Search

The underwater wireless sensor network (UWSNs) is an important communication facility supporting underwater monitoring applications. However, the transmission channel has the characteristics of high bit error rate, strong multipath effect, and many interference factors, and the network node has the characteristics of high energy consumption, difficult energy supply, and the node position vulnerable to change, which makes it extremely difficult for UWSNs to realize the reliable and efficient packet forwarding. To address the problem, we propose the Stackelberg Q-learning based multi-hop cooperative routing algorithm (SQMCR). The SQMCR builds the transmission routes based on the Q-learning algorithm, considering factors such as the delay, the remaining energy, and the network topology, which improves the rationality and adaptability of selecting the next-hop node. By balancing the packet forwarding benefits and the energy consumption costs based on the Stackelberg Q-learning algorithm, the SQMCR establishes the cooperative communication policy to ensure both the reliability and efficiency of underwater communications. It also adopts initializing Q-values and dynamic exploration probabilities optimization methods to further improve the performance of routing algorithms. Experimental results show that the SQMCR can help UWSNs increase the packet forwarding reliability and prolong the network lifetime by 17%. It has a better environment and application adaptability and is more suitable for underwater high-reliability applications.

Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it. [ABSTRACT FROM AUTHOR]

With the popularization of the two-layer distributed heterogeneous network between the macrocell and the femtocell, the interference problem in the heterogeneous network will become more and more complex. The interference of the femtocell base station (FBS) to the macrocell base station (MBS) limits the achievable rate throughput of the macrocell system. In this letter, a two-stage Stackelberg game was used to design a non-uniform pricing scheme to solve the problem. In order to get this scheme, it was first made that MBS management platform and femtocell system are equivalent. Then we set an incentive mechanism in both stages, and solve the optimization problem in two stages. In the first stage, MBS management platform charged for interference. In the second stage, the total benefit of the entire femtocell system could be get based on the cost and the benefit of each femtocell. FBS made a reasonable allocation of transmission power for each femtocell user (FUE) according to the total benefit. These two stages are mutually constrained and mutually supportive. Finally, we get Stackelberg equilibrium of the Stackelberg Game as a non-uniform pricing scheme. We use the simulation experiment to prove that this scheme is superior to the uniform pricing scheme. [ABSTRACT FROM AUTHOR]

Depression is the result of a complex interaction of social, psychological and physiological elements. Research into the brain disorders of patients suffering from depression can help doctors to understand the pathogenesis of depression and facilitate its diagnosis and treatment. Functional near-infrared spectroscopy (fNIRS) is a non-invasive approach to the detection of brain functions and activities. In this paper, a comprehensive fNIRS-based depression-processing architecture, including the layers of source, feature and model, is first established to guide the deep modeling for fNIRS. In view of the complexity of depression, we propose a methodology in the time and frequency domains for feature extraction and deep neural networks for depression recognition combined with current research. It is found that compared to non-depression people, patients with depression have a weaker encephalic area connectivity and lower level of activation in the prefrontal lobe during brain activity. Finally, based on raw data, manual features and channel correlations, the AlexNet model shows the best performance, especially in terms of the correlation features and presents an accuracy rate of 0.90 and a precision rate of 0.91, which is higher than ResNet18 and machine-learning algorithms on other data. Therefore, the correlation of brain regions can effectively recognize depression (from cases of non-depression), making it significant for the recognition of brain functions in the clinical diagnosis and treatment of depression. [ABSTRACT FROM AUTHOR]

Future industrial cyber-physical system (CPS) devices are expected to request a large amount of delay-sensitive services that need to be processed at the edge of a network. Due to limited resources, service placement at the edge of the cloud has attracted significant attention. Although there are many methods of design schemes, the service placement problem in industrial CPS has not been well studied. Furthermore, none of existing schemes can optimize service placement, workload scheduling, and resource allocation under uncertain service demands. To address these issues, we first formulate a joint optimization problem of service placement, workload scheduling, and resource allocation in order to minimize service response delay. We then propose an improved deep Q-network (DQN)-based service placement algorithm. The proposed algorithm can achieve an optimal resource allocation by means of convex optimization where the service placement and workload scheduling decisions are assisted by means of DQN technology. The experimental results verify that the proposed algorithm, compared with existing algorithms, can reduce the average service response time by 8–10%. [ABSTRACT FROM AUTHOR]

