A standards-based, fully-open software platform for smart embedded systems

This paper introduces iFringe, a mobile application that attempts to incorporate the resource heavy fringe analysis algorithms into the smart mobile devices platform. This first step taken towards mobility in the optical processing field aims to become a catalyst for modernization of various aspects of the field as well as to diversify developments to other applications. Predominantly, the motivation of this work stems from the vastly indifferent human interactive method of mobile devices, which enable images displayed on its touch screen to be manipulated in ways that could enhance the fringe analysis experience. Furthermore, given its hardware compatibility to the conventional fringe projection system, these mobile devices could potentially serve as a much more compact replacement. However, one imperative weakness that mobile devices pose is its limited computing ability. Therefore, to examine the feasibility of incorporating the fringe analysis algorithms into a mobile platform, we have implemented two fundamental fringe analysis techniques, namely the Fourier transform fringe analysis method and the phase-shifting technique. Formulas and processing procedures such as discrete Fourier transform (DFT) and quality-guided phase unwrapping, were included in accordance to their original algorithms to test their performance and usability on a smart mobile device. Details of the implementation and the performance results will also be presented in this paper to demonstrate the practicality of these algorithms on the smart mobile device platform.

Recently, application scenario of crowdsourcing IoT has covered to e-healthcare service, smart home, smart city, internet of vehicles due to the proliferation of smart devices such as smart mobile devices, smart wearable device, smart medical devices and smart furniture, etc. Patient's data collected by the smart devices send to the various remote medical servers. A group of medical professionals remote access patient data stored at the medical server database. Smart home users want to remote real-time access information of smart devices at home. All these operations need via wireless remote communication, which is suffering from various kinds of threat and attacks. Hence, there are a large number of multi-factor remote authentication and key agreement schemes designed for the application of crowdsourcing IoT. However, in most existing related multi-factor schemes, all factors for identity authentication only act as a parameter for encrypting the local secret key. In this paper, we propose a new secure remote multi-factor authentication scheme that includes three factors: 1) user identity; 2) password; and 3) user biometrics, which are authenticated by the remote server, act as a part of the secret key and participate in the key agreement process. We choose the chaotic map since it has a smaller key size and lower computational overhead, and then achieve remote multi-factor authentication and key agreement by artfully employ it to zero-knowledge technology and the fuzzy extractor technology. Our scheme is more secure and robust since the user revealing nothing sensitive information, and the adversary cannot impersonate any user even if he gets the server's master key. We have done security proof for our proposed scheme using the Random-Or-Real(ROR) model, Burrows-Abadi-Needham (BAN) logic, and ProVerif 2.00 to show that the presented scheme is secure. Also, we give an additional security analysis for other various attacks. Finally, according to the test and simulation result, the proposed scheme is very suitable for the power-constrained smart devices, and in the next generation 5G communication environment, its applicability and usability will be greatly enhanced.

There is tremendous growth in the amount of digital video creation and communication with the proliferation of mobile consumer electronic devices such as cellular phones with video capture and display capabilities. This trend will continue with the demand for higher spatial and temporal video resolutions accompanied by 3D and multiview video. Since it is not practical to store or transmit the raw digital video data, it is essential to reduce it with digital video compression. To cope with the vast amount of raw digital video data, digital video compression standards use increasingly more complex algorithms. These algorithms achieve higher compression at the same quality at the expense of significant computational complexity. Therefore, it is essential to design low power digital video compression hardware especially for mobile devices. In this talk, I will present two sets of power reduction techniques for digital video compression hardware. The ones in the first set reduce the power consumption with no impact on the video quality. The ones in the second set trade off video quality for power consumption which also enables adjusting video quality based on available energy in the battery of a mobile device for graceful degradation. I will show the impact of these techniques on the power consumption of digital video compression hardware implementing H.264/AVC and H.265/HEVC standards.

