Abstract

     The main objective of this research is a working example of how a hybrid methodology combining traditional methodologies and mobile devices can be used to contribute to the literature on mobile learning in teaching English as a second language. This work was carried out because, in many Latin American countries, students are taught English as a second language throughout their primary and secondary education. However, at the end of their studies, most students are unable to communicate with other people in English, let alone with native speakers. Moreover, it must be taken into account that nowadays English is the most widely used language in international communications, business transactions, finance and science. The professional who knows how to communicate in English has a positive differentiator in his or her professional profile and can easily access more relevant positions in any institution. For this purpose, a review of different methodologies for teaching oral expression in English has been carried out. Metrics have also been used to choose an effective mobile application to reinforce English speaking. These analyzed methodologies have been combined with the use of a mobile application to propose a hybrid methodology that contemplates an eight-week class guide. Due to the characteristics of mobile learning, this work can help to motivate students in their learning and in improving their communicative skills in the English language. High school teachers can use this methodology as an innovation in their educational programs.

Introduction

     In many countries in Latin America, students are taught English as a second language (L2) throughout their primary, secondary, and higher education. However, at the end of their studies, the vast majority of students are unable to communicate with others in English, even less with native English speakers [1].

     English is a universal language; it is the most learned language worldwide as an L2 [2]. Learning this language allows people to have access to more complete education and greater interaction with a huge amount of academic information [3]. In addition, it is fundamental to access new cultures, travel around the world and achieve personal and work goals [2]. It is for this reason that, nowadays, being able to communicate correctly in this language has become a necessary skill. Currently, there are multiple ways to learn English (courses, educational platforms, MOOCs, web apps, and mobile apps). However, the characteristics of mobile learning, the high penetration of mobile devices, and their use among students have allowed this tool to become one of the most used as a support for L2 teaching [4].

Results and analyses

     Studies, research, and this proposal provide information on how MALL apps can be used, in this case Duolingo, as a digital tool that contributes significantly to the improvement of oral proficiency in the English language. There are a variety of applications for L2 learning. However, using educational content as a basis, when choosing an educational application, it is important to consider the following points:

Pedagogical coherence
Feedback and self-correction
Motivation
Usability
Customization
Sharing

    In addition, due to the needs of our students and the experience of the teachers, it was concluded that Duolingo is the exact application to meet the proposed objective. There are multiple factors that can affect English language learning in high school students. That is why this proposal aims to help students improve oral communication without the barriers of fear and shyness, including a good command of vocabulary, pronunciation, fluency and comprehension of the English language. In recent years, multiple initiatives have been generated using mobile applications for education. Duolingo has been one of the most downloaded applications for this purpose. However, the work presented here defines a methodological guide that involves traditional teaching and mobile technology to improve and motivate learning in high school students.

1- Introduction

2- Background

3- Methodology

4- Results

5- Discussion

6- Limitations and future research

7- Conclusions

References

Abstract

During the last five years, game streaming has developed from a niche market into a mainstream activity and the supply of services and technology on offer has exploded. Today, some streamers garner audiences larger than big media houses, and services such as the game streaming service Twitch host millions of daily active users. While such activity is often waived merely as a manifestation of video game culture and an extension of online behaviour by adolescents, the phenomenon has begun to generate significant revenue and has managed to shift media consumption behaviour from large commercial organisations towards content created by private individuals. However, we still have a dearth in our understanding on how streamers undertake this activity and what tools they have in their disposal to facilitate successful endeavours in streaming. As this is an activity driven by individuals, are these individuals using vastly different modalities of communication, or have common trends emerged across broadcasters, as they have in traditional media? To build a better understanding of this, we utilize the existing understanding of affordance theory, and analyse the most popular elements and practices employed by streamers in their video streams and profile pages through the investigation of the 100 most popular individual streamers on the Twitch platform. The results show new aspects of social commerce that emerges from the novel forms of online business models of individual online video streamers.

