Showing papers in "Mobile Information Systems in 2019"
TL;DR: A novel algorithm based on deep convolutional neural network (DCNN), which classifies the stages of DR into five categories, labeled with an integer ranging between zero and four, and can achieve a recognition rate up to 86.17%, which is higher than previously reported in the literature.
Abstract: Diabetic retinopathy (DR) is a complication of long-standing diabetes, which is hard to detect in its early stage because it only shows a few symptoms. Nowadays, the diagnosis of DR usually requires taking digital fundus images, as well as images using optical coherence tomography (OCT). Since OCT equipment is very expensive, it will benefit both the patients and the ophthalmologists if an accurate diagnosis can be made, based solely on reading digital fundus images. In the paper, we present a novel algorithm based on deep convolutional neural network (DCNN). Unlike the traditional DCNN approach, we replace the commonly used max-pooling layers with fractional max-pooling. Two of these DCNNs with a different number of layers are trained to derive more discriminative features for classification. After combining features from metadata of the image and DCNNs, we train a support vector machine (SVM) classifier to learn the underlying boundary of distributions of each class. For the experiments, we used the publicly available DR detection database provided by Kaggle. We used 34,124 training images and 1,000 validation images to build our model and tested with 53,572 testing images. The proposed DR classifier classifies the stages of DR into five categories, labeled with an integer ranging between zero and four. The experimental results show that the proposed method can achieve a recognition rate up to 86.17%, which is higher than previously reported in the literature. In addition to designing a machine learning algorithm, we also develop an app called “Deep Retina.” Equipped with a handheld ophthalmoscope, the average person can take fundus images by themselves and obtain an immediate result, calculated by our algorithm. It is beneficial for home care, remote medical care, and self-examination.
91 citations
TL;DR: The results obtained from the structural equation modelling showed that the initial trust is considered as the most determinant and influencing factor in the decision of wearable health device adoption followed by health interest, consumer innovativeness, and so on.
Abstract: The advancement in wireless sensor and information technology has offered enormous healthcare opportunities for wearable healthcare devices and has changed the way of health monitoring. Despite the importance of this technology, limited studies have paid attention for predicting individuals’ influential factors for adoption of wearable healthcare devices. The proposed research aimed at determining the key factors which impact an individual's intention for adopting wearable healthcare devices. The extended technology acceptance model with several external variables was incorporated to propose the research model. A multi-analytical approach, structural equation modelling-neural network, was considered for testing the proposed model. The results obtained from the structural equation modelling showed that the initial trust is considered as the most determinant and influencing factor in the decision of wearable health device adoption followed by health interest, consumer innovativeness, and so on. Moreover, the results obtained from the structural equation modelling applied as an input to the neural network indicated that the perceived ease of use is one of the predictors that are significant for adoption of wearable health devices by consumers. The proposed study explains the wearable health device implementation along with test adoption model, and their outcome will help providers in the manufacturing unit for increasing actual users’ continuous adoption intention and potential users’ intention to use wearable devices.
64 citations
TL;DR: A heterogeneous network architecture incorporating multiple wireless interfaces installed on the on-board units to meet the requirements of pervasive connectivity for vehicular ad hoc networks to make them scalable and adaptable for IoV supporting a range of emergency services is proposed.
Abstract: The Internet of vehicles (IoV) is a newly emerged wave that converges Internet of things (IoT) into vehicular networks to benefit from ubiquitous Internet connectivity. Despite various research efforts, vehicular networks are still striving to achieve higher data rate, seamless connectivity, scalability, security, and improved quality of service, which are the key enablers for IoV. It becomes even more critical to investigate novel design architectures to accomplish efficient and reliable data forwarding when it comes to handling the emergency communication infrastructure in the presence of natural epidemics. The article proposes a heterogeneous network architecture incorporating multiple wireless interfaces (e.g., wireless access in vehicular environment (WAVE), long-range wireless fidelity (WiFi), and fourth generation/long-term evolution (4G/LTE)) installed on the on-board units, exploiting the radio over fiber approach to establish a context-aware network connectivity. This heterogeneous network architecture attempts to meet the requirements of pervasive connectivity for vehicular ad hoc networks (VANETs) to make them scalable and adaptable for IoV supporting a range of emergency services. The architecture employs the Best Interface Selection (BIS) algorithm to always ensure reliable communication through the best available wireless interface to support seamless connectivity required for efficient data forwarding in vehicle to infrastructure (V2I) communication successfully avoiding the single point of failure. Moreover, the simulation results clearly argue about the suitability of the proposed architecture in IoV environment coping with different types of applications against individual wireless technologies.
