Lieguang Zeng

Software-Defined and Virtualized Future Mobile and Wireless Networks: A Survey

With the proliferation of mobile demands and increasingly multifarious services and applications, mobile Internet has been an irreversible trend. Unfortunately, the current mobile and wireless network (MWN) faces a series of pressing challenges caused by the inherent design. In this paper, we extend two latest and promising innovations of Internet, software-defined networking and network virtualization, to mobile and wireless scenarios. We first describe the challenges and expectations of MWN, and analyze the opportunities provided by the software-defined wireless network (SDWN) and wireless network virtualization (WNV). Then, this paper focuses on SDWN and WNV by presenting the main ideas, advantages, ongoing researches and key technologies, and open issues respectively. Moreover, we interpret that these two technologies highly complement each other, and further investigate efficient joint design between them. This paper confirms that SDWN and WNV may efficiently address the crucial challenges of MWN and significantly benefit the future mobile and wireless network.

Evaluating the Impact of Social Selfishness on the Epidemic Routing in Delay Tolerant Networks

To cope with the uncertainty of transmission opportunities between mobile nodes, Delay Tolerant Networks (DTN) routing exploits opportunistic forwarding mechanism. This mechanism requires nodes to forward messages in a cooperative and altruistic way. However, in the real world, most of the nodes exhibit selfish behaviors such as individual and social selfishness. In this paper, we investigate the problem of how social selfishness influences the performance of epidemic routing in DTN. First, we model the message delivery process with social selfishness as a two dimensional continuous time Markov chain. Then, we obtain the system performance of message delivery delay and delivery cost by explicit expressions. Numerical results show that DTN is quite robust to social selfishness, which increases the message delivery delay, but there is more reducing of delivery cost.

The Impact of Node Selfishness on Multicasting in Delay Tolerant Networks

Due to the uncertainty of transmission opportunities between mobile nodes, delay tolerant networks (DTNs) exploit the opportunistic forwarding mechanism. This mechanism requires nodes to forward messages in a cooperative and selfish way. However, in the real word, most of the nodes exhibit selfish behaviors, such as individual and social selfishness. In this paper, we are the first to investigate how the selfish behaviors of nodes affect the performance of DTN multicast. We consider two typical multicast relaying schemes, namely, two-hop relaying and epidemic relaying, and study their performance in terms of average message transmission delay and transmission cost. Specifically, we model the message delivery process under selfish behaviors by a 3-D continuous time Markov chain; under this model, we derive closed-form formulas for the message transmission delay and cost. Then, we evaluate the accuracy of the proposed Markov chain model by comparing the theoretical results with the simulation results obtained by simulating the message dissemination under both two-hop and epidemic relaying with different network sizes and mobility models. Our study shows that different selfish behaviors may have different impacts on different performance metrics. In addition, selfish behaviors influence epidemic relaying more than two-hop relaying. Furthermore, our results show that the performance of multicast with selfish nodes depends on the multicast group size.

Energy-Efficient Optimal Opportunistic Forwarding for Delay-Tolerant Networks

Due to the uncertainty of transmission opportunities between mobile nodes, the routing of delay-tolerant networks (DTNs) exploits the mechanism of opportunistic forwarding. Efficient algorithms and policies for opportunistic forwarding are crucial for maximizing the message delivery probability while reducing the delivery cost. In this paper, we investigate the problem of energy-efficient opportunistic forwarding for DTNs. First, we model the message dissemination by introducing a continuous-time Markov framework. Based on this framework, we formulate the optimization problem of opportunistic forwarding, with the constraint of energy consumed by the message delivery for both two-hop and epidemic forwarding. Then, based on the solution of the optimization problem, we design different kinds of forwarding policies such as static and dynamic policies. Among these policies, we find that the threshold dynamic policy is optimal for both two-hop and epidemic forwarding. By simulation results, we show the accuracy of our continuous-time Markov analysis model. Furthermore, through extensive numerical results, we demonstrate that the performance of the threshold dynamic policy is the best among the static and continuous dynamic policies, and among the continuous dynamic policies, the negative-power policy provides relatively better performance.

