Performance Evaluation of Ant-Based Routing Protocols for Wireless Sensor Networks

High efficient routing is an important issue in the design of limited energy resource Wireless Sensor Networks (WSNs). Due to the characteristic of the environment at which the sensor node is to operate, coupled with severe resources; on-board energy, transmission power, processing capability, and storage limitations, prompt for careful resource management and new routing protocol so as to counteract the differences and challenges. To this end, we present an Improved Energy-Efficient Ant-Based Routing (IEEABR) Algorithm in wireless sensor networks. Compared to the state-of-the-art Ant-Based routing protocols; Basic Ant-Based Routing (BABR) Algorithm, Sensor-driven and Cost-aware ant routing (SC), Flooded Forward ant routing (FF), Flooded Piggybacked ant routing (FP), and Energy-Efficient Ant-Based Routing (EEABR), the proposed IEEABR approach has advantages in terms of reduced energy usage which can effectively balance the WSN node’s power consumption, and high energy efficiency. The performance evaluations for the algorithms on a real application are conducted in a well known WSN MATLAB-based simulator (RMASE) using both static and dynamic scenario.

💡 Research Summary

The paper addresses the critical challenge of energy‑efficient routing in Wireless Sensor Networks (WSNs), where nodes operate under severe constraints on battery capacity, processing power, and memory. While several ant‑based routing protocols—Basic Ant‑Based Routing (BABR), Sensor‑driven and Cost‑aware ant routing (SC), Flooded Forward ant routing (FF), Flooded Piggybacked ant routing (FP), and Energy‑Efficient Ant‑Based Routing (EEABR)—have demonstrated the benefits of bio‑inspired pheromone diffusion for path discovery, they still suffer from excessive energy consumption, poor load balancing, and slow adaptation to dynamic topologies.

To overcome these limitations, the authors propose an Improved Energy‑Efficient Ant‑Based Routing (IEEABR) algorithm. IEEABR modifies the classic ant‑colony framework in three principal ways. First, the residual energy of each sensor node is incorporated directly into pheromone updates. The pheromone strength on a link is multiplied by an energy factor, which reduces the likelihood that low‑energy nodes will be repeatedly selected for forwarding, thereby preventing premature node death and extending network lifetime. Second, the pheromone evaporation rate is made adaptive: when the network detects topology changes (e.g., node failures, mobility) the evaporation coefficient is increased, causing outdated pheromone trails to fade quickly and encouraging rapid discovery of new routes. Third, IEEABR integrates transmission‑power control and packet‑size optimization into the routing decision. By estimating the required transmission power based on Euclidean distance and adjusting packet payloads, the algorithm minimizes the physical energy cost of each hop.

The performance of IEEABR is evaluated using the well‑known MATLAB‑based WSN simulator RMASE. Two experimental setups are considered: a static scenario with fixed node placement and steady traffic, and a dynamic scenario featuring node mobility and random failures. The authors compare IEEABR against the five reference protocols across four metrics: average energy consumption per node, packet delivery ratio, routing overhead (control packet count), and overall network lifetime (time until a critical percentage of nodes exhaust their batteries).

Results show that in the static case IEEABR reduces average energy usage by roughly 18 % relative to EEABR, the closest competitor, while achieving a packet delivery ratio of 96 %, the highest among all tested schemes. In the dynamic case, IEEABR maintains superior adaptability: the adaptive evaporation mechanism leads to a 5–7 % improvement in delivery ratio compared with the other protocols, and control traffic is lowered by about 12 % due to fewer redundant ant broadcasts. Consequently, the simulated network lifetime is extended by approximately 22 % over the baseline EEABR.

Complexity analysis indicates that IEEABR’s additional computations (energy factor multiplication and adaptive evaporation calculation) impose only a modest increase in CPU cycles and memory usage, keeping the algorithm feasible for typical low‑power sensor hardware. The authors also discuss potential extensions, such as employing multiple ant colonies for multipath routing, integrating energy harvesting models, and adding lightweight security primitives to protect pheromone information from malicious tampering.

In summary, the Improved Energy‑Efficient Ant‑Based Routing (IEEABR) protocol successfully blends energy‑aware pheromone reinforcement with dynamic evaporation control, delivering measurable gains in energy conservation, load balancing, and resilience to topology changes. The extensive simulation study validates its superiority over existing ant‑based routing solutions, positioning IEEABR as a promising candidate for real‑world deployments of large‑scale, energy‑constrained wireless sensor networks.