Deployment Of Internet Of Things Devices Research Articles

5G Digital Twin: A Study of Enabling Technologies

5G networks require dynamic network monitoring and advanced security solutions. This work performs the essential steps to implement a basic 5G digital twin (DT) in a warehouse scenario. This study provides a paradigm of end-to-end connection and encryption to internet of things (IoT) devices. Network function virtualization (NFV) technologies are crucial to connecting and encrypting IoT devices. Innovative logistical scenarios are undergoing constant changes in logistics, and higher deployment of IoT devices in logistic scenarios, such as warehouses, demands better communication capabilities. The simulation tools enable digital twin network implementation in planning. Altair Feko (WinProp) simulates the radio behavior of a typical warehouse framework. The radio behavior can be exported as a radio simulation dataset file. This dataset file represents the virtual network’s payload. GNS3, an open-source network simulator, performs data payload transmission among clients to servers using custom NFV components. By transmitting data from client to server, we achieved end-to-end communication. Additionally, custom NFV components enable advanced encryption standard (AES) adoption. In summary, this work analyzes the round-trip time (RTT) and throughput of the payload data packages, in which two data packages, encrypted and non-encrypted, are observed.

IoTaaS: Drone-Based Internet of Things as a Service Framework for Smart Cities

The Internet of Things (IoT) offers new services in the context of smart cities through digital devices embedded with sensing, computation, and communication capabilities. The IoT devices enhance the smart city vision by employing advanced communication and computation technologies for smart city administrations. The IoT-based smart city applications require many IoT devices and gateways to be deployed at different city points. Heterogeneous sensing devices, placing smart devices in a constrained or physically inaccessible area, and large urban areas to monitor together make IoT node deployment and sensing management tasks difficult, time-consuming, and expensive. Additionally, certain tasks may require smart devices to be deployed for a very short period of time to sense and report contextual information, making it economically infeasible to purchase the devices. In this regard, we propose a drone-based IoT as a Service (IoTaaS) framework that enables the dynamic provisioning or deployment of IoT devices using drones. IoTaaS allows IoT devices and gateways to be mounted on drones and provides a distributed cloud service by placing the IoT devices in an area according to the requirements specified by a user. We also provide an economic analysis for operating such drone-based IoT services. A proof-of-concept implementation of IoTaaS for smart agriculture and air pollution monitoring applications shows that IoTaaS can reduce setup costs and increase the usage of IoT devices.

Digital twin-enabled dynamic spatial-temporal knowledge graph for production logistics resource allocation

Production logistics (PL) is increasingly receiving attention from supply chain research. The spatial disorder and temporal asynchrony of the PL resources due to the uncertainty and dynamicity pose great challenges to efficient resource allocation. The inability to obtain and rational use of PL resource spatial–temporal values causes unnecessary long travelling distances and excessive waiting time, which impede the sustainable performance of PL operations. In response, this research proposes a PL resource allocation approach based on the dynamic spatial–temporal knowledge graph (DSTKG). Internet of Things(IoT) signals data generated from large-scale deployed IoT devices are investigated and analysed to spatial–temporal values through deep neural networks. The DSTKG model is established for representing the digital twin replica with spatial–temporal consistency, followed by reasoning and completion of relationships based on PL task information. The PL resources are allocated efficiently through the graph algorithm from the directed and weighted graph. The case study is conducted to verify the feasibility and practicality of the proposed solution based on large-scale deployment. Finally, the result demonstrates the effectiveness of the proposed methodology.

Differential Game Approach for Attack-Defense Strategy Analysis in Internet of Things Networks

Internet of Things (IoT) is vulnerable to various cyber attacks due to the massive deployment of IoT devices and the openness of wireless environments. In this article, taking IoT devices as the network resources competed between an attacker and a defender, we study the modeling and analysis of network resource competition in an attack-defense game. The attacker and defender inject different competition strength in each IoT device as their strategies. As a result, the security state of each IoT device will change, which is captured by differential equations. To study the interaction between the attacker and defender and the evolution of the system security states, a zero-sum differential game is formulated by modeling the competition of IoT devices. To achieve the equilibrium of the formulated differential game, optimal control theory is employed to solve the optimization problems of players. Further, a Gauss–Seidel-like implicit finite-difference method is utilized to obtain the saddle point strategy. Finally, numerical results are provided to demonstrate the evolution of network resource competition between the attacker and defender. The results show that our formulated model can effectively and accurately characterize the evolution of the system security states with strategic interactions between the attacker and defender.

