Energy-Efficient Edge-Based Network Partitioning Scheme for ...

Energy-Efficient Edge-Based Network Partitioning Scheme for Wireless Sensor Networks Muni Venkateswarlu K

A. Kandasamy

K. Chandrasekaran

Department of Mathematical and Computational Sciences National Institute of Technology Karnataka, Mangalore, INDIA 575025 Email: [email protected]

Department of Mathematical and Computational Sciences National Institute of Technology Karnataka, Mangalore, INDIA 575025 Email: [email protected]

Department of Computer Science and Engineering National Institute of Technology Karnataka, Mangalore, INDIA 575025 Email: [email protected]

Abstract—The easy use of Wireless Sensor Networks has attracted applications from various fields. Day to day rise in wireless sensor network applications introduce new challenges to researchers. One such critical challenge is, optimal usage of network resources. Energy is one of the most important concerns in wireless sensor networks. Even though there has been an extensive research work done on this issue, the problem is still open with new requirements emerging every day. Exchange of control information consumes most of network resources to carry out network operations. An attempt has been made in the recent past to avoid this wastage of resources, by exploiting the properties of resource abundant sources in the network. Base station is one such source in wireless sensor network. The base station is resource abundant and less constrained network component in wireless sensor networks. The recent research works have focused more in this direction to explore the benefits of base station characteristics. In this perspective, a novel network partitioning mechanism is proposed here, to build energy efficient wireless sensor networks. The system proposed, distributes network load uniformly with little control overhead on energy resources in the network. The uniform distribution of sensor nodes in every part helps the network to distribute the load uniformly. From simulation results, it is noted that, the proposed system elevates the average lifetime of sensor nodes.

I.

I NTRODUCTION

Wireless Sensor Network(WSN) is a collection of densely populated low cost tiny sensor nodes with the capabilities of sensing, processing, computing and communicating with other nodes in the sensing field. The sensed values will be communicated to other nodes, generally by using node’s onboard radio transmitter or by using a gateway in the network field[1]. The node that receives sensed data from all other sensor nodes is referred as, Base Station(BS) or Sink node. WSN has become most popular networking technology in the recent past, because, it has the ability to meet a variety of requirements that any application demands. Initially, WSNs were introduced for military operations, like, Battlefield surveillance, Disaster management, Battle damage assessment, Nuclear and Explosive material detection etc.,. Later on, WSN has found its roots in various applications, like, Habitat monitoring, Medical and healthcare, Industrial fields, Flood detection, Home networks, etc.,[2][3]. WSN supports variety of requirements for various applications. This feature made researchers to pay greater attention towards its development in all the fields.

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The WSN has got much attention from various applications, because, it operates on its own without any human intervention. It has the ability to establish networks in hostile areas, like, forest, mountain terrains, etc., in ad-hoc mode without any infrastructure, even under extreme conditions where human cannot enter or survive[4][5]. Also, failure of some node does not affect regular network activities in WSN. Even though Ad-hoc network is one of the most widely accepted wireless network, the techniques used here cannot be ported directly into WSN. The characteristics, like, low battery power, less memory, limited data rates, bandwidth etc., introduce critical constraints over WSN network operations[6]. The WSN applications are looking forward for effective network management schemes for better utilization of network resources. Energy efficient methodologies are required for WSN, since, batteries cannot be replaced or recharged once they are deployed in the network field[7]. Rise in WSN usage in different applications is posing critical research challenges in designing and management of sensor node activities. An attempt is made here to establish, design and manage WSNs with little control overhead on network resources. Energy-efficiency has been focused from different aspects, like, energy conserving sleep scheduling, topology control, mobile data collectors and data aggregation[8]. Topological activity is one such hot spot in WSN, where energy is used in huge amounts to ensure well connected network. Control information exchange consumes most of the energy resources, to establish and maintain connectivity between the network components. Recent research focused at minimising energy consumption on control information exchange. In this context, a novel energy-efficient network organization scheme for WSN is proposed in this paper. The proposed model uses the characteristics of resource abundant and unconstrained network components, like, Base station, to produce energy-efficient WSN. Simulation results demonstrate that the proposed model achieves higher delivery rates with less energy consumption and prolongs the sensor node average life time. The rest of the paper is organized as follows. Section II reviews different edge-based wireless sensor network scenarios and related protocols. Section III presents a novel network partitioning scheme for energy-efficient wireless sensor network and section VI gives extensive experimental results. Finally, section V concludes the paper.

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II.

