DUCR: Distributed unequal cluster‐based routing algorithm for heterogeneous wireless sensor networks

Abstract

Designing energy efficient communication protocols for wireless sensor networks (WSNs) to conserve the sensors' energy is one of the prime concerns. Clustering in WSNs significantly reduces the energy consumption in which the nodes are organized in clusters, each having a cluster head (CH). The CHs collect data from their cluster members and transmit it to the base station via a single or multihop communication. The main issue in such mechanism is how to associate the nodes to CHs and how to route the data of CHs so that the overall load on CHs are balanced. Since the sensor nodes operate autonomously, the methods designed for WSNs should be of distributed nature, i.e., each node should run it using its local information only.Considering these issues, we propose a distributed multiobjective-based clustering method to assign a sensor node to appropriate CH so that the load is balanced. We also propose an energy-efficient routing algorithm to balance the relay load among the CHs. In case any CH dies, we propose a recovery strategy for its cluster members. All our proposed methods are completely distributed in nature. Simulation results demonstrate the efficiency of the proposed algorithm in terms of energy consumption and hence prolonging the network lifetime. We compare the performance of the proposed algorithm with some existing algorithms in terms of number of alive nodes, network lifetime, energy efficiency, and energy population.

A cluster‐based fault‐tolerant routing protocol for wireless sensor networks

“…This degrades the network performance as it makes on one hand the network lifespan limited to that of these CHs, and on the other hand, energy consumption is uneven among all WSN nodes. In Mazumdar and Om, 14 the authors have proposed DUCR. CHs and their specific cluster members are decided in clustering phase.…”

“…On the other hand, in some works, [12][13][14] this decision is performed using very normal parameters like residual energy of CH, distance from sensor node to CH, and distance from CH to sink. 15 Additionally, all these works [6][7][8][9][10][11][12][13][14] do not verify the energy sufficiency for the data transfer, which may not result in the best energy optimization. 15 Additionally, all these works [6][7][8][9][10][11][12][13][14] do not verify the energy sufficiency for the data transfer, which may not result in the best energy optimization.…”

“…In most works, [6][7][8][9][10][11] the decision to join a CH by sensor nodes is made considering a single parameter, but this may not be the best choice. On the other hand, in some works, [12][13][14] this decision is performed using very normal parameters like residual energy of CH, distance from sensor node to CH, and distance from CH to sink. Moreover, this may not lead to get the optimal solution.…”

“…Moreover, this may not lead to get the optimal solution. 15 Additionally, all these works [6][7][8][9][10][11][12][13][14] do not verify the energy sufficiency for the data transfer, which may not result in the best energy optimization. 16 Let us assume the WSN composed of two CHs and 13 sensor nodes as shown in Figure 1A.…”

“…We perform simulations of CFTR protocol and compare their results with those of distributed unequal clusterbased routing (DUCR) 14 and distributed fault-tolerant clustering and routing (DFCR). 12 The simulations results demonstrate that CFTR performs better than these protocols in terms of the network lifetime, the clustering overhead, the average residual energy of sensor nodes, the standard deviation of CHs' residual energy, the number of data packets received by the sink, and the energy and time costs of fault tolerance.…”