Cross-Layer Cluster-Based Energy-Efficient Protocol for Wireless Sensor Networks

Abstract

Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs). One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE) can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs) and a cluster head (CH). The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH) and hybrid energy-efficient distributed clustering (HEED).

A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks

“…According to [ 1 , 2 , 3 ], data (especially the multimedia data) gathered by nodes closed to each other often show some similarity in the same period. Thus, to decrease the amount of the redundant data, the definition of the “Data Gathering Area” (DGA) is proposed as follows.…”

“…For densely deployed Wireless Sensor Networks (WSNs), a typical way to send data is to the static Sink by multi-hop or points-to-point transmission [ 1 , 2 , 3 , 4 ]. However, nodes near the Sink tend to consume more energy as they are responsible for receiving and forwarding data from the whole network [ 5 ].…”

Wireless Sensor Networks for Smart Homes: A Fuzzy-Based Solution for an Energy-Effective Duty Cycle

“…Besides, the nodes can be grouped through a clustering algorithm. In this way, the data obtained by the nodes of a cluster can be aggregated and lessened by the cluster head before being sent, with a consequent energy saving [36,37]. At the data link layer, a Medium Access Control (MAC) protocol can implement energy-efficient methods for managing multiple accesses to the wireless channel.…”

Estimating the Lifetime of Wireless Sensor Network Nodes through the Use of Embedded Analytical Battery Models

“…Usually, these models are implemented in WSN simulators to estimate the operating behavior of the sensor nodes before their actual deployment. Within this context, the current battery condition is mathematically estimated to enable the deployment of energy-aware algorithms and protocols [12][13][14]. However, it is necessary to evaluate whether it is possible (or not) to implement similar differential equations-based models in real-world WSN nodes.…”