulation results, in six different real-world environments, show that our DAE-TRUST enhances the performance of CSS in IoT applications.

Sustainability Analysis of Opportunistic CR-IoT Network Employing Microwave Power Transfer

In this paper, we analyze a Cognitive Radio-based Internet-of-Things (CR-IoT) network comprising a Primary Network Provider (PNP) and an IoT operator. The IoT operator opportunistically accesses the licensed band to serve its IoT nodes. We employ the Microwave Power Transfer technique to harvest energy at IoT nodes for its superior energy transfer efficiency over long-distance. Due to the imperfect detection process, the IoT operator may jeopardize the PNP’s transmissions. To reduce the possibility of interference, the IoT operator may decide not to utilize a portion of perceived white space. Rest are used for data transmission and energy replenishment to maintain a balance between the average interference inflicted on PNP’s users and the Quality-of-Service (QoS) experienced by IoT nodes. The CR-IoT network is sustainable if the QoS requirements are satisfied and IoT nodes are not depleted of energy. We use Discrete Time Markov Chain method to model this framework. In contrast to the generic assumption that activities of PNP and IoT operator are mutually exclusive, our model incorporates possible overlaps between their activities to make it more realistic. Using our model, the sustainability region of the network can be obtained. The accuracy of our analysis is demonstrated via extensive simulation.

Medley: A Membership Service for IoT Networks

Efficient and correct operation of an IoT network requires the presence of a failure detector and membership protocol amongst the IoT nodes. This paper presents a new failure detector for IoT settings wherein nodes are connected via a wireless ad-hoc network. Our failure detector, named Medley, is fully decentralized, allows IoT nodes to maintain a local membership list of other alive nodes, detects failures quickly (and updates the membership list), and incurs low communication overhead. We adapt a failure detector originally proposed for datacenters (SWIM), for the IoT environment. This adaptation is non-trivial. In Medley each node picks a medley of ping targets in a randomized and skewed manner, preferring nearer nodes. We also provide optimizations to achieve time-bounded detection, as well as to reduce tail detection times. Via analysis, simulation, and Raspberry Pi deployments, we show that Medley can simultaneously optimize detection time and communication traffic.

Data Analytics and Modeling in IoT-Fog Environment for Resourceconstrained IoT-Applications: A Review

Introduction: The emergence of the concepts like Big Data, Data Science, Machine Learning (ML), and the Internet of Things (IoT) in recent years has added the potential of research in today's world. The continuous use of IoT devices, sensors, etc. that collect data continuously is putting tremendous pressure on the existing IoT network. Materials and Methods: This resource-constrained IoT environment is flooded with data acquired from millions of IoT nodes deployed at the device level. The limited resources of the IoT Network have driven the researchers towards data Management. This paper focuses on data classification at the device level, edge/fog level, and cloud level using machine learning techniques. Results: The data coming from different devices is vast and is of variety. Therefore, it becomes essential to choose the right approach for classification and analysis. This will help in optimizing the data at the device, edge/fog level for better performance of the network in the future. Conclusion: This paper presents data classification, machine learning approaches, and a proposed mathematical model for the IoT environment.

Queue-Aware Access Prioritization for Massive Machine-Type Communication

One of the pivotal services of the fifth generation (5G) of cellular technology is massive Machine-type Communications (mMTCs), which is intended to support connecting high density of machine-type devices (MTDs). Random access channel of long-term evolution (LTE)/LTE advanced needs to be modified in order to support the large simultaneous arrival of MTDs. 3GPP suggested access class barring (ACB) as a mechanism to inhibit network congestion in the mMTC or massive Internet of Things (IoT) scenario. Consequently, in devices that are repeatedly ignored by ACB, the queue of data packets keeps growing. In storage-constrained IoT nodes with limited buffer, this may lead to packet drop due to buffer overflow, causing a decline in the overall throughput of the system. To address this issue, a novel queue-aware prioritized access classification (QPAC)-based ACB technique is proposed in this article, where MTDs having data queue size close to its buffer limit are dynamically given higher priority in ACB. To study the queue build-up at each MTC device, a node-centric analysis of ACB in the buffer-constrained scenario is performed using a 2-D Markov chain. It is shown that the proposed QPAC scheme, with optimal model parameters obtained by maximizing overall system utility, offers up to 70% gain in throughput compared to the nearest competitive dynamic ACB scheme.

Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects.

The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.

Trusted Computing and Privacy Protection Method for Computer IoT Nodes Based on Fuzzy Logic Blockchain

With the continuous growth of the social economy, the trusted computing and privacy protection methods of IoT nodes are constantly innovating and changing, from the traditional single IoT node protection method to the new interactive IoT nodes supplemented by various network resources. That is, the item information server encrypts the detailed information of the item layer by layer with the session key of the adjacent node according to the node sequence of the response path. By completing the complete interactive design and the protection knot in the unique illusory environment, the application and discussion of privacy protection is achieved in the context of Internet of Things, creating a design concept for trusted computing and privacy protection methods for IoT nodes, and discussing the rationality of interactive design in the process of trusted computing and privacy protection methods for IoT nodes. The simulation results show that the application of fuzzy logic blockchain is very extensive, and the trusted computing and privacy protection methods of IoT nodes have a broader platform and a more open environment.

