Feng Zeng

Effective Social Relationship Measurement and Cluster Based Routing in Mobile Opportunistic Networks

In mobile opportunistic networks, the social relationship among nodes has an important impact on data transmission efficiency. Motivated by the strong share ability of “circles of friends” in communication networks such as Facebook, Twitter, Wechat and so on, we take a real-life example to show that social relationships among nodes consist of explicit and implicit parts. The explicit part comes from direct contact among nodes, and the implicit part can be measured through the “circles of friends”. We present the definitions of explicit and implicit social relationships between two nodes, adaptive weights of explicit and implicit parts are given according to the contact feature of nodes, and the distributed mechanism is designed to construct the “circles of friends” of nodes, which is used for the calculation of the implicit part of social relationship between nodes. Based on effective measurement of social relationships, we propose a social-based clustering and routing scheme, in which each node selects the nodes with close social relationships to form a local cluster, and the self-control method is used to keep all cluster members always having close relationships with each other. A cluster-based message forwarding mechanism is designed for opportunistic routing, in which each node only forwards the copy of the message to nodes with the destination node as a member of the local cluster. Simulation results show that the proposed social-based clustering and routing outperforms the other classic routing algorithms.

A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels

Traditional underground coalmine monitoring systems are mainly based on the use of wired transmission. However, when cables are damaged during an accident, it is difficult to obtain relevant data on environmental parameters and the emergency situation underground. To address this problem, the use of wireless sensor networks (WSNs) has been proposed. However, the shape of coalmine tunnels is not conducive to the deployment of WSNs as they are long and narrow. Therefore, issues with the network arise, such as extremely large energy consumption, very weak connectivity, long time delays, and a short lifetime. To solve these problems, in this study, a new routing protocol algorithm for multisink WSNs based on transmission power control is proposed. First, a transmission power control algorithm is used to negotiate the optimal communication radius and transmission power of each sink. Second, the non-uniform clustering idea is adopted to optimize the cluster head selection. Simulation results are subsequently compared to the Centroid of the Nodes in a Partition (CNP) strategy and show that the new algorithm delivers a good performance: power efficiency is increased by approximately 70%, connectivity is increased by approximately 15%, the cluster interference is diminished by approximately 50%, the network lifetime is increased by approximately 6%, and the delay is reduced with an increase in the number of sinks.

Energy-Efficient Contact Detection Model in Mobile Opportunistic Networks

Contact discovery is important to energy conservation in mobile opportunistic networks. In this paper, we propose an energy-efficient contact detection model with contact process following the Poisson distribution. Based on the proposed model, we find out the relation between the total number of effective contact detections and the interval of contact probing, and obtain the trade-offs between energy consumption and contact probing efficiency in different situations. Experiment is done, and the result shows the reasonability and correctness of the proposed contact detection model. Based on this work, the mobile devices can have the contact probing interval set according to the environment for energy conservation.

Synergistic Based Social Incentive Mechanism in Mobile Crowdsensing

Most Mobile Crowdsensing (MCS) applications are large-scale and the quality of sensing for sensing tasks is interdependent. Previous incentive mechanisms have focused on quantifying participants’ contribution to the quality of sensing and provide incentives directly to them, which are not applicable to the above scenario. To tackle this problem, in this article, we introduce a novel approach for MCS, called the synergistic based social incentive mechanism. The basic idea is to leverage the social ties among participants to promote cooperation. To maximize the utility of service provider, a moral hazard model is used to analyze the optimal contract between service providers and mobile users in the case of asymmetric information. Experiments show that the synergistic based social incentive mechanism can give users continuous encouragement while maximizing the utility of the principal.

