The main problem for event gathering in wireless sensor networks (WSNs) is the restricted communication range for each node. Due to the restricted communication range and high network density, event forwarding in WSNs is very challenging and requires multihop data forwarding. Currently, the energy-efficient ant based routing (EEABR) algorithm, based on the ant colony optimization (ACO) metaheuristic, is one of the state-of-the-art energy-aware routing protocols. In this paper, we propose three improvements to the EEABR algorithm to further improve its energy efficiency. The improvements to the original EEABR are based on the following: (1) a new scheme to intelligently initialize the routing tables giving priority to neighboring nodes that simultaneously could be the destination, (2) intelligent update of routing tables in case of a node or link failure, and (3) reducing the flooding ability of ants for congestion control. The energy efficiency improvements are significant particularly for dynamic routing environments. Experimental results using the RMASE simulation environment show that the proposed method increases the energy efficiency by up to 9% and 64% in converge-cast and target-tracking scenarios, respectively, over the original EEABR without incurring a significant increase in complexity. The method is also compared and found to also outperform other swarm-based routing protocols such as sensor-driven and cost-aware ant routing (SC) and Beesensor. 1. Introduction A sensor network is an infrastructure composed of sensing, computing, and communication elements that give a user or administrator the ability to instrument, observe, and react to events and phenomena in a specific environment [1, 2]. wireless sensor networks (WSNs) are collections of compact-size, relatively inexpensive computational nodes that measure local environmental conditions, or other parameters and forward such information to a central point for appropriate processing. Each node is equipped with embedded processors, sensor devices, storage, and radio transceivers. The sensor nodes typically have limited resources in terms of battery supplied energy, processing capability, communication bandwidth, and storage. WSN nodes can sense the environment, communicate with neighboring nodes, and in many cases perform basic computations on the data being collected. WSNs applications include commercial applications such as healthcare, target tracking, monitoring, smart homes, surveillance, and intrusion detection. Many applications of sensor networks deal with the static nature of nodes
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