IoT-based Applications Research Articles

Opinion on Different Classification Algorithms Used in Internet of Things Environment for Large Data Set

Nowadays, IoT is an emerging technique and has evolved in many areas such as healthcare, smart homes, agriculture, smart city, education, industries, automation, etc. Many sensor and actuator-based devices deployed in these areas collect data or sense the environment. This data is further used to classify the complicated problem related to the particular environment around us, which also increases efficiency, productivity, accuracy and the economic benefit of the devices. The main aim of this survey article is how the data collected by these sensors in the Internet of Things-based applications are handled and classified by classification algorithms. This survey article also identifies various classification algorithms such as KNN, Random forest logistic regression, SVM with different parameters, such as accuracy cross validation, etc., applied on the large dataset generated by sensor-based devices in various IoT-based applications to classify it. In addition, this article also gives a brief review on advance IoT called CIoT.

Selective Authentication Based Geographic Opportunistic Routing in Wireless Sensor Networks for Internet of Things Against DoS Attacks

Wireless Sensor Networks (WSNs) have been widely used as the communication system in the Internet of Things (IoT). In addition to the services provided by WSNs, many IoT-based applications require reliable data delivery over unstable wireless links. To guarantee reliable data delivery, existing works exploit geographic opportunistic routing with multiple candidate forwarders in WSNs. However, these approaches suffer from serious Denial of Service (DoS) attacks, where a large number of invalid data are deliberately delivered to receivers to disrupt the normal operations of WSNs. In this paper, we propose a selective authentication-based geographic opportunistic routing (SelGOR) to defend against the DoS attacks, meeting the requirements of authenticity and reliability in WSNs. By analyzing statistic state information (SSI) of wireless links, SelGOR leverages an SSI-based trust model to improve the efficiency of data delivery. Unlike previous opportunistic routing protocols, SelGOR ensures data integrity by developing an entropy-based selective authentication algorithm, and is able to isolate DoS attackers and reduce the computational cost. Specifically, we design a distributed cooperative verification scheme to accelerate the isolation of attackers. This scheme also makes SelGOR avoid duplicate data transmission and redundant signature verification resulting from opportunistic routing. The extensive simulations show that SelGOR provides reliable and authentic data delivery, while it only consumes 50% of the computational cost compared to other related solutions.

Case Study The Internet of Things and Ethics

The Internet of Things (IoT) may be defined as a network of networks, where the end devices are not user-handled devices but can be computing devices, mechanical and digital machines. In many businesses, IoT-based software is used increasingly as a means to deliver enhanced customer service and improved business management procedures. By using IoT to monitor business operations, through tracking-capable software, businesses are, for instance, able to track products and employees. The issue is further explored through literature review and a case study of a company developing IoT based monitoring software.The review focuses on the effects of using IoT as part of Smart Information Systems, especially systems supported by 5G networks in the near future. The effects on the users of SIS are referred to by the term Quality of Experience (QoE) and the specific effects of 5G networks on QoE are discussed in this background review. Since the user experience is also affected by such actions as employee and asset monitoring with the use of IoT, a brief overview of legal aspects follows the technological details of QoE in an IoT-aware 5G system. The legal/human rights analysis is presented through the literature, and takes into account some suggestions for guidelines and policies on monitoring is offered. A discussion on ethics and perceptions around monitoring and tracking is further presented.The CRM.COM case focuses thereafter on how the company provides tracking software as a service and as a product for businesses nationally and in several countries worldwide. The case study discusses the ethics of such IoT-powered software, by considering both their design and their usage.Overall, the area of using IoT-based tracking and monitoring applications to assist and enhance specific business processes is growing and becoming increasingly popular, both in terms of development and use. Being a new research area, however, it lacks sufficient literature that examines the ethical, social, economic and legal implications of the use of this technology. Such studies into the design, development and use of such IoT-based applications present important relevant information that enriches the state-of-the-art literature on the topic both from an academic and a practical perspective.This report offers an original case study on the use of an IoT related SIS in the software design and development area. Many of the ethical and legal issues discussed in this report have been analysed more generally within academia and assessed in other areas of application, but have rarely been associated with the IoT usage for tracking and monitoring. Therefore, this report will be highly valuable for the development and furthering of theory, knowledge and application for designing, developing and using such IoT based applications.