A Message Matching Routing Algorithm Based on Node Sociality in Opportunistic Networks

Abstract Inspired by the importance of grouping in the communication networks such as Facebook, Twitter, Wechat, we choose appropriate forwarding channels for different types of messages in order to improve the rate of message success forwarding, while reducing network overhead and addressing the problem of network congestion. In this paper, we put forward the concept of circle of friends and an effective classification method to assign the circle of friends that the node belongs according to social attributes of node. A message matching routing algorithm (MMAR) is designed to help messages forward, in which nodes select the corresponding circle of friends based on the type of message. The simulation results show that the proposed routing algorithm based on sociality is superior to other classical routing algorithms in forwarding rate, delay and routing overhead.

A Dynamic Human Contacts Prediction Method in Mobile Social Networks

Abstract Human contacts prediction is a challenging task in mobile social networks. In this paper, we extract some new features to predict human contacts and propose a new human contacts prediction method which is suitable for dynamic networks. The method first trains a classifier for each time period and assigns different weights to the classifiers of each time period. Then, an ensemble result of all the classifiers is used to predict human contacts. The experimental results show that our proposed method is efficient.

Joint interference optimization and user satisfaction improvement for multicast routing and channel assignment in wireless mesh networks

Multicast communication is a key technology for wireless mesh networks. In order to reduce co-channel interference in multicast transmission, we jointly study multicast tree construction and channel assignment in this paper. First, interference in multicast is analyzed and measured with considerations of hidden node problem and user coverage. Then, an algorithm called CIOMT is proposed to construct multicast routing tree, In which we find the path with minimum interference to root and add it to the constructing multicast tree, give an upper limit to the source-to-destination distance for QoS consideration, and tree structure is optimized to relieve interference among multicast links. Moreover, we propose a channel assignment algorithm for the constructed multicast tree with the name CIOCA. In CIOCA algorithm, in order to provide good service to the users as much as possible, we take the user coverage of each destination as the top priority in channel assignment, and the nodes are assigned the sending channels by descending order of priority. In each step, we find the channel with minimum interference to the whole tree, and assign it to the related nodes. For making full use of spectrum resources, the non-overlapping and overlapping channels are both taken into consideration. At last, simulation is done, and the results show the effectivity of the proposed multicast routing scheme. As far as user satisfaction is concerned, the proposed scheme has much better performance than the other two classic algorithms.

Efficient Listening and Sleeping Scheduling Mechanism Based on Self-Similarity for Duty Cycle Opportunistic Mobile Networks

In opportunistic mobile networks (OppNets), nodes should be in listening state to discover the neighbors for opportunistic message forwarding. While in OppNets, contacts between nodes are sparse, most of the node’s energy is consumed in idle listening state, which highlights the need for energy saving in contact probing. Duty cycle operation can be applied to address this problem. However, it may cause the degradation of network connectivity when the state of node is turned to be sleeping. In this paper, we propose an adaptive scheduling mechanism based on self-similarity, in which LMMSE predictor is used to predict the future contact information. The state of a node will be set as listening or sleeping adaptively according to the predicted result of future contacts with other nodes. Finally, we validate the effectiveness of the proposed mechanism by conducting a large amount of trace-driven simulations, which show that the proposed mechanism outperforms the random working mechanism and periodical working mechanism in terms of the number of effective contacts, delivery ratio, transmission delay and cost.

Channel Assignment with User Coverage Priority and Interference Optimization for Multicast Routing in Wireless Mesh Networks

In order to improve multicast performance in wireless mesh networks, we consider both multicast tree construction and channel assignment in this paper. To maximize the possibility of providing good service to the users, we treat the user coverage of each destination as the top priority in channel assignment. In addition, we propose the multicast tree constructing algorithm (namely CIOMT) and channel assignment algorithm with the name CIOCA. In the CIOMT algorithm, each destination selects the minimum interference path to the constructing tree and joins into the constructing tree. For QoS consideration, the source-to-destination hop count is up bounded. In the CIOCA algorithm, the nodes are assigned the sending channels by the descending order of priority. During the channel assignment process, the channel with minimum interference to the whole tree will be selected and assigned to the corresponding interfaces. Simulation results have shown that the proposed solution outperforms the other existing algorithms.