50 citations
TL;DR: A certificateless conditional privacy preserving authentication scheme based on certificateless cryptography and elliptic curve cryptography for secure vehicle-to-infrastructure communication in VANETs and achieves prominent performances of very little average message delay and average message loss ratio is proposed.
Abstract: Vehicular ad hoc networks (VANETs) are an increasing important paradigm for greatly enhancing roadway system efficiency and traffic safety. To widely deploy VANETs in real life, it is critical to deal with the security and privacy issues in VANETs. In this paper, we propose a certificateless conditional privacy preserving authentication (CCPPA) scheme based on certificateless cryptography and elliptic curve cryptography for secure vehicle-to-infrastructure communication in VANETs. In the proposed scheme, a roadside unit (RSU) can simultaneously verify plenty of received messages such that the total verification time may be sharply decreased. Furthermore, the security analysis indicates that the proposed scheme is provably secure in the random oracle model and fulfills all the requirements on security and privacy. To further improve efficiency, both map-to-point hash operation and bilinear pairing operation are not employed. Compared with previous CCPPA schemes, the proposed scheme prominently cuts down computation delay of message signing and verification by 66.9%–85.5% and 91.8%–93.4%, respectively, and reduces communication cost by 44.4%. Extensive simulations show that the proposed scheme is practicable and achieves prominent performances of very little average message delay and average message loss ratio and thus is appropriate for realistic applications.
43 citations
TL;DR: An approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result shows that this approach surpasses the original model obviously and is helpful for integrated pest management.
Abstract: In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).
41 citations
TL;DR: A combination method of learning-based and edge-based algorithms for iris segmentation with 95.49% accuracy on the challenging CASIA-Iris-Thousand database is presented.
Abstract: Iris segmentation is a critical step in the entire iris recognition procedure. Most of the state-of-the-art iris segmentation algorithms are based on edge information. However, a large number of noisy edge points detected by a normal edge-based detector in an image with specular reflection or other obstacles will mislead the pupillary boundary and limbus boundary localization. In this paper, we present a combination method of learning-based and edge-based algorithms for iris segmentation. A well-designed Faster R-CNN with only six layers is built to locate and classify the eye. With the bounding box found by Faster R-CNN, the pupillary region is located using a Gaussian mixture model. Then, the circular boundary of the pupillary region is fit according to five key boundary points. A boundary point selection algorithm is used to find the boundary points of the limbus, and the circular boundary of the limbus is constructed using these boundary points. Experimental results showed that the proposed iris segmentation method achieved 95.49% accuracy on the challenging CASIA-Iris-Thousand database.
39 citations
TL;DR: This study proposes a new version of the self-guided mental healthcare course using smartphones and chatbots to enhance its convenience for use and to maintain user motivation for daily and repeated use.
Abstract: In recent years, mental health management of employees in companies has become increasingly important. As the number of psychotherapists is not enough, it is necessary for employees to be able to keep their mental wellness on their own. A self-guided mental healthcare course using VR devices has been developed, and its stress reduction effect has been validated previously. This study proposes a new version of the course using smartphones and chatbots to enhance its convenience for use and to maintain user motivation for daily and repeated use. The effects of stress reduction and motivation maintenance were acknowledged.
35 citations
TL;DR: An improved collaborative filtering algorithm is proposed, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network.
Abstract: The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.
31 citations
TL;DR: The awareness level of smartphone users for different security-related parameters is fairly low, which needs considerable improvement, and in terms of age, the oldest group has the lowest level followed by the youngest group.
