Long Hu

CAP: community activity prediction based on big data analysis

Crowd sensing harnesses the power of the crowd by mobilizing a large number of users carrying various mobile and networked devices to collect data with the intrinsic multi-modal and large-volume features. With traditional methods, it is highly challenging to analyze the vast data volume generated by crowd sensing. In the era of big data, although several individual-oriented approaches are proposed to analyze human behavior based on big data, the common features of individual activity have not been fully investigated. In this article, we design a novel community- centric framework for community activity prediction based on big data analysis. Specifically, we propose an approach to extract community activity patterns by analyzing the big data collected from both the physical world and virtual social space. The proposed approach consists of community detection based on singular value decomposition and clustering, and community activity modeling based on tensors. The proposed approach is evaluated with a case study where a real dataset collected over a 15-month period is analyzed.

Cloud-based Wireless Network: Virtualized, Reconfigurable, Smart Wireless Network to Enable 5G Technologies

In recent years, information communication and computation technologies are deeply converging, and various wireless access technologies have been successful in deployment. It can be predicted that the upcoming fifth generation mobile communication technology (5G) can no longer be defined by a single business model or a typical technical characteristic. 5G is a multi-service and multi-technology integrated network, meeting the future needs of a wide range of big data and the rapid development of numerous businesses, and enhancing the user experience by providing smart and customized services. In this paper, we propose a cloud-based wireless network architecture with four components, i.e., mobile cloud, cloud-based radio access network (Cloud RAN), reconfigurable network and big data centre, which is capable of providing a virtualized, reconfigurable, smart wireless network.

Software defined healthcare networks

With the increasingly serious problem of the aging population, creating an efficient and real-time health management and feedback system based on the healthcare Internet of Things (HealthIoT) is an urgent need. Specifically, wearable technology and robotics can enable a user to collect the required human signals in a comfortable way. HealthIoT is the basic infrastructure for realizing health surveillance, and should be flexible to support multiple application demands and facilitate the management of infrastructure. Therefore, enlightened by the software defined network, we put forward a smart healthcare oriented control method to software define health monitoring in order to make the network more elastic. In this article, we design a centralized controller to manage physical devices and provide an interface for data collection, transmission, and processing to develop a more flexible health surveillance application that is full of personalization. With these distinguished characteristics, various applications can coexist in the shared infrastructure, and each application can demand that the controller customize its own data collection, transmission, and processing as required, and pass the specific configuration of the physical device. This article discusses the background, advantages, and design details of the architecture proposed, which is achieved by an open-ended question and a potential solution. It opens a new research direction of HealthIoT and smart homes.

A Cloudlet-Assisted Multiplayer Cloud Gaming System

The unstable network connectivity is the bottleneck of providing Gaming as a service (GaaS) for mobile devices. Therefore, the most critical technical challenge is to compress and transmit the real-time gaming video, so that during the gaming session, the expected server transmission rate over the bandwidth-limited mobile network can be minimized, while satisfying the quality of experience for the players. Inspired by the idea of peer-to-peer sharing between multiple players, we propose a cloudlet-assisted multiplayer cloud gaming system, in which the mobile devices are connected to the cloud server for real-time interactive game videos, while sharing the received video frames with their peers via an ad hoc cloudlet. Experimental results show that expected server transmission rate can be significantly reduced compared to the conventional video encoding schemes for cloud games.

Green and Mobility-Aware Caching in 5G Networks

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.

Localization Based on Social Big Data Analysis in the Vehicular Networks

Location-based services, especially for vehicular localization, are an indispensable component of most technologies and applications related to the vehicular networks. However, because of the randomness of the vehicle movement and the complexity of a driving environment, attempts to develop an effective localization solution face certain difficulties. In this paper, an overlapping and hierarchical social clustering model (OHSC) is first designed to classify the vehicles into different social clusters by exploring the social relationship between them. By using the results of the OHSC model, we propose a social-based localization algorithm (SBL) that use location prediction to assist in global localization in the vehicular networks. The experiment results validate the performance of the OHSC model and show that the presented SBL algorithm demonstrates superior localization performance compared with the existing methods.

