Ruiyun Yu

Multiple many-to-many multicast routing scheme in green multi-granularity transport networks

Due to the ubiquitous use of the Internet and huge proliferation of network devices, the energy consumed by todays networks has increased significantly, implying the need for designing and operating green networks. In this paper, we propose a power-efficient Quality of Service (QoS) routing scheme for multiple many-to-many multicast requests with given static traffic demands in green multi-granularity transport networks, which comprehensively considers both the IP and the optical layers. A chosen probability model is devised to describe the probability of a link being selected when routing, and a heuristic routing algorithm is proposed to construct multiple many-to-many multicast trees in order to decrease power consumption, enhance QoS evaluation and improve resource utilization evaluation under QoS and capacity constraints. Results from simulation experiments demonstrate that our proposed scheme is more power-efficient with higher QoS evaluation and better resource utilization compared with others.

RAQ-A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems.

Air quality information such as the concentration of PM2.5 is of great significance for human health and city management. It affects the way of traveling, urban planning, government policies and so on. However, in major cities there is typically only a limited number of air quality monitoring stations. In the meantime, air quality varies in the urban areas and there can be large differences, even between closely neighboring regions. In this paper, a random forest approach for predicting air quality (RAQ) is proposed for urban sensing systems. The data generated by urban sensing includes meteorology data, road information, real-time traffic status and point of interest (POI) distribution. The random forest algorithm is exploited for data training and prediction. The performance of RAQ is evaluated with real city data. Compared with three other algorithms, this approach achieves better prediction precision. Exciting results are observed from the experiments that the air quality can be inferred with amazingly high accuracy from the data which are obtained from urban sensing.

Multi-Label Classification Methods for Green Computing and Application for Mobile Medical Recommendations

With the explosive development of communication technologies, more customer friendly services have been designed for the next generation of cellular technology, that is, fifth-generation (5G) communication. However, such services require more computing resources and energy. Thus, the development of green and energy-efficient 5G application systems has become an important topic in communications. In this paper, we focus on high-performance multi-label classification methods and their application for medical recommendations in the domain of 5G communication. In machine learning, multi-label classification involves assigning multiple target labels to each query instance. The vast number of labels poses a challenge for maintaining efficiency. Several related approaches have been proposed to meet this challenge. In this paper, we propose two label selection methods for multi-label classification: clustering-based sampling and frequency-based sampling. We apply our proposed multi-label classification methods as an innovative 5G application to predict doctor labels for doctor recommendations. We perform experiments on real-world data sets. The experimental results show that our methods achieve the state-of-the-art performance compared with baselines. In addition, we develop a mobile application of a doctor recommendation system based on our proposed methods.

Reputation-based incentives for data dissemination in mobile participatory sensing networks

The ever-more-capable smart mobile phone gave birth to a novel sensing paradigm, participatory sensing. In the application environment of mobile participatory sensing networks, mobile equipment is usually weakly connected. Due to uncertainty of connection, mobile nodes sometimes need encounter opportunities to accomplish data communication and transmission. However, the participants’ reluctance would diminish their enthusiasm if there is no incentive mechanism. To address this nontrivial issue, we propose reputation-based incentive schemes to motivate participants to disseminate reliable data in participatory sensing system, named RIDD, while minimizing incentive cost for maintaining sufficient number of reliable participants. When an intended receiver receives the data packet from a participant, the receiver authorizes the participant by an acknowledgment message within an encryption code automatically generated by the data packet, which serves as a proof of successful data delivery. RIDD evaluates participants using reputation degree calculated according to the encryption code, encouraging reliable participants to keep being interested in the participatory service with rewards. We conduct simulations in different scenarios. The results show that RIDD remarkably increases the winning probability of participants who disseminate accurate data and reduces the cost for retaining sufficient number of reliable participants.