Abstract: As smartphone technology becomes more and more mature, its usage extends beyond and covers also applications that require security. However, since smartphones can contain valuable information, they normally become the target of attackers. A physically lost or a hacked smartphone may cause catastrophic results for its owner. To prevent such undesired events, smartphone users should be aware of existing threats and countermeasures to be taken against them. Therefore, user awareness is a critical factor for smartphone security. This study investigates the awareness level of smartphone users for different security-related parameters and compares the awareness levels of different user groups categorized according to their demographic data. It is based on a survey study conducted on a population with a different range of age, education level, and IT security expertise. According to the obtained results, in general, the awareness level of participants is fairly low, which needs considerable improvement. In terms of age, the oldest group has the lowest level followed by the youngest group. Education level, in general, has a positive effect on the awareness level. Having knowledge about IT is another factor increasing the security awareness level of smartphone users.
30 citations
TL;DR: This scheme extends the recent work with the proposed K-means-based D2D clustering method and the proposed game-based incentive mechanism, which can improve energy efficiency of multimedia content dissemination on the premise of ensuring the desired QoE for most multicast group members.
Abstract: While achieving desired performance, there exist still many challenges in current cellular networks to support the multimedia content dissemination services. The conventional multimedia transmission schemes tend to serve all multicast group members with the data rate supported by the receiving user with the worst channel condition. The recent work discusses how to provide satisfactory quality of service (QoS) for all receiving users with different quality of experience (QoE) requirements, but the energy efficiency improvement of multimedia content dissemination is not its focus. In this paper, we address it based on adaptive clustering and device-to-device (D2D) multicast and propose an energy-efficient multimedia content dissemination scheme under a consistent QoE constraint. Our scheme extends the recent work with the proposed K-means-based D2D clustering method and the proposed game-based incentive mechanism, which can improve energy efficiency of multimedia content dissemination on the premise of ensuring the desired QoE for most multicast group members. In the proposed scheme, we jointly consider the cellular multicast, intracluster D2D multicast, and intercluster D2D multicast for designing the energy-efficient multimedia content dissemination scheme. In particular, we formulate the energy-efficient multicast transmission problem as a Stackelberg game model, where the macro base station (MBS) is the leader and the candidate D2D cluster heads (DCHs) are the followers. Also, the MBS acts as the buyer who buys the power from the candidate DCHs for intracluster and intercluster D2D multicast communications, and the candidate DCHs act as the sellers who earn reward by helping the MBS with D2D multicast communications. Through analyzing the above game model, we derive the Stackelberg equilibrium as the optimal allocation for cellular multicast power, intracluster D2D multicast power, and intercluster D2D multicast power, which can maximize the MBS’s utility function. Finally, the proposed scheme is verified through the simulation experiments designed in this paper.
25 citations
TL;DR: Data flow mining technology framework for user’s mobile behavior trajectory based on location services in mobile e-commerce environment is designed to get user track data that incorporates location information, consumption information, and social information.
Abstract: With the deep cross-border integration of tourism and big data, the personalized demand of tourist groups is increasingly strong. Precision marketing has become a new marketing mode that the tourism industry needs to pay close attention to and explore. Based on the advantages of big data platform and location-based service, starting from the precise marketing demand of tourism, we design data flow mining technology framework for user’s mobile behavior trajectory based on location services in mobile e-commerce environment to get user track data that incorporates location information, consumption information, and social information. Data mining clustering technology is used to analyze the characteristics of users’ mobile behavior trajectories, and the precise recommendation system of tourism is constructed to provide support for tourism decision making. It can target the tourist group for precise marketing and make tourists travel smarter.
TL;DR: This paper considers a multiserver system where a single mobile device asks for computation offloading to multiple nearby servers, and proposes a light-weight algorithm that can provably converge to a bounded near-optimal solution.