Mobile Cloud Computing (MCC) is getting growing interest due to its wide applicability in variety of social, industrial, and commercial mobile applications. Mobile and smart devices can share complex computational operations with Cloud Service Providers (CSPs). It also provides storage, access polices enforcement, and security operations. In many cases, CSP requires services from crowd contributors CC s for data collection, sharing, and mobile application support. It requires trust management for CC s to guard against malicious CC s and ensure security and privacy of data. However, end users or data requesters also demand reliable security solutions for sharing their data or accessing data from unknown CC s. To ensure strong security, mobile devices are not computationally feasible to perform complex cryptographic operations for desired privacy. To resolve these issues, we propose Reputation-aware Trust and Privacy Preservation scheme for MCC. In first phase, we deal with the trust management by utilizing reputation aware selection of CC s and leverage cloud services where CSP maintains trust score for CC s and data requesters. In second phase, we manage privacy preservation by using our proposed Anonymous Secure-shell Ciphertext-policy Attribute Based Encryption (AS-CABE). We have also proposed a hybrid policy tree mechanism for dynamic attribute selection used for security solutions and key management. Next, an anonymous secure shell is maintained between the CC s and the crowd servers to ensure registration after approval from trusted authority. In the similar vein, we propose outsourced encryption and decryption mechanism for mobiles that further utilize encryption and decryption service providers for complex operations. To the best of our knowledge, we are the first one to deal with the trust issues of data requester and privacy concerns of CC s and users both at the same time. After that, we have presented the security analysis to analyze AS-CABE against security attacks. Finally, the results are presented that ensure the supremacy of our proposed scheme as compared to counterparts in terms of reputation score, storage, computation, trust, resilience, encryption, and decryption time.

Spatial data has become a critical issue in recent years. In the past years, nearly more than three quarters of databases, were related directly or indirectly to locations referring to physical features, which constitute the relevant aspects. Spatial data is necessary to identify or calculate the relationships between spatial objects when using spatial operators in programs or portals. Originally, calculations were conducted using Geographic Information System (GIS) programs on local computers. Subsequently, through the Internet, they formed a geospatial web, which is integrated into a discoverable collection of geographically related web standards and key features, and constitutes a global network of geospatial data that employs the World Wide Web to process textual data. In addition, the geospatial web is used to gather spatial data producers, resources, and users. Standards also constitute a critical dimension in further globalizing the idea of the geospatial web. The sensor web is an example of the real time service that the geospatial web can provide. Sensors around the world collect numerous types of data. The sensor web is a type of sensor network that is used for visualizing, calculating, and analyzing collected sensor data. Today, people use smart devices and systems more frequently because of the evolution of technology and have more than one mobile device. The considerable number of sensors and different types of data that are positioned around the world have driven the production of interoperable and platform-independent sensor web portals. The focus of such production has been on further developing the idea of an interoperable and interdependent sensor web of all devices that share and collect information. The other pivotal idea consists of encouraging people to use and send data voluntarily for numerous purposes with the some level of credibility. The principal goal is to connect mobile and non-mobile device in the sensor web platform together to operate for serving and collecting information from people.

