Path-Planning-Enabled Semiflocking Control for Multitarget Monitoring in Mobile Sensor Networks

Abstract

Mobile sensor networks (MSNs) are good candidates for large-scale unattended surveillance applications. However, it is challenging to track moving targets due to their complex dynamic behaviors. Semi-flocking algorithms have been proven to be efficient in controlling MSNs in both area coverage and target tracking applications. While many existing literatures on the study of semi-flocking algorithms often assume an area of interest (AoI) to be regular and with unified traversal cost, the uneven and rough landscapes in real-life applications have imposed extra challenges and raised demands for new management strategies. In this paper, a mobility map is used to incorporate different costs associated with irregular terrains which results in different maximum allowed speeds on nodes in different regions. In order to reduce target detection time and node energy consumption, a heuristic search algorithm is developed to find time-efficient and feasible paths between nodes and sensing targets. Under the proposed algorithm, nodes can effectively select a target to track or search for new targets in the AoI. Results of extensive experiments show that semi-flocking-controlled nodes together with path planning can reach their targets faster with lower energy consumption compared to three exiting flocking-based algorithms.

Application of Improved Ant Colony Algorithm Integrating Adaptive Parameter Configuration in Robot Mobile Path Design

“…The ACO algorithm corresponds to the robot avoiding O1 and O2 obstacles with the same movement mode by generating local target points, but the overall movement distance is 16.74 meters, which is significantly higher than the data of the IACO-A * planning model. Moreover, in various scenarios where the grid edge length of the simulated grid map changes from 10 to 100, the total path length of the planning model output in this study is consistently lower than that of all the comparative models, and also lower than the total path length under the same conditions as in references [9] and [11]. Finally, the IACO-A * model was placed in a domestic mobile robot product to carry out dynamic obstacle avoidance experiments in planar and curved scenes.…”

“…Compared with traditional www.ijacsa.thesai.org heuristic optimization algorithms, the four-way search scheme has strong advantages in solution space search, which helps to find high-quality paths with lower costs and satisfy various constraints. Yuan et al [9] proposed a mobile PP algorithm for robots equipped with mobile sensor networks. Compared with traditional planning models, this algorithm has good adaptability and environmental awareness.…”

Monitoring and Analysis of Cotton Planting Parameters in Multiareas Based on Multisensor

“…e N content of plant could effectively represent chlorophyll content, the leaf surface index could represent the growth state of plant leaves, and dry matter accumulation on the ground surface could represent the matter accumulation of cotton. Canopy coverage was used as a variable; the leaf area index (LAI) of plant N content and dry matter accumulation on the ground were used as stress variables to establish models [18,19].…”

Flocking for Multiple Subgroups of Multi-Agents With Different Social Distancing

“…Examples of this phenomenon include such as fish schools, bird flocks, ant colonies, and bacteria swarms, etc. Due to its broad applications in fields such as multi-target tracking of mobile sensor networks [4]- [6], cooperative control of swarm robots [7]- [9], and coordinated motion of unmanned aerial vehicles [10]- [12], etc., the flocking of multi-agents has attracted a great deal of attention among researchers from different disciplines [13]- [24].…”