Abstract: Mobile edge computing (MEC) provides cloud-computing services for mobile devices to offload intensive computation tasks to the physically proximal MEC servers. In this paper, we consider a multiserver system where a single mobile device asks for computation offloading to multiple nearby servers. We formulate this offloading problem as the joint optimization of computation task assignment and CPU frequency scaling, in order to minimize a tradeoff between task execution time and mobile energy consumption. The resulting optimization problem is combinatorial in essence, and the optimal solution generally can only be obtained by exhaustive search with extremely high complexity. Leveraging the Markov approximation technique, we propose a light-weight algorithm that can provably converge to a bounded near-optimal solution. The simulation results show that the proposed algorithm is able to generate near-optimal solutions and outperform other benchmark algorithms.
TL;DR: A cloud-assisted region monitoring strategy of mobile robots in a smart greenhouse that has better performance than the conventional methods and can reduce the number of monitoring points and time consumption, while the valid monitoring region area is enlarged.
Abstract: In smart agricultural systems, the macroinformation sensing by adopting a mobile robot with multiple types of sensors is a key step for sustainable development of agriculture. Also, in a region monitoring strategy that meets the real-scene requirements, optimal operation of mobile robots is necessary. In this paper, a cloud-assisted region monitoring strategy of mobile robots in a smart greenhouse is presented. First, a hybrid framework that contains a cloud, a wireless network, and mobile multisensor robots is deployed to monitor a wide-region greenhouse. Then, a novel strategy that contains two phases is designed to ensure valid region monitoring and meet the time constraints of a mobile sensing robot. In the first phase, candidate region monitoring points are selected using the improved virtual forces. In the second phase, a moving path for the mobile node is calculated based on Euclidean distance. Subsequently, the applicability of the proposed strategy is verified by the greenhouse test system. The verification results show that the proposed algorithm has better performance than the conventional methods. The results also demonstrate that, by applying the proposed algorithm, the number of monitoring points and time consumption can reduce, while the valid monitoring region area is enlarged.
TL;DR: Considering the defects of the Distance Vector-Hop (DV-Hop) localization algorithm making errors and having error accumulation in wireless sensor network (WSN), a new DV-Hop localization algorithm based on half-measure weighted centroid is proposed.
Abstract: Considering the defects of the Distance Vector-Hop (DV-Hop) localization algorithm making errors and having error accumulation in wireless sensor network (WSN), we proposed a new DV-Hop localization algorithm based on half-measure weighted centroid. This algorithm followed the two-dimensional position distribution, designed the minimum communication radius, and formed a reasonable network connectivity firstly. Then, the algorithm corrected the distance between the beacon node and its neighbour node to form a more accurate jump distance so that the shortest path can be optimized. Finally, we theorized the proposed localization algorithm and verified it in simulation experiments, including same communication radius, different communication radii, and different node densities in same communication radius, and have compared the localization error and localization accuracy, respectively, between the proposed algorithm and the DV-Hop localization algorithm. The experiment’s result shows that the proposed localization algorithm have reduced the localization’s average error and improved the localization’s accuracy.
TL;DR: A location-based TCP technique using the law of gravitation algorithm is proposed, which utilizes test case information, fault information, and location information to prioritize test cases and performs better than traditional TCP techniques.
Abstract: Considering that some intelligent software in mobile devices is related to location of sensors and devices, regression testing for it faces a major challenge. Test case prioritization (TCP), as a kind of regression test optimization technique, is beneficial to improve test efficiency. However, traditional TCP techniques may have limitations on testing intelligent software embedded in mobile devices because they do not take into account characteristics of mobile devices. This paper uses a smart mall as a scenario to design a novel location-based TCP technique for software embedded in mobile devices using the law of gravitation. First, test gravitation is proposed by applying the idea of universal gravitation. Second, a specific calculation model of test gravitation is designed for a smart mall scenario. Third, how to create a faulted test case set is designed by the pseudocode. Fourth, a location-based TCP using the law of gravitation algorithm is proposed, which utilizes test case information, fault information, and location information to prioritize test cases. Finally, an empirical evaluation is presented by using one industrial project. The observation, underlying the experimental results, is that our proposed TCP approach performs better than traditional TCP techniques. In addition, besides location information, the level of devices is also an important factor which affects the prioritization efficiency.
