Distributed Energy-Efficient And Position-Aware Routing Protocol For Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of tiny and low power devices that are usually scattered in a geographically isolated areas. In WSN sensors collect data, aggregate it then send it to the base station. The sensed data is then accessible to the end user. A fundamental challenge in the design of WSNs is to maximize their lifetimes because they have a limited and irreplaceable power supply. Clustering algorithm is the main method used to increase the lifetime of heterogeneous WSN. In this paper, we present and evaluate a Distributed Energy-Efficient and Position-Aware (DEEP) routing protocol for heterogeneous WSNs. In DEEP, cluster-heads (CHs) are elected using the same probability threshold used by LEACH-E. Moreover, it uses a second hierarchical level by selecting the most powerful. cluster head which we call master cluster head for data transmission to the base station. We impose the master cluster head (MCH) to be in a position close to the base station in order to reduce the transmission costs. Simulation results show that our proposed protocol DEEP increases the lifetime of the whole network and performs better than LEACH-E in term of energy dissipation and received messages at the base station.


Recent advances in wireless technology and electronics have enabled the creation of low-cost, low-power, multifunctional sensors that are tiny and able to communicate in short distances. A wireless sensor network consists of a large number of micro-sensors spatially dispersed over a geographical area to measure and monitor physical parameters of the environment and to organize the collected data in a base station in order to give the end-user the ability to monitor, observe and react to events and phenomena in a specified environment. Wireless Sensor Networks provide several applications from military applications such as battlefield mapping and target surveillance to creating smart homes and environment monitoring. In most of the applications, sensors are brought to collect data in a remote environment in order to detect an event and then communicate the collected information to a distant base station (BS) from where it’s made available for the end-user. [

Energy of sensor nodes is usually limited since harsh conditions and remote applications area make it quite impossible to recharge or replace their drained batteries. Energy of sensor nodes is consumed by sensing, processing and communicating the data and also in other operations performed by nodes. Communication is the most greedy part that consume the largest amount of energy in WSN.

Since energy is a major constraint, the main idea of WSN is to design energy-efficient algorithms to optimize energy consumption. Clustering is the most used method to optimize energy consumption in WSN. It consist of dividing sensors into groups, so that they communicate information only to cluster heads and then the cluster heads send the aggregated data to the processing centre saves energy. Furthermore, it’s better to divide the network into clusters, with each cluster having their own CH that sends collected data from their sensor neighbours to the BS. Thus, The CHs, which are elected randomly by using a probability threshold, aggregate the data of their cluster members and send it to the base station, from where the end-users can access this sensed data. Thus, only some nodes are required to transmit data over a long distance in behalf of the rest of the nodes that will only need to do short distance transmission. Therefore, more energy is saved and the overall network lifetime is extended. There are two kinds of clustering schemes. The clustering algorithms applied in homogeneous networks are referred to as homogeneous clustering schemes, where all nodes have the same initial energy, such as LEACH, PEGASIS, and HEED, and the clustering algorithms applied in heterogeneous networks are called heterogeneous clustering schemes, where all the nodes of WSN are equipped with different amount of energy, such as SEP, LEACH-E and DEEC. Based on LEACH-E protocol, we develop and validate a Distributed Energy-Efficient and Position-Aware (DEEP) routing protocol for heterogeneous wireless sensor networks. This protocol is intended to increase the whole network lifetime of heterogeneous WSN. DEEP adds a second hierarchical level concept based on two criteria : the position and energy level of the CH. This method improves and optimizes the use of the energy dissipated in the network. The use of an extra hierarchical layer for data transmission to the base station takes advantage of multi-hop and small distances transmissions and reduces the number of redundant messages to the BS. As a consequence, only one CH is required to transmit collected data in far distances to the BS. This allows a better distribution of the energy load and energy utilization through the sensor network and increases the whole network lifetime accordingly. The remainder of this paper is organized as follows. Section II presents the heterogeneous WSN model. Section III exhibits the details and analyzes the properties of DEEP. Section IV evaluates the performance of DEEP trough simulations and comparison of results with LEACH-E protocol. Finally, Section V gives concluding remarks and some perspectives.

Heterogeneous network model

The main goal of cluster-based routing protocol is to efficiently maintain the energy consumption of sensor nodes by involving them into multi-hop communication and by performing data aggregation and fusion in order to decrease the number of transmitted messages to the BS. In this section, we make a few statements and assumptions about the network scheme and introduce the network and energy model used in this work. In this study, we describe the network model. We assume that there are N sensor nodes, which are uniformly scattered over a M x M square region. The nodes always have data to transmit to the base station, which is far from the sensing area. This kind of WSN can be used in many fields such as space exploration, remote environment monitoring or in military and agriculture applications. The network is organized into a clustering hierarchy, and the CHs execute aggregation and fusion functions to reduce correlated data produced by sensor nodes.

In general CHs transmit the aggregated data to the base station directly. We assume that the nodes are stationary as supposed in. In the two-level heterogeneous networks, there are two types of sensor nodes, the advanced nodes and normal nodes. is the initial energy of the normal nodes, and is the fraction of the advanced nodes, which own times more energy than the normal nodes. Thus, there are advanced nodes equipped with initial energy of, and normal nodes equipped with initial energy of. The total initial energy of the two-level heterogeneous network is given by Eq. (1) : (1)Furthermore, we use the radio energy dissipation model and in order to achieve an acceptable Signal-to-Noise Ratio (SNR) in transmitting an L-bit message over a distance, the dissipated energy by the radio transmission is given by Eq. (2): (2).

