## 1 Introduction  
Typical sensor nodes are able to carry out sensing, data processing and communicating components, making them feasible for a wide range of promising applications, such as: environmental monitoring (e.g., humidity, temperature), disaster, healthcare, military, _etc_. [1]. Sensor nodes for these applications are usually deployed densely and operated autonomously. Sensor nodes are normally battery powered and left alone in adverse environments, making it quite challenging to recharge or replace node batteries. Hence, one of the crucial challenges in WSNs is to prolong network lifetime due to constrained energy resources. The critically-located sensors are those located near the sink, which carry the burden of relaying large amounts of data traffic, especially when multiple high-rate routes pass through these nodes. Thus, avoiding the failure of such nodes caused by early energy depletion is critical for improving the network lifetime. Another important challenge occurs, when each and every node wants to simultaneously transmit and receive data at the same time. This will lead to a lot of data collisions and congestion. As a result, a large amount of energy is wasted, and most of the nodes will run out of energy very quickly. Many proposals have concentrated on improving the energy efficiency. The research in data-centric WSNs is concentrated on clustering by reducing the number of transmissions to the sink, by selection of a proper MAC layer and an energy-efficient data aggregation mechanism to alleviate the challenges of WSNs. Clustering means grouping the nodes based on geographical location into different clusters. The clustering technique decreases the number of nodes required to forward data to the sink node, thereby reducing the channel load and improving the scalability. However, the advantages of clustering algorithms comes with additional overheads during CH selection and the cluster formation process. Moreover, some CHs die early as compared to the other sensor nodes. These challenges can be alleviated by using the rotation of CHs and selecting proper MAC protocols. Improvement in the MAC layer can increase the reliability and increase the sleep time of nodes. The MAC layer can be based on contention, contention-free or hybrid protocols. Specifically, using contention-based protocols, such as carrier sense multiple access with collision avoidance (CSMA/CA), can result in a long contention among nodes for using network resources [2]. Besides, the performance of CSMA/CA decreases dramatically when the network load and node density increases. Contention occurs when nearby sensors attempt to access the wireless channel at the same time. Contention increases packet dropping, which decreases the reliability of network. As a result, contention-based MAC protocols are not suitable for sensor networks with high density. In order to alleviate this issue, several contention-free protocols, such as TDMA, are proposed, where time is divided into transmission slots that are assigned to CMs, according to a slot scheduling algorithm in CH. Improving TDMA slot allocation by increasing sleep time could potentially lead to the improvement of the reliability and energy efficiency of WSNs. TDMA is suitable for real-time applications, and convergecast is a widely-used communication technique where CH collects information from CMs and forwards to the sink. Energy efficiency can be further increased by data aggregation algorithms in WSNs. Data aggregation (DA) techniques enhance the network lifetime by gathering and aggregating the data in an energy-efficient manner. The purpose of DA is to collect the useful information by eliminating repeated readings of a sensor. Aggregation reduces the communication cost by improving the energy consumption and network lifetime of the WSN. DA minimize the congestion by reducing the packet size. In this paper, we propose the cross-layer cluster-based energy-efficient (CCBE) algorithm based on the combination of clusters, hybrid MAC and data aggregation.