Energy-Efficient Multi-Disjoint Path Opportunistic Node Connection Routing Protocol in Wireless Sensor Networks for Smart Grids

Abstract

The gradual increase in the maturity of sensor electronics has resulted in the increasing demand for wireless sensor networks for many industrial applications. One of the industrial platforms for efficient usage and deployment of sensor networks is smart grids. The critical network traffic in smart grids includes both delay-sensitive and delay-tolerant data for real-time and non-real-time usage. To facilitate these traffic requirements, the asynchronous working–sleeping cycle of sensor nodes can be used as an opportunity to create a node connection. Efficient use of wireless sensor network in smart grids depends on various parameters like working–sleeping cycle, energy consumption, network lifetime, routing protocol, and delay constraints. In this paper, we propose an energy-efficient multi-disjoint path opportunistic node connection routing protocol (abbreviated as EMOR) for sensor nodes deployed in neighborhood area network. EMOR utilizes residual energy, availability of sensor node’s buffer size, working–sleeping cycle of the sensor node and link quality factor to calculate optimum path connectivity after opportunistic connection random graph and spanning tree formation. The multi-disjoint path selection in EMOR based on service differentiation of real-time and non-real-time traffic leads to an improvement in packet delivery rate, network lifetime, end-end delay and total energy consumption.

Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks

“…In this paper, we considered a heterogeneous WSN in which all sensor nodes are deployed uniformly in a field bounded by L × L m 2 region. Each static sensor node with a unique ID can acquire its neighbors' ID by sharing a probe message as discussed in [22,26]. Also, we assumed that all sensor nodes follow the asynchronous working-sleeping cycle strategy with their working time as W V and sleeping time as S V .…”

“…Anees et al [26] utilized the OR in WSNs using the asynchronous working-sleeping cycle strategy for sensor nodes deployed in neighborhood area networks (NANs) of smart grids (SGs). For the efficient utilization of OR, we have to model it using the working-sleeping schedule (W v /S v ), and the status transition frequency (F ST ) of each sensor node in the network.…”

“…Sensors 2020, 20, 913 7 of 28 where N is the total number of sensor nodes in the network, R int is the ratio of intermediate sensor nodes and R adv is the ratio of advanced sensor nodes in the network. Furthermore, we considered the simplified energy consumption model based on [26] for radio energy dissipation during transmission and reception. According to this model, the energy required to transmit l bits of data over distance d can be given in Equation (2) as:…”

“…In this paper, we have described the opportunistic clustering mechanism based on hesitant fuzzy entropy using several parameters, such as the time-frequency parameter, residual energy, LQR, distance from the BS and Nsupport. Anees et al in [26] proposed an energy-efficient multi-disjoint path opportunistic node connection routing protocol for smart grids in which the OCRG was utilized to calculate the link and path connectivity between sensor nodes for data forwarding purposes. Likewise, we have considered the neighborhood area networks (NANs) of smart grids in our case study to prove the significance of hesitant fuzzy entropy in heterogeneous clustering of WSNs.…”

“…In [25], Norman et al proposed a novel random graph modeling for heterogeneous sensor networks based on different transmission ranges and a new routing metric supporting opportunistic node connections. Anees et al [26] proposed an energy-efficient multi-disjoint path opportunistic node connection routing protocol for smart grids (SGs) neighborhood area networks (NAN) inspired by opportunistic connection random graph theory in WSNs with sink mobility. This routing protocol reduces the overall energy consumption, increases the PDR, increases the network lifetime and decreases the end-end network delay against existing benchmark schemes.Many recent studies have addressed the problem of WSN clustering.…”