Xianbin Cao

Proactive Drone-Cell Deployment: Overload Relief for a Cellular Network Under Flash Crowd Traffic

This paper is concerned with providing radio access network (RAN) elements (supply) for flash crowd traffic demands. The concept of multi-tier cells [heterogeneous networks (HetNets)] has been introduced in 5G network proposals to alleviate the erratic supply–demand mismatch. However, since the locations of the RAN elements are determined mainly based on the long-term traffic behavior in 5G networks, even the HetNet architecture will have difficulty in coping up with the cell overload induced by flash crowd traffic. In this paper, we propose a proactive drone-cell deployment framework to alleviate overload conditions caused by flash crowd traffic in 5G networks. First, a hybrid distribution and three kinds of flash crowd traffic are developed in this framework. Second, we propose a prediction scheme and an operation control scheme to solve the deployment problem of drone cells according to the information collected from the sensor network. Third, the software-defined networking technology is employed to seamlessly integrate and disintegrate drone cells by reconfiguring the network. Our experimental results have shown that the proposed framework can effectively address the overload caused by flash crowd traffic.

An evolutionary approach for dynamic single-runway arrival sequencing and scheduling problem

Aircraft arrival sequencing and scheduling is a classic problem in the air traffic control to ensure safety and order of the operations at the terminal area. Most of the related studies have formulated this problem as a static case and assume the information of all the flights is known in advance. However, the operation of the terminal area is actually a dynamic incremental process. Various kinds of uncertainties may exist during this process, which will make the scheduling decision obtained in the static environment inappropriate. In this paper, aircraft arrival sequencing and scheduling problem is tackled in the form of a dynamic optimization problem. An evolutionary approach, namely dynamic sequence searching and evaluation, is proposed. The proposed approach employs an estimation of distribution algorithm and a heuristic search method to seek the optimal landing sequence of flights. Compared with other related algorithms, the proposed method performs much better on several test instances including an instance obtained from the real data of the Beijing Capital International Airport.

Enhanced routing protocol for fast flying UAV network

Flying UAV network has tremendous potential for civilian and military applications. This paper is concerned with the design of a routing protocol for fast flying UAV network where UAVs are flying fastly and randomly in the sky. Due to the high mobility degree of UAVs, there may not exist instantaneous end-to-end communication path; thus, it is particularly challenging to design an available routing protocol with low transmission delay. The contribution of this paper is that we propose a Fountain-code based Greedy Queue and Position Assisted routing protocol, called FGQPA. It designs a Power Allocation and Routing (PAR) policy to relief the effect of the queue backlog on the overall network delay and employs a “nearest span” scheme to direct packets to the destination with a small delay. Our experimental results show that the proposed FGQPA can achieve lower transmission delay than the state-of-the-art disruption tolerant network routing protocol.

Routing protocol design for drone-cell communication networks

This paper is concerned with the design of routing protocol capable of congestion mitigation for drone-cells communication networks where drone-cells remain stationary in the sky as relays. All of the (distance or hop-count based) existing routing protocols can perform well when the network is lightly loaded. Once the network is heavily loaded, a large number of packets might be backlogged in queues of network nodes since these protocols can not be aware of the network congestion condition. In this paper, we propose a queuing delay and transmission delay based routing protocol (QDTD) to relieve the network congestion caused by heavily loaded traffic. First, QDTD designs a novel ForWard-Back (FWB) queue architecture that significantly reduces the number of queues maintained at each network node. Second, both queuing delay and transmission delay are leveraged as a routing metric to enhance the performance of QDTD. Experimental results show that QDTD can effectively relieve the network congestion and reduce the overall network delay and achieve high throughput.

Offline and Online Search: UAV Multiobjective Path Planning Under Dynamic Urban Environment

This paper is concerned with path planning for unmanned aerial vehicles (UAVs) flying through low altitude urban environment. Although many different path planning algorithms have been proposed to find optimal or near-optimal collision-free paths for UAVs, most of them either do not consider dynamic obstacle avoidance or do not incorporate multiple objectives. In this paper, we propose a multiobjective path planning (MOPP) framework to explore a suitable path for a UAV operating in a dynamic urban environment, where safety level is considered in the proposed framework to guarantee the safety of UAV in addition to travel time. To this aim, two types of safety index maps (SIMs) are developed first to capture static obstacles in the geography map and unexpected obstacles that are unavailable in the geography map. Then an MOPP method is proposed by jointly using offline and online search, where the offline search is based on the static SIM and helps shorten the travel time and avoid static obstacles, while the online search is based on the dynamic SIM of unexpected obstacles and helps bypass unexpected obstacles quickly. Extensive experimental results verify the effectiveness of the proposed framework under the dynamic urban environment.

The networked evolutionary algorithm: A network science perspective

Abstract The evolutionary algorithm is one of the most popular and effective methods to solve complex non-convex optimization problems in different areas of research. In this paper, we systematically explore the evolutionary algorithm as a networked interaction system, where nodes represent information process units and connections denote information transmission links. Within this networked evolutionary algorithm framework, we analyze the effects of structure and information fusion strategies, and further implement it in three typical evolutionary algorithms, namely in the genetic algorithm, the particle swarm optimization algorithm, and in the differential evolution algorithm. Our results demonstrate that the networked evolutionary algorithm framework can significantly improve the performance of these evolutionary algorithms. Our work bridges two traditionally separate areas, evolutionary algorithms and network science, in the hope that it promotes the development of both.