A Heuristic Algorithm of Cooperative Agents Communication for Enhanced GAF Routing Protocol in WSNs

Abstract

Rapid progress in technologies has led to the development of small sensor nodes. A wireless sensor network (WSN) is an interconnected collection of a large number of these small sensor nodes that is used to monitor and record the physical environment. WSNs have applications in diverse scenarios. They play an important role in tracking and monitoring in different domains, such as environmental research, military, and health care. In most of these applications, the WSN is composed of a large number of nodes deployed in an area of interest, and not all nodes are directly connected to the base station (BS). In some cases, batteries of nodes cannot be recharged or changed. For that, the most solution required to overcome these problems is to optimize energy consumed during communication. Data transmission in networks is maintained by routing protocols, which are responsible for discovering the required paths. This paper presents an improvement of the Geographic Adaptive Fidelity (GAF) routing protocol created on a smart actives node selection. The routing process works on cooperative agents communication where another node is activated in the same grid if the data collected are considered as important data, and a heuristic method is used to find an optimal path in terms of energy to transmit data collected until reaching the BS. Simulation results prove that the cooperative agents GAF (CAGAF) routing protocol proposed is more efficient compared to the basic version in terms of considering important data, energy consumed, and dead nodes.

Trustworthy and Efficient Routing Algorithm for IoT-FinTech Applications Using Nonlinear Lévy Brownian Generalized Normal Distribution Optimization

“…On the other hand, in [28] and [29] authors proposed new routing algorithm for IoT networks that using reinforcement learning and fuzzy logic. In another attempt and in order to further improve the routing efficiency in IoT networks, the authors in [30] have proposed a heuristic method to obtain the optimal path from source to destination. The proposed method has considered the energy of transmitted data from sensor node to base station.…”

Energy Efficient Strategy for WSN Technology Using Modified HGAF Technique

“…In sub-sell selection methods, all nodes of the sub cells having high energy levels will be selected and the remaining will go into sleep mode. [12]. Figure 2 present the different categories of protocols which are used in WSN where we selected hierarchical based protocol known as Geographic Adaptive Fidelity.…”

Enhanced GAF protocol based on graph theory to optimize energy efficiency and lifetime in WSN technology

“…Moreover, it is very conservative because it guesses its connectivity instead of measuring it directly, which results in less energy savings (Roychowdhury and Patra, 2010). To this end, some versions seem to exceed these limits such as the diagonal GAF (DGAF) protocol which allows direct communication between two diagonal grids (Shang and Liu, 2012), two-level GAF (TGAF) that overcome the problem of communication between only neighboring grids (Vaibhav and Dheeresh, 2015), optimized GAF that improve transitions between node states (Grover et al , 2014b), dislocated grid GAF (GAFDG) that help overcome the problem of limited energy (Zu-jue et al , 2012), hierarchical GAF (HGAF) which also improve energy efficiency by increasing cell size, and using a layered structure in each cell to select active nodes (Inagaki and Ishihara, 2009; Aznaoui et al , 2021a), cooperative agents GAF (CAGAF) that improve routing path to reduce energy consumed (Aznaoui et al , 2019), etc. Given the limitations of GAF protocol and its many improved versions to overcome the problems encountered in the wireless communication sector, we propose an enhanced version of GAF based on the theory of graphs, linear mathematical programming and data aggregation to save and reduce energy consumption and extend the network lifetime.…”

“…Then, the process is repeated until the sensed packets reach the base station. Next, the step 4 has undergone further improvements based on an integrated model of linear mathematical programming to overcome and improve problems encountered in routing (see our work Aznaoui et al , 2019). After that, we used a heuristic algorithm to solve linear programming.…”