Introduction

Contemporary media content producers, in the form of private individuals and small collectives, have begun competing for the attention of the audiences of many larger media conglomerates (Burling, 2015; Holland, 2016), through the utilization of digital services such as social media (Facebook, Instagram & Snapchat) and digital content sharing platforms (YouTube & Twitch) (see e.g. Grundberg & Hansegard, 2014). The increasing popularity of these content creation practices have been especially evident in video content creation, which has been spearheaded by the ease of use of video sharing platforms such as YouTube (Cha, Kwak, Rodriguez, Ahn, & Moon, 2007) and Twitch, as well as the incorporation of video as an integral part of social media platforms including Facebook and Instagram (Haimson & Tang, 2017; Raman, Tyson, & Sastry, 2018). Video content creation has become an integrated part of everyday life for digital natives (Tempelman, 2017), in the form of pre-recorded video sharing through services such as YouTube, and live video broadcasting, or live-streaming on services such as Twitch, Facebook Live or YouTube Live. The term streaming refers to the larger cultural phenomenon of streaming as a form of social live broadcasting on Twitch (Raman et al., 2018; Törhönen, Sjöblom, & Hamari, 2018), rather than only the technological solutions of streaming video and sound data over the internet. Additionally, the content creators on Twitch are most commonly referred to as streamers and the content on Twitch is primarily focused on gaming and creative endeavours. Although the service is less than ten years old, Twitch caters to 15 million unique daily visitors (Twitch, 2017a), and by 2018 its monthly viewership figures have reached similar numbers as some of the larger cable TV networks in the US (Gilbert, 2018).

1- Introduction

2- Background

3- Related work

4- Proposed solution

5- Performance evaluation

6- Conclusion and future work

References

Abstract

Internet of Things (IoT) and cyberphysical system (CPS) technologies play huge roles in the context of Industry 4.0. These technologies introduce cognitive automation to implement the concept of intelligent production, leading to smart products and services. One of the technological challenges related to Industry 4.0 is to provide support to big data cloud based applications which demand QoS-enabled Internet connectivity for information gathering, exchange, and processing. In order to deal with this challenge, in this article, a QoS-aware cloud based solution is proposed by adapting a recently proposed seamless resources sharing architecture to the IoT scenario. The resulting solution aims at improving device to cloud communications considering the coexistence of different wireless networks technologies, particularly in the domain of Industry 4.0. Results are obtained via simulations of three QoS demanding industrial applications. The outcomes of the simulations show that both delay and jitter QoS metrics are kept below their specific thresholds in the context of VoIP applications used for distributed manipulators fine tuning control. In the case of video-based production control, the jitter was controlled to meet the application demands, and even the throughput for best-effort supervisory systems HTTP access is guaranteed.

Introduction

Industry 4.0 introduces several changes to the original approach of industrial automation. IoT and CPS technologies play huge roles in this context introducing cognitive automation and consequently implementing the concept of intelligent production, leading to smart products and services (Drath and Horch, 2014). Besides the aforementioned technologies, Industry 4.0 typically relies on big-data cloud based applications which demand QoS-enabled Internet connectivity for information gathering, exchange and processing. Flexibility and scalability are important requirements of Industry 4.0, since the physical environment of the factories is in constant change. Implementing these requirements demands dealing with the disposition of the sensors and actuator devices in the factories, and how easily they can be displaced, introduced, and reorganized (Freitas et al., 2013). This flexible environment can be attained by diminishing the needs for cabling to connect the devices, which is possible using wireless technologies. Implementing a QoS-aware cloud based solution is challenging in the context of wireless connections and demands three main issues to be tackled (Wollschlaeger et al., 2017): (I) the coexistence of different wireless protocols and systems, (II) interoperability between communication systems, and (III) seamless engineering to allow adaptive factory operation. In order to deal with these issues, in this article, a recently proposed seamless resources sharing architecture (Kunst et al., 2016a,b; Fernandez et al., 2017) is adapted to the IoT scenario aiming at improving the device to cloud communications considering the coexistence of different wireless networks technologies, particularly in the domain of Industry 4.0.