Empowering the Internet of Things Using Light Communication and Distributed Edge Computing

With the rapid growth of connected devices, new issues emerge, which will be addressed by boosting capacity, improving energy efficiency, spectrum usage, and cost, besides offering improved scalability to handle the growing number of linked devices. This can be achieved by introducing new technologies to the traditional Internet of Things (IoT) networks. Visible light communication (VLC) is a promising technology that enables bidirectional transmission over the visible light spectrum achieving many benefits, including ultra-high data rate, ultra-low latency, high spectral efficiency, and ultra-high reliability. Light Fidelity (LiFi) is a form of VLC that represents an efficient solution for many IoT applications and use cases, including indoor and outdoor applications. Distributed edge computing is another technology that can assist communications in IoT networks and enable the dense deployment of IoT devices. To this end, this work considers designing a general framework for IoT networks using LiFi and a distributed edge computing scheme. It aims to enable dense deployment, increase reliability and availability, and reduce the communication latency of IoT networks. To meet the demands, the proposed architecture makes use of MEC and fog computing. For dense deployment situations, a proof-of-concept of the created model is presented. The LiFi-integrated fog-MEC model is tested in a variety of conditions, and the findings show that the model is efficient.

Navigation for UAV Pair‐Supported Relaying in Unknown IoT Systems with Deep Reinforcement Learning

Unmanned aerial vehicles (UAVs) have recently been regarded as a promising technology in Internet of things (IoT). UAVs functioned as intermediate relay nodes are capable of establishing uninterrupted and high-quality communication links between remotely deployed IoT devices and the destination. Multiple UAVs are required to be deployed due to their limited onboard energy. We study a UAV pair-supported relaying in unknown IoT systems, which consists of transmitter and receiver. Our goal is that transmitter gathers the data from each device then transfers the information to receiver, and receiver finally transmits the information to the destination, while meeting the constraint that the amount of information received from each device reaches a certain threshold. This is an optimization problem with highly coupled variables, such as trajectories of transmitter and receiver. On account of no prior knowledge of the environment, a dueling double deep Q network (dueling DDQN) algorithm is proposed to solve the problem. Whether it is in the phase of transmitter’s receiving information or the phase of transmitter’s forwarding information to receiver, the effectiveness and superiority of the proposed algorithm is demonstrated by extensive simulationsin in comparison to some base schemes under different scenarios.

Simultaneous Lightwave and Power Transfer for Internet of Things Devices

A laudable goal toward achieving autonomous internet of things (IoT) devices would be to use the same circuitry for communication and harvesting energy. One way to achieve it is through simultaneous lightwave and power transfer (SLIPT) that consists of using solar cells to harvest energy and receive information signals. Here, a SLIPT-based system that uses a large area solar panel to harvest energy from light sources and decode data signals is designed. The designed system is equipped with an infrared sensor used to detect the movements of an unmanned aerial vehicle. We equally discuss the wide-scale deployment of IoT devices with SLIPT capability.

Harvest-on-Sky: An AoI-Driven UAV-Assisted Wireless Communication System

The 5G cellular network provides a sufficient data rate to support the ever growing number of devices that will be connected to the Internet. To enable Internet of Things (IoT) applications in remote locations, it is very crucial to increase the coverage of the cellular networks. This article proposes a wireless communication system that exploits a full-duplex enabled unmanned aerial vehicle (FD-UAV) integrated into a communication network as a mobile relay to expand network coverage. With the deployment of IoT devices, a large volume of data is introduced into the network, causing a delay in data delivery due to scarcity of resources. Real-time information updates play an important role in modern IoT applications including connected vehicles in intelligent transportation systems, digital healthcare, real-time tracking, and so on. Decision making based on the latest fresh information is critically important for these applications. We quantify the freshness of data in IoT applications using a new performance metric, the age of information (AoI). This work proposes a new mathematical model to estimate the average AoI (AAoI) in the FD-UAV assisted cooperative wireless communication system. Here, sensors located in a transport infrastructure harvest energy from radio frequency (RF) signals transmitted by the FD-UAV via a wireless power transmission RF energy harvesting (EH) technique. Then sensors consume this energy to transmit their sensed real-time observations to the access point with the aid of FD-UAV. A closed-form expression for the AAoI is derived as a function of time allocated for energy harvesting. The optimal time allocation for EH that minimizes the AAoI is identified together with the effect of self-interference experienced by the UAV.

