Internet Of Things Nodes Research Articles

Secure data access using blockchain technology through IoT cloud and fabric environment

AbstractIn the current landscape, staying abreast of the latest technological advancements is a formidable challenge, especially given the deluge of data inundating the internet. The realization has dawned that effectively managing the surge in emerging data necessitates the integration of multiple technologies. In pursuit of this objective, the Internet of Things (IoT), renowned for its sensor‐based data capture capabilities, is frequently coupled with blockchain technology to ensure secure data storage and access. This amalgamation, in turn, leverages the cloud environment when data volume surpasses a machine's processing capacity, thus mitigating infrastructure and maintenance costs. This study endeavors to optimize data storage within the blocks of the blockchain (BCT) by storing an index that points to the actual data. This innovative approach not only conserves storage space but also enhances operational efficiency. Furthermore, it simplifies the task of identifying malicious or faulty nodes deployed at different locations for data capture within the prescribed time frame. To exemplify this implementation, a case study is presented, focusing on securing user votes through the creation of contracts. The results showcased underscore the preference for a permissioned blockchain, such as Fabric, over a permissionless one, like Ethereum, particularly in the context of security considerations. The findings reveal that as the number of operations (in this case, votes cast) increases, Ethereum's performance deteriorates, while Fabric exhibits exceptional robustness. Additionally, the study analyzes sensor data simulated via IoT nodes before and after the application of security algorithms to underscore the significance of the proposed Secure Cloud‐Based Blockchain (SCB2) model. The analysis encompasses various facets, including the creation, validation, and computation times of transactions and blocks within a node, and positions the model favorably in comparison to existing literature.

A Fully Privacy-Preserving Solution for Anomaly Detection in IoT using Federated Learning and Homomorphic Encryption

AbstractAnomaly detection for the Internet of Things (IoT) is a very important topic in the context of cyber-security. Indeed, as the pervasiveness of this technology is increasing, so is the number of threats and attacks targeting smart objects and their interactions. Behavioral fingerprinting has gained attention from researchers in this domain as it represents a novel strategy to model object interactions and assess their correctness and honesty. Still, there exist challenges in terms of the performance of such AI-based solutions. The main reasons can be alleged to scalability, privacy, and limitations on adopted Machine Learning algorithms. Indeed, in classical distributed fingerprinting approaches, an object models the behavior of a target contact by exploiting only the information coming from the direct interaction with it, which represents a very limited view of the target because it does not consider services and messages exchanged with other neighbors. On the other hand, building a global model of a target node behavior leveraging the information coming from the interactions with its neighbors, may lead to critical privacy concerns. To face this issue, the strategy proposed in this paper exploits Federated Learning to compute a global behavioral fingerprinting model for a target object, by analyzing its interactions with different peers in the network. Our solution allows the training of such models in a distributed way by relying also on a secure delegation strategy to involve less capable nodes in IoT. Moreover, through homomorphic encryption and Blockchain technology, our approach guarantees the privacy of both the target object and the different workers, as well as the robustness of the strategy in the presence of attacks. All these features lead to a secure fully privacy-preserving solution whose robustness, correctness, and performance are evaluated in this paper using a detailed security analysis and an extensive experimental campaign. Finally, the performance of our model is very satisfactory, as it consistently discriminates between normal and anomalous behaviors across all evaluated test sets, achieving an average accuracy value of 0.85.

A cost-effective LoRaWAN-based IoT localization method using fixed reference nodes and dual-slope path-loss modeling