1- Introduction

2- Related work

3- Preliminaries

4- System model

5- System design

6- Security analysis

7- Performance evaluation

8- Discussion

9- Conclusion

References

Abstract

With the development of the Internet of Things (IoT) technology, we are entering a new era of computing technology. This emerging technology allows connectivity for everyone and everything. Thus, IoT encapsulates some intelligence in Internet-connected objects to communicate, exchange information, sharing multimedia data, take a decision, invoke various actions and provide amazing services through smart devices. A huge amount of sensitive information and multimedia data such as images and videos are sharing which generates traffic in the network. Moreover, multimedia data contains high dimension property, which affects image processing including storage and retrieval in IoT environment. Additionally, limited storage capabilities and computing power are drawbacks of smart devices which makes the situation more complicated. To overcome this problem, users prefer to store their multimedia data on a cloud server to make local storage and computation flexible in smart devices, but still security and privacy concerns. To get rid of this challenges, the emergence of Intel Software Guard Extension (SGX) provide a new solution. In this paper, a novel secure similarity image retrieval (SSIR) scheme based on the primitive of content-based image retrieval (CBIR) is proposed to protect the images search. In addition, the proposed SSIR scheme enables the resource-constrained clients to move the process of images as well as the outsource searching operations to the cloud server which reduce the cost. Furthermore, the security analysis shows our scheme is secure in the proposed model. Experiment results illustrate that our scheme is well fitting for images retrieval in practical applications.

Introduction

With the rapid development of the Internet of Things, smart devices have been widely used in our daily life, which attracts people to share multimedia data such as images or videos. Thus, a large amount of image data are flooding on the Internet [39]. Although smart devices bring a lot of convenience for people, due to the disadvantages of smart devices such as limited storage capability and computing power, users on the mobile device side cannot perform complex processing, especially image data with a high dimension. Therefore, in order to save the storage space and reduce the computation overhead of the client side, users usually outsource their images to the cloud server. This situation leads to the image search operated by the cloud server. Currently, a lot of companies and research institutes are working on developing the image retrieval systems [35, 36, 40]. The most popular image search systems include Google Similar Images and Baidu Shitu. But the security and privacy issues are still concerned because these systems are searching based on the original images. For example, when a client attends to retrieve some similarity images, he needs to upload the query image to the cloud server. Then the image content and his interests will are known by the cloud server. This will result in a privacy leak, especially the image content relates to the private photos or medical images.

1- Introduction

2- Methods and materials

3- Results and discussion

4- Conclusion

References

Abstract

Increasing popularity of virtual reality (VR) gaming is causing increased concern, as prolonged use induces visual adaptation effects which disturbs normal vision. Effects of VR gaming on accommodation and convergence of young adults by measuring accommodative response and phoria before and after experiencing virtual reality were measured. An increase in accommodative response and a decrease in convergence was observed after immersion in VR games. It was found that visual symptoms were apparent among the subjects post VR exposure.

Introduction

Virtual reality (VR) is a simulated environment where the visual content and alternatively different senses are entirely computer-generated and the participant’s performance alters the appearance of the environmental status. The visual stimulus and other sensory channels such as touch, smell, sound, and taste are presented by a combination of virtual and augmented reality systems (Rebenitsch and Owen, 2016). Virtual reality has been rapidly developmental in the recent years, particularly the VR headsets, which are used by attaching a smartphone that contain the VR game and mounting it on the head, thus providing users with a virtually immersive experience (Desai et al., 2014). The current study uses a VR game as the stimulus as it is perceived to be more appealing to the user, enhancing maximum immersion; furthermore players show a higher anxiety level which would enhance their post VR-gaming response (Pallavicini et al., 2018). VR gaming blocks out the external environment whilst promoting sensory immersion due to the enlarged field of view (FOV) of the VR headset, providing users with a greater immersion experience (Martel and Muldner, 2017). The accommodation and vergence systems are reflexively linked, interacting with each other through accommodative vergence and vergence accommodation; where accommodation is stimulated by retinal blur whereas vergence is stimulated by depth (Hung, 2001). Accommodation and convergence are simultaneously occurring ocular systems that enable normal binocular vision. A disruption in one system could affect the other (Shiomi et al., 2013). The demand exerted on the accommodation and vergence systems by VR results to a reduction in visual performance due to the ocular discomfort experienced (Barnes, 2016). Moreover, discomfort in stereoscopic viewing is caused by the need of quick adaptation from the vergence system, despite the conflicting accommodation system (Hoffman et al., 2008; Lambooij et al., 2009). Studies have found a significant effect of VR on accommodation and convergence (Mon-Williams et al., 1993; Kooi and Toet, 2004; Rebenitsch and Owen, 2016), caused by a disruption in how these two systems work together. Shiomi and his colleagues found the incidence of mismatch between accommodation and convergence which resulted to complaints of visual fatigue after users were immersed in the VR world for a period of time (Shiomi et al., 2013). This paper presents investigations on how the accommodative and convergence systems are affected after using the VR headset for a period of time.

