Leveraging the concept of software-defined network (SDN), the integration of terrestrial 5G and satellite networks brings us lots of benefits. The placement problem of controllers and satellite gateways is of fundamental importance for design of such SDN-enabled integrated network, especially, for the network reliability and latency, since different placement schemes would produce various network performances. To the best of our knowledge, it is an entirely new problem. Toward this end, in this paper, we first explore the satellite gateway placement problem to obtain the minimum average latency. A simulated annealing based approximate solution (SAA), is developed for this problem, which is able to achieve a near-optimal latency. Based on the analysis of latency, we further investigate a more challenging problem, i.e., the joint placement of controllers and gateways, for the maximum network reliability while satisfying the latency constraint. A simulated annealing and clustering hybrid algorithm (SACA) is proposed to solve this problem. Extensive experiments based on real world online network topologies have been conducted and as validated by our numerical results, enumeration algorithms are able to produce optimal results but having extremely long running time, while SAA and SACA can achieve approximate optimal performances with much lower computational complexity.

Given the highly dynamic traffic loads of mobile Internet of Things (IoT) devices and their stringent quality-of-service (QoS) requirements, i.e., access delay particularly, as well as the heterogeneous infrastructures among IoT networks, it is a non-trivial task to efficiently deploy cloudlets among large number of access points (APs) in IoT networks, especially for the access delay and network reliability, since different placement schemes would produce various network performances. To combat this issue, we are motivated to investigate in details the optimal placement of cloudlets to minimize the average access delay by applying Software-Defined Networking (SDN) techniques to provide flexible and programmable management for cloudlets deployment in IoT networks with considering the complicated queuing process at numerous SDN-based APs. An enumeration-based optimal placement algorithm (EOPA) is first proposed as benchmark. Then we propose a ranking-based near-optimal placement algorithm (RNOPA) which is able to dynamically adapt to mobile IoT devices and their traffic loads, by treating each AP as a single server queue and adopting an efficient ranking mechanism. As corroborated by extensive simulation results, RNOPA reports access delay very close to that of EOPA. Note that RNOPA outperforms the famous K-medians clustering algorithm (KMCA) in both of average cloudlet access delay and reliability, while at the cost of a much lower computational complexity than KMCA.

The satellite gateway placement problem is of fundamental significance for the design of space-ground integrated network, especially for the network latency and reliability, since different strategies may produce various network performances. In this letter, we propose two solutions for solving this problem, one is an optimal brute force algorithm (OBFA) and the other is a particle swarm based optimization approximate algorithm (PSOA). Simulation results reveal that OBFA can obtain an optimal result with extremely high computational complexity, while PSOA can give approximate optimal performance with lower running time.

Leveraging unmanned aerial vehicles (UAVs) as relays, the space-air-ground integrated Internet of Things (SAG-IoT) networks can provide wireless access services to the connected devices with high data rate, high flexibility, and wide coverage. However, besides bringing lots of benefits, the SAG-IoT systems also face many challenges due to the high mobility, unreliable satellite links, and limited resources. Especially, when multiple UAVs act relays for cross-tier communication to transfer the collected data from ground network tier to the satellite tier, how to select an appropriate number of UAVs as gateways to improve system spectral efficiency (SE) is an important issue. To the best of our knowledge, it is an entirely new problem since most existing works utilize all UAVs as relay nodes and neglect the spectrum allocation. Toward this end, we present this paper to discuss the joint optimization problem of gateway selection, bandwidth allocation, and UAV deployment with the objective of maximizing the system SE. A Dinkelbach method based iterative algorithm is proposed as our solution by alternately adopting simulated annealing and successively convex programming technologies. Experimental results show that our designed joint optimization schemes are able to significantly improve the system SE and can make optimal decisions of gateway selection, bandwidth allocation, as well as UAV deployment.

With the explosive growth of various connected devices and the supported services, the satellite-aerial-terrestrial network (SATN) can fulfill the requirements of the increasing data traffic with enlarged coverage and enhanced capacity. In particular, utilizing multiple unmanned aerial vehicles (UAVs) as relays in SATN holds great promise for bringing lots of benefits in terms of high flexibility and high reliability. However, due to the limited power of UAV and the long distance of the aerial-satellite links, how to improve system energy efficiency (EE) is still a challenging issue since the existing works mainly paid their attentions to resource allocation while neglecting the relay strategy optimization among multiple UAVs. Toward this end, we present this article to explore the problem of system EE maximization through jointly optimizing relay selection and power allocation as well as UAV deployment. An iterative algorithm is presented as our solution via alternately leveraging coordinate ascent and Lagrangian dual decomposition methods. Extensive simulation results demonstrate that our devised optimization strategy can significantly improve the system EE and obtain the optimal relay selection and power allocation decisions.

Inter-segment gateway selection, as a critical issue for data delivery from ground segment to satellite via air network segment, is of great challenges for the design of space-air-ground converged networks (SAGCNs), especially for the transmission energy optimization due to the presence of unreliable or lossy wireless link in air network. To the best of our knowledge, it is an entirely new problem, since existing works on gateway selection mainly focused on one single network segment and gave no consideration to other segments. It is also noted that, we are the first to study the gateway selection problem with the objective of minimizing the transmission energy. Toward this end, in this paper, we formulate the issue of inter-segment gateway selection as a constrained optimization problem and propose two algorithms, i.e., an optimal enumeration algorithm (OEA) and a simulated annealing based algorithm (SOA). Extensive experiments based on different link error rate and relative velocity settings have been conducted and as validated by our numerical results, OEA can obtain an optimal result with extremely high computational complexity and SOA is able to achieve a near-optimal solution with much lower computational complexity.