Recently, RSSI-based localization has gained popularity for outdoor localization within the IoT ecosystem due to its cost-effectiveness, low-power and low-cost deployment, and ability to operate loT nodes for years on a single battery. However, this approach typically sacrifices accuracy, resulting in location estimates in the tens or hundreds of meters. For example, LoRaWAN communications have been widely used in IoT applications, being an attractive solution due to their long-range coverage, low implementation cost, and higher autonomy. However, RSSI-based LoRaWAN location methods suffer from multipath and fading interference which results in the near-far problem, impacting ranging accuracy for short and long distances, and thus leading to an overall decrease in the localization accuracy. In this article, we address the low accuracy of RSSI based localization challenge for outdoor IoT node localization by computing the Path Loss (PL) parameters of LoRaWAN, for short and long distances separately, thus providing a dual-slope PL model. In addition, some IoT nodes in specific locations are adopted as Reference Nodes (RNs), whose main task is to estimate the interference effect on the transmitted signals dynamically. The proposed model has been evaluated using a publicly available LoRaWAN dataset collected in urban areas in the city of Antwerp, Belgium, which serves as a benchmark for the evaluation of the results. Its effectiveness is assessed by simulation and comparison to the state-of-the-art. In addition, results are compared with the derived Cramer–Rao Lower Bound (CRLB). The localization error achieves a median error of 117 m and a mean error of 236 m.

EACH-COA: An Energy-Aware Cluster Head Selection for the Internet of Things Using the Coati Optimization Algorithm

In recent years, the Internet of Things (IoT) has transformed human life by improving quality of life and revolutionizing all business sectors. The sensor nodes in IoT are interconnected to ensure data transfer to the sink node over the network. Owing to limited battery power, the energy in the nodes is conserved with the help of the clustering technique in IoT. Cluster head (CH) selection is essential for extending network lifetime and throughput in clustering. In recent years, many existing optimization algorithms have been adapted to select the optimal CH to improve energy usage in network nodes. Hence, improper CH selection approaches require more extended convergence and drain sensor batteries quickly. To solve this problem, this paper proposed a coati optimization algorithm (EACH-COA) to improve network longevity and throughput by evaluating the fitness function over the residual energy (RER) and distance constraints. The proposed EACH-COA simulation was conducted in MATLAB 2019a. The potency of the EACH-COA approach was compared with those of the energy-efficient rabbit optimization algorithm (EECHS-ARO), improved sparrow optimization technique (EECHS-ISSADE), and hybrid sea lion algorithm (PDU-SLno). The proposed EACH-COA improved the network lifetime by 8–15% and throughput by 5–10%.

Learning autoencoder ensembles for detecting malware hidden communications in IoT ecosystems

Modern IoT ecosystems are the preferred target of threat actors wanting to incorporate resource-constrained devices within a botnet or leak sensitive information. A major research effort is then devoted to create countermeasures for mitigating attacks, for instance, hardware-level verification mechanisms or effective network intrusion detection frameworks. Unfortunately, advanced malware is often endowed with the ability of cloaking communications within network traffic, e.g., to orchestrate compromised IoT nodes or exfiltrate data without being noticed. Therefore, this paper showcases how different autoencoder-based architectures can spot the presence of malicious communications hidden in conversations, especially in the TTL of IPv4 traffic. To conduct tests, this work considers IoT traffic traces gathered in a real setting and the presence of an attacker deploying two hiding schemes (i.e., naive and “elusive” approaches). Collected results showcase the effectiveness of our method as well as the feasibility of deploying autoencoders in production-quality IoT settings.

Trust Based Mechanism for Isolation of Malicious Nodes in Internet of Things

The Internet of Things systems are prone to the attacks as they have ad-hoc and limited resource structure. IoT-based systems are utilized for managing a large volume of information and assist in services related to industrial and medical applications. Due to this, the IoT attains becomes a target for a multitude of attackers and adversaries namely occasional hackers, cybercriminals, hacktivists, government, etc. The major goal of potential attackers is to steal the sensitive information such as credit card numbers, location data, credential of financial account and information related to health, by hacking the Internet of Things devices. The version number attack is one of malicious activity of IoT which affect network performance to great extend. The version number attack is triggered by the malicious nodes which can flood unlimited hello packets in the network. The hello flood attack raised situation of denial of service in the network. The trust based mechanism is proposed in this research work in which trust value is assigned to each node based on their activities. The node which is least trusted will be marked as malicious and get isolated from the network. The proposed scheme is implemented in NS2 and results are analyzed in terms of throughput, packetloss, energy consumption and delay.