1- Introduction

2- Comparison of deep learning feature representation and conventional feature learning

3- Automatic feature extraction using deep learning methods

4- Deep learning approaches for human activity recognition using mobile and wearable sensor data

5- Classification algorithms and performance evaluation of human activities

6- Common datasets for deep learning based human activity recognition

7- Deep learning implementation frameworks

8- Open research challenges

9- Conclusion

References

Abstract

Human activity recognition systems are developed as part of a framework to enable continuous monitoring of human behaviours in the area of ambient assisted living, sports injury detection, elderly care, rehabilitation, and entertainment and surveillance in smart home environments. The extraction of relevant features is the most challenging part of the mobile and wearable sensor-based human activity recognition pipeline. Feature extraction influences the algorithm performance and reduces computation time and complexity. However, current human activity recognition relies on handcrafted features that are incapable of handling complex activities especially with the current influx of multimodal and high dimensional sensor data. With the emergence of deep learning and increased computation powers, deep learning and artificial intelligence methods are being adopted for automatic feature learning in diverse areas like health, image classification, and recently, for feature extraction and classification of simple and complex human activity recognition in mobile and wearable sensors. Furthermore, the fusion of mobile or wearable sensors and deep learning methods for feature learning provide diversity, offers higher generalisation, and tackles challenging issues in human activity recognition. The focus of this review is to provide in-depth summaries of deep learning methods for mobile and wearable sensor-based human activity recognition. The review presents the methods, uniqueness, advantages and their limitations. We not only categorise the studies into generative, discriminative and hybrid methods but also highlight their important advantages. Furthermore, the review presents classification and evaluation procedures and discusses publicly available datasets for mobile sensor human activity recognition. Finally, we outline and explain some challenges to open research problems that require further research and improvements.

Introduction

Human activity recognition is an important area of research in ubiquitous computing, human behaviour analysis and human-computer interaction. Research in these areas employ different machine learning algorithms to recognise simple and complex activities such as walking, running, cooking, etc. Particularly, recognition of daily activities is essential for maintaining healthy lifestyle, patient rehabilitation and activity shifts among the elderly citizens that can help to detect and diagnose serious illnesses. Therefore, human activity recognition framework provides mechanism to detect both postural and ambulatory activities, body movements and actions of users using different multimodal data generated by variety of sensors(Cao, Wang, Zhang, Jin, & Vasilakos, 2017; Ordonez & Roggen, 2016). Previous studies in human activity recognition can be broadly categorised based on diverse devices, sensor modalities and data utilised for detection of activity details. These include video based, wearable and mobile phone sensors, social network sensors and wireless signals. Video-based sensors are utilised to capture images, video or surveillance camera features to recognise daily activity (Cichy, Khosla, Pantazis, Torralba, & Oliva, 2016; Onofri, Soda, Pechenizkiy, & Iannello, 2016). With the introduction of mobile phones and other wearable sensors, inertial sensor data (S. Bhattacharya & Lane, 2016; Bulling, Blanke, & Schiele, 2014b) are collected using mobile or wearable embedded sensors placed at different body positions in order to infer human activities details and transportation modes. Alternatively, the use of social network methods (Y. Jia, et al., 2016) that exploit appropriate users’ information from multiple social network sources to understand user behaviour and interest have also been proposed recently. In addition, wireless signal based human activity recognition (Savazzi, Rampa, Vicentini, & Giussani, 2016) takes advantages of signal propagated by the wireless devices to categorise human activity. However, the use of sensor data generated using smartphones and other wearable devices have dominated the research landscape in human motion analysis, activity monitoring and detection due to their obvious advantages over other sensor modalities (Cornacchia, Ozcan, Zheng, & Velipasalar, 2017).