Recently, smart phones such as the Galaxy S and IPhone, and tablet PCs such as the Galaxy Tab have been introduced and become widespread in the market, thus the environment for wireless Internet services has been improving. As the market has improved, consumer preferences for mobile devices and the relationship between these devices and wireless Internet services regarding consumer preferences have become a very important issue. However, although there have been a considerable number of studies on the demand for mobile devices, it is not easy to find previous studies on the relationship between consumer preferences for specific attributes of wireless Internet services and the demand for mobile devices. The main purpose of this study is to analyze which mobile device is more likely to be preferred or become the main channel for wireless Internet services and how the demand for each mobile device is influenced by consumer preferences for specific attributes of wireless broadband Internet services used on it, and to induce managerial implications based on the main findings. To derive the results on consumer preferences, conjoint analysis is used. The survey was conducted using 1,000 Korean consumers. In the survey, respondents are allowed to choose more than one hypothetical alternative representing a mobile device combined with a certain type of wireless broadband Internet service if they desired. To estimate the consumer preferences quantitatively, a multivariate probit model is used. According to the estimation results, owing to their portability, smart phones are more likely to become a major channel for wireless Internet services despite their drawbacks in terms of display size and performance. Portability is the most important factor affecting the choice of a mobile device when using wireless Internet services. Therefore, manufacturers of mobile devices for wireless Internet services should improve the overall functionality and performances of mobile devices without negatively affecting portability. However, at the same time, consumer preferences for specific attributes of wireless Internet services are found to affect the demand of each mobile device much differently. Therefore, the prediction that smart phones will be the prevalent mobile device for use of wireless Internet services can be somewhat changed, depending on which and how wireless Internet services are provided to each mobile device and how they affect the demand for each mobile device. As mentioned above, there are significant differences between mobile devices with regard to the effects of mobility, data transfer speed, type of contents or Internet interface for wireless Internet services. It is found that the mobility of wireless Internet service is highly valued for smaller devices, but not much for notebook PCs. In contrast, data transfer speed and similarities in the type of contents or Internet interface to those provided by fixed broadband Internet services become more important for larger mobile devices. Thus, it is necessary for wireless Internet service providers to differentiate and optimize their contents and Internet interfaces properly for various mobile devices. Additionally, it is reasonable that manufacturers of smart phones focus on mobility and portability (or convenience) rather than adding sophisticated functions or pursuing higher-level specifications. Further, for smart phones, for which mobility and convenience are important, wireless Internet service providers should develop and provide easy and convenient programs, rather than advanced programs that require high device performance. Lastly, with respect to individual demographics, young and highly educated people are more likely to be consumers of newly introduced mobile devices for using wireless Internet services. Therefore, these young and highly educated consumers should be considered a major target for manufacturers of mobile devices for wireless Internet services.

The use of mobile devices in the classroom generates a continuous debate. Even if it involves some risks, there is no doubt that they are part of daily lifes of students, as well as the educational community. At higher education the use of these resources is even more relevant, as they all have at least one smart device. In this paper we show four experiences in the use of mobile devices for learning and teaching at the Faculty of Engineering (University of Deusto, Spain). We present four cases where mobile devices were used with educational purpose: generic activities for the learning management, activities in a laboratory, project management and ordinary activities in the classroom. We also provide the results of a questionnaire students answered about how they use mobile devices (in and out the classroom) and how they value their use in their learning.

Wireless networks make it easier for users to connect with each other in the sense of the Internet of Things infrastructure. Smart devices are dynamically connected and set up a network on their own. But there are more barriers to secure connectivity within this own network. Mobile devices like smart home automation access point, smart washing machines, smart platforms, temperature controller, colour-changing smart Lights, smart mobile phones, smartwatches and smart gadgets etc. are prevalent in our everyday lives and is becoming useful tools with wireless networking capabilities that use different wireless protocols that are traditionally used with an IEEE 802.11 access point. On the realism of the Internet, security has been perceived as a prominent inhibitor of embracing the cloud paradigm. It is resource storage and management that may lay in any since the cloud environment is a distributed architecture, which place of the world, many concerns have been raised over its vulnerabilities, security threats and challenges. The involvement of various parties has widened these concerns based on each party's perspective and objective. The Cloud point of view we mainly discuss the causes of obstacles and challenges related to security, reliability, privacy and service availability. Communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in constant growth in the use and popularity of cloud computing. Our purpose of this study is to create a framework of mobile ad hoc network mobility model using cloud computing for providing secure communication among smart devices network for the internet of things in 5G heterogeneous networks. Our main contribution links a new methodology for providing secure communication on the internet of smart devices in 5G. Our methodology uses the correct and efficient simulation of the desired study and can be implemented in a framework of the Internet of Things in 5G.