Caching content on mobile devices reduces not only the transmission of backhaul links but also the latency of content acquisition. However, in the existing caching schemes, the joint effect of the caching-aware indicator of users and content dimensions on the caching strategy is not considered. Thus, how to learn fine-grained caching- aware indicators and design caching schemes are still challenging issues. In order to solve these problems, in this article, we first propose the cognitive caching architecture and give the fine-grained caching-aware indicators metric. Then we design a cognitive caching scheme that includes what to do in cognitive caching and how to do cognitive caching. Finally, experimental results show that the proposed cognitive caching scheme is superior to other caching schemes in terms of learning regret and caching cost. [ABSTRACT FROM AUTHOR]

The rapid development of the wearable technology brings an explosive growth of wearable devices and imposes a new challenge to the current network. This is because the wearable devices require real-time interaction and data processing. To cope with this challenge and to realize reasonable utilization of resources, this paper first introduces the network slice-based 5G wearable networks, including the 5G ultra-dense cellular network, the edge caching, and the edge computing. Then, in order to realize the service aware and efficient management of network slicing resources, we propose a data-driven resource management framework which includes the service cognitive engine, the resources cognitive engine, and the global cognitive engine. Furthermore, through information perception, analytical prediction, policy decisions, and performance evaluation, the data-driven resources management method is realized. Finally, we set up a real testbed and conduct a related experiment. The experimental results show that the data-driven resources management scheme can realize the service-aware resources allocation and improve the utilization ratio of resources. [ABSTRACT FROM AUTHOR]

With the development of technologies such as the Internet of Things and artificial intelligence, mobile applications are becoming more and more intelligent. Compared to traditional applications realized by mobile cloud computing technology, these novel applications have a higher requirement for a task offloading scheme. However, the traditional task offloading schemes are hard pressed to meet latency and personalization requirements of these new applications. For intelligent application, how to realize personalized and fine-grained task offloading is still a challenging problem. Therefore, we propose a scheme called intelligent task offloading (iTaskOffloading) for a cloud-edge collaborative system, which can provide personalized task offloading. To be specific, we first propose the architecture of iTaskOffloading which includes the local device layer, edge cloud layer, remote cloud layer, and cognitive engine. Then we analyze the method of iTaskOffloading, which contains coarse-grained computing and fine-grained computing. Finally, we build a testbed to evaluate the proposed iTaskOffloading scheme using a typical intelligent application of emotion detection. The experimental results show that compared to the traditional cloud computing scheme, iTaskOffloading has less task duration. [ABSTRACT FROM AUTHOR]

With the development of communication technology and the explosive growth of video traffic brought by the rapid growth of mobile devices (such as smartphones and wearable devices), great business opportunities have been brought to video service providers. In this paper, we make full use of the cache and computing capacity of edge cloud. Considering the multi bitrate of video, we design the video caching and processing model that offers maximized profit to video service provider. Specifically, we model this problem as the 0–1 optimization problem and design the learning-based online upper confidence bound algorithm based on multi-arm bandit theory. This algorithm can design the corresponding cache and process strategy in real time according to the users’ request to video. Furthermore, this strategy can maximize the profit of video provider and satisfy the service quality for users. Finally, experimental results show that our proposed video caching and processing scheme is superior to other schemes. [ABSTRACT FROM AUTHOR]

With rapid development of unmanned aerial vehicle (UAV) technology, application of UAVs for task offloading has received increasing interest in academia. However, real-time interaction between one UAV and the mobile edge computing node is required for processing the tasks of mobile end users, which significantly increases the system overhead and is unable to meet the demands of large-scale artificial intelligence (AI)-based applications. To tackle this problem, in this article, we propose a new architecture for UAV clustering to enable efficient multi-modal multi-task offloading. With the proposed architecture, the computing, caching, and communication resources are collaboratively optimized using AI-based decision making. This not only increases the efficiency of UAV clusters, but also provides insight into the fusion of computation and communication. [ABSTRACT FROM AUTHOR]

The development of mobile communication technology, hardware, distributed computing, and artificial intelligence (AI) technology has promoted the application of edge computing in the field of heterogeneous IoT in order to overcome the defects of the traditional cloud computing model in the era of big data. In this article, we first propose a new AI-enabled smart edge with heterogeneous IoT architecture that combines edge computing, caching, and communication. Then we propose the Smart-Edge-CoCaCo algorithm. To minimize total delay and confirm the computation offloading decision, Smart-Edge-CoCaCo uses joint optimization of the wireless communication model, the collaborative filter caching model in edge cloud, and the computation offloading model. Finally, we built an emotion interaction testbed to perform computational delay experiments in real environments. The experiment results show that the computation delay of the Smart-Edge-CoCaCo algorithm is lower than that of the traditional cloud computing model with the increase of computing task data and the number of concurrent users. [ABSTRACT FROM AUTHOR]