TL;DR: It was discovered that social presence, directly and indirectly, influences tourist intentions towards MTS and the tourists’ perception of compatibility and relative advantages of MTS have insignificant influence on their intention to accept a mobile device for tourism shopping.
Abstract: Consumer adoption of mobile-based tourism shopping is an emerging but overlooked area in tourism research. Given the paybacks and potential scope of this new channel, this study attempts to bridge the gap by proposing a multimediation model investigating mobile tourism shopping (MTS) in a developing country, Pakistan. In particular, we applied structural equation modeling through partial-least-squares structural equation modeling (PLS-SEM) on 396 responses collected from mobile respondents who recently purchased tourism products using a mobile device(s). It was discovered that social presence, directly and indirectly, influences tourist intentions towards MTS. The results further show that the tourists’ perception of compatibility and relative advantages of MTS have insignificant influence on their intention to accept a mobile device(s) for tourism shopping. The findings and implications of the study furnish new vistas to research discourse and managerial significance. Economically, this research contributes to knowledge that could increase income and create jobs in the host country.
TL;DR: The aim of this work is to extract major keywords using text mining method, to identify prominent keyword from the keywords extracted from text mining analysis, and to confirm differences in influences of the keywords which affect corporate performance.
Abstract: We are aim firstly to extract major keywords using text mining method, secondly to identify prominent keyword from the keywords extracted from text mining analysis, and then to confirm differences in influences of the keywords which affect corporate performance. Results were as following. First, keywords have been found to show distinctive features. Since the keywords posted from the clients showed certain tendency, airlines accordingly need service management by identifying the service property through keyword analysis. Second, prominent keywords have been found out of the keyword extracted from text mining. Some of the keywords have significantly correlated with marketing performance, but others not. This implies that the company could uncover consumers’ needs through the prominent keywords and managing the properties related to the prominent keywords would help with improving corporate performance. Third, “recommend” should be treated distinctively with “satisfaction” in terms of service management through the keywords. Results suggest strategic implications to the practical business environment by analyzing keywords around the industry using text mining. We believe this work, which aims to establish common ground for understanding these analyses across multiple disciplinary perspectives, will encourage further research and development of service industry.
TL;DR: Using the activity signal from a smartband, it is possible to distinguish between depressive states, providing a preliminary and automated tool to specialists for the diagnosis of depression almost in real time.
Abstract: Depression is a mental disorder which typically includes recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, and in severe cases fatigue, causing inability to perform daily activities, leading to a progressive loss of quality of life. Monitoring depression (unipolar and bipolar patients) stats relays on traditional method reports from patients; however, bias is commonly present, given the patients’ interpretation of the experiences. Nevertheless, to overcome this problem, Ecological Momentary Assessment (EMA) reports have been proposed and widely used. These reports includes data of the behaviour, feelings, and other type of activities recorded almost in real time using different types of portable devices, which nowadays include smartphones and other wearables such as smartwatches. In this study is proposed a methodology to detect depressive patients with the motion data generated by patient activity, recorded with a smartband, obtained from the “Depresjon” database. Using this signal as information source, a feature extraction approach of statistical features, in time and spectral evolution of the signal, is done. Subsequently, a clever feature selection with a genetic algorithm approach is done to reduce the amount of information required to give a fast noninvasive diagnostic. Results show that the feature extraction approach can achieve a value of 0.734 of area under the curve (AUC), and after applying feature selection approach, a model comprised by two features from the motion signal can achieve a 0.647 AUC. These results allow us to conclude that using the activity signal from a smartband, it is possible to distinguish between depressive states, providing a preliminary and automated tool to specialists for the diagnosis of depression almost in real time.
TL;DR: The LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation and the unknown QoS values can be predicted by integrating locational cluster information and users’ reputation into a hybrid matrix factorization model.