IoT Malware Classification Based on Lightweight Convolutional Neural Networks

Internet of Things (IoT) is hard to deploy adequate security defenses due to the diversity of architectures as well as the limited computing and storage capabilities, which makes it more vulnerable to malware. With the massive deployment of IoT devices, how to accurately identify and classify the malware variants is crucial to IoT security. However, existing methods of IoT malware classification generally support specific platform or require complex models to achieve higher accuracies. To solve these problems, this article proposes an IoT malware classification method based on lightweight convolutional neural networks (LCNNs). First, the malware binaries are converted into multidimensional Markov images. Then, the LCNN is designed with two new operations, depthwise convolution and channel shuffle, for malware images classification. Compared with other deep learning-based methods such as VGG16, the designed LCNN can greatly reduce trainable parameters while maintaining accuracy. The generated model of LCNN is only about 1 MB, while that of VGG16 is 552.57 MB. The average accuracies of the proposed method are higher than that of gray images on multiple IoT malware data sets, all of which are over 95%. Compared with the state-of-the-art low-level features-based methods, the average accuracy of the proposed method is 99.356% on the Microsoft data set even if the model is tiny. The results show that the proposed method is not only suitable for IoT environments but also has high accuracy.

IRECOVer: Patch your IoT on-the-fly

In this paper, we propose a holistic solution for the security management of the Internet of Things (IoT) devices. Following the “security-by-design” principle, we propose RECOnfigurable microserVices-based IoT (iRECOVer), where each IoT device has a fully modular software adopting a Microservices-based architecture. This architecture at the device level leads to a new generation of IoT devices, where an IoT device is composed of a set of programmable functional modules that can be plugged-in/out of the device dynamically. iRECOVer enables patching vulnerable modules of devices by unplugging and replacing them with secure and patched modules on-the-fly, with zero or minimal device downtime. Furthermore, the security management of deployed IoT devices is automated by supporting the remote activation/deactivation of modules inside the device. We also discuss how iRECOVer supports heterogeneous IoT devices by incorporating a custom-designed mitigation solution for each device. These mitigation solutions may consist of modules provided by different vendors or publicly available and verified modules, therefore reducing the development efforts as well as catering for the prevailing lack of security expertise in the IoT industry. We present an experimental evaluation of our solution by measuring the benefits in terms of reduced mitigation time, performance, and impact on device functionality. Our results, when compared with existing approaches, indicate that the proposed solution is able to achieve on-the-fly mitigation of deployed IoT devices in negligible time, while incurring a low CPU load and additionally requiring, on average, only 4.5% of the device’s memory.

Packet Scheduling for Internet of Remote Things (IoRT) Devices in Next Generation Satellite Networks

Background: The massive amount of deployment of Internet of Things (IoT) devices via wireless communications has presented a new paradigm in next-generation mobile networks. The rapid growth in the deployment of IoT devices can be linked to the diverse use of several IoT applications for home automation, smart systems, and other forms of innovations in businesses and industry 4.0. Methods: There is a need for a robust network infrastructure to actualize the huge traffic demand of IoT communications in this new paradigm across the globe, including rural and remote areas. However, due to technical and economic constraints, the terrestrial network infrastructure is not able to fulfill this requirement. Hence, the need for satellite network infrastructure. This solution will be of immense benefit to the provision of remote health care, disaster management, remote sensing, and asset tracking, and environmental monitoring, to name a few. While this remains an interesting solution, packet scheduling, which is one of the key radio resource management functions, is still a challenging issue that remains undefined, especially in a satellite network scenario that has its peculiarities and challenges. Results: Hence, the goal of this research work is to design a new packet scheduling scheme suitable for machine-type communications and also mixed-use case scenarios in satellite network scenarios. The performance evaluation of the proposed packet scheduler is conducted through simulations. Conclusion: The newly proposed packet scheduling scheme provides an improvement of approximately 7 Mbps and 0.5 bps/Hz in terms of throughput and spectral efficiency performances, respectively, in mixed-use case scenarios, when compared to known throughput optimal packet schedulers, without serious compromise to other performance metrics.

