Optimal Relay Angle-Based Clustering Routing Protocol for Wireless Sensor Networks

Abstract

In this paper, a novel optimal relay angle-based clustering routing protocol called RACR is proposed to balance nodes’ energy consumption and prolong the network lifespan of wireless sensor networks (WSNs). In RACR, each node considers the parameters residual energy, number of neighbors, and average distance with neighbors as a criteria to determine whether to become a cluster head (CH) which cooperates with its neighbors to form a cluster. Afterward, the optimal relay angle is calculated for each CH to find its best relay node according to a target function considering the least energy consumption, which is applied to reduce the search range for finding routing paths. Consequently, all the CHs can find their best next-hop nodes within the determined field according to their residual energy, distance to the next-hop CH, and loads. Iteratively, the CHs obtain their best routing paths to the BS in the end. Simulation results demonstrate its effectiveness of RACR in terms of energy consumption, standard deviation of residual energy, data communication delay, network throughput, and lifespan.

Efficient cluster-based routing protocol for wireless sensor networks by using collaborative-inspired Harris Hawk optimization and fuzzy logic

“…In the first dimension, X 1 generates g 1 = (5,14,21,28) after synergising with gbest, X 2 generates g 2 = (10,14,21,28) after synergising with gbest, at this time, if f (g2)<f (gbest)<f (g1), then gbest and g 1 are discarded and g 2 is chosen as the new background variable, i.e., gbest = g 2 , then the above process is repeated in the second dimension, until the end of the last dimension, the optimal value is obtained. Repeat the above process in the second dimension, and finally get the more accurate global optimum gbest = (10,19,24,30). More accurate results are obtained after such a collaborative process of calculation.…”

“…Moreover, the search space is constrained in range (min(x ij ), max(x ij )). Once beyond the boundary, calculate the opposition using Eq (19).…”

“…In response, scholars have proposed various enhanced CH selection and routing path determination algorithms, often employing intelligent computing methods such as fuzzy logic [11], genetic algorithms [12], particle swarm optimization [13], ant colony optimization [14], and combinations like fuzzy logic with particle swarm optimization [15], fuzzy logic with ant colony optimization [16], and fuzzy logic with weed optimization [17]. Particularly, fuzzy logic stands out for its ability to handle uncertainties inherent in clustering and routing compared to other algorithms [18,19]. Moreover, it offers greater flexibility than explicit logic, enabling optimal solutions by combining input parameters more effectively.…”

IABPC: An Improved Angle-Based Path Clustering Protocol for Energy Efficient Routing in Wireless Sensor Networks

“…To achieve objectives like decreased energy use, WSNs use clustering. Clustering protocol implementation, as stated by Hu et al, [13], requires attention to not only energy e ciency but also the need to provide quality of service (QoS) and strike a balance between a number of competing concerns, including operational lifetime, scope, and throughput.…”