In the scenario of Industry 4.0, mobile smart devices (SDs) on production lines have to process massive amounts of data. These computing tasks sometimes far exceed the computing capability of SDs and require lots of energy and time to process. How to effectively reduce energy consumption and latency is necessary to be solved. To this end, we first propose an SDN (Software Defined Network)-based MEC (Mobile Edge Computing) system. In the MEC system, SDs can offload computation tasks to edge servers to decrease the processing latency and avoid the waste of energy. At the same time, taking advantage of SDNs programmability, scalability, and isolation of the control plane and the data plane, an SDN controller can manage edge devices within the MEC system. Secondly, based on a stochastic game, we study the computation offloading and resource allocation problems in the MEC system and establish a stochastic game-based computation offloading model. Furthermore, we prove that the multi-user stochastic game in this system can achieve Nash Equilibrium. We further consider each SD as an independent agent and design a stochastic game-based resource allocation algorithm with prioritized experience replays (SGRA-PER) to minimize energy consumption and processing latency with Multi-Agent Reinforcement Learning. Experiment results demonstrate that the proposed SGRA-PER is superior to MADDPG, Q-Mix, and MAPPO algorithms, which can significantly reduce the processing delay and energy consumption with dynamic resource allocation. Moreover, SGRA-PER can still keep a higher performance under the increase of SDs, which can be applied in a large-scale MEC system.

A large variety of digital content for smart mobile devices has entered the market. The demand for more diverse digital content has been accelerated through the implementation of an educational environment termed ‘Smart Education’ in Korea. Digital content can be displayed on smart devices using a content viewer application. However, existing contentonly supportstatic features, and do not support features that deliver analysis of big data. To address these shortcomings, here we propose a method for embedding dynamic educational content,which can be obtained frombig data analytics, into existing smart educational content.

<p>Although great strides have been made to tackle hospital-acquired pressure ulcers (HAPUs), there is a need for greater recognition of device-related pressure ulcers (DRPUs), including their causes, management and prevention. This consensus statement, an updated second edition, aims to continue raising awareness of these largely preventable injuries and, crucially, to stimulate action. DRPUs are relatively common and account for a growing proportion of HAPUs. Updated information on the incidence of DRPUs is described in chapter 1. Although it is recognised that DRPUs increase the financial burden of healthcare, there is little formal analysis of their economic impact. This needs to be addressed; robust evidence on the burden of DRPUs and the value that can be released by adopting prevention strategies is needed to help drive action. Our understanding of the pathophysiology of DRPUs has improved significantly over the past few years; this is described in chapter 2. One crucial difference between PUs and DRPUs is that body-weight forces are less significant in DRPUs, with the force being exerted from a device that is typically strapped or taped onto the body. Devices and their securement may generate high stress concentrations in tissues, leading to cell and tissue-damage pathways associated with sustained deformation. As more evidence is published on DRPUs, recurring themes are emerging, as outlined in chapter 3: The most vulnerable patients are bearing the brunt of DRPUs; paediatric and neonatal patients, and all those needing critical care are particularly susceptible. During the COVID-19 pandemic, a new high-risk population (people with severe COVID-19 infection) emerged. They are at increased risk of DRPUs because of their need for prolonged ventilatory support, especially when 'proning' Devices associated with DRPUs are often used to perform essential, life-saving functions. They include continuous positive airway pressure (CPAP) masks or endotracheal tubes. Minimising their use is clearly not an option, so practice innovation is needed Although the most common locations for DRPUs are the face, ears, lower legs and heels, any location where a device comes into close contact with the skin can be at risk. In the same vein, any device, whether needed for a medical purpose or not, has the capacity to cause injury if its use is not properly managed. Vigilance is needed for all patients. What can be done? The importance of routine risk assessment is covered in chapter 4. Although use of a validated risk assessment tool is the vital first step, this will not be enough on its own. Several steps can be taken to ensure the safe use of devices. These are described in chapter 5 and include device repositioning, cushioning with prophylactic dressings and moisture control (only where possible and clinically appropriate). Of key importance is the development of an institutional protocol and champions to ensure all necessary steps are adopted. For any of these changes to be put into practice, awareness of DRPUs needs to increase. A number of proposals are outlined in chapter 6. A change of focus among health professionals and policy makers, along with more investment in education and training, are needed. All patients being managed with a medical device must be considered as at high risk. The pandemic introduced the world to the problem of DRPUs in health professionals caused by the extended wear of personal protective equipment. Health professionals also have a right to expect institutional protocols and provision of devices that protect them from DRPUs. Cutting-edge ideas and technologies that may be available in the future are described in chapter 7. When designing new products, manufacturers of medical devices have a duty of care to investigate the risks of DRPUs associated with their products and mitigate them, wherever possible. Our developing understanding of how the design, structure and materials used in medical devices contribute to DRPUs will help us develop new solutions for tomorrow. The first step is for everyone involved to ask themselves, 'what can I do to help?' There is work to be done-your journey to reduce DRPUs starts here!.</p>