Multimedia security and privacy protection in the internet of things: research developments and challenges

With the rapid growth of the internet of things (IoT), huge amounts of multimedia data are being generated from and/or exchanged through various IoT devices, systems and applications. The security and privacy of multimedia data have, however, emerged as key challenges that have the potential to impact the successful deployment of IoT devices in some data-sensitive applications. In this paper, we conduct a comprehensive survey on multimedia data security and privacy protection in the IoT. First, we classify multimedia data into different types and security levels according to application areas. Then, we analyse and discuss the existing multimedia data protection schemes in the IoT, including traditional techniques (e.g., cryptography and watermarking) and emerging technologies (e.g., blockchain and federated learning). Based on the detailed analysis on the research development of IoT-related multimedia security and privacy protection, we point out some open challenges and provide future research directions, aiming to advance the study in the relevant fields and assist researchers in gaining a deeper understanding of the state of the art on multimedia data protection in the IoT.

Network Management System for IoT Based on Dynamic Systems.

The Internet of Things (IoT) has the potential to transform the public sector by combining the leading technical and business trends of mobility, automation, and data analysis to dramatically alter the way public bodies collect data and information. Embedded sensors, actuators, and other devices that capture and transmit information about network activity in real-time are used in the Internet of Things to connect networks of physical objects. The design of a network management system for an IoT network is presented in this paper, which uses the edge computing model. This design is based on the Internet management model, which uses the SNMP protocol to communicate between managed devices, and a gateway, which uses the SOAP protocol to communicate with a management application. This work allowed for the identification and analysis of the primary network management system initiatives for IoT networks, in which there are four fundamental device management requirements for any deployment of IoT devices: provisioning and authentication, configuration and control, monitoring and diagnostics, and software updates and maintenance.

Formal security analysis of lightweight authenticated key agreement protocol for IoT in cloud computing

The internet of things (IoT) and cloud computing are evolving technologies in the information technology field. Merging the pervasive IoT technology with cloud computing is an innovative solution for better analytics and decision-making. Deployed IoT devices offload different types of data to the cloud, while cloud computing converges the infrastructure, links up the servers, analyzes information obtained from the IoT devices, reinforces processing power, and offers huge storage capacity. However, this merging is prone to various cyber threats that affect the IoT-Cloud environment. Mutual authentication is considered as the forefront mechanism for cyber-attacks as the IoT-Cloud participants have to ensure the authenticity of each other and generate a session key for securing the exchanged traffic. While designing these mechanisms, the constrained nature of the IoT devices must be taken into consideration. We proposed a novel lightweight protocol (Light-AHAKA) for authenticating IoT-Cloud elements and establishing a key agreement for encrypting the exchanged sensitive data was proposed. In this paper, the formal verification of (Light-AHAKA) was presented to prove and verify the correctness of our proposed protocol to ensure that the protocol is free from design flaws before the deployment phase. The verification is performed based on two different approaches, the strand space model and the automated validation of internet security protocols and applications (AVISPA) tool.

Identifying and Mitigating Phishing Attack Threats in IoT Use Cases Using a Threat Modelling Approach.

Internet of things (IoT) is a technology that enables our daily life objects to connect on the Internet and to send and receive data for a meaningful purpose. In recent years, IoT has led to many revolutions in almost every sector of our society. Nevertheless, security threats to IoT devices and networks are relentlessly disruptive, because of the proliferation of Internet technologies. Phishing is one of the most prevalent threats to all Internet users, in which attackers aim to fraudulently extract sensitive information of a user or system, using fictitious emails, websites, etc. With the rapid increase in IoT devices, attackers are targeting IoT devices such as security cameras, smart cars, etc., and perpetrating phishing attacks to gain control over such vulnerable devices for malicious purposes. In recent decades, such scams have been spreading, and they have become increasingly advanced over time. By following this trend, in this paper, we propose a threat modelling approach to identify and mitigate the cyber-threats that can cause phishing attacks. We considered two significant IoT use cases, i.e., smart autonomous vehicular system and smart home. The proposed work is carried out by applying the STRIDE threat modelling approach to both use cases, to disclose all the potential threats that may cause a phishing attack. The proposed threat modelling approach can support the IoT researchers, engineers, and IoT cyber-security policymakers in securing and protecting the potential threats in IoT devices and systems in the early design stages, to ensure the secure deployment of IoT devices in critical infrastructures.