In this paper, we proposed an emotion-aware video quality of experience (QoE) assessment. Specifically, we first detect the user's emotion via transfer learning. Then, we design a personalized video QoE prediction model. Experiments show that compared to the traditional video QoE assessment, emotion-aware video QoE assessment has higher score. [ABSTRACT FROM AUTHOR]

With the development of intelligent applications (e.g., self-driving, real-time emotion recognition), there are higher requirements for cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed LLTC. By use of the limited computing and storage resources at the edge cloud, LLTC evaluates the value of data that will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission. [ABSTRACT FROM AUTHOR]

With the development of recent innovative applications (e.g., augmented reality, natural language processing, and various cognitive applications), more and more computation-intensive and rich-media tasks are delay-sensitive. Edge cloud computing is expected to be an effective solution to meet the demand for low latency. By the use of content offloading and/or computation offloading, users' quality of experience is improved with shorter delay. Compared to existing edge computing solutions, this article introduces a new concept of computing task caching and gives the optimal computing task caching policy. Furthermore, joint optimization of computation, caching, and communication on the edge cloud, dubbed Edge-CoCaCo, is proposed. Then we give the solution to that optimization problem. Finally, the simulation experimental results show that compared to the other schemes, Edge-CoCaCo has shorter delay. [ABSTRACT FROM AUTHOR]

With the growing popularity of mobile devices, a new type of peer-to-peer communication mode for mobile cloud computing has been introduced. By applying a variety of short-range wireless communication technologies to establish connections with nearby mobile devices, we can construct a mobile cloudlet in which each mobile device can either works as a computing service provider or a service requester. Although the paradigm of mobile cloudlet is cost-efficient in handling computation-intensive tasks, the understanding of its corresponding service mode from a theoretic perspective is still in its infancy. In this paper, we first propose a new mobile cloudlet-assisted service mode named Opportunistic task Scheduling over Co-located Clouds (OSCC), which achieves flexible cost-delay tradeoffs between conventional remote cloud service mode and mobile cloudlets service mode. Then, we perform detailed analytic studies for OSCC mode, and solve the energy minimization problem by compromising among remote cloud mode, mobile cloudlets mode and OSCC mode. We also conduct extensive simulations to verify the effectiveness of the proposed OSCC mode, and analyze its applicability. Moreover, experimental results show that when the ratio of data size after task execution over original data size associated with the task is smaller than 1 (i.e., [ABSTRACT FROM AUTHOR]

Recent advances in wireless networking and big data technologies, such as 5G networks, medical big data analytics, and the Internet of Things, along with recent developments in wearable computing and artificial intelligence, are enabling the development and implementation of innovative diabetes monitoring systems and applications. Due to the life-long and systematic harm suffered by diabetes patients, it is critical to design effective methods for the diagnosis and treatment of diabetes. Based on our comprehensive investigation, this article classifies those methods into Diabetes 1.0 and Diabetes 2.0, which exhibit deficiencies in terms of networking and intelligence. Thus, our goal is to design a sustainable, cost-effective, and intelligent diabetes diagnosis solution with personalized treatment. In this article, we first propose the 5G-Smart Diabetes system, which combines the state-of-the-art technologies such as wearable 2.0, machine learning, and big data to generate comprehensive sensing and analysis for patients suffering from diabetes. Then we present the data sharing mechanism and personalized data analysis model for 5G-Smart Diabetes. Finally, we build a 5G-Smart Diabetes testbed that includes smart clothing, smartphone, and big data clouds. The experimental results show that our system can effectively provide personalized diagnosis and treatment suggestions to patients. [ABSTRACT FROM AUTHOR]

With the development of recent innovative applications (e.g., augment reality, self-driving, and various cognitive applications), more and more computation-intensive and data-intensive tasks are delay-sensitive. Mobile edge computing in ultra-dense network is expected as an effective solution for meeting the low latency demand. However, the distributed computing resource in edge cloud and energy dynamics in the battery of mobile device makes it challenging to offload tasks for users. In this paper, leveraging the idea of software defined network, we investigate the task offloading problem in ultra-dense network aiming to minimize the delay while saving the battery life of user’s equipment. Specifically, we formulate the task offloading problem as a mixed integer non-linear program which is NP-hard. In order to solve it, we transform this optimization problem into two sub-problems, i.e., task placement sub-problem and resource allocation sub-problem. Based on the solution of the two sub-problems, we propose an efficient offloading scheme. Simulation results prove that the proposed scheme can reduce 20% of the task duration with 30% energy saving, compared with random and uniform task offloading schemes. [ABSTRACT FROM AUTHOR]

