In the fields of industrial production or safety monitoring, wireless sensor networks are often content with unreliable and time-varying channels that are susceptible to interference. Consequently, ensuring both transmission reliability and data accuracy has garnered substantial attention in recent years. Although multipath routing-based schemes can provide transmission reliability for wireless sensor networks, achieving high data accuracy simultaneously remains challenging. To address this issue, an Energy-efficient Multipath Routing algorithm balancing data Accuracy and transmission Reliability (EMRAR) is proposed to balance the reliability and accuracy of data transmission. The multipath routing problem is formulated into a multi-objective programming problem aimed at optimizing both reliability and power consumption while adhering to data accuracy constraints. To obtain the solution of the multi-objective programming, an adaptive artificial immune algorithm is employed, in which the antibody initialization method, antibody incentive calculation method, and immune operation are improved, especially for the multipath routing scheme. Simulation results show that the EMRAR algorithm effectively balances data accuracy and transmission reliability while also saving energy when compared to existing algorithms.

Underwater Acoustic Sensor Networks (USANs) which adopt acoustic communication have been widely used in underwater, such as oceans and rivers, monitoring applications. However, the USANs designed for urban lake monitoring are facing challenges and seldom regarded. Based on the analysis of the features of the urban lakes and the monitoring requirements, a USAN system is designed in this paper. The node deployment strategy is discussed to balance the node density and transmission bandwidth. Then a hybrid topology is proposed to meet the monitoring requirements, in which the nodes on the water surface construct a two-dimensional ring topology, and the nodes underwater construct a three-dimensional grid structure. Base on this topology, a MAC protocol i-HALO is designed which improves the existing handshaking mechanism with a dynamic adaptive back off algorithm to avoid unnecessary backoff. As the result, high delay problem is addressed and the “one handshake, multiple transmission” is realized. The channel access delay is analyzed theoretically and the formula of waiting time for a frame before sending is given. The performance of throughput and transmission delay under different node densities and frame lengths are simulated. The results indicate that a node having about 13 neighbors will have the best performance on throughput, and when the frame length is longer than 100 bytes, the throughput and delay will have fluctuation. In comparison with existing protocols, i-HALO has better performance on throughput, delay, frame reception rate and fairness.

Efficient data dissemination protocols play a crucial role in transmitting messages among vehicles in vehicular ad hoc networks (VANETs). In scenarios where the destination nodes in VANETs are unknown, it becomes imperative to disseminate the data across as many locations as possible, thereby enhancing the likelihood of reaching potential destinations. While multihop broadcasting facilitates rapid data coverage in networks, it often results in challenges, such as broadcast storms, data redundancy, and transmission delays. This work addresses these issues from the perspective of maximizing the diversity of system evolution, consequently increasing the probability of receiving data by potential destination nodes. To achieve this objective, an efficient data dissemination algorithm causal entropy increase-based dissemination (CEID) is proposed based on causal path entropy theory. By modeling the topology of urban VANETs and calculating the degrees of nodes centrality, the algorithm selects the relay nodes that can increase the diversity of the system during the data dissemination process. This approach aims to maximize the increase of causal path entropy, allowing as many vehicles as possible to receive data quickly while reducing redundancy and collisions during the data dissemination. In addition, a multidirectional synchronous data dissemination strategy is designed for the environment of urban road intersections to achieve fast and efficient data dissemination in the network. Simulation experiments show the superiority of CEID in terms of system entropy increase rate, data transmission delay, and data redundancy when compared to existing algorithms.

The Cyber-physical system is highly desirable for real-time perception and dynamic control, especially in task scheduling and network communication for large and complex engineering systems. In most of applications, the resource scheduling of cyber and physical network are independent to each other, which may cause severe deterioration of the network performance. To implement the cognitive cooperation between the upper layer of industrial services and the underlying network, we propose a co-optimization method in this paper. We adopt the SDN technology to extract the network control logic, which is integrated with the upper-level task scheduling. This co-optimization scheme can dynamically calculate optimal end-to-end virtual paths over the underlying network infrastructures. Meanwhile, it can also adjust the task scheduling of the upper-level network by signals derived from the underlying network. Simulation results show that our proposed scheme outperforms the traditional method in terms of task acceptance rate, average end-to-end delay and network load balance degree.

In multi-end-to-end path request planning, the control plane may not be able to meet all path request requirements under limited bandwidth resources. Moreover, suboptimal path planning can lead to localized network congestion, which in turn causes an overall imbalance in network load. Therefore, the multi-domain control plane needs to consider more network resource states during the path selection, such as link weights, load saturation, and resource occupancy rates, in order to select the optimal paths to maximize the satisfaction of data plane requirements while maintaining network load balance. To address such issues, we first derive a cross-domain communication load balancing objective function based on network modeling. Through collaborative processing among multi-domain controllers, the coordinated planning of cross-domain paths and the collaborative installation of flow tables are achieved. Then, we transform the cross-domain path planning problem into a clique-finding problem under a set of backup paths. Finally, we provide a heuristic approximate solution method for this problem. In terms of cross-domain communication, we adopt a collaborative approach among multiple controllers to achieve coordinated planning of cross-domain paths and collaborative installation of flow tables. Simulation results show that our proposed scheme outperforms the traditional method in terms of path allocation success rate, network load balancing degree, and data transmission delay, especially in cross-domain communication under high-density path requests in SDN networks.