R ELATED W ORK

Base station is crucial asset for any WSN[9]. The potentials, like, resource abundance, less constrained, unrestricted behavioural characteristics etc.,. made BS as a valuable asset in WSN, whereas sensor nodes are tiny and resource constrained. Hence energy conservation is the most important concern in WSN. Resource efficient network activities are being hot research goals now a days. The following section explains a new paradigm introduced for WSN, where, BS characteristics are exploited to carry out network activities with minimum burden on sensor nodes.

node decides whether the sensed data should be forwarded or not. Besides its advantages, BeamStar also has some pitfalls. The number of nodes varies in large number in far away rings to closer rings from BS. Use of controlled broadcasting for data transmission is a kind of data flooding, which causes energy drain in the network. Also, for the regular network health check-up, every node should sacrifice its valuable energy resources. Complete sector needs to be configured to correct broken parts of the network, which also requires considerable amount of network resources.

A. BeamStar

B. CHIRON

Mao and Hou[10] have introduced a novel edge-based routing protocol, called, BeamStar for WSNs. The aim of BeamStar is to reduce, size and cost of the sensor node. BeamStar considers a WSN equipped with directional antenna with power control capabilities as a base station. By varying antenna’s transmission power level and beam width, BS can reach any part of the network, to provide control information for the sensor nodes. This avoids, the burden of exchange in control information between the sensor nodes. Since, BS sits at one corner of the network, these type of networks are referred as, ”Edge-Based Networks”.

Kuong-Ho and Jyh-Ming[11] have proposed a routing protocol, called, CHIRON for edge-based WSNs. This is like, an extension of BeamStar routing protocol. Here, a well known routing protocol, Power-Efficient GAthering in Sensor Information Systems(PEGASIS)[12], is used for data transmission process with required changes. Other than this, CHIRON assumes and uses similar properties of BeamStar.

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Fig. 2: Different phases of CHIRON (a) First scan SN=1; RN=1

(c) Third scan SN=1; RN=3

(b) Second scan SN=1; RN=2

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Fig. 1: BeamStar’s node location discovery process

Base station(BS) scans entire network field, by varying antenna’s transmission power level(Sector number(SN)) in different angles(Ring Number(RN), to provide location information for each sensor node in the network. This location information is used to communicate data between the nodes and BS during data transmission process. BeamStar uses controlled broadcasting to transmit the data between BS and sensor nodes. BS provides data forwarding rules and other control information directly to sensor nodes, whenever it is required. In this way, intra node communication cost is reduced by BeamStar. With simple data forwarding rules provided, sensor

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CHIRON operates in four phases. In the first phase, called, Group construction phase, network is divided into smaller groups using BeamStar methodology. Nodes with same location information or Id are gathered together to from as groups. Grouped nodes are used to form chains using PEGASIS protocol in the second phase, called, Chain formation phase. Nodes with high residual energy are chosen as Leaders in the next phase, known as, Leader node election phase from each chain. In the last phase, data collection and transmission phase, Leader nodes collect data from its members and send aggregated data to BS. Leader to leader communication delivers the data to BS. For the next round, nodes with high residual energies are chosen as new leaders. Since, CHIRON is derived from existing work, BeamStar, it has inherited few drawbacks as well. Like, chain length varies when we move away form BS as the number of sensor nodes rises in each group. This variation incurs greater data propagation delay between the nodes and BS. Also, Leaderto-leader data communication is always a bottleneck. Single chain leader is hot spot for intruders and malware. C. Cluster Based BeamStar Hao-Li Wang and Yu-Yang Chao[13] introduced another routing protocol, called, Cluster Based BeamStar(CBS), for

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edge-based WSNs. CBS is introduced to overcome some of the drawbacks of BeamStar. CBS uses, the same technique of BeamStar with refined scan process to provide location information to sensor nodes. Well established, Low Energy Adaptive Clustering Hierarchy(LEACH)[14] protocol is used here to transmit data between the sensor nodes and BS. CBS is successful in utilizing network resources optimally and reduces inter-node communication burden. CBS is explained in three different phases: Locating phase, Cluster building phase and Data transmission phase. In the first phase entire field is scanned using different transmission power levels(R). The R value for each round of scan is chosen from the equation(1). This scan gives ring number(RN) to all the sensor nodes in the network. Then, the sector wise scan starts, by varying antenna’s beam width. This assigns sector number(SN) for each sensor node. The combination of ring number and sector number gives location details to sensor nodes, like, BeamStar. 