Guest Editorial Special Issue on Real-Time Data Processing for Internet of Things

With the development of the Internet of Things (IoT), various large-scale real-time data processing applications for handling real-time sensor data are becoming one of the important applications in cloud computing. The academia, the industry, and even the government institutions have already begun to pay close attention to how to efficiently process large amounts of sensor data in real-time using cloud computing technology. Although cloud computing technology has attracted much attention with high-performance, there are strong needs for improving data processing efficiency of large-scale real-time data for IoT-based applications. In addition to this, currently the IoT paradigm is facing increasing difficulty to handle the data generated from IoT applications. As a result of this, it is challenging to ensure low latency and network bandwidth consumption, optimal utilization of computational recourses, scalability, security, and energy efficiency of IoT devices while moving all data to the cloud. Therefore, this centralized computing model is starting to shift to a decentralized model termed as edge computing, that allows data to be handled from the cloud to local devices such as smartphones, smart gateways or routers, local PCs or sensor nodes on a smaller scale in real-time.

Building IoT based applications for Smart Cities: How can ontology catalogs help?

The Internet of Things (IoT) plays an ever-increasing role in enabling Smart City applications. An ontology-based semantic approach can help improve interoperability between a variety of IoT-generated as well as complementary data needed to drive these applications. While multiple ontology catalogs exist, using them for IoT and smart city applications require significant amount of work. In this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? We consider four ontology catalogs that are relevant for IoT and smart cities: READY4SmartCities, LOV, OpenSensingCity (OSC) and, LOV4IoT. To support semantic interoperability with the reuse of ontology-based smart city applications, we present a methodology to enrich ontology catalogs with those ontologies. Our methodology is generic enough to be applied to any other domains as is demonstrated by its adoption by OSC and LOV4IoT ontology catalogs. Researchers and developers have completed a survey based evaluation of the LOV4IoT catalog. The usefulness of ontology catalogs ascertained through this evaluation has encouraged their ongoing growth and maintenance. The quality of IoT and smart city ontologies have been evaluated to improve the ontology catalog quality. We also share the lessons learned regarding ontology best practices and provide suggestions for ontology improvements with a set of software tools.

A Topology Control with Energy Balance in Underwater Wireless Sensor Networks for IoT-Based Application.

As part of the IoT-based application, underwater wireless sensor networks (UWSN), which are typically self-organized heterogeneous wireless network, are one of the research hot-spots using various sensors in marine exploration and water environment monitoring application fields, recently. Due to the serious attenuation of radio in water, acoustic or hybrid communication is a usual way for transmitting information among nodes, which dissipates much more energy to prevent the network failure and guarantee the quality of service (QoS). To address this issue, a topology control with energy balance, namely TCEB, is proposed for UWSN to overcome time-delay and other interference, as well as make the entire network load balance. With the given underwater network model and its specialized energy consumption model, we introduce the non-cooperative-game-based scheme to select the nodes with better performance as the cluster-heads. Afterwards, the intra-cluster and inter-cluster topology construction are, respectively, to form the effective communication links of the intra-cluster and inter-cluster, which aim to build energy-efficient topology to reduce energy consumption. With the demonstration of the simulation, the results show the proposed TCEB has better performance on energy-efficiency and throughput than three other representative algorithms in complex underwater environments.

Decentralized detection of hybrid faults in mobile sensor nodes

The widespread use of sensor nodes that are operating under the Internet of Things paradigm motivates researcher to step forward and build reliable systems capable of detecting their faulty nodes. These nodes lead to decrease in the accuracy and functionality of the networks, which finally result in quality degradation of network services. From the temporal point of view, the faults can be either permanent or intermittent. The detection of the latter one is more challenging since the nodes show contradictory behaviors at different times. from the topological point of view, the mobility of the sensor nodes is an intrinsic characteristic in many IoT-based applications, where numerous mobile nodes are managed by static overlay nodes. The dynamics of these network introduces the second challenge in identifying faulty nodes. Several works have been conducted to address the problem, but there is a research gap in identifying hybrid soft sensor faults in the aforementioned networks. The focus of attention in this paper is the detection of soft faults in the sensing unit of the nodes. We devised a new method, called Hybrid Fault Detection in Mobile Sensors, to detect nodes with mixed permanent and intermittent faults. A software debugging approach inspired the main idea. We also applied data mining techniques such as DBSCAN and K-means to validate sensed data, and differentiate the classes of faults, respectively. We evaluated the devised method using the NS2 simulator in various situations. One of the outcomes of the method is that the mobility of the nodes does not reduce the accuracy, in contrast to most of the traditional methods. Moreover, the evaluation demonstrates promising results for the networks with more than 50% faulty nodes. The results also show perfect performance in detecting permanent and intermittent faults in the networks with various percentage of faulty nodes.