2- MOTIVATION AND CONTRIBUTION

3- LITERATURE REVIEW METHOD

4- ANALYSIS OF THE RESULTS AND DISCUSSION

5- RESEARCH CHALLENGES

6- LIMITATIONS OF THE STUDY

7- RELATED WORK

8- CONCLUSIONS AND FUTURE WORK

9- BEYOND SM

REFERENCES

INTRODUCTION

To include external web data in the traditional data warehouse (DW) systems, and the traditional online analytical processing (OLAP) processes, is a promising way for broadening traditional business intelligence (BI) analysis Abelló et al. (2013). In the context of BI, a DW is used to collect, organize and store subject-oriented, integrated, time-variant, and nonvolatile data (Inmon, 1992). Traditionally, a DW has been defined as a historical data repository containing data collected from a wide variety of heterogeneous sources by means of extraction–transformation–loading (ETL) processes (Kimball & Ross, 2002). On the other hand, multidimensional (MD) processing, also called OLAP, is an approach for responding to MD analytical queries. Research focusing on DWs and OLAP has led to the creation of important technologies for the design, administration, and use of information systems in decision-making support. Part of the interest in, and success of this field, can be attributed to the demonstrated need for software and tools that help improve data analysis and administration. This is mainly due to the large quantity of information that is being accumulated by corporations as well as scientific databases. Traditional BI tools, such as OLAP, have been successfully applied to large amounts of data coming from operational databases. However, there is a trend whereby DWs are becoming more and more dynamic, with updates occurring almost in real-time, and with the inclusion of more complex types of data (Henschen, 2015). This situation has forced traditional BI to open its gates to external data in order to encompass a more heterogeneous and open analysis scenario (Chen, Chiang, & Storey, 2012). Current research envisions that Semantic Web technologies are required for realizing the next generation of DWs (Abelló et al., 2015), as an increasing quantity of semantically annotated data is available over the Internet.1 To include Semantic Web information in a traditional OLAP analysis process is therefore a promising way to augment traditional BI analyses (Trujillo & Maté, 2012). The Semantic Web is an extension of the Web proposed by the World Wide Web Consortium.2 Its intended objective is to facilitate the creation of technologies that publish legible data for informatic applications, which are implemented by adding semantic metadata and ontologies to the web (Shadbolt, Berners-Lee, & Hall, 2006). In practical terms, the strength of the Semantic Web lies in its ability to aggregate the semantic annotations of web-published content so that the information can be effectively retrieved and processed, either by humans or by machines, for a wide variety of tasks (Hendler, 2001). The above goals is achieved through the use of a diverse variety of software technologies, such as ontologies (Coral, Francisco, & Mario, 2006) and markup languages (e.g., RDF and OWL) (Saha, 2007), which allow semantic annotations to be added to resources that can either be very simple, or very complex annotations, depending on the requirements.

1- Introduction

2- Details of mechanism

3- Networked automatic washing streetlight system

4- SMS control commands of SIM900 GSM/GPRS modem

5- Conclusions

References

Abstract

The illumination of the streets and public area in metropolitan cities is a vital service, which is not only related to the type of the light but also the dirtiness of the surface of the light. In this paper, both subjects are considered to increase the productivity of the light. To achieve this goal, a novel Automatic washing system (AWS) of LED street/public light surface was designed, manufactured and installed practically. The proposed mechanism consists of two main parts comprising mechanical and electrical systems. AWS operates based on internet interconnection technique known as Internet of Things (IoT) with a high productivity. The system has the potential to be designed and employed by four types of control system; (i) using a timer switch, (ii) using a GSM 900, (iii) using a push button manually by an operator, and (iv) using a remote-control module such as GSM, SIM 808 or GPRS/GPS/SMS through the Ethernet network. A practical system has been manufactured and installed in Kermanshah city in Iran, due to its low cost, low maintenance, upgradability, and feasibility of installing different recognition sensors such as rain and dust sensors.

