Dynamic low altitude networks offer significant potential for efficient and reliable data transport via unmanned aerial vehicles (UAVs) relays which usually operate with pre-determined trajectories. However, it is challenging to optimize the data routing and resource allocation due to the time-varying topology and the need to control interference with terrestrial systems. Traditional schemes rely on time-expanded graphs with uniform and fine time subdivisions, making them impractical for interference-aware applications. This paper develops a dynamic space-time graph model with a cross-layer optimization framework that converts a joint routing and predictive resource allocation problem into a joint bottleneck path planning and resource allocation problem. We develop explicit deterministic bounds to handle the channel uncertainty and prove a monotonicity property in the problem structure that enables us to efficiently reach the globally optimal solution to the predictive resource allocation subproblem. Then, this approach is extended to multi-commodity transmission tasks through time-frequency allocation, and a bisection search algorithm is developed to find the optimum solution by leveraging the monotonicity of the feasible set family. Simulations verify that the single commodity algorithm approaches global optimality with more than 30 dB performance gain over the classical graph-based methods for delay-sensitive and large data transportation. At the same time, the multi-commodity method achieves 100X improvements in dense service scenarios and enables an additional 20 dB performance gain by data segmenting.

The rapidly growing human and autonomous robot activities in the low-altitude airspace brings new requirements and challenges to wireless networks. It is needed but challenging to design the aerial transmission strategy to ensure the transmission requirements of aerial nodes and avoid interference with ground terminals in a dynamic network topology. In this work, we study a predictive data transportation problem in aerial networks and formulate a integer programming problem to control the transmission route, handover time, and power. Specifically, a cache-and-pass transmission graph is introduced to convert the integer programming problem to a shortest path problem. Then, a graph-based alternating optimization algorithm with convergence guarantee is developed by using shortest path, alternating optimization algorithms. Simulation verifies that the proposed scheme is able to adjust the transmission strategy according to the data requirements in time-varying topology, reaching almost global optimality, and achieves an order of magnitude improvement compared with the classical static scheme, trivial routing scheme, and space-time routing scheme.

This paper studies a delay-tolerant data transportation problem over an aerial ad hoc network where the aerial nodes have predetermined trajectories. The challenge is how to exploit the large-scale channel information predicted from the predetermined trajectories of the aerial nodes to optimize for the communication strategy in a distributive way. The objective is to minimize both the communication energy and the communication time to control the interference leakage to the ground. Most existing approaches for aerial network communications require intensive centralized coordination, but the trajectory information may not be globally available. To tackle these issues, this paper develops a large timescale two-layer optimization strategy using a game theoretical approach. In the inner layer, a mixed timescale optimization on the power allocation and transmission timing is formulated, which is converted into a one-parameter optimization with an optimality guarantee for a deterministic proxy of the original problem. In the outer layer, ahandover timegame is formulated, and a neighbor coordinate response strategy based on local information exchange is developed, demonstrating rapid convergence and near-global optimality in simulations. Numerical experiments demonstrate that, under large timescale optimization, an order of magnitude of cost saving can be achieved.

Herein, an interference-aware predictive aerial-and-terrestrial communication problem is studied, where an unmanned aerial vehicle (UAV) delivers some data payload to a few nodes within a communication deadline. The first challenge is the possible interference to the ground base stations (BSs) and users possibly at unknown locations. This paper develops a radio-map-based approach to predict the channel to the receivers and the unintended nodes. Therefore, a predictive communication strategy can be optimized ahead of time to reduce the interference power and duration for the ground nodes. Such predictive optimization raises the second challenge of developing a low-complexity solution for a batch of transmission strategies over T time slots for N receivers before the flight. Mathematically, while the proposed interference-aware predictive communication problem is non-convex, it is converted into a relaxed convex problem, and solved by a novel dual-based algorithm, which is shown to achieve global optimality at asymptotically small slot duration. The proposed algorithm demonstrates orders of magnitude of the computational time saving compared to several existing solvers. Simulations show that the radio-map-assisted scheme can reduce the interference to the unintended receivers at known locations below a prescribed threshold and significantly reduce the interference to the users at unknown locations.

This paper studies a decentralized transmission strategy for delay-tolerant wireless data transportation over unmanned aerial vehicle (UAV) networks, where a team of UAVs is employed for data transportation as a secondary task without altering the preset courses of the UAVs from their primary tasks. The key novelty yet challenge is to exploit the predicted channel knowledge among the UAVs to optimize the transmission strategy. Much prior work focuses on multi-UAV cooperation from a centralized perspective, or in a distributed manner but requiring intensive message exchange. This paper develops a team strategy following a game-theoretical approach, where the data packets are passed from one node to the other according to the handover time that is negotiated in a fully distributive way. A Handover Game is thus formulated and is proven that the Nash equilibriums (NEs) are the stationary points of an energy minimization problem for coordinated data transportation. A neighbor coordinate response strategy is designed, which is shown to converge faster than a classical asynchronous response in a multi-player game. Numerical experiments show that the proposed scheme substantially reduces transmission energy from classical relay or data ferry schemes.

The use of radio-frequency identification (RFID) technology in supply chain has been a fairly mature application in recent years, which can be extended to the field of carrier management for the inventory and access control of sensitive files and mobile storage medium. To address the inherent defects of false readings of RFID, we present RF-AMOC, a tag movement identification system that leverages the signal variation patterns between the opposite antennas and the tag to accurately determine whether someone takes the sensitive carrier out of the room or just the normal carrier usage activity in the room. Particularly, we focus on two kinds of signal variation modes: Direct side models, where the RSSI is sensed by one antenna on the tag side, and obstruction side models, where the RSSI is sensed by the other antenna that was obstructed by the person. Then, Pearson Coefficient and crest comparison algorithms are adopted to match the theoretical and actual RF-signal curves on the two sides, respectively. Additionally, a starting point acquisition method is proposed to extract the meaningful time period. A prototype of RF-AMOC is realized in two different environments with various persons, and the results validate that it is superior in terms of sensitivity and specificity with strong robustness.

In this paper, a novel framework for cluster detection in co-operative wireless networks is proposed. This framework is modeled by a dynamic game with incomplete information, in which each player in the game aspires to improve its position in the network by forming cooperative groups. Instead of static systems, the attention we paid in this paper is highly dynamic networks, where the users' high mobility brings a huge challenge in clustering. In order to mitigate that impact, this paper is from the perspective of social relations to clustering, extracting users' social nature from their mobile patterns and designing distributed cluster strategy based on game model. The introduction of social nature with generally long-term characteristics makes clustering framework more predictive and stable. Simulations on real-world networks show that the proposed approach performs well in clustering in cooperative wireless networks.