According to a report by the World Health Organization, diseases caused by an unhealthy lifestyle represent the leading cause of death all over the world. Therefore, it is crucial to monitor and avoid users' unhealthy behaviors. Existing health monitoring approaches still face many challenges of limited intelligence due to insufficient healthcare data. Therefore, this article proposes a smart personal health advisor (SPHA) for comprehensive and intelligent health monitoring and guidance. The SPHA monitors both physiological and psychological states of the user. The SPHAScore model is proposed to evaluate the overall health status of the user. Finally, a testbed for verification of feasibility and applicability of the proposed system was developed. The experimental and simulation results have shown that the proposed approach is efficient for proper user state monitoring. [ABSTRACT FROM AUTHOR]

Recently, there has been increasing interest of deploying computation-intensive and rich-media applications on mobile devices, and ultra-low latency has become an important requirement to achieve high user QoE. However, conventional mobile communication systems are incapable of providing considerable communication and computation resources to support low latency. Although 5G is expected to effectively increase communication capacity, it is difficult to achieve ultra-low end-to-end delay for the ever growing number of cognitive applications. To address this issue, this article first proposes a novel network architecture using a resource cognitive engine and data engine. The resource cognitive intelligence, based on the learning of network contexts, is aimed at a global view of computing, caching, and communication resources in the network. The data cognitive intelligence, based on data analytics, is critical for the provisioning of personalized and smart services toward specific domains. Then we introduce an optimal caching strategy for the small-cell cloud and the macro-cell cloud. Experimental results demonstrate the effectiveness of the proposed caching strategy, and its latency is lower than that of the two conventional approaches, that is, the popular caching strategy and the greedy caching strategy. [ABSTRACT FROM PUBLISHER]

With the drastic increase of mobile devices, there are more and more mobile traffic and repeated requests for content. In 5G networks, small cell base stations (SBSs) caching and caching in wireless device-to-device network can effectively decrease the mobile traffic during peak hours. Currently, most of the related work is focused on how to cache content on SBSs and on mobile devices, and it is assumed that the user can download the entire requested content through the connected SBSs and mobile devices. However, few works have taken user mobility and the randomness of contact duration into consideration. How to improve the caching strategy by exploiting user mobility is still a challenging problem. Thus, in this paper, we first investigate the problem of how to conduct caching placement on SBS and on mobile devices leveraging user mobility, aiming to maximize the cache hit ratio. Specifically, the caching placement on SBSs and on mobile devices is formulated as an integer programming problem, and submodular optimization is adopted to solve the formulated problem. Then, we give the optimal transmission power of SBSs and mobile devices to deliver the caching content in order to reduce the energy cost. Simulation results prove that our caching strategy is more efficient than other existing caching strategies in terms of both cache hit ratio and energy efficiency. [ABSTRACT FROM PUBLISHER]

As mobile devices are equipped with more memory and computational capability, a novel peer-to-peer communication model for mobile cloud computing is proposed to interconnect nearby mobile devices through various short range radio communication technologies to form mobile cloudlets, where every mobile device works as either a computational service provider or a client of a service requester. Though this kind of computation offloading benefits compute-intensive applications, the corresponding service models and analytics tools are remaining open issues. In this paper we categorize computation offloading into three modes: remote cloud service mode, connected ad hoc cloudlet service mode, and opportunistic ad hoc cloudlet service mode. We also conduct a detailed analytic study for the proposed three modes of computation offloading at ad hoc cloudlet. [ABSTRACT FROM PUBLISHER]

Early diagnosis and intervention of depression promote complete recovery, with its traditional clinical assessments depending on the diagnostic scales, clinical experience of doctors and patient cooperation. Recent researches indicate that functional near-infrared spectroscopy (fNIRS) based on deep learning provides a promising approach to depression diagnosis. However, collecting large fNIRS datasets within a standard experimental paradigm remains challenging, limiting the applications of deep networks that require more data. To address these challenges, in this paper, we propose an fNIRS-driven depression recognition architecture based on cross-modal data augmentation (fCMDA), which converts fNIRS data into pseudo-sequence activation images. The approach incorporates a time-domain augmentation mechanism, including time warping and time masking, to generate diverse data. Additionally, we design a stimulation task-driven data pseudo-sequence method to map fNIRS data into pseudo-sequence activation images, facilitating the extraction of spatial-temporal, contextual and dynamic characteristics. Ultimately, we construct a depression recognition model based on deep classification networks using the imbalance loss function. Extensive experiments are performed on the two-class depression diagnosis and five-class depression severity recognition, which reveal impressive results with accuracy of 0.905 and 0.889, respectively. The fCMDA architecture provides a novel solution for effective depression recognition with limited data.