Applications of ontology in the Internet of Things: a systematic analysis

Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions

Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence

Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented

Deep Learning Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks

Perfect channel state information (CSI) is required in most of the classical physical layer security techniques, while it is difficult to obtain the ideal CSI due to the time varying wireless fading channel. Although imperfect CSI has a greatly impact on the security of MIMO communications, deep learning is becoming a promising solution to handle the negative effect of imperfect CSI. In this work, we propose two types of deep learning based secure MIMO detectors for heterogeneous networks, where the macro base station (BS) chooses the null-space eigenvectors to prevent information leakage to the femto BS. Thus, the bit error rate of the associated user is adopted as the metric to valuate the system performance. With the help of deep convolutional neural networks (CNNs), the macro BS obtains the refined version from the imperfect CSI. Simulation results are provided to validate the proposed algorithms. The impacts of system parameters, such as the correlation factor of imperfect CSI, the normalized doppler frequency, the number of antennas are investigated in different setup scenarios. The results show that considerable performance gains can be obtained from the deep learning based detectors compared with the classical maximum likelihood algorithm

Role of smart vehicles concept in reducing traffic congestion on the road

The aim of this simple qualitative review was to provide an overview of how smart vehicles concept facilitates reducing traffic congestion on the road. Google Scholar was searched for literature sources using the topic itself as the search term. The search yielded 40 usable papers for this review. Many elements of smart city are inter-mixed with the smart vehicles concept. On the other hand in the smart vehicle concept, enabling technologies like VANET, IoV, SDN, use of mobiles and even use of electric poles on the road as IoT gateway were tested in the different frameworks proposed by different researchers. Many other traffic management systems have also been tested especially in Japan and India. In general, two scenarios have been considered-one of current types of roads and the other automated highways. Understandably, the requirements and approaches are different for the two scenarios. Some limitations of this review have also been listed at the end. Maximum of works dealt with VANET technolog

Present Scenario of Fog Computing and Hopes for Future Research

According to the forecast that billions of devices will get connected to the Internet by 2020. All these devices will produce a huge amount of data that will have to be handled rapidly and in a feasible manner. It will become a challenge for real-time applications to handle this huge data while considering security issues as well as time constraints. The main highlights of cloud computing are on-demand service and scalability; therefore the data generated from IoT devices are generally handled in cloud infrastructure. Though, dealing with IoT application requests on the cloud exclusively is not a proficient result for some IoT applications particularly time-sensitive ones. These issues can be settled by utilizing another idea called, Fog computing. Fog computing has become one of the major fields of research from both academia and industry perspectives. The ongoing research commitments on few issues in fog computing are figuring out in this paper. At long last, this paper also highlights some open issues in fog with IoT, which will determine the future research direction for implementing Fog computing paradigm

Applications of ontology in the internet of things: A systematic analysis

Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions

Application of the cybernetic approach to price-dependent demand response for underground mining enterprise electricity consumption

The article considers a cybernetic model for the price-dependent demand response (DR) consumed by an underground mining enterprise (UGME), in particular, the main fan unit (MFU). A scheme of the model for managing the energy consumption of a MFU in the DR mode and the implementation of the cybernetic approach to the DR based on the IoT platform are proposed. The main functional requirements and the algorithm of the platform operation are described, the interaction of the platform with the UGME digital model simulator, on which the processes associated with the implementation of the technological process of ventilation and electricity demand response will be simulated in advance, is shown. The results of modeling the reduction in the load on the MFU of a mining enterprise for the day ahead are given. The presented solution makes it possible to determine in advance the necessary power consumption for the operation of the main power supply unit, manage its operation in an energy-saving mode and take into account the predicted changes in the planned one (e.g., when men hoisting along an air shaft) and unscheduled (e.g., when changing outdoor air parameters) modes. The results of the study can be used to reduce the cost of UGME without compromising the safety of technological processes, both through the implementation of energy-saving technical, technological or other measures, and with the participation of enterprises in the DR market. The proposed model ensures a guaranteed receipt of financial compensation for the UGME due to a reasonable change in the power consumption profile of the MFU during the hours of high demand for electricity, set by the system operator of the Unified Energy System

Blockchain technology research and application: a systematic literature review and future trends

Blockchain, as the basis for cryptocurrencies, has received extensive attentions recently. Blockchain serves as an immutable distributed ledger technology which allows transactions to be carried out credibly in a decentralized environment. Blockchain-based applications are springing up, covering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockchain technology such as scalability, security and other issues waiting to be overcome. This article provides a comprehensive overview of blockchain technology and its applications. We begin with a summary of the development of blockchain, and then give an overview of the blockchain architecture and a systematic review of the research and application of blockchain technology in different fields from the perspective of academic research and industry technology. Furthermore, technical challenges and recent developments are also briefly listed. We also looked at the possible future trends of blockchain

Evaluasi Kinerja Protokol Perutean AODV dan SDGR+R pada VANET dengan Studi Kasus Pelabuhan Lembar

The Vehicular ad-hoc Network (VANET) is a subclass of Mobile ad-hoc networks (MANETs).VANET is a wireless network created from the concept of building a vehicle network (node) toexchange data information (data communication). There is a new concept technique forVANET communication used, namely the use of the concept of Software Defined Network(SDN) on VANET. For data communication between vehicles, a routing protocol required. Themost common routing protocol used on VANET since 2003 is AODV. In 2014 several studieswere using the SDN paradigm tried on VANET technology to improve the performance ofQuality of Service (QoS), one of which is a Geographic-based SDN, called SDGR in 2016.Multicast is a method of routing data on a network that allows one node or a group of nodes tocommunicate efficiently with the receiving node. The multicast concept supports one-to-manyrouting in nodes that send packet data to a group of nodes. The development of the SDGRrouting protocol using the idea of multicast technique to SDGR based on the Direction calledSDGR + R carried out in 2019. This study uses a case study of vehicle transportationsimulations in the Lamber Port area of Lombok. The simulation results knew that SDGR + Ris better than AODV in terms of service quality (QoS) at a latency of 15.58% and packet deliveryratio (PDR) of 47.78%

Dynamic Power Provisioning System for Fog Computing in IoT Environments

Large amounts of data are created from sensors in Internet of Things (IoT) services and applications. These data create a challenge in directing these data to the cloud, which needs extreme network bandwidth. Fog computing appears as a modern solution to overcome these challenges, where it can expand the cloud computing model to the boundary of the network, consequently adding a new class of services and applications with high-speed responses compared to the cloud. Cloud and fog computing propose huge amounts of resources for their clients and devices, especially in IoT environments. However, inactive resources and large number of applications and servers in cloud and fog computing data centers waste a huge amount of electricity. This paper will propose a Dynamic Power Provisioning (DPP) system in fog data centers, which consists of a multi-agent system that manages the power consumption for the fog resources in local data centers. The suggested DPP system will be tested by using the CloudSim and iFogsim tools. The outputs show that employing the DPP system in local fog data centers reduced the power consumption for fog resource providers