Internet Of Things Deployments Research Articles

Analysis of the interplay between RPL and the congestion control strategies for CoAP

The Constrained Application Protocol (CoAP) is gaining attention as a standardised RESTful interface for the Internet of Things (IoT). Recent studies have focused on different congestion control strategies for CoAP, in order to ensure proper operation of large-scale IoT deployments. In this paper, we carry out a performance evaluation of different congestion control policies for CoAP in a realistic environment by exploiting WiSHFUL, a platform for large-scale experimentation of network architectures. Our goal is to analyse different congestion control policies and their interplay with the routing protocol in a real environment, where unstable links and route fluctuations are frequent, due to channel variability. The results of our experiments highlight that the dynamics of the routing protocol have a noticeable impact and can impair significantly the performance of the congestion control algorithm. Specifically, the influence of the routing protocol depends on the specific congestion control policy adopted: an aggressive policy that re-transmits messages more frequently, e.g. CoCoA, is more penalised than others, in terms of throughput.

Sensing the City: Designing for Privacy and Trust in the Internet of Things

The push for ‘smart cities’ is seeing increasing interest and investment in public deployments of Internet of Things (IoT) technologies. In many cases, however, these deployments are ‘hidden in plain sight’, with inadequate attention given to the communication of practices regarding the collection, use and sharing of data. This lack of information may impact negatively upon perceptions of trust by the general public, and lead to diminished engagement and comfort with IoT activities. In this paper, we report the results of a survey undertaken in the United Kingdom in November 2018 designed to gain information on perceptions of trust, risk, and informational desires related to public IoT deployments. Findings are expected to contribute to the development of communication practices regarding IoT deployments in smart city environments.

تاثیر فناوری اینترنت اشیاء بر عملکرد بخش ذخیرهسازی زنجیره تامین محصول راهبردی گندم

The wheat supply chain has always been one of the most critical management issues at different times. What distinguishes the wheat supply chain is the importance of factors such as the possibility of endangering the wheat safety and quality and its high volume of waste, especially in the storage sector. Some authors believe that the Internet of Things (IoT) can consider as one of the best solutions to solve agricultural supply chain problems. Therefore, this study aimed to evaluate the impact of IoT deployment on wheat supply chain storage. This research's data collected through distributing questionnaires between 54 companies from the private and public sector in Khorasan Razavi province in 2020. Confirmatory Factor Analysis, One-sample Wilcoxon Test, and Friedman Test was used to analyze of hypothesis. The results show that IoT deployment in the wheat supply chain's storage sector can have several positive consequences in the economy, performance, and marketing area and cost reduction, increase revenue, maintaining quality, improve monitoring, and increase customer satisfaction positively affected. Moreover, the findings of this study indicated that increasing revenue with an average of 3.52 is the most critical impact of IoT deployment. Then, maintaining the wheat quality and customer satisfaction (3.06), improving monitoring (2.71), and cost reductions (2.70) is in the next ranks of IoT impact on the wheat storage sector.

A Model for the Remote Deployment, Update, and Safe Recovery for Commercial Sensor-Based IoT Systems.

Internet of Things (IoT) systems deployments are becoming both ubiquitous and business critical in numerous business verticals, both for process automation and data-driven decision-making based on distributed sensors networks. Beneath the simplicity offered by these solutions, we usually find complex, multi-layer architectures—from hardware sensors up to data analytics systems. These rely heavily on software running on the on-location gateway devices designed to bridge the communication between the sensors and the cloud. This will generally require updates and improvements—raising deployment and maintenance challenges. Especially for large scale commercial solutions, a secure and fail-safe updating system becomes crucial for a successful IoT deployment. This paper explores the specific challenges for infrastructures dedicated to remote application deployment and management, addresses the management challenges related to IoT sensors systems, and proposes a mathematical model and a methodology for tackling this. To test the model’s efficiency, we implemented it as a software infrastructure system for complete commercial IoT products. As proof, we present the deployment of 100 smart soda dispensing machines in three locations. Each machine relies on sensors monitoring its status and on gateways controlling its behaviour, each receiving 133 different remote software updates through our solution. In addition, 80% of the machines ran non-interrupted for 250 days, with 20% failing due to external factors; out of the 80%, 30% experienced temporary update failures due to reduced hardware capabilities and the system successfully performed automatic rollback of the system, thus recovering in 100% of the temporary failures.