Smart devices--including speakers, cameras, TVs, and sensors--are increasingly used to enhance comfort, efficiency, security, and entertainment in and outside the home. These devices collect significant audio, video, and environmental data, and that data may be shared with device owners, manufacturers, and/or third-parties. Social computing researchers have devoted significant attention to the privacy risks posed by these devices, although the focus has largely been on primary users who have access and control of devices and data. More recently, researchers have begun examining the privacy risks to incidental users --those who do not own or manage devices but still have their data collected due to their presence in a smart environment. In this paper, we present findings from 25 interviews with US-based adults who have encountered and/or used smart devices as incidental users in rental properties, as part of their work, at friends' and family's houses, and in shared living environments. We explore their reactions to these devices across different contexts, as well as their expectations for how devices should be disclosed and managed by primary users. We found a prevailing sense of resignation across our participants, as well behavioral and environmental modifications in response to smart devices. At the same time, participants were disinterested in gaining access and control to other people's devices, and they were hesitant or uncomfortable with the idea of initiating communication with device owners regarding their concern. Many wanted clearer and more prominent disclosures about device and data management that shifted responsibility from the incidental user to the device owner. Based on our findings, we argue for researchers to focus more closely on sociotechnical approaches to help incidental users navigate privacy risks in smart environments. Such approaches must account for interpersonal factors like trust, power dynamics, and social norms in addition to the many technical solutions that have been introduced in recent years.

As the most immediately impacted population, survivors’ mobility and distribution characteristics are closely linked to earthquake emergency response. Accurate population distribution and mobility data are vital foundational resources for post-disaster decision-making. With the help of mobile phone location data within the earthquake-stricken area, we explore novel rapid assessment approaches to identifying perceived impact area.The widespread adoption of smart mobile devices in China has led to the installation of numerous third-party applications. These applications rely on push notification services enabled by Push Software Development Kits (SDKs) provided by mobile service providers. These SDKs, compliant with data security standards, collection scopes, and transmission protocols, utilize built-in functional modules to periodically collect user-authorized geolocation data. This data encompasses device identifiers, GPS coordinates, WiFi signatures, cellular base station logs, and network connectivity metadata. After encryption, these multi-source data streams inputs are aggregated into structured mobile location datasets. They can provide high-resolution insights into population mobility patterns during disasters.At 9:05 (Beijing time) on 7 January 2025, an earthquake with magnitude Ms 6.8 hit Dingri County, Tibet Autonomous Region, which killed 126 people. We collected a mobile phone location dataset covering a 150 km radius from the epicenter, spanning 38 hours from 9:00 on 6 January to 1:00 on 8 January. It comprises 146,123 records. Each record includes four mobile location-based indicators, namely (1) Active Base Stations (base stations scanned and periodically reported by mobile devices), (2) Active Wi-Fi Hotspots (Wi-Fi hotspots scanned and periodically reported by mobile devices), (3) Mobile Devices (mobile devices that obtain services through multiple positioning methods) and (4) Wi-Fi-Connected Devices (mobile devices connected to Wi-Fi hotspots). Utilizing natural neighbor interpolation and Thiessen polygon interpolation methods, we analyzes changes in four mobile location-based indicators and their spatiotemporal distribution characteristics before and after the earthquake, summarizing crowd movement patterns and communication behaviors after the Dingri earthquake. The results indicate an uneven distribution of population and differing dynamics in mobile phone signal activity. This reflects different behavioral patterns and the potential perceived extent of the earthquake. Within 50 km of the epicenter, all four indicators showed varying degrees of decline post-earthquake, while areas beyond 100 km exhibited short-term surges, reflecting differentiated behavioral responses based on seismic impact severity. In areas experiencing strong shaking, risk avoidance behavior predominated, while in areas where shaking was noticeable but less severe, communication behavior was more prominent. Mobile data decline zones showed high spatial correlation with intensity VIII+ regions, proving their effectiveness as rapid indicators for identifying strongly affected areas. Notably, mobile location data enabled accurate identification of strongly affected zones within 30 min post-earthquake.The research establishes a theoretical-technical framework supporting three critical post-disaster applications: (1) dynamic population distribution sensing, (2) behavioral pattern analysis of affected populations, and (3) rapid evaluation of seismic perception zones.