Resilient Deployment of Smart Nodes for Improving Confident Information Coverage in 5G IoT

The development of 5G has brought new opportunities for the application of Internet of Things (IoT). The integration of 5G and IoT technologies promote high availability, resilience, and reliability of the network infrastructures. IoT deployment optimization is the core issue of 5G IoT. Traditionally, IoT node deployment methods mostly used disk coverage model or probabilistic detection coverage model, which only utilizes the sensing capability of a single IoT node, which results in higher deployment costs. In this article, we study the network resilience of coverage estimation error and solve the coverage problem of resilient deployment of smart nodes in 5G IoT. The coverage formulation in the deployment optimization method is defined based on the confident information coverage (CIC). In order to obtain the optimal deployment with a given coverage quality and with a given budget, the mixed-integer linear programming models (CICILP-COST) and (CICILP-ERROR) are proposed based on the CIC model. After analyzing the model complexity, the proposed models are solved by the variable relaxation algorithm (CICVR-COST) and dichotomous search algorithm (CICDS-ERROR), respectively. Simulations on air pollution datasets in Lyon, France, show that the proposed model yields a lower cost optimal deployment than existing peer schemes.

Riding the Waves Towards Generic Single-Cycle Masking in Hardware

Research on the design of masked cryptographic hardware circuits in the past has mostly focused on reducing area and randomness requirements. However, many embedded devices like smart cards and IoT nodes also need to meet certain performance criteria, which is why the latency of masked hardware circuits also represents an important metric for many practical applications.The root cause of latency in masked hardware circuits is the need for additional register stages that synchronize the propagation of shares. Otherwise, glitches would violate the basic assumptions of the used masking scheme. This issue can be addressed to some extent, e.g., by using lightweight cryptographic algorithms with low-degree Sboxes, however, many applications still require the usage of schemes with higher-degree S-boxes like AES. Several recent works have already proposed solutions that help reduce this latency yet they either come with noticeably increased area/randomness requirements, limitations on masking orders, or specific assumptions on the general architecture of the crypto core.In this work, we introduce a generic and efficient method for designing single-cycle glitch-resistant (higher-order) masked hardware of cryptographic S-boxes. We refer to this technique as (generic) Self-Synchronized Masking (“SESYM”). The main idea of our approach is to replace register stages with a partial dual-rail encoding of masked signals that ensures synchronization within the circuit. More concretely, we show that WDDL gates and Muller C-elements can be used in combination with standard masking schemes to design single-cycle S-box circuits that, especially in case of higher-degree S-boxes, have noticeably lower requirements in terms of area and online randomness. We apply our method to DOM-based S-boxes of Ascon and AES and compare the resulting circuits to existing latency optimized circuits based on TI, GLM, and LMDPL. The latency of all three designs is reduced to single-cycle operation and are dth-order secure. Compared to GLM-masked Ascon, our approach comes with a 6.4 times reduction in online randomness for all protection orders. Compared to 1st-order LMDPL-masked AES, our approach achieves comparable results, while it is more generic, amongst others, by also supporting higher-order designs. We also underline the practical protection of our constructions against power analysis attacks via empirical and formal verification approaches.

An approach for data integrity authentication and protection in fog computing

The data integrity verification process in cloud has become more promising research area in several Internet of Things (IoT) applications. The traditional data verification approaches use encryption in order to preserve data. Moreover, fog computing is considered as extensively employed virtualized platform and it affords various services including storage as well as services interconnected to computing and networking between user and data center based on standard cloud computing. Moreover, fog computing is an extensive description of cloud computing. Thus, fog servers effectively decrease the latency by integrating fog servers. In this paper, novel model for data integrity authentication and protection is designed in IoT cloud-fog model. This method mainly comprises fog nodes, cloud server, IoT nodes, and key distribution center. Here, dynamic and secure key is produced based on the request to key distribution center based on hashing, Exclusive OR (XOR), homomorphic encryption and polynomial. The fog nodes are employed to encrypt the data gathered from IoT nodes as well as allocate the nearby nodes based on Artificial Bee Colony-based Fuzzy-C-Means (ABC FCM) – based partitioning approach. The proposed data integrity authentication approach in IoT fog cloud system outperformed than other existing methods with respect to detection rate, computational time and memory usage of 0.8541, 34.25 s, and 54.8 MB, respectively.

Fault tolerant data offloading in opportunistic fog enhanced IoT architecture

Internet of Things (IoT) is characterized by the large volumes of data collection. Since IoT devices are themselves resource-constrained, this data is transferred to cloud-based systems for further processing. This data collected over a period of time possesses high utility as it is useful for multiple analytical, predictive and prescriptive tasks. Therefore, it is crucial that IoT devices transfer the collected data to network gateways before exhausting their storage to prevent loss of data; this issue is referred to as the “data offloading problem”. This paper proposes a technique for fault tolerant offloading of data by IoT devices such that the data collected by them is transferred to the cloud with a minimal loss. The proposed technique employs opportunistic contacts between IoT and mobile fog nodes to provide a fault tolerant enhancement to the IoT architecture. The effectiveness of the proposed method is verified through simulation experiments to assess the reduction in data loss by use of proposed data offloading scheme. It is demonstrated that the method outperforms a state-of-art method.