IoT Services and Virtual Objects Management in Hyperconnected Things Network

In recent past, Internet of Things- (IoT-) based applications have experienced tremendous growth in various domains, and billions of devices are expected to be connected to the Internet in near future. The first step for development of IoT-based applications is to virtualize the physical devices by abstracting device properties in virtual objects. Later, these virtual objects can be combined to compose different services for diverse applications. Many existing systems provide virtualization service for physical devices and service composition. But, with the growth of the network, when too many devices and services are added in the IoT network, its management will become a cumbersome task. This paper presents an architecture of IoT services and virtual objects management in hyperconnected things network to facilitate the management tasks. We also have implemented a Service and Virtual Objects Management (SVOM) system prototype to effectively organize and monitor the physical devices through corresponding virtual objects and services composed in the IoT environment. The proposed system also provides interface for user interaction to perform supported control operations on selected device and check device operational and fault status. For scalability analysis of the proposed system, we have performed simulation in the OMNeT++ simulator to study impact of the IoT network size on key performance measures like response time, throughput, and packet delivery ratio. Simulation results reveal that with the growing network size, the gateway nodes become the performance bottleneck. We have also performed resources requirement analysis for virtual objects and control overhead analysis of the proposed management system. Simulation results reveal that control overhead is insignificant in normal scenarios; however, in extreme network conditions, we may have to sacrifice fewer bits which is, in fact, worth nothing when compared to the flexibility and control offered by the proposed management system.

Collaborative Working Architecture for IoT-Based Applications.

The new sensing applications need enhanced computing capabilities to handle the requirements of complex and huge data processing. The Internet of Things (IoT) concept brings processing and communication features to devices. In addition, the Cloud Computing paradigm provides resources and infrastructures for performing the computations and outsourcing the work from the IoT devices. This scenario opens new opportunities for designing advanced IoT-based applications, however, there is still much research to be done to properly gear all the systems for working together. This work proposes a collaborative model and an architecture to take advantage of the available computing resources. The resulting architecture involves a novel network design with different levels which combines sensing and processing capabilities based on the Mobile Cloud Computing (MCC) paradigm. An experiment is included to demonstrate that this approach can be used in diverse real applications. The results show the flexibility of the architecture to perform complex computational tasks of advanced applications.

Context Design and Tracking for IoT-Based Energy Management in Smart Cities

The advent of the Internet of Things (IoT) and its innumerous applications for Smart Cities emphasizes the need for context-aware systems able to adapt behavior automatically to instant environment conditions. Currently, there is a gap in terms of understanding how context information is interrelated, as well as tracking which events occurred under which conditions within certain context scopes. Visualizing, specifying, tracking and monitoring typical contexts involved in IoT-based applications are still challenging activities since context modeling remains a low level process that requires much human expertise. In this paper we propose a new context life cycle that involves context design and context tracking. We developed a context-aware management framework, where contexts are modeled as graphs and can be explicitly designed and their occurrences can be tracked down. We believe that this feature of designing and tracking context graphs may help developers, administrators, and end-users in harnessing the wealth of information generated by highly scalable IoT systems.

Spatio-Temporal Analysis of Urban Acoustic Environments with Binaural Psycho-Acoustical Considerations for IoT-Based Applications

Sound pleasantness or annoyance perceived in urban soundscapes is a major concern in environmental acoustics. Binaural psychoacoustic parameters are helpful to describe generic acoustic environments, as it is stated within the ISO 12913 framework. In this paper, the application of a Wireless Acoustic Sensor Network (WASN) to evaluate the spatial distribution and the evolution of urban acoustic environments is described. Two experiments are presented using an indoor and an outdoor deployment of a WASN with several nodes using an Internet of Things (IoT) environment to collect audio data and calculate meaningful parameters such as the sound pressure level, binaural loudness and binaural sharpness. A chunk of audio is recorded in each node periodically with a microphone array and the binaural rendering is conducted by exploiting the estimated directional characteristics of the incoming sound by means of DOA estimation. Each node computes the parameters in a different location and sends the values to a cloud-based broker structure that allows spatial statistical analysis through Kriging techniques. A cross-validation analysis is also performed to confirm the usefulness of the proposed system.

