The wireless sensor network (WSN) consists of a large number of small smart devices called sensor nodes distributed randomly in a remote area for monitoring or controlling such area (Sung and Hsiao, 2013; Darabkh et al., 2016). WSNs have many attractive characteristics such as fast deployment, self-organization, low cost, and fault tolerance. Such features make WSNs very favourable for many applications (e.g. military, environmental, etc.) (Barolli et al., 2016; Matrouk and Landfekdt, 2009).

The sensor node is comprised of a sensing unit, a processing unit, a transmission unit, and a power unit (Darabkh et al., 2019, 2017a). The most crucial aspect of designing issues of software and hardware of WSNs is energy consumption (Ismail et al., 2015; Al-Zubi et al., 2018). This is because the energy source in most WSN applications is a battery and the sensor nodes need energy for various operations; sensing data, processing data, and data communication (Yaseen et al., 2018). Consequently, the transmission and routing protocols that are designed for WSNs must be energy efficient in order to mitigate energy consumption limitation in the network by guaranteeing minimum power communication paths (Vu et al., 2014). Many transmission and routing protocols have been proposed for WSNs (Parwekar, 2014; Awan et al., 2018; Darabkh and Zomot, 2018; Darabkh et al., 2017b). One category of these protocols is known as clustering-based routing protocols. Such protocols have improved the network lifetime and they have proven to be the most energy efficient routing protocols (Darabkh and Alsaraireh, 2018).

A cluster based routing protocol is based on network fragmentation principle, where the nodes in a WSN are partitioned into independent groups, which are called clusters (Darabkh et al., 2018; Khalifeh et al., 2017). Each of these clusters has one node working as a leader of the cluster referred to as Cluster Head (CH), while the other nodes are normal nodes referred to as Member Nodes (MNs) (Darabkh et al., 2017c). CH is responsible for collecting sensed data from all MNs, aggregating it, and sending the aggregated data to a base station (BS) (Darabkh et al., 2017d, 2017e). The sensed data is sent by MNs to the CH rather than sending it to the BS directly because sending it directly would drain the batteries of the nodes quickly due to a high power consumption occurrence (Darabkh et al., 2017f).

In this paper, an energy efficient lifetime improvement fixed-clustering based routing protocol for WSNs is proposed and investigated. It is called load balancing cluster head (LBCH) protocol. It focuses on balancing the workload among the nodes evenly and reduces the energy consumption of the network. However, Section 2 presents some of the related works. In Section 3, we introduce the LBCH protocol. In Section 4, we evaluate the LBCH protocol and present the simulation results. In Section 5, we discuss and conclude the paper.

There are a lot of interesting cluster-based routing protocols proposed in the literature. In this section, we introduce closely related protocols (Heinzelman, 2000; Heinzelman et al., 2000; Azim and Islam, 2012; Nam and Min, 2007; Baek et al., 2010; Darabkh and Al-Jdayeh, 2018). In Heinzelman (2000), LEACH-Fixed was proposed. It is the first fixed clustering routing protocol based on LEACH (Heinzelman et al., 2000). Clusters in LEACH-F are constructed initially at the network setup phase and then kept fixed using a centralized cluster formation algorithm. At the end of each round, the role of a CH is rotated among the cluster nodes in a round-robin manner. The steady state phase of LEACH-F is identical to that of the original LEACH. LEACH-F is not scalable since no nodes can be added to the network after construction.

In Azim and Islam (2012), the dynamic round time-based fixed LEACH scheme was proposed. The main reason behind finding this scheme is to mitigate the fixed round time problem in LEACH-F. The round time of the Dynamic Round Time-based Fixed LEACH, is modified based on current energy of MNs, not on their initial energy, and the total energy consumption in the cluster for that round. Hence, by reducing the probability of CHs early death and enhanced network lifetime were achieved. The round time is not the same for all clusters due to the diversity in energy consumption between clusters.