Even the most cautious user cannot guarantee that avoidance alone will completely protect them from cell phone spyware because mobility plays an increasingly important role in both business and leisure. Advanced Android spyware has become more prevalent as a result of the recent and obvious rise in mobile devices. Currently available Android spyware and other malicious software are frequently camouflaged and buried among the many useful Android apps. These sophisticated spyware skulks in the user-trusted third-party program market may jeopardize the security of the user’s smart device, resulting in financial or personal loss. The spyware makers also distributed their programs under names that were obscured to entice users to install them on their mobile devices. Applications run in the background throughout this procedure and display adverts to prevent users from detecting spyware. Correctly classifying spyware using traditional detection approaches takes a lot of time and human work. Deep learning (DL) based spyware detection and classification approaches have recently been created to address this problem. This study’s objective is to provide a revolutionary methodology that, can successfully detect Android spyware even in the face of tricky and evasive attacks. The main advantage was the utilization of recently specialized datasets from the real-world environment to create more reliable and adaptive training and testing materials, which were then combined with different algorithms to create the most accurate and time-efficient model feasibly. The best outcomes were obtained with 92% accuracy from the Random Forest method. The dataset is thought to offer greater potential and good outcomes, although the model is not assured against recently developing spyware technologies and obfuscation.

The leaf area index (LAI) is an essential indicator for assessing vegetation growth and understanding the dynamics of forest ecosystems and is defined as the ratio of the total leaf surface area in the plant canopy to the corresponding surface area below it. LAI has applications for obtaining information on plant health, carbon cycling, and forest ecosystems. Due to their price and portability, mobile devices are becoming an alternative to measuring LAI. In this research, a new method for estimating LAI using a smart device with a fisheye lens (SFL) is proposed. The traditional Otsu method was enhanced to improve the accuracy and efficiency of foreground segmentation. The experimental samples were located in Gansu Ziwuling National Forest Park in Qingyang. In the accuracy parameter improvement experiment, the variance of the average LAI value obtained by using both zenith angle segmentation and azimuth angle segmentation methods was reduced by 50%. The results show that the segmentation of the front and back scenes of the new Otsu method is more accurate, and the obtained LAI values are more reliable. In the efficiency parameter improvement experiment, the time spent is reduced by 17.85% when the enhanced Otsu method is used to ensure that the data anomaly rate does not exceed 10%, which improves the integration of the algorithm into mobile devices and the efficiency of obtaining LAI. This study provides a fast and effective method for the near-ground measurement of forest vegetation productivity and provides help for the calculation of forest carbon sequestration efficiency, oxygen release rate, and forest water and soil conservation ability.