In this paper, a wireless communication network that uses unmanned aerial vehicles (UAVs) in the sky to transmit information between ground users is considered. We highlight a delay-tolerant network, where information is relayed in a store-and-forward fashion by establishing two types of intermittent communication links: between a UAV and a user (UAV-to-user) and between UAVs (UAV-to-UAV). Thus, a flight algorithm that controls the movement of the UAVs is crucial in achieving rapid information transmission. Our study proposes new flight algorithms that simultaneously consider the two types of communication links. In UAV-to-UAV links, the direct information transmission between two UAVs and the indirect transmission through other UAVs are considered separately. The movement of the UAVs is controlled by solving an optimization problem at certain time intervals, with a variable consideration ratio of the two types of links. In addition, we investigate not only the case where all UAVs move cooperatively but also the case where each UAV moves autonomously. Simulation results show that the proposed algorithms are effective. Moreover, they indicate the existence of an optimal consideration ratio of the two types of communication and demonstrate that our approach enables the control of frequencies of establishing the communication links. We conclude that increasing the frequency of indirect communication between UAVs improves network performance.

Machine-to-Machine (M2M) communication plays a pivotal role in the evolution of Internet of Things (IoT). Cellular networks are considered to be a key enabler for M2M communications, which are originally designed mainly for Human-to-Human (H2H) communications. The introduction of M2M users will cause a series of problems to traditional H2H users, i.e., interference between various traffic. Resource allocation is an effective solution to these problems. In this paper, we consider a shared resource block (RB) and power allocation in an H2H/M2M coexistence scenario, where M2M users are subdivided into delay-tolerant and delay-sensitive types. We first model the RB-power allocation problem as maximization of capacity under Quality-of-Service (QoS) constraints of different types of traffic. Then, a learning framework is introduced, wherein a complex agent is built from simpler subagents, which provides the basis for distributed deployment scheme. Further, we proposed distributed Q-learning based autonomous RB-power allocation algorithm (DQ-ARPA), which enables the machine type network gateways (MTCG) as agents to learn the wireless environment and choose the RB-power autonomously to maximize M2M pairs' capacity while ensuring the QoS requirements of critical services. Simulation results indicates that with an appropriate reward design, our proposed scheme succeeds in reducing the impact of delay-tolerant machine type users on critical services in terms of SINR thresholds and outage ratios.

This paper considers an emergency communication system controlling multiple unmanned aerial vehicles (UAVs) in the sky over a large-scale disaster-affected area. This system is based on delay-tolerant networking, and information from ground users is relayed by the UAVs through wireless transmission and the movement of UAVs in a store-and-forward manner. Each UAV moves autonomously according to a predetermined flight method, which uses the positions of other UAVs through communication. In this paper, we propose a new method for UAV flight considering the non-uniformity of user distributions. The method is based on the Voronoi cell using the predicted locations of other UAVs. We evaluate the performance of the proposed method through computer simulations with a non-uniform user distribution generated by a general cluster point process. The simulation results demonstrate the effectiveness of the proposed method.

One of the important technologies for the Future Internet is the delay-tolerant network, which enables data transfers even when mobile nodes are connected intermittently. Routing algorithms for a delay-tolerant network generally aim to increase the message delivery rate and decrease the number of forwarded messages in the situation of an intermittent connection. A fame-based strategy for delay-tolerant network routing is suggested in this work. The number of contacts of a node with other nodes, known as the fame degree in this work, is counted to rank the fame degree of the node. By utilizing the fame degree, the proposed routing algorithm determines the probability of forwarding the messages of a node to the contact node. Due to the characteristics of the proposed algorithm, it can be combined harmonically with the PROPHET routing algorithm. Through experiments on well-known benchmark datasets, the proposed algorithms shows better delivery rates with much lower number of forwarded messages and lower average hop counts of delivered messages compared to Epidemic, PROPHET and SimBet.