Introduction

The rapid increase of urbanization and constructions at large cities around the world caused the expansion of the industrial activities and transportation, which significantly have produced a huge amount of pollutants and dusts. This is one of the main challenges in metropolitan cities [1] in which pollutants are spread everywhere widely and the street light surfaces are not exceptional. Interestingly, due to the humidity and heavy materials, the quality and quantity of pollutants and dusts have different persistent deposition [2] even on the surface of the street lights. Therefore, the influence of deposition can be irreversible on the lights both in the reduction of the illumination and increase the temperature, which damages the lights and electrical connections. It is obvious that the reduction of illumination in cities has negative effects on safety, which can increase crimes and accidents. In addition, due to the dirt accumulation of the lights, the required illumination significantly reduces without more power saving. It has been shown that the illumination of lights is reduced up to 60% due to the dirt accumulation and surface deposition [3]. A relatively new analytical investigation performed by researchers have shown that dusts and heavy materials that are the consequences of hydrocarbons could be the result of different kinds of dirt accumulation such as dry and humid depositions.

1- Introduction

2- Background

3- Related work

4- Real WSN system

5- The graph-based big data model

6- Prototype implementation for proposed model

7- Simulation

8- A case study: surveillance application

9- Experimental work

10- Conclusions

References

Abstract

Sensors are present in various forms all around the world such as mobile phones, surveillance cameras, smart televisions, intelligent refrigerators and blood pressure monitors. Usually, most of the sensors are a part of some other system with similar sensors that compose a network. One of such networks is composed of millions of sensors connected to the Internet which is called Internet of Things (IoT). With the advances in wireless communication technologies, multimedia sensors and their networks are expected to be major components in IoT. Many studies have already been done on wireless multimedia sensor networks in diverse domains like fire detection, city surveillance, early warning systems, etc. All those applications position sensor nodes and collect their data for a long time period with real-time data flow, which is considered as big data. Big data may be structured or unstructured and needs to be stored for further processing and analyzing. Analyzing multimedia big data is a challenging task requiring a high-level modeling to efficiently extract valuable information/knowledge from data. In this study, we propose a big database model based on graph database model for handling data generated by wireless multimedia sensor networks. We introduce a simulator to generate synthetic data and store and query big data using graph model as a big database. For this purpose, we evaluate the well-known graph-based NoSQL databases, Neo4j and OrientDB, and a relational database, MySQL. We have run a number of query experiments on our implemented simulator to show that which database system(s) for surveillance in wireless multimedia sensor networks is efficient and scalable.

Introduction

A wireless multimedia sensor network (WMSN) is a distributed wireless network that consists of a set of multimedia sensor nodes, which are connected to each other or connected to leading gateways. Nowadays, smart devices such as mobile phones, smart televisions, and smart watches are equipped with sensors and network connections. Hence, with the advances in wireless communication technologies, multimedia sensor networks are expected to be one of the major components in the Internet of things (IoT). A typical application for a WMSN is a surveillance system or a monitoring system. Smart city surveillance cameras with 7/24 recording, or one million sensor nodes reporting meteorological data produce data in various formats as video, audio, and text [1]. All that huge structured or unstructured data is considered as big data, which is defined by a number of Vs; Volume, Velocity, Variety, Veracity, and Value. Min et al. [2] present a comprehensive survey of big data and they identify that “defining the structural model of big data” is a fundamental problem.

1- Introduction

2- Research framework

3- Research model

4- Method

5- Results and discussion

6- Discussion

7- Limitations and future research

8- Conclusion

References

Abstract

The current reliance on different modes for delivering learning materials and establishing connections among peers can be significantly attributed to the teaching and learning practices in higher education. Here, the possible effects of students’ engagement in multiple learning delivery modes on their academic performance were examined. This study specifically examined the effects of students’ engagement in three learning delivery modes—face-to-face (F2F) learning, learning management system (LMS)-based learning, and web-based learning (WBL)—on their learning experience and performance. A total of 196 questionnaires were administrated to university students (104 male; 92 female) to assess their perception of the three modes. Log records were also obtained to investigate the mediation effect of students’ learning experience in LMS-based learning (by access, time spent, tools usage) and WBL (by web tools usage and self-learning time) on their learning performance. Results of a partial least-squares structural equation modeling (PLS-SEM) analysis revealed that students’ engagement in the F2F mode had a significant positive effect on their engagement in the LMS and WBL modes. Moreover, LMS time and tools usage had a positive influence on students’ learning performance in the blended learning environment. Thus, the study results evince the effectiveness of multiple learning delivery modes on the learning development of students in higher education.