Evaluation of an IoT Application-Scoped Access Control Model over a Publish/Subscribe Architecture Based on FIWARE.

The Internet of Things (IoT) brings plenty of opportunities to enhance society’s activities, from improving a factory’s production chain to facilitating people’s household tasks. However, it has also brought new security breaches, compromising privacy and authenticity. IoT devices are vulnerable to being accessed from the Internet; they lack sufficient resources to face cyber-attack threats. Keeping a balance between access control and the devices’ resource consumption has become one of the highest priorities of IoT research. In this paper, we evaluate an access control architecture based on the IAACaaS (IoT application-Scoped Access Control as a Service) model with the aim of protecting IoT devices that communicate using the Publish/Subscribe pattern. IAACaaS is based on the OAuth 2.0 authorization framework, which externalizes the identity and access control infrastructure of applications. In our evaluation, we implement the model using FIWARE Generic Enablers and deploy them for a smart buildings use case with a wireless communication. Then, we compare the performance of two different approaches in the data-sharing between sensors and the Publish/Subscribe broker, using Constrained Application Protocol (CoAP) and Hypertext Transfer Protocol (HTTP) protocols. We conclude that the integration of Publish/Subscribe IoT deployments with IAACaaS adds an extra layer of security and access control without compromising the system’s performance.

Machine Learning Methods for Monitoring of Quasiperiodic Traffic in Massive IoT Networks

One of the central problems in massive Internet of Things (IoT) deployments is the monitoring of the status of a massive number of links. The problem is aggravated by the irregularity of the traffic transmitted over the link, as the traffic intermittency can be disguised as a link failure and vice versa. In this work we present a traffic model for IoT devices running quasi-periodic applications and we present both supervised and unsupervised machine learning methods for monitoring the network performance of IoT deployments with quasi-periodic reporting, such as smart-metering, environmental monitoring and agricultural monitoring. The unsupervised methods are based on the Lomb-Scargle periodogram, an approach developed by astronomers for estimating the spectral density of unevenly sampled time series.

Guest Editorial Special Issue on Edge-Cloud Interplay Based on SDN and NFV for Next-Generation IoT Applications

With significant and continuing advances in information and communication technologies, the Internet of Things (IoT) will play an increasingly important role in domains, such as healthcare, transportation, finance, and energy. In an IoT system, billions of devices (e.g., sensors, wearables, and smart appliances) are connected to the global network infrastructure, and one associated phenomenon is the generation of a large volume of data. Apart from data volume, the velocity, variety, and veracity of these data will pose a significant burden on conventional networking infrastructures. However, as sensor and fifth-generation (5G) cellular technologies advance, so will the pervasiveness of IoT deployment. Parallel to this trend, cloud computing has been integrated with IoT in order to address limitations in existing IoT networks (e.g., storage and computing resources), and examples include Google cloud dataflow and Amazon IoT. However, cloud-centric IoT solutions may not be suited for delay-sensitive and computationally intensive applications, for example, due to resource availability, end-to-end latency, bandwidth, etc. Increasingly, large-scale IoT deployments demand high connectivity, interoperability, and orchestration which are necessary for minimizing latency and maximizing throughput. This highlights the importance of a distributed computing platform that can support the interactions between IoT and cloud computing systems.

A Survey of IoT Security Based on a Layered Architecture of Sensing and Data Analysis.

The Internet of Things (IoT) is leading today’s digital transformation. Relying on a combination of technologies, protocols, and devices such as wireless sensors and newly developed wearable and implanted sensors, IoT is changing every aspect of daily life, especially recent applications in digital healthcare. IoT incorporates various kinds of hardware, communication protocols, and services. This IoT diversity can be viewed as a double-edged sword that provides comfort to users but can lead also to a large number of security threats and attacks. In this survey paper, a new compacted and optimized architecture for IoT is proposed based on five layers. Likewise, we propose a new classification of security threats and attacks based on new IoT architecture. The IoT architecture involves a physical perception layer, a network and protocol layer, a transport layer, an application layer, and a data and cloud services layer. First, the physical sensing layer incorporates the basic hardware used by IoT. Second, we highlight the various network and protocol technologies employed by IoT, and review the security threats and solutions. Transport protocols are exhibited and the security threats against them are discussed while providing common solutions. Then, the application layer involves application protocols and lightweight encryption algorithms for IoT. Finally, in the data and cloud services layer, the main important security features of IoT cloud platforms are addressed, involving confidentiality, integrity, authorization, authentication, and encryption protocols. The paper is concluded by presenting the open research issues and future directions towards securing IoT, including the lack of standardized lightweight encryption algorithms, the use of machine-learning algorithms to enhance security and the related challenges, the use of Blockchain to address security challenges in IoT, and the implications of IoT deployment in 5G and beyond.