In Nam and Min (2007), the Round-Robin Cluster Header (RRCH) protocol was proposed. It is an energy efficient fixed clustering protocol which balances the energy consumption and achieves high energy efficiency in WSNs due to fixed clustering approach. Initially, the setup phase of the RRCH is performed only once and it is identical to the one of the LEACH where the CHs selection, clusters’ construction, and TDMA scheduling are created for cluster member nodes. In the advanced rounds, the selection of CH nodes within the clusters is based on round robin method. The rotation innovation of CHs roles is the responsibility of the initial CHs where a CH role sequence tables for nodes in the clusters are created. The sequence information is broadcasted by the CHs to the MNs combined with TDMA schedules. In the steady state phase, the CHs are modified according to the sequence information of each node based on round robin method. RRCH consumes less energy than LEACH by E, which is calculated as follows: (1) E = E setup × ( N r − 1 ) , where N r is the number of rounds, E setup is the total energy consumption of the entire sensor node region and it is calculated as follows: (2) E setup = l ( N ( 4 E elec + E schedule + E fs M 2 2 π k ) + k ( E mp d 4 − 2 E elec ) ) , where l is the data size, N is the number of nodes in the network, E elec is the electronics energy, E schedule is the amount of energy consumption due to scheduling in the cluster head node, E fs is the amplifier energy (free space model), M is the length of the region, k is the number of clusters, E mp is the amplifier energy (multipath model), and d is the distance to the base station.

Self-incentive and semi re-clustering (SISR) protocol was proposed in Baek et al. (2010). SISR is a fixed clustering data routing protocol for WSNs. During the setup phase, each node elects itself as a candidate CH with probability P and then broadcasts an ADVERTISE-MESSAGE with the initial radio range RR. Gradually, RR is increased until the node receives an ADVERTISE-MESSAGE from at least one node. According to the received ADVERTISE-MESSAGE, the node checks P value of nodes; if there is a node with P value higher than its value, it selects it as an associated CH. Otherwise, a node gives up the competition. The elected CHs broadcast an INVITE-MESSAGE with their RR and wait for a response from normal nodes. While each normal node determines its associated CH, they send a JOIN-REQ-MESSAGE to the CHs to inform them about their decisions. After the clusters are constructed, each CH decides its CH sequence based on the signal strength of JOIN-REQ-MESSAGE from normal nodes in its cluster. In the steady state phase, the round length differs between clusters where the number of frames in each round equal to the number of nodes in a cluster. The frames are recognized according to the CH sequences that were sent by initial CHs. At the end of each frame, HEARTBEAT-MESSAGE broadcasts by the CHs to their MNs. The nodes that do not respond by HEARTBEAT-ACK-MESSAGE are listed as dead nodes. These listed dead nodes are included in a DEAD-NODES-MESSAGE that is broadcasted by the CHs to their MNs in order to remove the dead nodes from the sequential schedule. Finally, alive nodes send their HEARTBEAT-ACK-MESSAGEs that include their incentive values to be CHs.