Multiple mobile data offloading through delay tolerant networks

To cope with the explosive traffic demands and limited capacity provided by the current cellular networks, Delay Tolerant Networking (DTN) is used to migrate traffic from the cellular networks to the free and high capacity device-to-device networks. The current DTN-based mobile data offloading models do not address the heterogeneity of mobile traffic and are based on simple network assumptions. In this paper, we establish a mathematical framework to study the problem of multiple mobile data offloading under realistic network assumptions, where 1) mobile data is heterogeneous in terms of size and lifetime, 2) mobile users have different data subscribing interests, and 3) the storage of offloading helpers is limited. We formulate the maximum mobile data offloading as a Submodular Function Maximization problem with multiple linear constraints of limited storage and propose greedy, approximated and optimal algorithms for different offloading scenarios. We show that our algorithms can effectively offload data to DTNs by extensive simulations which employ real traces of both humans and vehicles.

OpenRAN: a software-defined ran architecture via virtualization

With the rapid growth of the demands for mobile data, wireless network faces several challenges, such as lack of efficient interconnection among heterogeneous wireless networks, and shortage of customized QoS guarantees between services. The fundamental reason for these challenges is that the radio access network (RAN) is closed and ossified. We propose OpenRAN, an architecture for software-defined RAN via virtualization. It achieves complete virtualization and programmability vertically, and benefits the convergence of heterogeneous network horizontally. It provides open, controllable, flexible and evolvable wireless networks.

Game Theory Based Bandwidth Allocation Scheme for Network Virtualization

Running multiple virtual networks over a shared physical network is a promising way to support diverse applications, consequently network virtualization is viewed as the keystone of the next-generation architecture. However, decoupling the role of traditional ISPs into Infrastructure Providers (InPs) and Service Providers (SPs), also brings some new challenges to us. For example, how to fairly and efficiently share the sacred physical resources of InPs among multiple SPs is a key problem. The interaction between InPs and SPs, such as cooperation and competition, makes this topic even more complicated. In this paper, we develop a novel approach to encourage efficient behavior in solving the interaction between InPs and SPs by introducing economic incentives, in the form of Game Theory. Based on the non-cooperative game model, a bandwidth allocation scheme in the network virtualization environment is established, using the concept of the Nash Equilibrium. Then we propose an iterative algorithm to find the Nash Equilibrium and solve the bandwidth allocation problem. Finally, we demonstrate the convergence and the effectiveness of our scheme in the experiments.

Collaborative Vehicular Content Dissemination with Directional Antennas

We study the performance of collaborative vehicular content dissemination, where the content is distributed within the network by vehicle-to-vehicle opportunistic communications and the vehicle nodes are equipped with directional antennas. Through analysing a large real-world vehicle trace, we adopt an accurate mobility model of Levy-walk to set up the realistic vehicular network simulation environment. Using a fluid approximation, we derive a theoretical model to depict the system performance of content dissemination time. The accuracy of the proposed analysis is confirmed by simulation results, which also show that the directional antenna performs better than the omni-directional antenna in our considered scenario, especially when the antenna beam is well scheduled with small beamwidth and high beam steering rate.

Opportunistic Spectrum Sharing Based Resource Allocation for Wireless Virtualization

Wireless virtualization has been proposed as a promising way to overcome the current plight of the wireless network, i.e. spectrum crisis, by enabling multiple concurrent wireless virtual networks running on shared wireless substrate resource. However, making efficient use of the underlying spectrum requires effectively assigning the physical spectrum resources to several virtual networks. Since opportunistic spectrum sharing makes wireless communication more intelligent and efficient, we present an opportunistic spectrum sharing based resource allocation scheme for wireless virtualization. First, we formulate the resource allocation problem as an NP-Hard integer program through employing opportunistic spectrum sharing. Then, we propose a dynamic programming algorithm and a heuristic algorithm to solve it. Simulation results confirm the advantage of our algorithms and show that opportunistic spectrum sharing significantly improves the revenue, resource utilization and accept rate while decreasing the payments of virtual networks.

Optimal Beaconing Control for Epidemic Routing in Delay-Tolerant Networks

Owing to the uncertainty of transmission opportunities between mobile nodes, the routing in delay-tolerant networks (DTNs) exploits the mechanism of opportunistic forwarding. Energy-efficient algorithms and policies for DTN are crucial to maximizing the message delivery probability while reducing the delivery cost. In this contribution, we investigate the problem of energy-efficient optimal beaconing control in a DTN. We model the message dissemination under variable beaconing rate with a continuous-time Markov model. Based on this model, we then formulate the optimization problem of the optimal beaconing control for epidemic routing and obtain the optimal threshold policy from the solution of this optimization problem. Furthermore, through extensive numerical results, we demonstrate that the proposed optimal threshold policy significantly outperforms the static policy with constant beaconing rate in terms of system energy consumption savings.