scholarly journals Virtualization Based Efficient Service Matching and Discovery in Internet of Things

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1007
Author(s):  
Zulfiqar Ali Khan ◽  
Israr Ullah ◽  
Muhammad Ibrahim ◽  
Muhammad Fayaz ◽  
Ayman Aljarbouh ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Service Discovery ◽  
Processing Time ◽  
Service Selection ◽  
Geographic Area ◽  
Residual Energy ◽  
Service Matching ◽  
Domain Specific ◽  
Proposed Model ◽  
Iot Devices

Internet of Things (IoT) is getting more popular day by day, which triggers its adoption for solving domain specific problems. Cities are becoming smart by gathering the context knowledge through sensors and controlling specific parameters through actuators. Dynamically discovering and integrating different data streams from different sensors is a major challenge these days. In this paper, a service matchmaking algorithm is presented for service discovery utilizing IoT devices and services in a particular geographic area. It helps us to identify services based on a variety of parameters (location, query size and processing time, etc.). Customization of service selection and discovery are also explored. The conceptual framework is provided for the proposed model along with a matchmaking algorithm based on IoT devices virtualization. The simulation results elaborate the increased complexity of processing time with respect to the increasing pool of available services. The average processing time varies as the number of conditions are multiplied. Query size and complexity increases with additional number of filters and conditions which results in the reduction of the number of matching services. Moreover, upon decreasing the radius of geographic search area, the number of candidate services decreases for service matching algorithm. This is based on the assumption that IoT devices and services are evenly distributed in a given geographic area. Similarly, the remaining energy of IoT devices is also assumed to be uniformly distributed and, therefore, if we are interested in IoT devices or services with more residual energy, then a limited number of IoT devices or services will fulfill this criterion.

scholarly journals A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

2021 ◽  
pp. 182-189
Author(s):  
S. Arokiaraj ◽  
Dr. N. Viswanathan
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Short Term Memory ◽  
The Other ◽  
Learning Models ◽  
Computational Overhead ◽  
Temporal Features ◽  
Human Movements ◽  
Proposed Model ◽  
Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

Enhanced Service Discovery Model for Wireless Mesh Networks

2018 ◽  
Vol 22 (1) ◽  
pp. 44-53 ◽  
Author(s):  
Lungisani Ndlovu ◽  
◽  
Okuthe P. Kogeda ◽  
Manoj Lall
Keyword(s):  
Wireless Mesh Networks ◽  
Service Discovery ◽  
Mesh Networks ◽  
Service Selection ◽  
Cost Effective ◽  
Ant Colony ◽  
Area Network ◽  
Wireless Mesh ◽  
Proposed Model ◽  
Link Congestion

Wireless mesh networks (WMNs) are the only cost-effective networks that support seamless connectivity, wide area network (WAN) coverage, and mobility features. However, the rapid increase in the number of users on these networks has brought an upsurge in competition for available resources and services. Consequently, factors such as link congestion, data collisions, link interferences, etc. are likely to occur during service discovery on these networks. This further degrades their quality of service (QoS). Therefore, the quick and timely discovery of these services becomes an essential parameter in optimizing the performance of service discovery on WMNs. In this paper, we present the design and implementation of an enhanced service discovery model that solves the performance bottleneck incurred by service discovery on WMNs. The proposed model integrates the particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms to improve QoS. We use the PSO algorithm to assign different priorities to services on the network. On the other hand, we use the ACO algorithm to effectively establish the most cost-effective path whenever each transmitter has to be searched to identify whether it possesses the requested service(s). Furthermore, we design and implement the link congestion reduction (LCR) algorithm to define the number of service receivers to be granted access to services simultaneously. We simulate, test, and evaluate the proposed model in Network Simulator 2 (NS2), against ant colony-based multi constraints, QoS-aware service selection (QSS), and FLEXIble Mesh Service Discovery (FLEXI-MSD) models. The results show an average service discovery throughput of 80%, service availability of 96%, service discovery delay of 1.8 s, and success probability of service selection of 89%.

scholarly journals Hyperledger Fabric Blockchain for Securing the Edge Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 359
Author(s):  
Houshyar Honar Pajooh ◽  
Mohammad Rashid ◽  
Fakhrul Alam ◽  
Serge Demidenko
Keyword(s):  
Internet Of Things ◽  
Performance Metrics ◽  
Security And Privacy ◽  
Practical Implementation ◽  
Blockchain Technology ◽  
Performance Requirements ◽  
Processing Power ◽  
Proposed Model ◽  
Iot Devices ◽  
Research Challenge

Providing security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems’ scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results show that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios.