A Voronoi Diagram Approach for Mobile Element Scheduling in Sparse Sensor Networks

Relaying data with the help of mobile elements seems an effective way to bridge the gaps in sparse sensor networks. In this paper, we propose the Voronoi-diagram-based mobile element scheduling (VDMES) algorithm to construct the shortest possible path for mobile elements. The mobile elements are scheduled to visit a small subset of Voronoi vertices rather than the sensor nodes, which is carefully constructed through iterative virtual site insertion, and exactly covers all sensor nodes in a given transmission radius. The path concatenating the Voronoi vertices is much shorter than that formed by regular sensor nodes.

A Location Cloaking Algorithm Based on Combinatorial Optimization for Location-Based Services in 5G Networks

In order to satisfy the various requirements of future network services, 5G wireless network is proposed and becoming a hot topic in academic and industrial field. Location-based services are widely used with the development of wireless communication and mobile Internet technology. A number of popular spatial-temporal cloaking technologies have been proposed, and the number of users in an anonymizing spatial region (ASR) is uncontrollable. This paper, based on the semi-trusted server architecture proposes a location cloaking algorithm (LCA) based on combinatorial optimization. First, the semi-trusted server architecture divides the information of mobile users into three parts, so adversaries are unable to obtain the location and identity at the same time, then utilize the spatial k -anonymity algorithm LCA to hide the real locations of user, which controls the number of real users to around k in the anonymous result sets (Aset), so that both the number of users in ASR and the area of ASR are minimized, thereby improving query precision and decreasing the resources consumption. Finally, complete further improvement that the location is distinguishable in ASR and the query contents are diverse in Aset. Simulations show that the LCA performs well on cloaking success rate, query precision, and resources saving.

Efficient data gathering using mobile elements in partially connected sensor networks

Wireless sensor networks are usually densely-deployed, while sparse sensor networks have emerged as a special class of sensor networks in recent studies which created a number of new challenges. Relaying data using mobile elements seems an effective way for data gathering in such networks. In this paper, we propose two grid-based mobile element scheduling (GBMES) schemes that schedule a mobile element (ME) to periodically gather data from a partially connected sensor network. As shown in simulations, the GBMES schemes perform well on avoiding data loss due to buffer overflow of sensor nodes.

LAPM: the location aware prediction model in human sensing systems

The mobile human social network actually might be the hugest and best “sensor network” because of the explosive growth in social network content. Nowadays, more and more mobile social applications offer a much easier way for people to share their feeling including vision, haptic, hearing, and smell with the location information by words, images, or even videos. These new sharing methods appearing in the mobile social network actually give us a precious chance to sense the world. Extra systems, which are specialized in particular sensing, do not need to be created any more. The specific sensing data can be acquired from the social network by handling the heterogeneous data. The contribution of this paper lies in developing a model that collects samples considering the relevancy from the perspective of location from different mobile social networks and estimating the occurrence likelihood of the perceived event with collected samples. The simulations and real-world case studies are also developed to verify the reliability of the model and the effectiveness of the Location Aware EM algorithm.

NDI: Node-dependence-based Dynamic gaming Incentive algorithm in opportunistic networks

Opportunistic networks are lack of end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the “store-carry-forward” strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors, and hence improves the reliability and robustness of the networks. In this paper, we propose the Node-dependence-based Dynamic gaming Incentive (NDI) algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective on increase the delivery ratio and decrease average latency when there are a lot of selfish nodes in the opportunistic networks.

Real-time carbon dioxide emission monitoring system based on participatory sensing technology

Based on participatory sensing technology, a real-time carbon dioxide emission monitoring system is built to expand the sensor network. In this system, the sense node uses sensors to collect carbon dioxide emission concentration data, and sends them to a smartphone with GPS via Bluetooth; the smartphone obtains its location and time information and sends the data to the web server. In a given area, the web server uses an optimized interpolation algorithm to process the raw data for real-time web display on ArcGIS map. Based on the analysis and processing of a large number of experimental data, information of carbon dioxide emission on GIS map can be obtained to help solve the traffic route planning, environmental protection, public health and other issues.