Deep Learning Based Resources Allocation for Internet-of-Things Deployment Underlaying Cellular Networks

Resources allocation (RA) is a challenging task in many fields and applications including communications and computer networks. The conventional solutions of such problems usually come with a time and memory cost, especially for massive networks such as Internet-of-Things (IoT) networks. In this paper, two RA deep network models are proposed for enabling a clustered underlay IoT deployment, where a group of IoT nodes are uploading information to a centralized gateway in their vicinity by reusing the communication channels of conventional cellular users. The RA problem is formulated as a two-dimensional matching problem, which can be expressed as a traditional linear sum assignment problem (LSAP). The two proposed models are based on the recurrent neural network (RNN). Specifically, we investigate the performance of two long short-term memory (LSTM) based architectures. The results show that the proposed techniques could be used as replacement of the well-known Hungarian algorithm for solving LSAPs due to its ability to find the solution for the problems with different sizes, high accuracy, and very fast execution time. Additionally, the results show that the obtained accuracy outperforms the state-of-the-art deep network techniques.

UAV-Supported Clustered NOMA for 6G-Enabled Internet of Things: Trajectory Planning and Resource Allocation

The sixth-generation (6G) communication requires supporting massive Internet of Things (IoT) devices and extremely differentiated IoT applications for the air–space–ground integrated network. Relying on the aerial superiority, unmanned aerial vehicle (UAV) is capable of acting as an aerial base station (BS) and supporting IoT deployment in remote and disaster areas. A UAV-supported clustered nonorthogonal multiple access (C-NOMA) system is put forward in this article. Specifically, the UAV provides services to IoT terminals as an aerial BS based on the wireless-powered communication (WPC) technique. According to this system, we propose a synergetic scheme for UAV trajectory planning and subslot allocation. Our goal is to maximize the uplink average achievable sum rate of IoT terminals by synergistically planning UAV trajectory and subslot duration, while guaranteeing the uplink achievable sum rate and the UAV mobility constraints. As the formulated problem suffers nonconvexity and complication, an efficient iterative algorithm is proposed to address it. First, for fixed UAV trajectory, all the terminals are clustered and a subslot allocation algorithm based on the Lagrange multiplier and bisection method is proposed. Then, for a fixed clustering state and subslot duration, we optimize the UAV trajectory. Finally, we solve these two subproblems alternatively until the objective function converges. The effectiveness of the proposed scheme in the UAV-supported C-NOMA system is verified by the numerical results.

An In-Depth Analysis of IoT Security Requirements, Challenges, and Their Countermeasures via Software-Defined Security

Internet of Things (IoT) is transforming everyone's life by providing features, such as controlling and monitoring of the connected smart objects. IoT applications range over a broad spectrum of services including smart cities, homes, cars, manufacturing, e-healthcare, smart control system, transportation, wearables, farming, and much more. The adoption of these devices is growing exponentially, that has resulted in generation of a substantial amount of data for processing and analyzing. Thus, besides bringing ease to the human lives, these devices are susceptible to different threats and security challenges, which do not only worry the users for adopting it in sensitive environments, such as e-health, smart home, etc., but also pose hazards for the advancement of IoT in coming days. This article thoroughly reviews the threats, security requirements, challenges, and the attack vectors pertinent to IoT networks. Based on the gap analysis, a novel paradigm that combines a network-based deployment of IoT architecture through software-defined networking (SDN) is proposed. This article presents an overview of the SDN along with a thorough discussion on SDN-based IoT deployment models, i.e., centralized and decentralized. We further elaborated SDN-based IoT security solutions to present a comprehensive overview of the software-defined security (SDSec) technology. Furthermore, based on the literature, core issues are highlighted that are the main hurdles in unifying all IoT stakeholders on one platform and few findings that emphases on a network-based security solution for IoT paradigm. Finally, some future research directions of SDN-based IoT security technologies are discussed.