SEHIDS: Self Evolving Host-Based Intrusion Detection System for IoT Networks.

The Internet of Things (IoT) offers unprecedented opportunities to access anything from anywhere and at any time. It is, therefore, not surprising that the IoT acts as a paramount infrastructure for most modern and envisaged systems, including but not limited to smart homes, e-health, and intelligent transportation systems. However, the prevalence of IoT networks and the important role they play in various critical aspects of our lives make them a target for various types of advanced cyberattacks: Dyn attack, BrickerBot, Sonic, Smart Deadbolts, and Silex are just a few examples. Motivated by the need to protect IoT networks, this paper proposes SEHIDS: Self Evolving Host-based Intrusion Detection System. The underlying approach of SEHIDS is to equip each IoT node with a simple Artificial Neural Networks (ANN) architecture and a lightweight mechanism through which an IoT device can train this architecture online and evolves it whenever its performance prediction is degraded. By this means, SEHIDS enables each node to generate the ANN architecture required to detect the threats it faces, which makes SEHIDS suitable for the heterogeneity and turbulence of traffic amongst nodes. Moreover, the gradual evolution of the SEHIDS architecture facilitates retaining it to its near-minimal configurations, which saves the resources required to compute, store, and manipulate the model’s parameters and speeds up the convergence of the model to the zero-classification regions. It is noteworthy that SEHIDS specifies the evolving criteria based on the outcomes of the built-in model’s loss function, which is, in turn, facilitates using SEHIDS to develop the two common types of IDS: signature-based and anomaly-based. Where in the signature-based IDS version, a supervised architecture (i.e., multilayer perceptron architecture) is used to classify different types of attacks, while in the anomaly-based IDS version, an unsupervised architecture (i.e., replicator neuronal network) is used to distinguish benign from malicious traffic. Comprehensive assessments for SEHIDS from different perspectives were conducted with three recent datasets containing a variety of cyberattacks targeting IoT networks: BoT-IoT, TON-IOT, and IoTID20. These results of assessments demonstrate that SEHIDS is able to make accurate predictions of 1 True Positive and is suitable for IoT networks with the order of small fractions of the resources of typical IoT devices.

Misbehavior of nodes in IoT based vehicular delay tolerant networks VDTNs

Misbehavior of nodes in IoT based vehicular delay tolerant networks VDTNs

Artificial neural network and symmetric key cryptography based verification protocol for 5G enabled Internet of Things

AbstractDriven by the requirements for entirely low communication latencies, high bandwidths, reliability and capacities, the Fifth Generation (5G) networks has been deployed in a number of countries. One of the most prevalent application scenarios of 5G networks is the Internet of Things (IoT) that can potentially boost convenience and energy savings. However, the information exchanged over the open wireless 5G networks is susceptible to numerous attacks such as malicious modifications. Although many protocols have been developed to protect against these attacks, the provision of optimum security and privacy issues in 5G networks is still an open challenge. This is attributed to the high device density, frequent handovers and resource constrained nature the 5G IoT nodes. In this article, a network selection and authentication protocol that securely verifies the authenticity of all the communicating entities is presented. The network selection is accomplished using Artificial Neural Network (ANN) for increased efficiency. In addition, all the security tokens are independently derived at the end devices without the involvement of any central authority. Formal security analysis based on the Burrows–Abadi–Needham (BAN) logic shows that all the terminals securely authenticate each other before the onset of packet exchanges. In addition, it is shown that this protocol thwarts majority of the conventional 5G attack vectors and is robust under the Dolev–Yao (DY) threat model. Moreover, a comparison with other related schemes shows that the proposed protocol offers many adorable security features at relatively low communication and computation costs. The simulation results show that the deployed ANN yields low packet loss ratio and latency variations.

Application of embedded Linux in the design of Internet of Things gateway

AbstractTo study the development trend of embedded platforms in home appliances Internet of Things (IoT), an application of embedded Linux in the design of IoT gateway is designed. ARM microprocessor S3C2410, EM310 radio frequency (RF) module, and Ethernet DM9000A module are used to realize the home network information-processing platform. The platform utilizes existing network facilities, RF modules, and network interface modules to realize the interconnection of the home perception network through wireless or wired links. For experimental analysis, Ping the central server (gateway) from the IoT node B is set to twice: The first time ttl = 255 time = 1.9 ms, the second time: ttl = 255 time = 2.4 ms. After application verification, the system has the characteristics of low cost, small size, low power consumption, and vital ease of use. It can not only integrate the home Internet and the IoT gateway but also directly run application-layer communication protocols such as HTTP/MQTT between IoT nodes and Internet terminals. The design supports multi-hop communication and significantly increases the deployment range of IoT nodes without signal relay equipment, hence reducing the cost of IoT networking and the complexity of the network architecture.