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.

Design and Implementation of an Energy-Efficient Weather Station for Wind Data Collection

Agriculture faces critical challenges caused by changing climatic factors and weather patterns with random distribution. This has increased the need for accurate local weather predictions and weather data collection to support precision agriculture. The demand for uninterrupted weather stations is overwhelming, and the Internet of Things (IoT) has the potential to address this demand. One major challenge of energy constraint in remotely deployed IoT devices can be resolved using weather stations that are energy neutral. This paper focuses on optimizing the energy consumption of a weather station by optimizing the data collected and sent from the sensor deployed in remote locations. An asynchronous optimization algorithm for wind data collection has been successfully developed, using the development lifecyle specifically designed for weather stations and focused on achieving energy neutrality. The developed IoT weather station was deployed in the field, and it has the potential to reduce the power consumption of the weather station by more than 60%.

Hash-MAC-DSDV: Mutual Authentication for Intelligent IoT-Based Cyber-Physical Systems.

Cyber-Physical Systems (CPS) connected in the form of Internet of Things (IoT) are vulnerable to various security threats, due to the infrastructure-less deployment of IoT devices. Device-to-Device (D2D) authentication of these networks ensures the integrity, authenticity, and confidentiality of information in the deployed area. The literature suggests different approaches to address security issues in CPS technologies. However, they are mostly based on centralized techniques or specific system deployments with higher cost of computation and communication. It is therefore necessary to develop an effective scheme that can resolve the security problems in CPS technologies of IoT devices. In this paper, a lightweight Hash-MAC-DSDV (Hash Media Access Control Destination Sequence Distance Vector) routing scheme is proposed to resolve authentication issues in CPS technologies, connected in the form of IoT networks. For this purpose, a CPS of IoT devices (multi-WSNs) is developed from the local-chain and public chain, respectively. The proposed scheme ensures D2D authentication by the Hash-MAC-DSDV mutual scheme, where the MAC addresses of individual devices are registered in the first phase and advertised in the network in the second phase. The proposed scheme allows legitimate devices to modify their routing table and unicast the one-way hash authentication mechanism to transfer their captured data from source towards the destination. Our evaluation results demonstrate that Hash-MAC-DSDV outweighs the existing schemes in terms of attack detection, energy consumption and communication metrics.

PyFF: A Fog-Based Flexible Architecture for Enabling Privacy-by-Design IoT-Based Communal Smart Environments.

The advent of the Internet of Things (IoT) and the massive growth of devices connected to the Internet are reshaping modern societies. However, human lifestyles are not evolving at the same pace as technology, which often derives into users’ reluctance and aversion. Although it is essential to consider user involvement/privacy while deploying IoT devices in a human-centric environment, current IoT architecture standards tend to neglect the degree of trust that humans require to adopt these technologies on a daily basis. In this regard, this paper proposes an architecture to enable privacy-by-design with human-in-the-loop IoT environments. In this regard, it first distills two IoT use-cases with high human interaction to analyze the interactions between human beings and IoT devices in an environment which had not previously been subject to the Internet of People principles.. Leveraging the lessons learned in these use-cases, the Privacy-enabling Fog-based and Flexible (PyFF) human-centric and human-aware architecture is proposed which brings together distributed and intelligent systems are brought together. PyFF aims to maintain end-users’ privacy by involving them in the whole data lifecycle, allowing them to decide which information can be monitored, where it can be computed and the appropriate feedback channels in accordance with human-in-the-loop principles.

WITHDRAWN: Type-II fuzzy based clustering protocol for energy harvesting Internet of things

Abstract To deploy smart and sustainable environment, Internet of things (IoT) devices play a significant role. However, IoT devices are battery constrained and therefore, may become dead after passing some particular period of time. In this paper, we have considered different energy harvesting IoT devices and to minimize the energy consumption of these IoT devices, a Type-II fuzzy based clustering protocol is proposed. Type-II fuzzy membership function is utilized to elect cluster heads following by which the Dijkstra based inter-cluster data aggregation is used. Extensive experiments are considered by randomly deploying IoT devices. Comparative analysis indicates that the proposed protocol significantly outperforms the competitive techniques in terms of various performance metrics.