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Delay tolerant networks (DTNs) is a network evolved from mobile networks. Differing from the traditional network, which has a stable end‐to‐end transmission path, DTNs are sparse and intermittently connected mobile ad hoc network, which are widely used in harsh environments, such as battlefields, seabed, space communication networks, and so on. In DTNs, intermittent connectivity, partitioned network, long delays and node mobility characteristics make the network fail to communicate frequently, therefore, how to successfully forward the message is of extreme importance. Up to now, almost all the traditional models in DTNs use the store‐carry‐forward method. This paper proposes a novel clustered DTN routing model based on sensing node relationship strength. The routing mechanism takes advantage of the number of other nodes encountered by the nodes in the process of movement and the changes in the number of nodes to calculate the strength of the relationship between nodes, and clusters DTN routing according to the strength of the relationship between nodes. Moreover, the relationship between nodes in a cluster and other clusters is used to transmit messages between clusters, and messages are transmitted within clusters according to the strength of the relationship between nodes. Simulation results show that the routing mechanism not only increases the success rate of message transmission, but also reduces the transmission delay of messages and improves network performance. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node. [ABSTRACT FROM AUTHOR]

Processing queries in wireless sensor networks causes quality of service challenges such as data latency and validity. In fact, this paper is going to shed light on identifying the right trade‐off between latency and the freshness of the data. For this purpose, a fuzzy logic‐based decision‐making system that assigns incoming queries either to the sensor network, where the queries are waiting to be processed into a queue, or a central database which provides stored and potentially stale data or to a cache server which in turn provides acceptable latency and recent data, is provided. Thus, a new rule‐based system decision is introduced, that minimizes the request latency and returns the most recent data. In addition, a comparison between the approach and other work that is restricted to the database server and sensor network with no focus on the caching server is provided. This paper is going to carry on with the theory of Markov Chain which is more complicated compared to the fuzzy logic system. This approach is confirmed in the basis of an experience is developed with python. [ABSTRACT FROM AUTHOR]

Clustering algorithm is the primary technology used in target clustering and group status analysis which are key features of the Unmanned Aerial Vehicles (UAVs) control system. Due to variable application environment, the stability of the algorithm in the UAVs control system needs to be considered. K‐means clustering is a widely used method in intelligent systems. However, K‐means algorithm is susceptible to the local optimum due to the influence of the initial centroid. For this problem, the predecessors have proposed various effective solutions. These algorithms perform better on real and large‐scale datasets, but they are unable to achieve optimum results with unbalanced datasets. Herein, a simpler and more effective algorithm for seed initialization is proposed, it has a better accuracy rate than the alternative algorithms.Moreover, after running tests multiple times with each algorithm independently, it has the highest stability and the lowest overall volatility. With unbalanced datasets, the proposed algorithm performs significantly better than several other algorithms and therefore can solve the problems that other algorithms have with unbalanced datasets. [ABSTRACT FROM AUTHOR]