Multi-Domain Resource Multiplexing Based Secure Transmission for Satellite-Assisted IoT: AO-SCA Approach

Due to the wireless broadcasting and broad coverage in satellite-supported Internet of things (IoT) networks, the IoT nodes are susceptible to eavesdropping threats. Considering the distance difference between satellite and nearby destinations is negligible, the main and wiretapping channels between satellite and IoT node are similar, it poses great challenges to reach physical layer security in satellite-assisted IoT networks. In this paper, to guarantee secure transmissions for satellite-assisted IoT downlink communications, the multi-domain resource multiplexing based secure approach is proposed. Particularly, the self-induced co-channel interference between adjacent nodes is leveraged to increase the difference of signal transmission quality over both main and wiretapping channels. By comprehensively optimizing multi-domain resources, i.e., frequency, power, and spatial domains, secure transmissions from satellite to IoT nodes are reached. Specifically, the problem to maximize the sum secrecy rate of IoT nodes is formulated with a constraint of common communication rate of IoT nodes. To solve this non-convex problem, an alternating optimization (AO) algorithm with two inner successive convex approximation (SCA) algorithms are executed to solve the power allocation, spectral multiplexing, and precoding. In addition, simulation results are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.

An improved energy-efficient cloud-optimized load-balancing for IoT frameworks

As wireless communication grows, so does the need for smart, simple, affordable solutions. The need prompted academics to develop appropriate network solutions ranging from wireless sensor networks (WSNs) to the Internet of Things (IoT). With the innovations of researchers, the necessity for enhancements in existing researchers has increased. Initially, network protocols were the focus of study and development. Regardless, IoT devices are already being employed in different industries and collecting massive amounts of data through complicated applications. This necessitates IoT load-balancing research. Several studies tried to address the communication overheads produced by significant IoT network traffic. These studies intended to control network loads by evenly spreading them across IoT nodes. Eventually, the practitioners decided to migrate the IoT node data and the apps processing it to the cloud. So, the difficulty is to design a cloud-based load balancer algorithm that meets the criteria of IoT network protocols. Defined as a unique method for controlling loads on cloud-integrated IoT networks. The suggested method analyses actual and virtual host machine needs in cloud computing environments. The purpose of the proposed model is to design a load balancer that improves network response time while reducing energy consumption. The proposed load balancer algorithm may be easily integrated with peer-existing IoT frameworks. Handling the load for cloud-based IoT architectures with the above-described methods. Significantly boosts response time for the IoT network by 60 %. The proposed scheme has less energy consumption (31 %), less execution time (24\\%), decreased node shutdown time (45 %), and less infrastructure cost (48\\%) in comparison to existing frameworks. Based on the simulation results, it is concluded that the proposed framework offers an improved solution for IoT-based cloud load-balancing issues.

Internet of things-blockchain integration: a robust data security approach for end-to-end communication

<span>The integration of internet of things (IoT) and blockchain technology has been proposed as a promising solution to address the challenges related to security, scalability, and privacy. This integration enables the creation of secure and decentralized systems that allow devices to interact with each other and exchange data in a transparent and trustworthy manner. The significant research work has been carried out by researcher on IoT and blockchain integration to utilize their combine potential in smart applications. However, due to limitation and scope of emerging technologies, it is essential to develop robust security frameworks that address the challenges and ensure the secure data communication from IoT node to blockchain network. IoT devices come up with constrained nature and limited security mechanisms along with weak access control policy. Our work includes heterogeneous IoT and blockchain ecosystem that senses and store data intelligently after proper validation with verification into decentralized distributed blockchain network. Our proposed work includes a robust approach to convey potential data security issue to protect end to end information with suitable authorization, access control policy, device to device data protection and data integrity specially when data moving from IoT devices to blockchain network.</span>

Outage Analysis of UAV-Aided Networks With Underlaid Ambient Backscatter Communications

