Yongfeng Qian

Software-Defined Mobile Networks Security

The future 5G wireless is triggered by the higher demand on wireless capacity. With Software Defined Network (SDN), the data layer can be separated from the control layer. The development of relevant studies about Network Function Virtualization (NFV) and cloud computing has the potential of offering a quicker and more reliable network access for growing data traffic. Under such circumstances, Software Defined Mobile Network (SDMN) is presented as a promising solution for meeting the wireless data demands. This paper provides a survey of SDMN and its related security problems. As SDMN integrates cloud computing, SDN, and NFV, and works on improving network functions, performance, flexibility, energy efficiency, and scalability, it is an important component of the next generation telecommunication networks. However, the SDMN concept also raises new security concerns. We explore relevant security threats and their corresponding countermeasures with respect to the data layer, control layer, application layer, and communication protocols. We also adopt the STRIDE method to classify various security threats to better reveal them in the context of SDMN. This survey is concluded with a list of open security challenges in SDMN.

Data-Driven Computing and Caching in 5G Networks: Architecture and Delay Analysis

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.

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.

Edge cognitive computing based smart healthcare system

Abstract With the rapid development of medical and computer technologies, the healthcare system has seen a surge of interest from both the academia and industry. However, most healthcare systems fail to consider the emergency situations of patients, and are unable to provide a personalized resource service for special users. To address this issue, in this paper, we propose the Edge-Cognitive-Computing-based (ECC-based) smart-healthcare system. This system is able to monitor and analyze the physical health of users using cognitive computing. It also adjusts the computing resource allocation of the whole edge computing network comprehensively according to the health-risk grade of each user. The experiments show that the ECC-based healthcare system provides a better user experience and optimizes the computing resources reasonably, as well as significantly improving in the survival rates of patients in a sudden emergency.

Node Scheduling for All-Directional Intrusion Detection in SDR-Based 3D WSNs

For intrusion detection in 3D wireless sensor networks, the monitoring quality and the energy efficiency are both of great significance. In this paper, we develop a novel globoid model to ensure the all-directional detection quality while saving the network energy effectively, which divides the sensing area into outermost shell and interior region. We first propose an outermost shell coverage algorithm to guarantee the recognition quality of intruding events. Then, a Markov prediction model is designed to predict the motion probability in the adjacent area based on the historical trajectories of intruders. According to the predicted results, different working frequencies will be allocated to the covered nodes by using software defined radio technology. Moreover, a trajectory correction strategy is proposed to relocate the missing intruders during the operation. The performance evaluations show the efficiency of our scheme in terms of the network lifetime, trajectory prediction accuracy, and success rate of correction strategy.

Cloud-assisted humanoid robotics for affective interaction

In recent years, the humanoid robot is received great attention, and gradually develop to households and personal service field. The prominent progress of cloud computing, big data, and machine learning fields provides a strong support for the research of the robot. With affective interaction ability of robot has a broad market space and research value. In this paper, we propose a cloud-assisted humanoid robotics for affective interaction system architecture, and introduce the essential composition, design and implementation of related components. Finally, through an actual robot emotional interaction test platform, validating the feasibility and extendibility of proposed architecture.

AIEM: AI-enabled affective experience management

Abstract Nowadays, with rapid development of artificial intelligence technology, the emerging human–machine interaction application researches grow up with machine intelligence, cognitive science and CEM (Customer Experience Management). This paper puts forward a new AIEM (AI-enabled affective experience management) method, blends AI and CEM in the emotion recognition and interactive intelligence application. Besides, in order to create good user experience, AIEM method also strives for the intelligence at various phases of emotion acquisition, emotion recognition, and emotion interaction. This paper introduces the composition and architecture of AIEM from three aspects, i.e. intelligent management of emotion data collection, accuracy management of emotion recognition, and real-time management of emotion interaction. Then we use advanced algorithm and model in two phases of emotion recognition algorithm and emotion computing offloading. Moreover, we select two deep learning algorithms (VGG-Net and Alex-Net) for facial expression recognition and speech emotion recognition, respectively. In the experiment using AIWAC system in real environment, we evaluate the emotion interaction delay in different computing nodes (Cloud and Edge) using AIEM method. Experiment results show that our method can provide intuitive and reasonable user experience management, and select suitable computing nodes for users. Finally, we provide summary and prospect for the future research proposal.

Towards decentralized IoT security enhancement: A blockchain approach

Abstract With the rapid development of internet of things (IoT), it has brought great convenience to users in different fields, such as smart home, smart transportation and so on. However, it also carries potential security risks. In order to solve this challenge, in this paper, we first introduce three layers of IoT, i.e., perception layer, network layer and application layer, then corresponding security problems of three layers are introduced. Second, we propose a high-level security management scheme based on blockchain for different IoT devices in the full life cycle. Finally, we give open research problems and future work.

Photo Crowdsourcing based Privacy-Protected Healthcare

In this paper, the concept of crowdsourcing is applied to the medical field and a health monitoring mechanism based on photo crowdsourcing is proposed. Specifically, with photo crowdsourcing by many participators, the routine circumstances of users may be represented. However, these photos may include other people than the user, such as the visibility requestor, the invisibility requestor, and the passerby. The visibility and invisibility requestor are the participators in the system, whose identity can be set as visible or invisible, while the passerbys do not participate in the system. Hence, a privacy protection mechanism is proposed for this system, which includes two categories: i) The image fuzzy processing is provided for the invisibility requestor, while the original image is reserved for the visibility requestor. ii) The passerbys image is directly fuzzy processed for privacy protection.