Dynamic Resource Allocation for Remote IoT Data Collection in SAGIN

In this article, we investigate a dynamic resource allocation problem for remote Internet of Things (IoT) data collection in space–air–ground integrated networks (SAGINs), in which the aerial platforms are deployed to bridge the communications between IoT nodes and satellites. To obtain an efficient resource allocation strategy that accommodates the stochastic data arrivals of IoT nodes and the dynamic network topology due to the high mobility of nongeostationary orbit (NGSO) satellites, we first formulate a resource allocation problem with queue stability constraints. Our objective is to maximize the long-term network utility, ensuring a balance between throughput and fairness among the IoT nodes. The formulated long-term problem is challenging to solve due to the unknown future network states and the coupling between continuous and integer variables. Therefore, we adopt the Lyapunov optimization theory to transform the problem into a deterministic problem in each time slot. Moreover, an online resource allocation algorithm is proposed to dynamically determine data admission, subchannel assignment, and power control in each time slot based on the current network status and data backlog. In addition, theoretical analysis indicates that there is an
$[{\cal O({1 / V}),{\mathcal{ O}}(V)}]$
tradeoff between network utility and data backlog with control parameter V. Numerical results demonstrate that the proposed algorithm can greatly enhance the system throughput and reduce data queue backlog as well as preserve queue stability as compared with the benchmarks.