Goal-Based Semantic Queries for Dynamic Processes in the Internet of Things

2016 ◽  
Vol 10 (02) ◽  
pp. 269-293 ◽  
Author(s):  
Steffen Huber ◽  
Ronny Seiger ◽  
André Kühnert ◽  
Vasileios Theodorou ◽  
Thomas Schlegel
Keyword(s):  
Internet Of Things ◽  
Domain Knowledge ◽  
Service Discovery ◽  
Modeling Languages ◽  
Goal Modeling ◽  
Modeling Methodology ◽  
Novel Approach ◽  
Process Execution ◽  
Process Engine ◽  
Iot Devices

Internet of Things-aware process execution imposes new requirements on process modeling that are outside the scope of current modeling languages. Internet of Things (IoT) devices may vanish, appear or stay unknown during process execution, which renders process resource allocation at design time infeasible. Devices’ capabilities are often only available in a particular real-world context at runtime. This is not considered by current approaches that use services for encapsulating device functionality. We propose a novel approach to enable both service discovery and invocation for IoT-aware processes based on users’ goals that are defined as part of a process. We apply the Tropos goal modeling methodology to represent the dependencies between these goals and IoT device capabilities. Furthermore, we present a Semantic Access Layer (SAL) to transform these goals into service invocations using generated SPARQL queries. The SAL executes the queries on a knowledge base representing runtime domain knowledge about IoT services, their capabilities, and context. As a result, it invokes the identified IoT services and transfers the responses back to the process engine. The evaluation of our approach within several Smart Home scenarios shows an increase of flexibility and separation of concerns for scalable, IoT-aware process execution.

scholarly journals Recognition of the Cybersecurity State of IoT devices

TEM Journal ◽  
2021 ◽  
pp. 1912-1918
Author(s):  
M.E. Sukhoparov ◽  
I.S. Lebedev
Keyword(s):  
Time Series ◽  
Internet Of Things ◽  
Processing Time ◽  
External Information ◽  
Classification Methods ◽  
Internal Resources ◽  
Software And Hardware ◽  
Mass Development ◽  
Process Execution ◽  
Iot Devices

The identification of the cybersecurity (CS) state of Internet of things (IoT) devices determines the necessity to search for and improve approaches to detecting various threat types. The unification used in the mass development of IoT devices facilitates software and hardware modification to block certain built-in protective functions from the side of a potential intruder. A need arises to develop universal methods for identifying the cybersecurity state of devices using comprehensive approaches to analyzing data from internal and external information channels. The article presents an approach to identifying the cybersecurity of IoT devices based on processing time series recorded from sensors during various processes, and internal and external (thirdparty) sources. The approach is based on classification methods. The presented solution uses template sequences containing synchronized time series showing numerical values obtained from various probes and sensors during process execution. The proposed approach makes it possible to identify IoT device cybersecurity states without increasing the volume of information stored and processed in internal resources.

scholarly journals Investigating the Role of Internet of Things in Knowledge Management Systems (Case Study: Offering A Resource Description Model Based on Ontological Study of Smart Store Management (Smart Shopping Cart)

2018 ◽  
Vol 7 (3.5) ◽  
pp. 43
Author(s):  
Siamak Shahpasand ◽  
Omid Rahimzadeh
Keyword(s):  
Knowledge Management ◽  
Internet Of Things ◽  
Performance Optimization ◽  
Business Performance ◽  
Human Beings ◽  
Shopping Cart ◽  
Proposed Model ◽  
Important Challenge ◽  
Iot Devices ◽  
Resource Description

Rapid changes in the knowledge management (KM) area are substantially dependent on the considerable progresses made by the mankind in the information technology (IT) during these years. In fact, Internet of Things (IoT), as part of the applied technologies in the IT world, has rendered feasible the fast growth and sharing of knowledge. IoT records the data pertinent to the natural phenomena and classifies and calculates them for the purpose of facilitating a better and easier perception thereby to enable the human beings better perceive the phenomena. The quality of achieving an integrated source in regard of resource description is an important challenge in IoT for a large number of heterogeneous devices.According to the absence of an integrated description model for IoT devices, the present article tries proposing an ontology-based resource description model (ORDM). These resources in IoT include the description of such classes as specifications, statuses, controls, situations, performances, histories and privacies inherent of the things. The ontology-based description model (ORDM) can be completely implemented for the performance optimization of a smart store through its offering of a smart shopping cart. The experiment results indicated that the proposed model is of a considerable applied value and prospect in the optimization of devices’ access and business performance in IoT.

