Opportunistic networks are mobile self-organizing networks that use the encounter opportunities brought by node movement to achieve communication. However, existing opportunistic routing algorithms rarely consider node context information and cache management at the same time, which leads to network congestion and high energy consumption problems in opportunistic networks. To solve the above problems, this paper defines the node historical activity degree and encounter duration based on the context information of nodes, and designs the AD-AC (historical Activity degree and encounter Duration of nodes-Acknowledgment deletion mechanism) opportunistic routing algorithm based on the context information of nodes by incorporating ACK (Acknowledgment) deletion mechanism. The simulation results indicate that AD-AC can substantially improve the message delivery rate while reducing the network overhead as well as the average hop count of messages.

The energy consumed by a node in the opportunity network to forward messages determines its service time and survival period. In order to solve the problem of energy consumption imbalance, the paper proposes an energy equalization opportunity network routing algorithm (EOGH) based on game theory and historical similarity. The algorithm selects a suitable relay node to forward the message based on the remaining energy of the node and its encounter probability with the destination node. Simulation results show that EOGH can get high delivery rate with the energy balance consumption and prolongs the network lifetime.

The paper proposes an Opportunistic Network Routing Strategy Based on Relay Node Collaboration (BRCR), which solves the problem of low effectiveness of data forwarding caused by existing algorithms that ignore the social nature of node movement. The paper introduces a hybrid data forwarding service collection consisting of a cluster of fixed relay nodes and mobile relay nodes, which a relatively stable communication link is established between the source and target nodes. The experimental results show that BRCR proposed in this paper improves the efficiency of data forwarding as compared to classical routing algorithms.

Routing strategy is one of the most important researches in Opportunistic Internet of Things (IoT), and it highly influences the efficiency of data transmission. In this paper, a hybrid Opportunistic IoT secure routing strategy based on node intimacy and trust value (HIRouter) is proposed to resolve the problem of unbalanced transmission efficiency and security in the message delivery process. According to the records of node encounter and message forwarding, the strategy proposed in this paper can calculate nodes’ intimacy and trust value. The messages are then forwarded based on the intimacy and trust value between nodes. Experimental results verify that HIRouter algorithm we proposed can improve the message delivery rates and reduce the overhead rate in the Opportunistic IoT with dense nodes and frequent interactions between nodes.

Existing opportunistic network routing algorithms usually have two main problems: excessive calculation of key nodes leads to the uneven energy consumption of nodes, and limited remaining cache of nodes leads to the loss of important messages. To solve the above problems, this paper proposed a new opportunistic network routing algorithm-EC-CW, which forwards messages according to the multi-copy mechanism and the communication willingness between nodes. The simulation results show that EC-CW reduces the average latency and the overhead rate in the nodes-sparse opportunistic network scenarios composed of high-cache nodes; EC-CW improves the delivery rate and reduces the overhead rate in the nodes-intensive opportunistic network scenarios composed of low-cache nodes.

An opportunistic network is a special type of wireless mobile ad hoc network that does not require any infrastructure, does not have stable links between nodes, and relies on node encounters to complete data forwarding. The unbalanced energy consumption of ferry nodes in an opportunistic network leads to a sharp decline in network performance. Therefore, identifying the ferry node group plays an important role in improving the performance of the opportunistic network and extending its life. Existing research studies have been unable to accurately identify ferry node clusters in opportunistic networks. In order to solve this problem, the concepts of k-core and structural holes have been combined, and a new evaluation indicator, namely, ferry importance rank, has been proposed in this study for analyzing the dynamic importance of nodes in a network. Based on this, a ferry cluster identification model has been designed for accurately identifying the ferry node clusters. The results of the simulations conducted for verifying the performance of the proposed model show that the accuracy of the model to identify the ferry node clusters is 100%.

How we manage Web services depends on how we understand their variable parts and invariable parts. Studying them separately could make Web service research much easier and make our software architecture much more loose-coupled. We summarize two variable parts that affect Web service compositions: uncertain invocation results and uncertain quality of services. These uncertain factors affect success rate of service composition. Previous studies model the Web service problem as a planning problem, while this problem is considered as an uncertain planning problem in this paper. Specifically, we use Partially Observable Markov Decision Process to deal with the uncertain planning problem for service composition. According to the uncertain model, we propose a reinforcement learning method, which is an uncertainty planning method, to compose web services. The proposed method does not need to know complete information of services, instead it uses historical data and estimates the successful possibilities that services are composed together with respect to service outcomes and QoS. Simulation experiments verify the validity of the algorithm, and the results also show that our method improves the success rate of the service composition.