ASA: Against statistical attacks for privacy-aware users in Location Based Service

Abstract The fusion of mobile devices and social networks is stimulating a wider use of Location Based Service (LBS) and makes it become an important part in our daily life. However, the problem of privacy leakage has become a main factor that hinders the further development of LBS. When a LBS user sends queries to the LBS server, the user’s personal privacy in terms of identity and location may be leaked to the attacker. To protect user’s privacy, Niu et al. proposed an algorithm named enhanced-Dummy Location Selection (en-DLS). In this paper, we introduce two attacks to en-DLS, namely long-term statistical attack (LSA) and regional statistical attack (RSA). In the proposed attacks, an attacker can obtain the privacy contents of a user by analyzing LBS historical data, which causes en-DLS to be invalid for user’s privacy protection. Furthermore, this paper proposes a set of privacy protection schemes against both LSA and RSA. For LSA, we propose two protection methods named multiple user name (MNAME) and same user name (SNAME). To solve the regional privacy issue, we divide the map into various regions with different requirements on privacy protection. For this purpose, four levels of protection requirements (PLs) are defined, and true location is protected by allocating a certain number of positions from the dummies according to the location’s PL. Performance analysis and simulation results show that our proposed methods can completely avoid the vulnerabilities of en-DLS to both LSA and RSA, and incur marginal increase of communication overhead and computational cost.

Privacy Protection and Intrusion Avoidance for Cloudlet-based Medical Data Sharing

With the popularity of wearable devices, along with the development of clouds and cloudlet technology, there has been increasing need to provide better medical care. The processing chain of medical data mainly includes data collection, data storage and data sharing, etc. Traditional healthcare system often requires the delivery of medical data to the cloud, which involves users’ sensitive information and causes communication energy consumption. Practically, medical data sharing is a critical and challenging issue. Thus in this paper, we build up a novel healthcare system by utilizing the flexibility of cloudlet. The functions of cloudlet include privacy protection, data sharing and intrusion detection. In the stage of data collection, we first utilize Number Theory Research Unit (NTRU) method to encrypt users body data collected by wearable devices. Those data will be transmitted to nearby cloudlet in an energy efficient fashion. Second, we present a new trust model to help users to select trustable partners who want to share stored data in the cloudlet. The trust model also helps similar patients to communicate with each other about their diseases. Third, we divide users’ medical data stored in remote cloud of hospital into three parts, and give them proper protection. Finally, in order to protect the healthcare system from malicious attacks, we develop a novel collaborative intrusion detection system (IDS) method based on cloudlet mesh, which can effectively prevent the remote healthcare big data cloud from attacks. Our experiments demonstrate the effectiveness of the proposed scheme.

Enabling RFID technology for healthcare: application, architecture, and challenges

Information processing is the cornerstone of patient safety and healthcare quality. In the current healthcare system, critical gaps exist in the collection of vital information from patients and transferring that information to healthcare providers. The information collection problems are particularly challenged in patients lacking verbal communication or under other serious conditions. Information handover among medical staff can also introduce human errors which may place a patient’s health and life at risk. Radio frequency identification (RFID) is a kind of electronic identification technology that is becoming widely deployed. RFID technology allows crucial personal information to be saved in a low-cost chip attached to the patient. This innovative technology has tremendous potential to improve patient healthcare quality by eliminating human errors and ambiguity presented during patient-physician and physician-physician interactions.

Design of QoS-Aware Multi-Level MAC-Layer for Wireless Body Area Network

With the advances in wearable computing and various wireless technologies, there is an increasing trend to outsource body signals from wireless body area network (WBAN) to outside world including cyber space, healthcare big data clouds, etc. Since the environmental and physiological data collected by multimodal sensors have different importance, the provisioning of quality of service (QoS) for the sensory data in WBAN is a critical issue. This paper proposes multiple level-based QoS design at WBAN media access control layer in terms of user level, data level and time level. In the proposed QoS provisioning scheme, different users have different priorities, various sensory data collected by different sensor nodes have different importance, while data priority for the same sensor node varies over time. The experimental results show that the proposed multi-level based QoS provisioning solution in WBAN yields better performance for meeting QoS requirements of personalized healthcare applications while achieving energy saving.