Bringing Security to The Smallest Embedded Systems

2017 ◽  
Author(s):  
JOSEPH YIU
Keyword(s):  
Internet Of Things ◽  
Embedded Systems ◽  
Low Cost ◽  
Security Requirements ◽  
The Internet ◽  
Security Measures ◽  
Significant Challenge ◽  
Iot Devices

The increasing need for security in microcontrollers Security has long been a significant challenge in microcontroller applications(MCUs). Traditionally, many microcontroller systems did not have strong security measures against remote attacks as most of them are not connected to the Internet, and many microcontrollers are deemed to be cheap and simple. With the growth of IoT (Internet of Things), security in low cost microcontrollers moved toward the spotlight and the security requirements of these IoT devices are now just as critical as high-end systems due to:

Agricultural Application of Web of Things Architecture

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 61-63 ◽  
Author(s):  
Akihiro Fujii
Keyword(s):  
Internet Of Things ◽  
Agricultural Sector ◽  
Electronic Device ◽  
Specific Work ◽  
Science And Engineering ◽  
Unique Identifier ◽  
Agricultural Application ◽  
Iot Devices ◽  
The Internet Of Things ◽  
Type Pressure

The Internet of Things (IoT) is a term that describes a system of computing devices, digital machines, objects, animals or people that are interrelated. Each of the interrelated 'things' are given a unique identifier and the ability to transfer data over a network that does not require human-to-human or human-to-computer interaction. Examples of IoT in practice include a human with a heart monitor implant, an animal with a biochip transponder (an electronic device inserted under the skin that gives the animal a unique identification number) and a car that has built-in sensors which can alert the driver about any problems, such as when the type pressure is low. The concept of a network of devices was established as early as 1982, although the term 'Internet of Things' was almost certainly first coined by Kevin Ashton in 1999. Since then, IoT devices have become ubiquitous, certainly in some parts of the world. Although there have been significant developments in the technology associated with IoT, the concept is far from being fully realised. Indeed, the potential for the reach of IoT extends to areas which some would find surprising. Researchers at the Faculty of Science and Engineering, Hosei University in Japan, are exploring using IoT in the agricultural sector, with some specific work on the production of melons. For the advancement of IoT in agriculture, difficult and important issues are implementation of subtle activities into computers procedure. The researchers challenges are going on.

scholarly journals Internet of things issues related to psychiatry

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Scott Monteith ◽  
Tasha Glenn ◽  
John Geddes ◽  
Emanuel Severus ◽  
Peter C. Whybrow ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Cognitive Abilities ◽  
Healthcare Providers ◽  
Emotional Reactions ◽  
The Body ◽  
Psychiatric Research ◽  
Main Body ◽  
Personal Safety ◽  
Healthcare Organizations ◽  
Iot Devices

Abstract Background Internet of Things (IoT) devices for remote monitoring, diagnosis, and treatment are widely viewed as an important future direction for medicine, including for bipolar disorder and other mental illness. The number of smart, connected devices is expanding rapidly. IoT devices are being introduced in all aspects of everyday life, including devices in the home and wearables on the body. IoT devices are increasingly used in psychiatric research, and in the future may help to detect emotional reactions, mood states, stress, and cognitive abilities. This narrative review discusses some of the important fundamental issues related to the rapid growth of IoT devices. Main body Articles were searched between December 2019 and February 2020. Topics discussed include background on the growth of IoT, the security, safety and privacy issues related to IoT devices, and the new roles in the IoT economy for manufacturers, patients, and healthcare organizations. Conclusions The use of IoT devices will increase throughout psychiatry. The scale, complexity and passive nature of data collection with IoT devices presents unique challenges related to security, privacy and personal safety. While the IoT offers many potential benefits, there are risks associated with IoT devices, and from the connectivity between patients, healthcare providers, and device makers. Security, privacy and personal safety issues related to IoT devices are changing the roles of manufacturers, patients, physicians and healthcare IT organizations. Effective and safe use of IoT devices in psychiatry requires an understanding of these changes.

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