Introduction

The literature has emphasized that a blended learning environment typically involves face-to-face (F2F) and other complementary online learning delivery modes (Garrison & Kanuka, 2004; Oliver & Trigwell, 2005; Heirdsfield, Walker, Tambyah, & Beutel, 2011). Typically, students attend conventional instructor-directed F2F classes with synchronous communication (Graham, 2006), and use advanced online learning technologies (Iiyoshi & Kumar, 2008), including an online learning management system (LMS) (e.g., Blackboard (BB)), to create a blended learning environment (Black, Beck, Dawson, Jinks, & DiPietro, 2007; Jain, Abhyankar, & Venugopal, 2013). The experiences gained from using these tools not only support learning communities but also promote students’ learning engagement (Baragash, & AlSamarraie, 2018; Coates, 2005) and success in higher education (Álvarez, Martín, Fernández–Castro, & Urretavizcaya, 2013; Islam, 2013). With the recent development of information and communication technologies (ICTs), university students must be familiar with different learning delivery modes to effectively learn in the online environment (Gros, Garcia, & Escofet, 2012).

I- Introduction

II- LTE physical layer

III- Downlink physical layer decoding protocol

IV- Types of jamming

V- Jamming vulnerability analysis of LTE physical layer

VI- Test setup for jamming margin measurement

VII- Mitigation techniques

VIII- Conclusion

References

Abstract

In Wireless/mobile communication, the requirement of higher throughput and data rates are increasing day-byday. For meeting this requirement, Long Term Evolution (LTE) technology has become the prime choice. Communication using wireless technology has vulnerabilities. LTE wireless technology has vulnerabilities associated with the processing technique as well as well-defined positioning of channels and signals of physical layer. This drawback is of concern, as it can completely or partially block the communication, due to intentional jamming or unintentional interference. To understand the jamming vulnerabilities in LTE, it is essential to understand various physical channels, signals, frame structure, decoding protocols and various types of jamming. This paper provides jamming vulnerability analysis of LTE physical layer, physical channels and signals. Practical lab test setup for measuring jamming margins along with the result is provided in this paper. Various mitigation techniques have been provided in this paper to reduce jamming vulnerabilities.

Introduction

3rd Generation Partnership Project (3GPP) has standardized Long Term Evolution (LTE) technology to meet the demand of growing data rates and higher throughput for cellular communication. As compared to the existing cellular technology, LTE provides higher data rate, lower latency, higher spectral efficiency and higher capacity [1]. Hence, LTE has become the prime choice to meet the growing demand of high data traffic. LTE has already been proposed for public safety network and is in investigation for end use in military application fields. Wireless technologies are vulnerable to radio frequency interference and jamming, due to which the communication link may suffer partial or complete blocking. Unfortunately, wireless communication link undergoes unintentional interference and intentional jamming. Like any wireless technology, LTE is also vulnerable to disruption. Various types of attack possible on LTE are Denial of Service (DoS) and information extraction [2]. Typically, DoS is caused by jamming attacks due to which communication link gets disrupted. DoS at higher layers and attacks for extracting information, falls under cyber attack. In this paper, we have focused to assess the physical layer vulnerabilities and threats which can potentially cause DoS by intentional jamming. The objective of the paper is to provide the insight to the jamming vulnerabilities in LTE physical layer and the techniques to mitigate and withstand jamming.

