A blockchain bee colony double inhibition labor division algorithm for spatio-temporal coupling task with application to UAV swarm task allocation

It is difficult for the double suppression division algorithm of bee colony to solve the spatio-temporal coupling or have higher dimensional attributes and undertake sudden tasks.Using the idea of clustering,after clustering tasks according to spatio-temporal attributes,the clustered groups are linked into task sub-chains according to similarity.Then,based on the correlation between clusters,the child chains are connected to form a task chain.Therefore,the limitation is solved that the task chain in the bee colony algorithm can only be connected according to one dimension.When a sudden task occurs,a method of inserting a small number of tasks into the original task chain and a task chain reconstruction method are designed according to the relative relationship between the number of sudden tasks and the number of remaining tasks.Through the above improvements,the algorithm can be used to process tasks with spatio-temporal coupling and burst tasks.In order to reflect the efficiency and applicability of the algorithm,a task allocation model for the unmanned aerial vehicle(UAV)group is constructed,and a one-to-one correspondence between the improved bee colony double suppression division algorithm and each attribute in the UAV group is proposed.Task assignment has been constructed.The study uses the self-adjusting characteristics of the bee colony to achieve task allocation.Simulation verification and algorithm comparison show that the algorithm has stronger planning advantages and algorithm performance.

Human condition monitoring in hazardous locations using pervasive RFID sensor tags and energy-efficient wireless networks

Tracking persons in dangerous situations as well as monitoring their physical condition, is often crucial for their safety. The systems commonly used for this purpose do not include individual monitoring or are too expensive and intrusive to be deployed in common situations. In this project, a mobile system based on energy-efficient wireless sensor networks (WSNs) and active radio frequency identification (RFID) has been developed to achieve ubiquitous positioning and monitoring of people in hazardous situations. The system designed can identify each individual, locate him/her, send data regarding their physical situation, and ascertain whether they are located in a confined space. A new algorithm called time division double beacon scheduling (TDDBS) has been implemented to increase operation time and data transmission rate of the nodes in the system. The results show that the use of this system allows us to find the location and state of a person, as well as to provide an analysis of the potential risks at each moment, in real time and in an energy-efficient way. In an emergency, the system also allows for quicker intervention, as it not only provides the location and causes of the event, but also informs about the physical condition of the individual at that moment.