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Summary Ant colony optimization (ACO) and unequal clustering algorithms in wireless sensor networks (WSNs) prove their efficiency in protracting the network lifetime. However, the existing ACO and unequal clustering algorithms, respectively, do not consider jointly energy efficiency and reliability and focus only on some normal parameters to adjust the cluster radius, then neglecting the cluster head (CH) neighborhood information as it is wise to reduce the cluster radius when there are more neighbor CHs in order to balance the load and energy consumption. To resolve these problems, we propose a fault‐tolerant distributed ACO‐based routing (DACOR) protocol for mitigating the hot spot problem in fog‐enabled WSN architecture. To improve the performance of the network, we propose a multiple fog nodes (FNs) and unequal clustering‐based network model. The proposed model is energy efficient as it avoids repetitive clustering and affects CHs to FNs based on distance. Also, unlike the existing works which use either single FN/sink‐based unequal clustering or multiple FNs/sinks to mitigate hot spot problem, we propose to distribute unequal clustering to multiple FNs (partitions). Additionally, we formulate a different rule to calculate the cluster radius based on significant parameters ensuring energy efficiency and balancing. To route data from source to destination, we devise a new probabilistic formula which considers not only energy efficiency but also reliability. The performance of the proposed DACOR protocol has been investigated under different scenarios through simulations. The results show that the proposed DACOR protocol outperforms the existing protocols in terms of various main metrics.

We address the problem of energy efficiency and reliability for forest fires monitored by a distributed bandwidth-constrained Wireless Sensor Network (WSN). To improve energy-efficiency, data routing is an important approach that is being considered in the context of WSNs. An attractive and widely used method to find the optimal communication paths is the Ant Colony Optimization (ACO) algorithm. However, the traditional ACO-based routing protocols only consider the energy-efficiency while ignoring the overall network reliability (before and after failures) which is critical in the context of WSNs. In addition, the existing protocols are not application-specific (i.e., the parameters cannot be adapted to the application's requirements). In this paper, we propose a novel Energy-efficient and Reliable ACO-based Routing Protocol (E-RARP) for WSNs. The proposed protocol not only guarantees high quality communication paths in terms of energy efficiency but also ensures the communication reliability. Critical events in delay-intolerant applications (e.g., forest fires detection) require reliable transmission in order to perform reliable decisions and take appropriate actions in a timely fashion. The simulations results re-

Summary Wireless Sensor Networks (WSNs) present numerous constraints, which motivate many researchers and which reside mainly in the fact that their resources are limited in terms of communication, calculations, and energy. In particular, the stress linked to energy is considered as a fundamental problem. Indeed, all the sensor elements need energy to function. Thus, controlling the energy consumption of a node remains a major problem for maximizing the lifetime of the network. The clustering and Ant Colony Optimization (ACO) algorithms have been seen as a good solution to route data while maintaining a better network lifetime. However, the existing state‐of‐the‐art cluster and ACO‐based routing protocols perform re‐clustering and fail to restrict the ant searching scope. This consumes a high amount of energy and makes the convergence speed slow. For these reasons, this paper presents an Improved ACO‐based Energy‐efficient Routing Protocol (IACO‐ERP) to mitigate the hot spot problem by proposing a multiple Fog Nodes (FNs)‐based energy‐efficient network model. IACO‐ERP avoids re‐clustering and rotates the Cluster Head (CH) role based on energy among cluster members without any need for control overhead, and also, it proposes to limit the scope of ant searching for optimal route toward the sink based on some specific parameters such as energy and distance, thus avoiding routing loop, slow convergence speed, and excessive energy consumption. The proposed routing protocol is evaluated against some recent and relevant existing solutions. The simulations results reveal that IACO‐ERP provides significant improvement in comparison to its counterparts in terms of various main metrics.