Driven by the increasing demand for continuous health monitoring, fitness tracking, and virtual reality, there has been a fast-growing market for wearable devices. The total value of wearable devices was estimated to be around $22.0 billion in 2016 and the worldwide revenue is expected to reach $97.8 billion by 2023, growing at a compound annual growth rate of around 24.1% from 2017 to 2023.[1] Wearable sensors that can be laminated onto the surface of skin or integrated with textiles have received tremendous attention from both academia and industry. Such sensors can monitor individual health parameters and environmental exposures with high sensitivity. Tracking of key health indicators such as body temperature, pulse rate, respiration rate, blood pressure, electrocardiogram (ECG), and glucose level can greatly benefit diagnosis, disease treatment, and postoperative rehabilitation. Long-term and continuous monitoring of vital signs is particularly important in early diagnosis of diseases, management of chronic diseases such as diabetes, asthma, hypertension, and severe obesity, and timely response to life-threatening situations such as seizure and cardiac arrest.[2,3] Tracking of environmental conditions such as ambient temperature, ultraviolet (UV) radiation humidity level, and concentration of harmful gases, and airborne particulates can provide valuable information for the prediction, management, and treatment of chronic diseases.[4,5] In addition to the applications in wearable health monitoring, continuous tracking of daily and sports activities can offer an efficient way to assess the well-being and athletic performances.[6] In order to ensure a robust and conformal contact with the curvilinear, coarse, and dynamic surface of skin without impeding daily activities, the wearable sensor should have low modulus and high stretchability. The epidermis has a modulus of 140–600 kPa while the dermis has an even lower modulus of 2–80 kPa.[7] Skin itself can be stretched elastically up to 15% and with the help of wrinkles and creases, the overall stretchability can reach as high as 100% during daily motions.[8] In addition to good wearability, wearable sensors should be highly sensitive, lightweight, low-cost, and with low power consumption. To achieve these features, nanomaterials that are compliant, possessing larger surface area and exceptional material properties, and compatible with low-cost fabrication processes are widely employed as building blocks for developing wearable sensors. In this review, we start from a brief overview of nanomaterials, their related structural designs and fabrication processes (Section 2). Following that, recent advances of nanomaterialenabled wearable sensors including temperature, electrophysiological, strain, tactile, electrochemical, and other sensors will be summarized (Section 3). A survey on the integration of multiple sensors and other components into wearable systems will be given in Section 4. The application of nanomaterialenabled wearable sensors for healthcare including health monitoring, activity tracking, and electronic skin will be presented in Section 5. The challenges and opportunities will be discussed at the end.

Major depression (MDD) has a lifetime prevalence of 14.4% (Kessler, et al., 2012) and a negative impact on overall functioning that is often greater than many physical disorders (World Health Organization, 2011). Although several psychological treatments including cognitive-behavior therapy (Dobson, 1989; Hollon, Shelton, & Davis, 1993; ; Kessler, Petukhova, Sampspn, Zaslavsky, & Wittchen, 2012; Butler, Chapman, & Forman, & Beckl., 2006) and pharmacotherapy (de Maat, Dekker, Schoevers, & de Jonghe, 2007; Thase, Greenhouse, et al., 1997) have been demonstrated to be effective, most people who have MDD do not receive such treatments (Hirshfeld, et al., 1997; ; Davidson & Meltzer-Brody, 1999). For example, the World Health Organization (2011) reported that 76%–85% of those in low/middle income countries and 35%–50% in high income countries receive no mental health treatment. In response to this, technological applications have been suggested as one possible solution to the problem of under-treatment of MDD (Spurgeon & Wright, 2010)Proudfoot, Parker, Pavlovic, Manicavasagar, Adler, & Whitton, 2010).). Ninety-two percent of the U.S. population own cell phones (Anderson, 2015). As of 2015, 68% of cell phone owners were using a smartphone, outnumbering other types of mobile phones, which is a 40% increase since 2011 (Smith, 2015). Ownership is broadly represented among gender, age, and economic groups and has increased across a wide range of demographic groups, which now include nearly equal proportions of African-American (68%), Hispanic (64%), and Caucasian users (66%) (Smith, 2015). The majority of these users own a computer (i.e., 73% own a desktop or laptop,and 45% own a tablet computer. (Anderson, 2015). The national average for Internet use is 87% (Pew Research Center, 2014). (Zickuhr, 2012) argues that these trends and other technological advancements have spurred increased development of a variety of technology-based tools to deliver or augment treatment of MDD. Moreover, the price of data plan fees (that offer Internet and e-mail capability) has been reduced significantly by cellular service providers, which broadens the population of consumers willing and able to invest in the smartphone concept (Klassen, 2011). These advances prompt us to present the usage of these technologies—and other related technologies—to address the issues of under or inadequate treatment of MDD.