Energy-Efficient Connectivity Algorithm for Directional Sensor Networks in Edge Intelligence Systems

The proliferation of edge intelligence systems necessitates efficient and reliable connectivity for sensor networks deployed at the edge. This paper proposes a novel energy-efficient connectivity algorithm called Constrained Angle-aware Connectivity Optimization (CA-Opt), designed for directional sensor networks to address the challenges of limited resources and asymmetric network constraints in edge environments. CA-Opt constructs a hop-constrained, degree-bounded network topology while considering the directional coverage of sensor nodes. The algorithm incorporates an angle-aware child selection strategy to optimize the energy consumption by minimizing the number of active links and the total communication distance. Extensive simulations demonstrated that CA-Opt achieved comparable connectivity to the traditional Breadth-First Search (BFS) algorithms while significantly reducing the energy consumption. Furthermore, the impact of key parameters, such as the communication range, node density, maximum degree, and directional coverage angle, on CA-Opt’s performance was analyzed. The results underscore the potential of CA-Opt to balance asymmetry-driven connectivity control with energy-efficient operation, making it particularly suitable for resource-constrained edge applications, such as smart manufacturing, environmental monitoring, and intelligent transportation systems.