Achieving consensus is a crucial problem for large scale drone swam to perform collaboration tasks. There are some critical issues for the consensus of large-scale drone swarms, such as limited-time convergence and high robustness against drone failure due to stringent requirements of mission cycle and hostile environment. Note that traditional consensus cannot guarantee limited-time convergence and a robust consensus against node failure. Existing finite-time consensus faces challenges of strict prerequisites and high complexity. Moreover, leaderless and leader-follower consensus adopts different models respectively. Toward these ends, a unified consensus model for both leaderless and leader-follower modes is proposed. It achieves limited-time convergence by using distributed energy minimization. A dynamic spanning tree algorithm is designed to ensure consensus under dynamic topology. Furthermore, a robust method against node failure is proposed by combining grey prediction, average consensus, and Molly-Reed criterion. Simulation results show that the proposed methods can be adopted in both leaderless and leader-follower situations with advantages of limited-time convergence and high robustness.

Achieving Robust and Efficient Consensus for Large-Scale Drone Swarm

Due to the vast demand for monitoring a structure or area in linear topology, linear sensor networks (LSNs) have recently attracted plenty of attention. Since sensor nodes are usually battery-powered, duty-cycling techniques have been widely studied to improve energy efficiency, which, however, introduces a significant issue known as sleep latency. Thereafter pipelined forwarding has been proposed in the literature as a promising way to alleviate this issue. This paper focuses on interference analysis for data collection services in a multihop LSN running a duty-cycling and pipelined-forwarding protocol, where multiple concurrent transmissions along a data collection path can severely interfere with each other. We first obtain the nodal distance distributions associated with all concurrent transmissions. Based on the obtained distance distributions and the path-loss model in an interference-limited environment, we analyze the distributions of signal-to-interference-plus-noise ratio (SINR) and link capacity. The obtained SINR distribution indicates the link outage probability at a given SINR threshold. By investigating the transmission which receives the strongest cumulative interference, our model can provide useful guidelines for duty cycle setting to achieve a desired network performance.

Modeling and Analysis for Data Collection in Duty-Cycled Linear Sensor Networks With Pipelined-Forwarding Feature

A Review on Wireless Sensor Networks: Routing

Real-time optimizations in energy profiles and end-to-end delay in WSN using two-hop information

Existing duty-cycling and pipelined-forwarding (DCPF) protocols applied in battery-powered wireless sensor networks can significantly alleviate the sleep latency issue and save the energy of networks. However, when a DCPF protocol applies to a linear sensor network (LSN), it lacks the ability to handle the bottleneck issue called the energy-hole problem, which is mainly manifested due to the excessive energy consumption of nodes near the sink node. Without overcoming this issue, the lifespan of the network could be greatly reduced. To that end, this paper proposes a method of deploying redundant nodes in LSN, and a corresponding enhanced DCPF protocol called redundancy-based DCPF (RDCPF) to support the new topology of LSN. In RDCPF, the distribution of energy consumption of the whole network becomes much more even. RDCPF also brings improvements to the network in terms of network survival time, packet delivery latency, and energy efficiency, which have been shown through the extensive simulations in comparison with existing DCPF protocols.

https://www.mdpi.com/1424-8220/20/19/5608/pdf

Shensheng Tang

Papers

EasyGo: Low-cost and robust geographic opportunistic sensing routing in a strip topology wireless sensor network