In wireless sensor networks (WSNs), collecting data with mobile sinks is an effective way to solve the “energy hole problem”. However, most of existing algorithms of mobile sinks ignore the load balance of rendezvous nodes, which will significantly shorten the network lifetime. Moreover, most mobile sinks are usually required to visit locations of sensor nodes without taking advantage of their communication ranges. Therefore, this paper proposes an energy-efficient trajectory planning algorithm (EETP) based on multi-objective particle swarm optimization (MOPSO) to shorten the trajectory length of the mobile sink and balance the load of rendezvous nodes. EETP aims to reduce the delay in data delivery and prolong the network lifetime. To shorten the trajectory length of the mobile sink, we design a mechanism to select potential visiting points within communication overlapping ranges of sensor nodes, rather than locations of sensor nodes. Additionally, according to trajectory characteristics of the mobile sink, we design an effective trajectory encoding method that can generate a trajectory containing an unfixed number of visiting points. The simulation results show that the proposed EETP is superior to existing WRP, CB and the MOPSO-based algorithm, in terms of delay in data delivery, network lifetime and energy consumption.

Existing routing protocols for multi-sink wireless sensor networks (WSNs) attempt to optimize energy efficiency and routing survivability from the perspective of the network itself, without considering the impact of external environment, making them unable to respond to environmental changes in a timely manner. Therefore, the routing survivability of these routing protocols in harsh environments is questionable. To solve this problem, we design a sustainable multipath routing protocol SMRP, in which the routing decisions are made according to a mixed potential field in terms of depth, residual energy and environment. The basic idea of SMRP is to instruct messages to select paths with a tradeoff among delivery latency, energy balance and routing survivability. As the environmental field is constructed and updated using the sensing capability of WSN itself, the constructed multipath can be secured by avoiding passing through the dangerous areas. We explore the impact of the number of sink nodes and the number of paths on routing performance, and compare SMRP with commonly used routing protocols (i.e., EDGR and IPF). Through extensive experiments, SMRP has proved to have better performance in terms of packet delivery ratio and network lifetime under harsh environments.

In practical cyber-manufacturing systems (CMS), the node component is the forwarder of information and the provider of services. This dual role makes the whole system have the typical physical-services interaction characteristic, making CMS more vulnerable to cascading failures than general manufacturing systems. In this work, in order to reasonably characterize the cascading process of CMS, we first develop an interdependent network model for CMS from a physical-service networking perspective. On this basis, a realistic cascading failure model for CMS is designed with full consideration of the routing-oriented load distribution characteristics of the physical network and selective load distribution characteristics of the service network. Through extensive experiments, the soundness of the proposed model has been verified and some meaningful findings have been obtained: (1) attacks on the physical network are more likely to trigger cascading failures and may cause more damage; (2) interdependency failures are the main cause of performance degradation in the service network during cascading failures; and (3) isolation failures are the main cause of performance degradation in the physical network during cascading failures. The obtained results can certainly help users to design a more reliable CMS against cascading failures.

Low-latency data delivery in sparse deployment scenarios is a challenging problem. The emergence of UAV-assisted IoTs provides a viable path to solve this problem. In the existing UAV-assisted Internet of Things system, UAVs usually complete data collection through task collaboration, but there is no data relay transmission between UAVs. The main bottleneck of this mode is that the UAV responsible for the edge region of the ground sensor network needs to fly a long distance to send data to the base station, which will seriously affect the age of information (AoI) performance of the system. Therefore, in this work, we design a UAV-assisted IoT data collection mechanism based on aerial collaborative relay and propose an AoI-sensitive data collection scheme. In this scheme, we design a general AoI expectation (GAE) function to achieve iterative optimization of UAV flight paths. Extensive experimental results have shown that the proposed data collection scheme requires less deployment preparation time while obtaining better AoI performance than existing data collection schemes.

Despite the fact that current research related to the invulnerability of wireless sensor networks (WSNs) against cascading failures has made some progress, most of it focuses on the peer-to-peer structure, which is far from the reality. In this paper, we build a cascading model of clustering WSNs by introducing the concept of sensing load and relay load. We discuss the impacts of model parameters on network invulnerability and evaluate the invulnerability performance of two types of WSN topologies, i.e., scale-free network and random network. Simulation and theoretical results show that the network invulnerability is negatively related to the proportion of cluster heads and positively related to the allocation coefficient. When the degree of each sensor node bears a linear relationship with its initial load, the network invulnerability is strongest. Moreover, to alleviate the damage level caused by cascading failures, we design capacity-expansion schemes and indicate the recommended model parameter settings for each scheme.