Ambient backscatter communication is an energy efficient technique for massive Internet of Things (IoT). Combining with flexibly deployed unmanned aerial vehicles (UAVs), the UAV-aided ambient backscatter communication can establish wireless links for isolated IoT nodes efficiently. In this paper, we investigate the outage performance of the UAV-aided air-ground network with underlaid ambient backscatter communications, where the emitted signals from the air-ground link are leveraged as radio frequency (RF) carrier for ambient backscattering. The air-ground channel is modeled as a probabilistic line-of-sight (LoS) channel with Nakagami- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> fading. Then, the ground communication is modeled as a non-line-of-sight (NLoS) channel with Rayleigh fading. For the downlink, we derive the expressions of the outage probabilities of the backscatter link and the air-ground link. In addition, the asymptotic cases of infinite transmit power and infinite fading parameter are analyzed. For the uplink, the outage probabilities of the backscatter link and air-ground are analyzed, with the cases of infinite transmit power and fading parameter discussed. Simulation results show that the analytical results match well with the Monte Carlo results, which verifies the effectiveness of the proposed scheme.

Trust based Mechanism for Isolation of Malicious Nodes in Internet of Things

The Internet of Things systems are prone to the attacks as they have ad-hoc and limited resource structure. IoT-based systems are utilized for managing a large volume of information and assist in services related to industrial and medical applications. Due to this, the IoT attains becomes a target for a multitude of attackers and adversaries namely occasional hackers, cybercriminals, hacktivists, government, etc. The major goal of potential attackers is to steal the sensitive information such as credit card numbers, location data, credential of financial account and information related to health, by hacking the Internet of Things devices. The version number attack is one of malicious activity of IoT which affect network performance to great extend. The version number attack is triggered by the malicious nodes which can flood unlimited hello packets in the network. The hello flood attack raised situation of denial of service in the network. The trust based mechanism is proposed in this research work in which trust value is assigned to each node based on their activities. The node which is least trusted will be marked as malicious and get isolated from the network. The proposed scheme is implemented in NS2 and results are analyzed in terms of throughput, packetloss, energy consumption and delay.

Data-similarity-based IoT node selection for UAV trajectory optimization

Data collected from densely deployed IoT sensor nodes via UAVs may incur remarkable redundancy, unnecessarily wasting the UAV power. This paper introduces a framework of UAV trajectory planning for optimizing energy efficiency via data-similarity-based node selection while maintaining sufficient information integrity out of the refined data. The proposed framework consists of three phases, namely data similarity determination, redundant nodes removal, and UAV trajectory planning. In particular, we propose a sliding-window dynamic time warping (SDTW) algorithm to quantify the data similarity between nodes. Then a hybrid genetic ant colony algorithm (HGACA) is introduced for the redundant node removal, where data similarity and UAV energy consumption are jointly considered. Finally, we formulate the trajectory planning problem as a three-stage integer linear programming (ILP) model, which clusters the nodes with minimal overlap and finds the shortest UAV cruising path that traverses each cluster head once and only once. The simulation result demonstrates that the proposed framework outperforms all the considered counterparts under various threshold values of data similarity in terms of execution time and power consumption while maintaining information integrity.

Solar Powered IoT Sensors to Increase the Network Longevity

A wide variety of industries, such as the manufacturing business, the transportation industry, the environmental industry, and even the medical industry, are beginning to comprehend the significance of wireless sensor networks. This problem is being further amplified by the rapidly increasing use of devices connected to the Internet of Things across a wide variety of enterprises. Because of this, having an independent source of energy is essential because it determines the adaptability of placement and the simplicity of maintenance, both of which are essential to the widespread adoption of the technology, the successful application of the technology, and the long-term viability of the technology. In this paper we enable a solar powered IoT node communication using energy management system. Having an independent source of energy is essential because it determines the adaptability of placement and the simplicity of maintenance. The creation of energy-aware wireless sensor nodes has become increasingly feasible as a result of recent advancements in fields such as the manufacturing of a large number of highly efficient energy converters and the reduction of energy consumption in hardware, software, and communication protocols.