Internet of Things (IoT) in high-risk Environment, Health and Safety (EHS) industries: A comprehensive review

The rise of ubiquitous systems is sustained by the development and progressive adoption of the Internet of Things (IoT) devices and their enabling technologies. IoT has been shown to have significant potential in high-risk Environment, Health, and Safety (EHS) industries. In these industries, human lives are at stake and IoT-based applications are primed to offer safe, reliable, and efficient solutions due to their ability to operate at a fine granular level and provide rich low-level information. We review existing published research on IoT-based applications in high-risk EHS industries with specific emphasis on healthcare industry, food supply chain (FSC), mining and energy industries (oil & gas and nuclear), intelligent transportation (e.g., connected vehicles), and building & infrastructure management for emergency response operations until 2016. We also highlight IoT-related challenges and proposed solutions in high risk EHS industries. We then conclude by presenting research challenges and expected trends for IoT in these industries.

Flexible IoT security middleware for end-to-end cloud–fog communication

IoT (Internet of Things) based smart devices such as sensors have been actively used in edge clouds i.e., ‘fogs’ along with public clouds. They provide critical data during scenarios ranging from e.g., disaster response to in-home healthcare. However, for these devices to work effectively, end-to-end security schemes for the device communication protocols have to be flexible and should depend upon the application requirements as well as the resource constraints at the network-edge. In this paper, we present the design and implementation of a flexible IoT security middleware for end-to-end cloud–fog communications involving smart devices and cloud-hosted applications. The novel features of our middleware are in its ability to cope with intermittent network connectivity as well as device constraints in terms of computational power, memory, energy, and network bandwidth. To provide security during intermittent network conditions, we use a ‘Session Resumption’ algorithm in order for our middleware to reuse encrypted sessions from the recent past, if a recently disconnected device wants to resume a prior connection that was interrupted. In addition, we describe an ‘Optimal Scheme Decider’ algorithm that enables our middleware to select the best possible end-to-end security scheme option that matches with a given set of device constraints. Experiment results show how our middleware implementation also provides fast and resource-aware security by leveraging static properties i.e., static pre-shared keys (PSKs) for a variety of IoT-based application requirements that have trade-offs in higher security or faster data transfer rates.

A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model

Internet of Things (IoT) is considered as one of the future disruptive technologies, which has the potential to bring positive change in human lifestyle and uplift living standards. Many IoT-based applications have been designed in various fields, e.g., security, health, education, manufacturing, transportation, etc. IoT has transformed conventional homes into Smart homes. By attaching small IoT devices to various appliances, we cannot only monitor but also control indoor environment as per user demand. Intelligent IoT devices can also be used for optimal energy utilization by operating the associated equipment only when it is needed. In this paper, we have proposed a Hidden Markov Model based algorithm to predict energy consumption in Korean residential buildings using data collected through smart meters. We have used energy consumption data collected from four multi-storied buildings located in Seoul, South Korea for model validation and results analysis. Proposed model prediction results are compared with three well-known prediction algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Trees (CART). Comparative analysis shows that our proposed model achieves 2.96 % better than ANN results in terms of root mean square error metric, 6.09 % better than SVM and 9.03 % better than CART results. To further establish and validate prediction results of our proposed model, we have performed temporal granularity analysis. For this purpose, we have evaluated our proposed model for hourly, daily and weekly data aggregation. Prediction accuracy in terms of root mean square error metric for hourly, daily and weekly data is 2.62, 1.54 and 0.46, respectively. This shows that our model prediction accuracy improves for coarse grain data. Higher prediction accuracy gives us confidence to further explore its application in building control systems for achieving better energy efficiency.

Security of LoRaWAN v1.1 in Backward Compatibility Scenarios

We present security vulnerabilities of LoRaWAN v1.1 in backward compatibility scenarios and propose countermeasures. Novel low-power wireless communication technologies are continuously introduced into the IoT ecosystem, while the existing ones continue their evolution by adding new features and fixing known problems. Since most of IoT-based applications involve communication of sensitive data with remote users, it is crucial to scrutinize the security of the emerging low-power wireless communication technologies. LoRaWAN is a Low Power Wide Area Network (LPWAN) protocol that is optimized for wireless battery-powered IoT devices. The current LoRaWAN v1.1 states only one possible backward compatibility scenario without defining all the associated security behaviors and their implications for the system’s overall security. This work examines the applicability of v1.0.2 attacks in fall-back cases by consolidating the vulnerabilities of v1.0.2 and the resulting attacks. After determining the possible attacks, countermeasures to prevent DoS attacks that target the join procedure, replay of data, and eavesdropping in backward compatibility scenarios are proposed.

