Improving the performance of hierarchical wireless sensor networks using the metaheuristic algorithms: efficient cluster head selection

Abstract

Purpose Efficient resource utilization in wireless sensor networks is an important issue. Clustering structure has an important effect on the efficient use of energy, which is one of the most critical resources. However, it is extremely vital to choose efficient and suitable cluster head (CH) elements in these structures to harness their benefits. Selecting appropriate CHs and finding optimal coefficients for each parameter of a relevant fitness function in CHs election is a non-deterministic polynomial-time (NP-hard) problem that requires additional processing. Therefore, the purpose of this paper is to propose efficient solutions to achieve the main goal by addressing the related issues. Design/methodology/approach This paper draws inspiration from three metaheuristic-based algorithms; gray wolf optimizer (GWO), incremental GWO and expanded GWO. These methods perform various complex processes very efficiently and much faster. They consist of cluster setup and data transmission phases. The first phase focuses on clusters formation and CHs election, and the second phase tries to find routes for data transmission. The CH selection is obtained using a new fitness function. This function focuses on four parameters, i.e. energy of each node, energy of its neighbors, number of neighbors and its distance from the base station. Findings The results obtained from the proposed methods have been compared with HEEL, EESTDC, iABC and NR-LEACH algorithms and are found to be successful using various analysis parameters. Particularly, I-HEELEx-GWO method has provided the best results. Originality/value This paper proposes three new methods to elect optimal CH that prolong the networks lifetime, save energy, improve overhead along with packet delivery ratio.

A multi-strategy sand cat swarm optimization to efficient resource allocation and scheduling in fog-cloud environment

“…In this section, the last analysis is the performance evaluation of each algorithm on the multi-objective fitness function (Figure 10). The effective values of the weights used in this function (α1, α2, α3) according to the problem were obtained from [41,42] studies. According to the results obtained, it is determined that the proposed algorithm performed better than other algorithms.…”

“…In order to choose these weights in an optimized way, they need to be tuned or found with an approach such as MH. The approach in [41,42] is applied to find the optimal values for the relevant coefficients. According to the result, α1=0.41, α2=0.24, α3=0.…”

Cluster based energy efficient routing protocol for heterogeneous wireless sensor networks

“…So, the throughput of our proposed algorithm is also increased. Our proposed CEER routing protocol is compared with optimization algorithms such as PDU‐SLnO, 21 LEACH‐GA, 22 I‐HEEL methods, 23 and LU‐DA 24 . The statistical and performance analysis of our proposed CEER routing protocol with optimization algorithms are shown in the Tables 2 and 3.…”

“…Genetic algorithm is widely used for solving optimization problems that have a large number of possible solutions. A novel metaheuristic algorithm, I‐HEEL proposed in Reference 23 includes two phases; In the first phase, the parameter coefficients are based on gray wolf optimizers. In the second phase, the appropriate CH for each cluster is selected.…”

Adaptive Metaheuristic-Based Methods for Autonomous Robot Path Planning: Sustainable Agricultural Applications

“…For an autonomous robot, the problem of searching for a safe path from a source to a destination is called path planning [5,6]. This issue can be addressed using various new technologies (e.g., Wireless Sensor Networks (WSNs) and Internet of Things (IoT)) that have a wide range of applications [7][8][9] since they can be designed with heterogeneous or homogeneous devices in distributed, central, or Peer-to-Peer (P2P) architectures. One of the application areas of these technologies which has become popular in recent years is agriculture [10][11][12][13].…”