The Internet of Things in the Oil and Gas Industry: A Systematic Review

The low oil price environment is driving the oil and gas (O&G) industry to become more innovative and deploy smart field technologies, to increase operational and asset efficiency, minimize health, safety, and environmental (HSE) risks, improve asset portfolio, reduce capital and operation costs, and maximize capital productivity. The Internet of Things (IoT) is at the forefront of this digital transformation, enabling seamless real-time data collection, processing, and analysis from a range of equipment, processes, and operations to achieve these objectives. There are various operations/applications in the upstream, midstream, and downstream sectors (e.g., condition-based monitoring and location tracking) for which IoT-enabled solutions have a significant impact and offer a range of opportunities to increase socioeconomic benefits. However, there are several impediments (e.g., vulnerability to cyber attacks, lower technological readiness for deploying in zone-0 and zone-1 hazardous environments, unavailability of communication infrastructure, labor concerns, and maintenance and obsolescence) that slow the pace of adoption of IoT technologies for regular upstream, midstream, and downstream operations. This review article provides an overview and assessment of the role, impact, opportunities, challenges, and current status of IoT deployment in the O&G industry.

Joint trajectory and power optimization for UAV-relay-assisted Internet of Things in emergency

In emergency situations, existing Internet of Things (IoT) infrastructure is vulnerable to natural disasters and unable to provide reliable services to users in disaster areas. Due to flexible maneuverability and fast deployment, unmanned aerial vehicle (UAV) plays an important role in the IoT deployment in emergency since it can be fast deployed as aerial relay to significantly enhance the communication capacity in disaster areas by designing efficient UAV trajectory. In this paper, a UAV relay communication model for multi-user data transmission is designed. When the communication between the user area and base station (BS) is interrupted, BS first sends information to the UAV, and then the UAV acts as a relay to forward the information to the users. According to the model, a joint UAV trajectory and power allocation scheme is proposed. The goal is to maximize the downlink achievable sum rate of all users by jointly optimizing UAV trajectory and BS/UAV power subject to the UAV mobility and information causality constraints. In view of the non-convexity of the formulated problem, an efficient iterative algorithm is proposed which optimizes the UAV trajectory and the BS/UAV transmit power alternately to obtain the approximately optimal solution to the original problem. Simulation results show that the proposed iterative algorithm can not only optimize the UAV trajectory, but also effectively improve the downlink achievable sum rate compared with the benchmark schemes.

Analysis of Passive RFID Applicability in a Retail Store: What Can We Expect?

The Internet of Things (IoT) has a lot to offer and contribute to the retail industry, from the innovations in retail store experience to the increased efficiency in the store management and supply chain optimization. On its way to real-world applications, Radio Frequency IDentification (RFID) became the main enabler for the final IoT deployment. However, to improve the technology performance even further, it is important to overcome the fundamental limitations of its physical layer and, consequently, to better understand how to use the technology in an optimal way. The analysis provided in this paper employs the simulation/measurement study on RFID technology advancement and the influence of radio propagation in a realistic model of the retail environment. The results are provided for different types of the retail layouts and materials that influence tag responsiveness.

Made-Up Rubbish: Design Fiction as a Tool for Participatory Internet of Things Research

As Internet of Things (IoT) technologies become embedded in public infrastructure, it is important that we consider how they may introduce new challenges in areas such as privacy and governance. Public technology implementations can be more democratically developed by facilitating citizen participation during the design process, but this can be challenging. This work demonstrates a novel method for participatory research considering the privacy implications of IoT deployments in public spaces, through the use of worldbuilding design fictions. Using three fictional contexts and their associated tangible design fiction objects, we report on findings to inform transparency and governance in public space IoT deployments.

Security Architecture for Defining and Enforcing Security Profiles in DLT/SDN-Based IoT Systems.

Despite the advantages that the Internet of Things (IoT) will bring to our daily life, the increasing interconnectivity, as well as the amount and sensitivity of data, make IoT devices an attractive target for attackers. To address this issue, the recent Manufacturer Usage Description (MUD) standard has been proposed to describe network access control policies in the manufacturing phase to protect the device during its operation by restricting its communications. In this paper, we define an architecture and process to obtain and enforce the MUD restrictions during the bootstrapping of a device. Furthermore, we extend the MUD model with a flexible policy language to express additional aspects, such as data privacy, channel protection, and resource authorization. For the enforcement of such enriched behavioral profiles, we make use of Software Defined Networking (SDN) techniques, as well as an attribute-based access control approach by using authorization credentials and encryption techniques. These techniques are used to protect devices’ data, which are shared through a blockchain platform. The resulting approach was implemented and evaluated in a real scenario, and is intended to reduce the attack surface of IoT deployments by restricting devices’ communication before they join a certain network.

Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework.

Cellular broadband Internet of Things (IoT) applications are expected to keep growing year-by-year, generating demands from high throughput services. Since some of these applications are deployed over licensed mobile networks, as long term evolution (LTE), one already common problem is faced: the scarcity of licensed spectrum to cope with the increasing demand for data rate. The LTE-Unlicensed (LTE-U) forum, aiming to tackle this problem, proposed LTE-U to operate in the 5 GHz unlicensed spectrum. However, Wi-Fi is already the consolidated technology operating in this portion of the spectrum, besides the fact that new technologies for unlicensed band need mechanisms to promote fair coexistence with the legacy ones. In this work, we extend the literature by analyzing a multi-cell LTE-U/Wi-Fi coexistence scenario, with a high interference profile and data rates targeting a cellular broadband IoT deployment. Then, we propose a centralized, coordinated reinforcement learning framework to improve LTE-U/Wi-Fi aggregate data rates. The added value of the proposed solution is assessed by a ns-3 simulator, showing improvements not only in the overall system data rate but also in average user data rate, even with the high interference of a multi-cell environment.

An Anonymous Device to Device Authentication Protocol Using ECC and Self Certified Public Keys Usable in Internet of Things Based Autonomous Devices

Two party authentication schemes can be good candidates for deployment in Internet of Things (IoT)-based systems, especially in systems involving fast moving vehicles. Internet of Vehicles (IoV) requires fast and secure device-to-device communication without interference of any third party during communication, and this task can be carried out after registration of vehicles with a trusted certificate issuing party. Recently, several authentication protocols were proposed to enable key agreement in two party settings. In this study, we analyze two recent protocols and show that both protocols are insecure against key compromise impersonation attack (KCIA) as well as both lack of user anonymity. Therefore, this paper proposes an improved protocol that does not only resist KCIA and related attacks, but also offers comparable computation and communication. The security of proposed protocol is tested under formal model as well as using well known Burrows–Abadi–Needham (BAN) logic along with a discussion on security features. While resisting the KCIA and related attacks, proposed protocol also provides comparable trade-of between security features and efficiency and completes a round of key agreement in just 13.42 ms, which makes it a promising candidate to be deployed in IoT environments.

Communications for IoT: Connectivity and Networking

The Internet of Things (IoT) is revolutionizing many industries by enabling machines to directly work with each other without human intervention, and communication and networking technologies are fundamental to enabling it. IoT systems are also creating a significant amount of new data which can now be processed to analyze and learn from using novel machine-learning techniques. However, the requirements for communications can vary significantly depending on the target applications, and they can range from ultra-low power for enabling a vast deployment of sensors with multi-year battery life to ultra-reliable low latency communications (URLLC) for smart factories and remote robot control. 5G, as an example, is expected to deliver a number of enhancements to support massive IoT deployments as well as URLLC capabilities for new verticals such as Smart Cities, Industrial IoT, E-Health, Public Safety, and Autonomous Vehicles (V2V, V2X). The purpose of this Special Issue is to provide researchers and practitioners working on Connectivity and Networking technologies for IoT systems a means to share experiences and disseminate successful applications of IoT technologies and identify recent trends and opportunities poised to revolutionize the IoT industry.

Adoption of Smart Cities with a Practical Smart Building Implementation

Smart cities, driven by the rapid urbanization of the world, are currently a hot topic among decision makers (mayors, city councilors, economic planners, policy makers), as well as industries (communications and cloud computing providers, device manufacturers, big data analytics companies). Within this category, applications for buildings, using automation and wireless communication for efficiency purposes, are prominent examples of this trend. It is especially the case for residential buildings due to the recent trends in population growth of urban areas. This is also known as PropTech. This article discusses the position of the Internet of Things (IoT) in the context of smart cities and presents an example of a practical implementation in a smart building. The realized IoT system uses state-of-the-art technologies, namely cloud computing, virtualization, and automation, for infrastructure management. The authors focus on the monitoring and management aspects of IoT deployment, which are important when delivering solutions with a multitude of gateways and sensors for scalability reasons.