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Passive secret disclosure attack on an ultralightweight authentication protocol for Internet of Things

Internet of Things (IoT) is a technology in which for any object the ability to send data via communications networks is provided. Ensuring the security of Internet services and applications is an important factor in attracting users to use this platform. In the other words, if people are unable to trust that the equipment and information will be reasonably safe against damage, abuse and the other security threats, this lack of trust leads to a reduction in the use of IoT-based applications. Recently, Tewari and Gupta (J Supercomput 1–18, 2016) have proposed an ultralightweight RFID authentication protocol to provide desired security for objects in IoT. In this paper, we consider the security of the proposed protocol and present a passive secret disclosure attack against it. The success probability of the attack is ‘1’ while the complexity of the attack is only eavesdropping one session of the protocol. The presented attack has negligible complexity. We verify the correctness of the presented attack by simulation.

Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid

Internet of Things (IoT) technologies have been broadly applied in smart grid for monitoring physical or environmental conditions. Especially, state estimation is an important IoT-based application in smart grid, which is used in system monitoring to get the best estimate of the power grid state through an analysis of the meter measurements and power system topologies. However, false data injection attack (FDIA) is a severe threat to state estimation, which is known for the difficulty of detection. In this paper, we propose an efficient detection scheme against FDIA. First, two parameters that reflect the physical property of smart grid are investigated. One parameter is the control signal from the controller to the static Var compensator (CSSVC). A large CSSVC indicates there exists the intense voltage fluctuation. The other parameter is the quantitative node voltage stability index (NVSI). A larger NVSI indicates a higher vulnerability level. Second, according to the values of the CSSVC and NVSI, an optimized clustering algorithm is proposed to distribute the potential vulnerable nodes into several classes. Finally, based on these classes, a detection method is proposed for the real-time detection of the FDIA. The simulation results show that the proposed scheme can detect the FDIA effectively.

Design of CPW-Fed Slot Antenna with Rhombus Patch for IoT Applications

A CPW-fed slot antenna with rhombus patch radiator is proposed for 1.2276/1.5754/1.8/2.4/2.45/5.0/5.2/5.8 GHz wireless communication applications. The pentagon and rectangular slots are etched on the metallic layer of a single sided printed circuit board and rhombus patch radiator with a tuning stub in the feeding structure is embedded to form the proposed slot antenna. The dimensions of pentagon slot and tuning stub are changed to design and fabricate the antenna which can be operated at 1.2276/1.5754/1.8/2.4/2.45/5.0/5.2/5.8 GHz successfully. IE3D software is used to design this CPW-fed slot antenna and choose the better parameters to manufacture the proposed antenna. The influences of slot and stub dimension parameters of the proposed antenna on resonant frequency, input reflection coefficient expressed in decibel and impedance bandwidth are described. The proposed antenna with the volume of 100mm×80mm×0.6mm has been fabricated. The measured result shows that the proposed antenna can be successfully operated at 1.2-2.8 GHz and 3.35-7.56 GHz bands. These bands are suitable for IoT-based applications.

BSNCare+: A Robust IoT-Oriented Healthcare System with Non-Repudiation Transactions

Recently, the rapid advancement in technologies of modern intelligent objects has led to a new network paradigm, called the Internet of Things (IoT), in which every networked and automated object has been connected in a pervasive manner. New types of IoT-based application services are thus presented. In a healthcare oriented environment, the usage of IoT has brought opportunities for assisting physicians (or nurses) to provide on-demand and real-time body-care services to patients with higher accuracy and better efficiency. However, while IoT-oriented techniques deliver such advantages, they may encounter system security vulnerabilities and patient privacy threats not seen in the past. In this paper, we propose a robust IoT-based healthcare system, called BSNCare+, in which body sensor networks (BSNs) are adopted as the underlying communication architecture. In the proposed healthcare system, we exploit lightweight crypto-primitives to construct a secure communication mechanism that does achieve data confidentiality and entity authentication among intelligent body sensors, the mobile gateway and the backend BSN-Care server. In addition, we evaluate the performance of the proposed healthcare system using the Raspberry PI series platform. The results show the practicability and feasibility of BSNCare+.