Abstract: With the great development of mobile services, the Quality of Services (QoS) becomes an essential factor to meet end users’ personalized requirement on the nonfunctional performance of mobile services. However, most of the QoS values in real cases are unattainable because a service user would only invoke some specific mobile services. Therefore, how to predict the missing QoS values and recommend high-quality services to end users becomes a significant challenge in mobile service recommendation research. Previous QoS prediction researches demonstrate that the nonfunctional performance of mobile services is closely related to users’ location information. However, most location-aware QoS prediction methods ignore the premise that the obtainable QoS values observed by different users in same location region would probably be untrustworthy, which will lead to inaccurate and unreliable prediction results. To make credible location-aware QoS prediction, we propose a hybrid matrix factorization method integrated location and reputation information (LRMF) to predict the unattainable QoS values. Our approach firstly cluster users into different locational region based on their geographical distribution, and then we compute users’ reputation to identify untrustworthy users in every locational region. Finally, the unknown QoS values can be predicted by integrating locational cluster information and users’ reputation into a hybrid matrix factorization model. Comprehensive experiments are conducted on a public QoS dataset which contains sufficient real-world service invocation records. The evaluation results indicate that our LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation.
TL;DR: A dual-user nonorthogonal multiple access (NOMA) with the help of full-duplex decode-and-forward (DF) relay systems with respect to Nakagami-m fading channel environment is considered to evaluate system performance in terms of outage probability, achievable throughput, and energy efficiency.
Abstract: In this paper, we consider a dual-user nonorthogonal multiple access (NOMA) with the help of full-duplex decode-and-forward (DF) relay systems with respect to Nakagami-m fading channel environment. Especially, we derive the analytical expressions to evaluate system performance in terms of outage probability, achievable throughput, and energy efficiency. The main investigation is on considering how the fading parameters and transmitting power at the base station make crucial impacts on system performance in the various scenarios. Finally, simulations are conducted to confirm the validity of the analysis and show the system performance of NOMA under different fading parameters of Nakagami-m fading channels.
TL;DR: A novel approach to identify hand gestures in complex scenes by the Single-Shot Multibox Detector (SSD) deep learning algorithm with 19 layers of a neural network is proposed.
Abstract: Hand gesture recognition is an intuitive and effective way for humans to interact with a computer due to its high processing speed and recognition accuracy. This paper proposes a novel approach to identify hand gestures in complex scenes by the Single-Shot Multibox Detector (SSD) deep learning algorithm with 19 layers of a neural network. A benchmark database with gestures is used, and general hand gestures in the complex scene are chosen as the processing objects. A real-time hand gesture recognition system based on the SSD algorithm is constructed and tested. The experimental results show that the algorithm quickly identifies humans’ hands and accurately distinguishes different types of gestures. Furthermore, the maximum accuracy is 99.2%, which is significantly important for human-computer interaction application.
TL;DR: A novel key negotiation scheme based on blockchain is proposed, which can be deployed in blockchain-enabled contexts such as data sharing or facilitating electric transactions between vehicles (e.g., unmanned vehicles).
Abstract: While key negotiation schemes, such as those based on Diffie–Hellman, have been the subject of ongoing research, designing an efficient and security scheme remains challenging. In this paper, we propose a novel key negotiation scheme based on blockchain, which can be deployed in blockchain-enabled contexts such as data sharing or facilitating electric transactions between vehicles (e.g., unmanned vehicles). We propose three candidates for flexible selection, namely, key exchanges via transaction currency values through value channels (such as the amount in transactions), automated key exchanges through static scripts,and dynamic scripts, which can not only guarantee key availability with timeliness but also defend against MITM (man-in-the-middle) attacks, packet-dropping attacks, and decryption failure attacks.
TL;DR: A local topology information sensing technology-based broadcast (LISCast) protocol is proposed, making use of the advantage of probability-based forwarding scheme in redundancy inhibition, and simulation results show that LISCast improves the ability to adapt to dynamic topology by optimizing the performance of delay, redundancy, and broadcast efficiency upon the condition of satisfying the high level of transmission reliability.