Where is the energy dissipated per bit to run the transmitter or the receiver circuit, and depend on the transmitter amplifier model used and is the distance between the sender and the receiver. We have fixed the value of at 87. 7 meters. In most WSN, sensor nodes have limited power supply since they are usually powered by batteries. Energy plays an important role in WSN design. Therefore, optimizing energy consumption in each node is an important factor when developing routing protocol for WSNs. One of the drawbacks of LEACH-E is that CH communicate directly to the BS.

Moreover, All CHs send data to the BS, this can cause redundant or unnecessary information transmitted to the BS Normal node + advanced node. In this regard, we develop a Distributed Energy-Efficient and position-aware routing protocol for heterogeneous wireless sensor networks called DEEP. Based on a LEACH-E probability threshold to elect CHs, DEEP achieves a large reduction in the energy consumption and increases the WSN lifespan by adding a new hierarchical layer for data transmission to the BS. In next section, we describe the DEEP protocol in more details.

Our DEEP protocol

DEEP protocol uses the same probability formula as in LEACH-E. In order to optimize energy consumption, we add a second hierarchical layer for data transmission to the BS. Furthermore, we set a condition about the position of the CH which act as relay between all CHs and the BS. We select the most powerful CH that we call MCH as intermediate hierarchical level between CHs and the BS. The MCH is chosen based on its energy level and its position to be close to the BS. In fact, by using this method we reduce the number of far distance transmissions since the nodes will only have to make short distance communications while the MCH will handle far distance transmissions to the BS. The main characteristics of DEEP protocol are:

  • All nodes in the network are heterogeneous and have limited energy.
  • All nodes are able to communicate with CHs.
  • CHs perform data compression and aggregation.
  • CHs communicate to BS trough the MCH.
  • The MCH is chosen among all CHs.
  • The MCH is the CH having the highest energy level and position close to BS.
  • The BS is immobile and located far from the sensing area. We consider a network of N nodes, uniformly distributed within M×M square region and that the network topology remains unchanged over time and the BS is located in (x = 50, y = 175).

In DEEP, we put the probability threshold, in which each node uses to determine whether itself to become a cluster-head in each round, as in Eq. (3): (3). Where is the set of nodes that are eligible to become cluster heads at round. In each round, when node finds it is eligible to be a cluster head, it will choose a random number between 0 and 1. If the number is less than threshold, the node becomes a cluster head during the current round.

In the “cluster-head-advertisement” phase, the cluster-heads use a CSMA MAC protocol, and all cluster-heads transmit their advertisement using the same amount of transmit energy. The non-cluster-head nodes must keep their receivers on during this phase of set-up to hear the advertisements of all the cluster-head nodes. The sent messages include in addition the Id nodes and the position of coordinates. After this phase is finished, each non-cluster-head node decides the cluster to which it will belong for that round. This decision is based on strength of the received signal of advertisement. Assuming symmetric propagation channels, the cluster-head advertisement heard with the largest signal strength is the cluster-head to whom the minimum amount of transmitted energy is needed for communication. Based on the position of coordinates and the broadcasted message, the CHs elected can select the MCH. Consequently, the CH with the highest energy level and close to the BS will be chosen as MCH in this round. This last node gather all data coming from all CHs, compress it into a single signal and send it directly to the BS. We have chosen the MCH as intermediate hierarchical level, because only this latter will grant transmission in long distance. In fact, other nodes will not have to waste energy in long transmission to the BS since they get involved in multi-hop and short distance transmissions. Initially, all the nodes need to know the initial energy and lifetime R of the network, which can be determined a priori. In DEEP, the base station could broadcast the initial energy and estimation of R to all nodes. When a new epoch starts, each node will use this information to calculate its probability by Eq. (6) and Eq. (4). A node will substitute into Eq. (3), and get the election threshold, which is used to decide if a node will be a CH or not in the current round.

Results and Discussion

In this section, we evaluate the performance of DEEP protocol using MATLAB. We consider a wireless sensor network with N = 100 nodes randomly distributed over a two dimensional area of. To compare the performance of DEEP with LEACH-E protocol, we will consider the following scenario and examine several metrics. After deployment of WSN, the nodes consume energy during the course of the WSN lifetime. More precisely, energy is reduced whenever a node transmits or receives data and whenever it performs data aggregation. Once a node runs out of energy, it is considered dead and can no longer transmit or receive data. First, we examine the period of stability of our proposed protocol DEEP and compare it to that of LEACH-E.


Most of existing clustering protocols aims to maximize lifetime of the sensor nodes in wireless sensor networks. In this paper we propose a Distributed Energy-Efficient and Position-aware (DEEP) protocol for heterogeneous wireless sensor networks. In DEEP, We select the most powerful CH with an imposed position close to the BS called Master Cluster Head (MCH). The MCH is selected as gateway between the CHs and the BS. The simulation results shows that DEEP outperformed LEACH-E in terms of network lifetime, energy consumption and number of messages received at the BS. As perspective, we aim to extend our work by using more than one MCH in multi-level energy WSN.

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