An Adaptive Clustering Algorithm for Balanced Energy Consumption in WSNs (ACBEC-WSNs) was proposed in Darabkh and Al-Jdayeh (2018). It is an adaptive fixed clustering based solution that has a single setup phase that is executed once at the beginning till the end of the network lifetime and has one long steady state phase. The aggregated data in this scheme are not sent directly from CHs to the BS. Instead, aggregated data go through a multi-hop path from CHs to the BS through nearby nodes called Relay nodes (RNs). The cycle of each RN or CH is not fixed; it depends on the energy consumption level in the cluster. In the setup phase, the initial CHs and RNs are selected, the clusters are constructed, and multi-hop paths are initiated, whereas in the steady state phase, the data is communicated and the roles of CHs and RNs are circulated. The circulation of CH or RN roles between nodes is to balance the load and distribute the energy consumption among them evenly. In CH role switching, the CH will switch its role if the current energy level of CH in cluster i is smaller than or equal to a certain level. This level is calculated as follows: (3) L th = α ∑ n = 1 N i E n N i , where E n is the residual energy of the node n, N i is the number of nodes in cluster i, and α is a CH switching ratio determined by simulation. The new CH is selected based on a calculated weight for each node. This weight is calculated as follows: (4) CH w ( n ) = E n β × d ( n , R N i ) + ( 1 − β ) × d n , where CH w ( n ) is the weight of node n for the CH nomination, d ( n , R N i ) is the distance between node n and the relay node of the cluster i, β is a constant ratio referred to as the impact ratio which is found by simulation, and d n is the average intra-cluster communication distance of node n, which is calculated as follows: (5) d n = ∑ k = 1 N i d ( n , k ) N i n , k ∈ cluster i , where d ( n , k ) is the distance between node n and node k. For the RN, it continues working as an RN until its energy level is falling below a predefined level.

The main difference between our proposed protocol and ACBEC-WSNs is the method of rotating the roles of CH and RN among the nodes in the network. First, ACBEC-WSNs depends on different parameters (α, β, and γ) which are calculated by simulation. This makes such protocol, practically, non-adaptive for any changes in the network features. However, our proposed protocol does not depend in its operation on any parameter that is calculated by simulation. Second, in our proposed protocol, roles of CH and RN are done every certain period called round. This ensures more load balancing among all nodes in the network. However, in ACBEC-WSNs, the CH or RN may not be changed for many rounds.

The main goal of our proposed LBCH protocol is to improve the network lifetime through balancing the load among all nodes in the network and minimizing the energy consumption in the network. It ensures the minimization of the transmission distances via a multi-hop communication model through RNs. RNs represent links between the CHs and the BS. They deliver the aggregated data from the CHs instead of forwarding it directly to the BS by the CHs. In the following sections, we present the details of the LBCH protocol.

We conduct extensive simulations to evaluate the LBCH protocol and compare its performance with other protocols. The simulations are conducted using MATLAB.

In this article, we proposed a lifetime improvement fixed-clustering based routing protocol in WSNs. It is called Load Balancing Cluster Head (LBCH) protocol. The main target of the LBCH protocol is to balance the workload and minimize the energy consumption among all nodes in the network, thereby improving the performance of the network. In particular, LBCH uses a fully centralized control for picking the initial CHs and RNs, constructing the clusters, and switching the role of CHs and RNs. To increase the efficiency and achieve the desired goal, a multi-hop communication is also incorporated within the model. LBCH performance is evaluated by conducting different simulation scenarios; different network densities under continuous data and event-based data models. The simulation results are compared with that of several related protocols (i.e. ACBEC-WSNs-CD, Adaptive LEACH-F, LEACH-F, and RRCH). The results show the superiority in the performance of LBCH over Adaptive LEACH-F, LEACH-F, and RRCH protocols in terms of number of alive nodes, first node died, network throughput, and load balancing. However, the LBCH protocol slightly outperforms ACBEC-WSNs-CD protocol in some scenarios. This is because LBCH and ACBEC-WSNs-CD employ similar procedures for dividing the network into grid-like subfields and for selecting the initial CHs and RNs. However, LBCH applies different conditions; the highest energy node and the nearest node to the BS will be elected as the new RN in the cluster and a node must achieve three conditions to be selected as a new CH which include the maximum energy, the nearest from the associated RN, and has the minimum average distance to all other nodes in the cluster.

For future work, even through the LBCH protocol results in promising performance, there are some areas for improvement. In the current implementation of LBCH, the number of subfields that will partition the network area depends on a pre-defined percentage of CHs (i.e. P). This parameter could not be suitable for any distribution or density of nodes and any network size. Therefore, in the future work, we are in the process of proposing a method to adaptively derive this parameter based on the number of nodes, distribution of nodes, and the network size.