Clustering in Wireless Sensor Networks Based on Node Ranking

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Mariam Alnuaimi1 , Khaled Shuaib1, Klaithem Alnuaimi1 and Mohammed Abed-Hafez 2. 1College of Information Technology, UAEU. P.O. Box 15551 Al Ain, ...
Clustering in Wireless Sensor networks Based on Node Ranking Mariam Alnuaimi1 , Khaled Shuaib1, Klaithem Alnuaimi1 and Mohammed Abed-Hafez 2 1

College of Information Technology, UAEU P.O. Box 15551 Al Ain, UAE e-mail: {mariam.alnuaimi, k.shuaib, klaithem.nuaimi}@uaeu.ac.ae 2

College of Engineering, Electrical Engineering Department, UAEU P.O. Box 15551, Al Ain, UAE e-mail: [email protected]

Abstract— The need for efficient optimization of network resources to prolong the life time of large-scale and dense deployments of Wireless Sensor Networks opened the door for researchers to study and produce efficient clustering techniques. Clustering has been proven to be an effective approach for organizing a large scale WSN into connected groups increasing the life time and the reliability of such networks. Distance of the nodes from the base station and internode distances can have a high influence on saving energy and prolonging the network life time. In this paper, a new algorithm based on node ranking is proposed. The proposed algorithm uses node ranking to elect cluster heads based on energy levels and positions of the nodes in reference to the base station used as a sink for gathered information. Also the algorithm provides a stronger coverage through maintaining a global knowledge at the base station which in turn ensures that all nodes are reachable through connected cluster heads. Since the BS calculates the number of rounds a cluster head can remain through in advance, this feature will have a great effect in prolonging the network life time as it reduces the amount of energy wasted on replacing cluster heads. The performance of this algorithm compared to other well-known algorithms will be presented.

and wireless communication capabilities [2]. These nodes or terminals communicate with each other by forming a network of nodes and maintaining connectivity in a distributed way. An unstructured WSN contains a dense collection of sensor nodes that are deployed in an ad hoc manner into an area of choice. In such an environment, network maintenance such as managing connectivity and detecting failures is difficult due to the large number of deployed nodes and the large coverage areas. However, such deployments are critical to have in certain harsh environment where the deployment of preplanned networks can be difficult if not impossible. In a structured WSN, the deployment of the sensor nodes is preplanned. The advantage of a structured network is that fewer nodes can be deployed with lower network maintenance and management cost. When deployed in large quantities in a sensor field, these sensors can automatically organize themselves to form an ad hoc multi-hop network to communicate with each other and with sink nodes. Typically, a WSN has one or more sinks (or base-stations) which collect data from sensors within the WSN. These sinks are considered the gateways through which a WSN interacts with the outside world. In a WSN, nodes communicate over wireless links; thus, effects of radio communication, such as noise, fading, and interferences cannot be avoided and need to be addressed. The denseness and random distribution of WSNs makes it quite difficult to replace or recharge node batteries especially in applications such as: disasters recovery areas, environment monitoring, border monitoring, battle fields, under water sensing, oil fields and many others. Therefore, energy efficiency and management is a major design goal in these networks. In addition, reliability and scalability are two other major goals which are targeted by researchers to further expand the deployments of such networks in applications requiring these features such as military, environment and health. Research related to WSNs is not new and several related problems have been exposed and addressed within the last few years. This research effort has been categorized into three main areas: clustering algorithms, data dissemination techniques and routing protocols [8]. In [9, 10, 11], a hierarchical clustering algorithm for sensor networks, the Low Energy Adaptive Clustering Hierarchy (LEACH) was introduced. The idea is to form clusters of the sensor nodes based on the received wireless signal strength. Local cluster

Keywords- Clustering protocols; wireless sensor networks; load balancing; routing protocols; energy efficiency protocols;

I.

INTRODUCTION

Recent improvement in hardware electronics technology enabled manufacturers to develop low cost, low power and small size sensors [1, 2, 3, 4]. Hundreds and thousands of these sensors are deployed as wireless sensor networks (WSN) serving many applications based on the specific requirements of each one [5, 6, 7]. Nowadays, there are many applications of sensor networks covering different fields such as agriculture, medicine, military, environment monitoring, toys, intrusion detection, motion tracking, machine malfunction, and many others [2]. Sensors can be deployed to continuously report environmental data for long periods of time. This is a very important improvement with respect to previous operating conditions where humans had to operate in the fields and periodically take manual measurements resulting in fewer data, higher errors, higher costs and nonnegligible interference with life conditions of the observed species or phenomena [1]. In general, a wireless sensor network is a collection of nodes with sensing, computation,

