Dilip Kumar* and Tarunpreet Kaur Pages 1 - 14 ( 14 )
Over the decades, wireless sensor networks (WSNs) have reached its greatest heights and started to emerge into various applications, ranging from health care to multimedia transmission. In these application domains, smart autonomous low power tiny devices known as sensor nodes form a wireless network to transmit their sensed data to the base station (BS) via multi-hop routing or directly. However, the conventional routing protocols based on computational intelligence techniques have some drawbacks viz., slow convergence rate, large memory constraints, highly sensitive to initial value, large communication overheads, and high learning period. These issues have received considerable research attention at the network layer, which leads to the development of hybrid intelligence techniques to address and solve the routing problems. Therefore, this paper presents a systematic survey on hybrid intelligence techniques based routing protocols in WSNs. Moreover, a comparative analysis of reviewed protocols with their strengths and limitations is also included in the survey.
Fuzzy logic (FL), reinforcement learning (RL), ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), and genetic algorithms (GA)
Electronics and Communication Department, SLIET Longowal, Sangrur, Electronics and Communication Department, SLIET Longowal, Sangrur