An ensemble learning framework for the detection of RPL attacks in IoT networks based on the genetic feature selection approach

The rapid proliferation of Internet of Things (IoT) devices has raised critical concerns regarding the security of corresponding IoT networks. The Routing Protocol for Low-Power and Lossy Networks (RPL), a foundational element in IoT communication, is susceptible to diverse routing attacks due to IoT nodes’ constrained resources and open nature. This underscores the necessity for an Intrusion Detection System (IDS) to safeguard RPL-based IoT networks. Existing anomaly-based IDS suffer from high false alarm rates (FAR). In response to these challenges, this paper presents the Ensemble Learning-based Intrusion Detection System (ELG-IDS), which employs stacking and extreme parameter optimization to detect three RPL internal attacks: version number, decreased rank, and DIS flooding attacks. ELG-IDS employs enhanced feature extraction and genetic algorithm (GA)-based feature selection. Experimental results on a dedicated dataset demonstrate ELG-IDS's remarkable accuracy: 99.18%, 99.38%, 99.66%, and 97.90% for version number, rank attack, DIS flooding, and an average accuracy of 97.90% in multi-classification mode, respectively. This study advances IoT network security through ELG-IDS, enhancing protection against evolving security challenges.

An Enhanced Lightweight Security Gateway Protocol for the Edge Layer

With the rapid expansion of the Internet of Things (IoT), the necessity for lightweight communication is also increasing due to the constrained capabilities of IoT devices. This paper presents the design of a novel lightweight protocol called the Enhanced Lightweight Security Gateway Protocol (ELSGP) based on a distributed computation model of the IoT layer. This model introduces a new type of node called a sub-server to assist edge layer servers and IoT devices with computational tasks and act as a primary gateway for dependent IoT nodes. This paper then introduces six features of ELSGP with developed algorithms that include access token distribution and validation, authentication and dynamic interoperability, attribute-based access control, traffic filtering, secure tunneling, and dynamic load distribution and balancing. Considering the variability of system requirements, ELSGP also outlines how to adopt a system-defined policy framework. For fault resiliency, this paper also presents fault mitigation mechanisms, especially Trust and Priority Impact Relation for Byzantine, Cascading, and Transient faults. A simulation study was carried out to validate the protocol’s performance. Based on the findings from the performance evaluation, further analysis of the protocol and future research directions are outlined.

An innovative approach for dynamic key dependent S-Box to enhance security of IoT systems

The Internet of Things (IoT) foresees pervasive and connected smart nodes that interact independently while offering varied services. Today, IoT nodes are a treasure house of data and hence security has become a major concern in IoT networks. Conventional cryptography faces many challenges during implementation in IoT devices. These challenges can be tackled by lightweight cryptography which is highly secure with less resource requirements. The vital characteristic of security is fulfilled by one out of 6 inner structures such as Substitution-Permutation Network, Feistel Network, General Feistel Network, Add-Rotate-XOR, Non Linear-Feedback Shift Register and Hybrid. PRESENT, which is a NIST approved lightweight cryptographic algorithm with SPN structure uses fixed or static S-Box. This permits attackers to explore S-Box and identify weaknesses of the algorithm. Key dependent approaches are better because it impedes an attacker from any offline analyses of attacks on the S-Box. Additionally, this is susceptible to linear and differential attacks. The static S Box does not possess superior diffusion and does not have a good rate of avalanche effect. So, the solution is to create a robust dynamic key dependent algorithm with nonlinear S-Boxes to secure the communication between IoT devices and platform. Security analysis is done on Non-linearity, Differential and Linear Cryptanalysis and Avalanche characteristics, besides, encryption of IoT data to demonstrate its aptness for cold chain security applications.© 2017 Elsevier Inc. All rights reserved.

QoS-Aware and Energy Data Management in Industrial IoT

Two crucial challenges in Industry 4.0 involve maintaining critical latency requirements for data access and ensuring efficient power consumption by field devices. Traditional centralized industrial networks that provide rudimentary data distribution capabilities may not be able to meet such stringent requirements. These requirements cannot be met later due to connection or node failures or extreme performance decadence. To address this problem, this paper focuses on resource-constrained networks of Internet of Things (IoT) systems, exploiting the presence of several more powerful nodes acting as distributed local data storage proxies for every IoT set. To increase the battery lifetime of the network, a number of nodes that are not included in data transmission or data storage are turned off. In this paper, we investigate the issue of maximizing network lifetime, and consider the restrictions on data access latency. For this purpose, data are cached distributively in proxy nodes, leading to a reduction in energy consumption and ultimately maximizing network lifetime. To address this problem, we introduce an energy-aware data management method (EDMM); with the goal of extending network lifetime, select IoT nodes are designated to save data distributively. Our proposed approach (1) makes sure that data access latency is underneath a specified threshold and (2) performs well with respect to network lifetime compared to an offline centralized heuristic algorithm.