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heads are used by members of the cluster as routers to the sink. The essence of this approach is to save node energy consumption as the transmissions of gathered data to the sink will only be done by cluster heads rather than by all sensor nodes. LEACH randomly selects a number of sensor nodes as cluster heads and then rotates this role to uniformly distribute the energy load among the sensors in the network. Each elected cluster head broadcasts an advertisement message to the rest of the nodes in the network informing them of their new role as cluster heads. All the non-cluster head nodes, after receiving this advertisement, choose the cluster to which they want to belong to. This decision is based on the signal strength of the received advertised message. LEACH uses single-hop routing where each node can transmit directly to the cluster head which in turn transmits directly to the sink, regardless of the distance. This technique might work well in dense WSNs but not in large scale networks with large distances between nodes due to a direct proportional energy consumption relationship with distance. LEACH elects cluster heads randomly regardless of their energy level and thus, it is not suitable for networks deployed at a large scale. Furthermore, the idea of dynamic clustering brings extra overhead, e.g. head changes, advertisements etc., which may diminish any gain realized in energy consumption. It also assumes that nodes always have data to send, and nodes located close to each other have correlated data. In addition, it is not obvious how the number of the predetermined CHs is going to be uniformly distributed through the network. Therefore, there is a possibility that the elected CHs will be more concentrated in one part of the network over other parts. As a consequence of this, some nodes will not have any CHs in their neighborhood and will not be covered. Finally, the protocol assumes that all nodes begin with the same amount of energy capacity in each election round, assuming that being a CH consumes approximately the same amount of energy for each node. In order to mitigate some of these problems Multi-hop LEACH was proposed in [12]. Multi-hop LEACH is another extension of the LEACH routing protocol to increase energy efficiency through using multi-hopping to reach the base station of the wireless sensor network [9], [12]. Cluster-heads receive data from all nodes at a single-hop and send to the base station through intermediate cluster-heads. However, some of the above mentioned problems are still considered open research issues and have not been solved yet. Grid Schemes Power-efficient GAthering in Sensor Information Systems (PEGASIS) approach [13] which is a chain based algorithm showing an improvement over the LEACH protocol. PEGASIS forms chains from sensor nodes instead of forming multiple clusters. Only one node is selected from that chain to transmit to the base station or sink. Gathered data moves from a node to other neighboring nodes, aggregated and eventually sent to the base station. Each node uses the signal strength to measure the distance to all neighboring nodes, and then adjusts the signal strength so that only one node can be heard. Therefore, the chain will consist of those nodes that are closest to each other and form a path to the base-station. The aggregated form of data will be sent to the base-station by any node in the chain and the nodes in the

chain will take turns in sending to the base station. Unlike LEACH, PEGASIS avoids cluster formation and uses only one node in a chain to transmit to the base station instead of using multiple nodes thus saving energy consumed by the rest of the nodes within the network. However, PEGASIS adds more delay for distant nodes on the chain especially if the wrong direction to the base station is taken. In addition to that the chain leader can become a bottleneck for the whole chain. In addition to the above two, several other research issues have been considered by researchers recently related to large scale WSNs. The authors in [16] proposed a mixed unequal clusters size algorithm (MNUC) to prolong the life time of the network. It is addressing the problem of hot spot where nodes have to do more processing and transmission related work compared to other parts of the network. Therefore, their energy will be drained more quickly than the others. The idea of the algorithm is to form clusters with unequal sizes. Nodes closer to the base stations will be gathered into smaller sizes clusters and nodes that are far will have bigger clusters sizes. Nodes that are closer to the base station will be more used but the transmission’s ranges will be less. In [18], a single hop or multi hops energy-aware clustering algorithm (EADC) was proposed. Clusters of equal sizes are formed in this algorithm. The algorithm uses the less dense, fewer nodes associated with them, cluster heads to forward the data of the other denser cluster heads to the base station in an attempt to balance the load among all cluster heads. Universal LEACH (ULEACH) in [17] was proposed as an improvement over LEACH. Selection of cluster heads in ULEACH is based upon initial and residual energy of nodes. Data is sent in a multi-hop approach from farthest node to cluster heads and from cluster heads to master cluster heads (MCH). Master cluster heads are responsible to send gathered data to the base station. This algorithm incorporates some features of HEED [4] and PEGASIS into LEACH. Although it utilized the multi-hop data transmission approach, it doesn’t take into account the distance of the master cluster heads from the base station. Therefore, there might be more delay in delivering the data if the master cluster heads are far from the base station which will also results in an additional transmission cost. Despite recent achievements, challenges still exist in active areas of research, gaining the focus of many researchers who are working on areas such as quality of service, security, energy harvesting and prolonging the network life time by conserving energy on deployed nodes. In this paper, a new clustering algorithm based on node ranking is being proposed. This proposed algorithm will save energy through using node ranking to elect cluster heads contrary to other existing cluster-based protocols that select a random node or the node with the higher energy at a particular time instance as the new cluster head. Moreover, a global knowledge strategy is used to maintain a global knowledge about existing nodes in the covered area and avoid the problem of disconnected or forgotten nodes. This paper is organized as follows. In Section II, the proposed clustering algorithm is described. In Section III, performance evaluation of the proposed algorithm is compared to other existing well