Abstract: Internet of Vehicle (IoV) is playing an increasingly important role in constructing an Intelligent Transport System (ITS) of safety, efficiency, and green. Safety applications such as emergency warning and collision avoidance require high reliability and timeliness for data transmission. In order to address the problems of slow response and local broadcast storm commonly existing among waiting-based relay schemes of emergency messages, a local topology information sensing technology-based broadcast (LISCast) protocol is proposed in this paper, making use of the advantage of probability-based forwarding scheme in redundancy inhibition. According to the beacon broadcasted periodically between vehicles, LISCast collects information about number and distribution of neighbor, from which the characteristic information such as effective candidate number, maximum forwarding distance, and global traffic density are extracted. Through embedding the characteristic information into the head of broadcast packets by the message sender for assisting in making relay decision, the alternative receivers uniformly schedule forwarding priorities in a distributed and adaptive way. LISCast works without the help of a roadside unit and generates a little more overhead. The simulation results show that LISCast improves the ability to adapt to dynamic topology by optimizing the performance of delay, redundancy, and broadcast efficiency upon the condition of satisfying the high level of transmission reliability.
TL;DR: A mobile edge assisted live streaming system for omnidirectional video (MELiveOV), which can intelligently offload the processing tasks to the edge computing enabled 5G base stations and can reduce the network bandwidth requirement and the transmission delay while ensuring the quality of the user’s experience.
Abstract: As a popular form of virtual reality (VR) media, omnidirectional video (OV) has been continuously developed in recent years. OV contains the view of the scene in every direction, which will ask for around 120 Mbps with 8k resolution and 25 fps (frames per second). Although there has been a lot of work to optimize the transmission for on-demand of OV, the research on the live streaming of OV is still very lacking. Another big challenge for the OV live streaming system is the huge demand for computing resources. The existing terminal devices are difficult to completely carry tasks such as stitching, encoding, and rendering. This paper proposes a mobile edge assisted live streaming system for omnidirectional video (MELiveOV); the MELiveOV can intelligently offload the processing tasks to the edge computing enabled 5G base stations. The MELiveOV consists of an omnidirectional video generation module, a streaming module, and a viewpoint prediction module. A prototype system of MELiveOV is implemented to prove its complete end-to-end OV live streaming service. Evaluation result demonstrates that compared with the traditional solution, MELiveOV can reduce the network bandwidth requirement by about 50% and the transmission delay of more than 70% while ensuring the quality of the user’s experience.
TL;DR: A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address the problem of excessive consumption in dynamic mobile cloud environments.
Abstract: Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynamic mobile cloud environments with the consideration of reducing the offloading decision algorithm’s consumption. A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address this problem. Simulations show that the proposed algorithm helps offloaded applications save more energy and time while consuming fewer computing resources.
TL;DR: The energy efficiency cost minimization problem is formulated, which satisfies the completion time deadline constraint of MDs in an MEC system, and the corresponding Karush–Kuhn–Tucker conditions are applied to solve the optimization problem.
Abstract: Mobile edge computing (MEC) is considered a promising technique that prolongs battery life and enhances the computation capacity of mobile devices (MDs) by offloading computation-intensive tasks to the resource-rich cloud located at the edges of mobile networks. In this study, the problem of energy-efficient computation offloading with guaranteed performance in multiuser MEC systems was investigated. Given that MDs typically seek lower energy consumption and improve the performance of computing tasks, we provide an energy-efficient computation offloading and transmit power allocation scheme that reduces energy consumption and completion time. We formulate the energy efficiency cost minimization problem, which satisfies the completion time deadline constraint of MDs in an MEC system. In addition, the corresponding Karush–Kuhn–Tucker conditions are applied to solve the optimization problem, and a new algorithm comprising the computation offloading policy and transmission power allocation is presented. Numerical results demonstrate that our proposed scheme, with the optimal computation offloading policy and adapted transmission power for MDs, outperforms local computing and full offloading methods in terms of energy consumption and completion delay. Consequently, our proposed system could help overcome the restrictions on computation resources and battery life of mobile devices to meet the requirements of new applications.
TL;DR: Performance analysis shows that the LIAU scheme is able to resist various types of security attacks and it performs well in terms of communication cost and operation time.