RETRACTED ARTICLE: Improvement of short video transmission effect based on IoT node technology

RETRACTED ARTICLE: Improvement of short video transmission effect based on IoT node technology

Energy efficient routing using adaptive elephant herding optimization for IoT-WSN

WSNs(Wireless Sensor Networks) has been developed with applications in many domains including agriculture, telecommunication, manufacturing industry, healthcare, and surveillance. More specifically, WSN plays a pivotal role in IoT (Internet of Things). The IoT sensors provide information about the physical phenomena in the deployed fields. As the sensors contain only limited resources, the factors like data processing, power consumption, transmission, and storage capabilities adversely affect the efficiency. Thus, the process of routing is necessary for network longevity. The data from IoT-based sensors is routed to the destination through a multi-hop routing system. The Energy aware Routing is motivated by the nature inspired Fuzzy Butterfly Optimization (E2RFBOA). Further a new data aggregation method is introduced in this article customized for IoT based WSN to acquaint higher crop yield in precision farming. Nevertheless, the scalability becomes a primary concern when deployed in larger and denser networks. This is due to the fact that all nodes in IoT and WSN are mostly alive depending on higher usage of bandwidth and power. The primal aim is to build a novel routing protocol developed for IoT-WSN. Apart from this, an Energy aware Clustered Routing that is motivated by Adaptive Elephant Herding Optimization (E2CR-AEHO) is proposed, which sensors collect data and find a group of Cluster Heads (CHs). In the AEHO Algorithm, the formed CH is rotated depending on power consumption. This also prevents frequent re-clustering; at the same time it can effectively adapt to the changes in network topology. According to the AEHOA, the node population comprises of nodes that can choose its CHs among the other nodes. This algorithm takes into account a number of criteria, including power consumption, residual power of Sensor Nodes (SN), network reliability, and data reliability. The suggested approach can efficiently represent the network environment, allowing the routing algorithm to avoid passing over marked zones. Network-specific performances measures including PDRs (Packet Delivery Ratios), NLs (Network Lifetimes), PLRs (Packet Loss Ratios), and AE2E (Average End To End) delay are used to evaluate simulation outcomes. This proposed framework aggregates IoT, which can gradually reduce the amount of data, hence extending network lifetime.

A Constellation Diagram Learning-Based Adaptive Sparse Nonorthogonal Wavelet Division Multiplexing for Sonar Image Underwater Acoustic Transmission

In underwater Internet of Things (IoT) applications, efficient and reliable transmission is demanding. Recently, the non-orthogonal multicarrier (MC) modulation is utilized as a promising spectrum-efficient solution in the UWA channel, where generally, the constellation mapping (CM) methods (such as 8-PSK and 16-QAM) are utilized to further increase the transmission rate. In this paper, we focus on the transmission of sonar images among IoT nodes and a constellation diagram learning-based adaptive sparse non-orthogonal wavelet division multiplexing (CDLA-SNOWDM) system is proposed to combat complex channel states between IoT nodes. We found that different CM methods contribute to unique time-frequency characteristics in constellation diagrams. We first utilize the time-frequency characteristics in designing the non-orthogonal subcarriers. The complex-valued CM signals are regarded as a new type of image. We introduce dictionary learning (DL) to non-orthogonal subcarrier designing, with the idea that designing CM signal adaptive non-orthogonal subcarriers can be regarded as designing an adaptive subcarrier dictionary. The CDLA-SNOWDM modulation is performed as a projection of the CM image to the subcarriers with an adaptive dictionary. The learned adaptive subcarriers can represent constellation diagrams more efficiently and improve transmission reliability. Both simulations and experiments show that the proposed scheme improved the reliability in transmitting sonar images over other orthogonal and non-orthogonal modulation schemes in UWA scenarios.