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known ones. Section IV, discusses conclusions and intended future work. II.

proposed algorithm where data can be sent through the correct path or direction with respect to the BS and by the BS maintaining a global knowledge of all nodes in the WSN area to ensure that all alive nodes are connected through the proper choice of cluster heads. In addition, we propose the use of an energy threshold technique in making decisions to replace cluster heads which prolongs the life time of nodes closer to the BS. This in effect prolongs the overall network lifetime as nodes closer to the BS are more critical than those far away nodes in maintaining connectivity to the sink.

PROPOSED ALGORITHM

Since data transmission can account for up to 70% of the power consumed in typical sensor nodes [19], substantial energy can be reduced by reducing the distance travelled and the amount of data transmitted to the base station. Distance of the nodes from the base station and inter-node distances can have a high impact on saving nodes’ energy and thus prolonging the network life time which can be defined either as the time for the first node to die, the time for the last node to die or the time for a certain percentage of nodes in the WSN to die [20]. Moreover, in dense deployments of sensor nodes in a WSN, nodes can cooperate to send data and therefore distribute the consumption of energy between them [22]. In this paper we propose a node ranking clustering algorithm (NRCA). The difference between this algorithm and other algorithms is that this algorithm uses a more efficient mechanism to select cluster heads. This is done by measuring the distance, the current energy level of nodes and calculating the number of rounds that they can be cluster heads for, to maximize the network life time and decrease excessive communication overheads used for electing new cluster heads. In this algorithm, nodes are ranked based on their current energy level (En) and their positions (Dn) in reference to the BS. This ranking is used for choosing cluster heads which are also ranked into levels based on their position, Euclidean distance, from the BS. Therefore, each node is assigned a rank Rn (En, Dn) reflecting its candidacy for being elected as a cluster head. Moreover, we avoided the problem of disconnected or forgotten nodes by maintaining a global knowledge of nodes’ positions at the BS through an initial exchange of information. The proposed algorithm is shown to be energy efficient and provides a more comprehensive node coverage. These aspects are achieved by utilizing the correct direction and the shortest distance to the BS, incorporating cluster heads in sending data to BS and managing the nodes using the BS. Next, we introduce the proposed algorithm in more details.

Figure 1: WSN clustering example of sending data in the wrong direction

In the proposed algorithm, the Base Station (BS) is placed in a fixed position and has unlimited energy. Thus no constraints are assumed with regards to power consumption due to data processing and communication. Through the initial step of the below algorithm, the BS becomes aware of the locations of all sensor nodes either via collecting their GPS coordinates or any other mechanism [15]. The following steps give a description of the algorithm and cluster heads’ selection process: • Similar to the initial step done in [4, 10, 21], each node at the set up phase broadcasts a message of its energy level and location to its neighbors. Therefore, each node sets up a neighbor information table recording the energy levels and positions of its neighbors and broadcast this information to its neighbors. This is conducted by all nodes in the network until information about all nodes in the network is received by the BS. This will provide the BS with a global knowledge of the network. • The BS divides the area into smaller partitions called clusters. • Nodes with the highest energy level (En) and least distance from the BS (Dn) in each cluster become a Candidate Cluster Head (CCH). • Candidate Cluster Head Nodes are then ranked based on their location from the BS, (Dn), and the node with the least distance from the BS becomes the first level cluster head. • If two CCHs have the same energy, distance (Dn) is used to choose between the two. • If a node has the highest energy, but not the closest Dn to the BS, the accumulative transmission energy that will be used by all nodes belonging to the same cluster to reach the highest energy node (En1) will be

A. Node Ranking Clustering Algorithm In most of the previously proposed clustering algorithms a node is elected as a cluster head either randomly or based on having the highest residual energy in a cluster. This selection might lead to inefficiencies [21]. For example and as was previously shown in [21], node A in Figure1 has higher residual energy than the other nodes M and S belonging to the same cluster as A. Thus, this node is typically elected as the new cluster head. As a result, this causes M and S in the same cluster to send data through A to the base station taking a longer path as the location of A is toward the opposite direction from the base station. The additional distance data need to travel to get to the base station will cause more energy consumption. Also, there can be forgotten nodes or disconnected nodes which are not covered by any of the cluster heads chosen due to being far from any reachable cluster heads. These two problems can be avoided in our