Abstract: Communication in VANETs is vulnerable to various types of security attacks since it is constructed based on an open wireless connection. Therefore, a lightweight authentication (LIAU) scheme for vehicle-to-vehicle communication is proposed in this paper. The LIAU scheme requires hash operations and uses cryptographic concepts to transfer messages between vehicles, in order to maintain the required security. Moreover, we made the LIAU scheme lightweight by introducing a small number of variable parameters in order to reduce the storage space. Performance analysis shows that the LIAU scheme is able to resist various types of security attacks and it performs well in terms of communication cost and operation time.
TL;DR: A joint optimization method considering user association and small-cell base station (SBS) on/off strategies in UDNs achieves energy efficiency performance enhancements.
Abstract: The widespread application of wireless mobile services and requirements of ubiquitous access have resulted in drastic growth of the mobile traffic and huge energy consumption in ultradense networks (UDNs). Therefore, energy-efficient design is very important and is becoming an inevitable trend. To improve the energy efficiency (EE) of UDNs, we present a joint optimization method considering user association and small-cell base station (SBS) on/off strategies in UDNs. The problem is formulated as a nonconvex nonlinear programming problem and is then decomposed into two subproblems: user association and SBS on/off strategies. In the user association strategy, users associate with base stations (BSs) according to their movement speeds and utility function values, under the constraints of the signal-to-interference ratio (SINR) and load balancing. In particular, taking care of user mobility, users are associated if their speed exceeds a certain threshold. The macrocell base station (MBS) considers user mobility, which prevents frequent switching between users and SBSs. In the SBS on/off strategy, SBSs are turned off according to their loads and the amount of time required for mobile users to arrive at a given SBS to further improve network energy efficiency. By turning off SBSs, negative impacts on user associations can be reduced. The simulation results show that relative to conventional algorithms, the proposed scheme achieves energy efficiency performance enhancements.
TL;DR: It is demonstrated that it is feasible to use portable motion-sensing methods to measure gait characteristics among Chinese adults and suggested suggestions proposed through user evaluation could be of value to improve the user experience of the motion-Sensing system.
Abstract: Wearable motion sensors with built-in accelerometers have been deployed for gait assessment. This study aims at exploring gait patterns between younger and older adults using a motion-sensing system and exploring sensor technology acceptance among participants. The motion-sensing system was formed by a smart bracelet, an Android application, and a website based on Microsoft Azure. The study employed quasi-experimental, nonexperimental, and qualitative design. A total of 28 younger and 28 older adults were recruited. The gait assessment result indicated that the root mean square (RMS) acceleration increased significantly as the walking pace increased based on the right ankle sensor. Older participants usually presented a lower magnitude of acceleration patterns in the anteroposterior and mediolateral direction compared with the younger participants, while the stride regularity and variability were not significantly different between younger and older participants. User evaluation indicated that the user experience of the motion-sensing system could be further enhanced by providing feedback on the smart bracelet display, generating an analysis report on the gait visualization website, and involving family members in data sharing for older adults. Study findings demonstrated that it is feasible to use portable motion-sensing methods to measure gait characteristics among Chinese adults. Suggestions proposed through user evaluation could be of value to improve the user experience of the motion-sensing system.
TL;DR: In this article, location prediction techniques are used to provide an anchorless mobility management solution in order to ensure seamless handover of the producer during real-time multimedia communication in NDN.
Abstract: Information-centric networking (ICN) is one of the promising solutions that cater to the challenges of IP-based networking. ICN shifts the IP-based access model to a data-centric model. Named Data Networking (NDN) is a flexible ICN architecture, which is based on content distribution considering data as the core entity rather than IP-based hosts. User-generated mobile contents for real-time multimedia communication such as Internet telephony are very common these days and are increasing both in quality and quantity. In NDN, producer mobility is one of the challenging problems to support uninterrupted real-time multimedia communication and needs to be resolved for the adoption of NDN as future Internet architecture. We assert that mobile node’s future location prediction can aid in designing efficient anchor-less mobility management techniques. In this article, we show how location prediction techniques can be used to provide an anchor-less mobility management solution in order to ensure seamless handover of the producer during real-time multimedia communication. The results indicate that with a low level of location prediction accuracy, our proposed methodology still profoundly reduces the total handover latency and round trip time without creating network overhead.