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calculated and if it is less than the difference in the energy between this node and the node with the second highest (En2) node then this node will be the head, otherwise the node with the second highest energy will be selected. The BS informs each cluster head about the nodes which can associate with it. Then, Each CH node broadcast a message to those nodes. Nodes join the cluster head by replying to the broadcast message. The BS calculates the number of rounds (a round is a random time slot where cluster heads election phase and data transmission phase occurred) cluster heads can serve based on their residual energy and a predefined energy threshold, then relays this information to each cluster head. Cluster heads are replaced only when their energy level drops below the pre-defined energy threshold. Cluster heads, which are located the closet to the network base station, are referred to as the first level cluster heads. The cluster heads that are located at more distant positions from the base station are considered as second level, third level…etc. Higher cluster heads’ levels transmit to lower cluster heads level in order to reach the BS with the least energy consumption. If there is a change in the network topology, due to nodes being considered dead or having residual energy below a certain threshold, the BS determines the next appropriate cluster head in each cluster while considering the new changes.

by all researcher in this field. The simulated area is 100 x 100 m. Every node was given an initial energy of 5 J. The energy for data aggregation is 5 nJ/bit. The energy to run the radio is 50 nJ/bit. The amplifier transmitting energy is 100 nJ/bit/m2. Table 1: Parameters used in the simulation Notation N = 100 Eo = 0.5J / node Eelec = 50nJ / bit EDA = 5nJ / bit Eamp=100 pJ/bit/m2 Area = 100 x 100

In our performance evaluation, we focused our attention on the main two algorithms, LEACH and PEGASIS which were used as the baseline for all researchers in the field. In [24] we showed how PEGASIS outperformed HEED, therefore HEED was not selected. SEP was also not considered here as it uses heterogeneous nodes with different initial energy levels. Using simulation, we have considered several metrics to evaluate the performance of NRCA as reported in the following sub-sections. A. Network lifetime Network Lifetime is defined here as the time interval from the time the sensor network starts its operation until the death of the last node in the network. From Figure 2 and Table 2, we can see that the last node in the simulated WSN died in LEACH at round 2330 making it the least achiever with the shortest network life time among the other considered protocols. On the other hand, it is shown how NRCA has the longest network life time followed by PEGASIS as their last nodes died at rounds 3200 and 2774 respectively. Table 1 and Figure 2 show how NRCA out performed PEGASIS by 15% and LEACH by almost 70% for the network life time criterion.

B. Used Energy Model In this paper, the energy model adopted is the same used by [9, 13, 18, 21, 23] and as shown in Table 1 where Eelec is the radio dissipated energy which is assigned a value of 50 nJ/bit to run the transmitter or receiver circuitry. The Eamp is the used energy for the transmitting amplifier and assigned a value of 100 pJ/bit/m2. ETx(k, d) is the energy that a node dissipates for the radio transmission of a message of k bits over a distance d and expressed by equation (1).

Table 2: Simulation Results for the network life time

 =   ×  +  ×  ×  

Protocols

Measurements Round first node dies

LEACH NRCA PEGASIS

821 1179 1086

Round last node dies 2330 3200 2774

B. Connectivity and coverage There exists a connectivity between the cluster head and nodes in the cluster if and only if the physical Euclidean distance between the cluster head and any node in the cluster is less than or equal to the transmission range of the cluster head. The more cluster heads nodes there are the better coverage or connectivity the network will have and the less distance and energy will be needed to send data. Better coverage also implies minimal or no forgotten or disconnected nodes. From Figure 3, we can see that NRCA has the highest number of cluster heads almost in all rounds when compared with the other two protocols and as such its performance with respect to connectivity and coverage is considered better.

In the same way, the equation of the energy dissipated by a node for the reception ERx(k) of a message of k bits which is due to running the receiver circuitry Eelec (k) can be expressed by equation (2):  =   ×  

III.

Description Total number of sensor nodes Initial energy of each node Per bit energy consumption Energy for data aggregation Amplifier transmitting energy Area used in the simulation

PERFORMANCE EVALUATION

To evaluate the performance of the proposed algorithm against two other well-known algorithms (LEACH and PEGASIS) we used MATLAB for simulating the considered algorithms. Table 1 shows the parameters used in this simulation environment which are a standard parameters used

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Figure 4: Data sent per round

minimizing the number of overhead messages needed for cluster heads selections and replacing processes. D. Energy consumed As shown in Figure 5 the energy consumed per round in NRCA is less than LEACH and PEGASIS with LEACH consuming the most. The amount of energy wasted on the frequent replacement of cluster head nodes by allowing them to serve as CHs in several rounds as long as their energy did

C. Data sent As shown in Figure 4, data sent in NRCA was more than the data sent using the other two algorithms. Data implies both control data sent in cluster head selections or network setup and the sensed data which is sent through sensors (control data was < 10%). This was due to NRCA choosing the most appropriate nodes as cluster heads based on both energy and the correct path to the BS. It is also due to

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y (E n e rg y c o n s u m e d )

not drop below the specified threshold level, was the main factor in achieving this.

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