R =

√ i × R, ∀i = 1, 2, 3, .....n

is based on initial transmission power level(R) chosen. The number of rings rises exponentially with network size. This creates need for more number of CHs and causes interference between the data paths. The rise in rings causes more number of CHs participate in data transfer, thereby increases the data propagation delay. Even though, the above models utilize the advantages of power controlled capabilities of BS, none of them is successful in producing effective performance. This is because, the network partition model distributes sensor nodes with irregular densities at different parts of the network. The uneven node distribution fails to distribute the load uniformly and causes energy drops in the network. To overcome the drawbacks discussed above, a novel network partitioning system is proposed here, to distribute the sensor nodes equally in every part of the network. This will helps, to distribute the network load uniformly across the networked elements and leads to better utilization of network resources. III.

(1) 

where R is transmission power level for first ring, R is transmission power level for ith ring, i is current ring number and n represents maximum number of rings in the network. In the second phase, called, Cluster building up phase, clusters are formed by grouping nodes having same Id. Like in CHIRON, node with maximum residual energy is chosen as Cluster Head (CH) from each cluster. Once the CH energy falls below the given threshold, next round starts and node with maximum residual energy is chosen as new CH. In the last phase, called, Data transmission phase, LEACH protocol is used to transmit data between the sensor nodes and BS. CH collects and process the sensed data from its cluster members and forwards the same to other CHs. CH to CH data communication, delivers the data to BS.

BASE S TATION A SSISTED N OVEL N ETWORK PARTITION M ECHANISM

Major concern in the above discussed models is, uniform load distribution with less over head on the network devices The following section describes the conceptual model of proposed novel network partitioning mechanism. The proposed model tries to distribute the network load uniformly across the network, by dividing the network field into smaller pieces with equal area. By doing like this, we have, consistent number of nodes in each part of the network. A WSN with n sensor nodes, deployed randomly within the radius R from sink node is considered here. A power control capability directional antenna is located at, center of the network field and it can reach any part of the network by varying its transmission power level and beam width. For illustration, we consider one quarter of the circle here. The same scan order of CBS [13] is followed here, to provide location information for the sensor nodes. Network is divided into given number of rings and each ring is scanned with the given transmission power level ri . This provides, Ring Number, one of the two values of location information, that uniquely identifies the ring that a sensor node belongs to. By varying beam width of directional antenna, each sector of the network is scanned, to provide second value, Sector Number, for each the sensor node. Like, BeamStar, the combination of these two values gives the location information of a sensor node. The ri transmission power level or radius of the ith ring is calculated form the equation(2). ri = i ∗ r1 , where r1 is radius of 1

st

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(2)

ring.

The beam width(Θi ) for ith ring is obtained from the following equation. Fig. 3: CBS network scanning mechanism

From equation(1), it is observed that, the R value chosen creates more number of rings. The number of rings formed

Θi = 90/((2 ∗ i) − 1),

∀i = 1, 2, 3, ...

(3)

Using ri , we get area(ai ) of ith ring as ai = Π ∗ ri2 ÷4 = i2 ∗ a1 ,

∀i = 1, 2, 3, ...

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(4)

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where ai is area of ith ring with radius ri . From the equation(4), we get area of i Ai = [(2 ∗ i) − 1] ∗ A1 ,

th

region as

2)

∀i = 1, 2, 3, ...

(5)

where Ai is the area of ith region. Form equation(5), each region i can be divided into ((2 ∗ i) − 1) equal partitions, called, Zones. Zji represents j th partition of ith region. The complete process of network partition mechanism is illustrated in Fig.(4). First quadrant shows first phase of network division process, where the network is divided into set of rings with different transmission power levels. Also, it represents corresponding regions with their areas. Second and third quadrants show, how the beam width is used to divide each region into zones. The beam width value lets antenna to scan the regions and provides the location details for each sensor node. The last quadrant represents, the partitioned network with the proposed mechanism. 105  120

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performance evaluation. This protocol is used over an unpartitioned network(plain network). CluserBasedBeamStarRouting: This represents CBS routing protocol defined in [13]. This protocol is used over the architecture defined in [13]. ResidualEnergyLEACHRouting:This is a variant of original LEACH routing protocol. Like in CBS, here also, node with maximum residual energy is selected as cluster head and the other sensor nodes are chosen as cluster members from each cluster or zone. This protocol is used over the network partitioned according to the proposed network partition model.

The routing metrics, like, packet delivery ratio, energy consumed and average lifetime of node are considered here, to evaluate performances of these protocols over their respective network architectures. The data forwarding technique followed by BeamStar[10], CHIRON[11], Cluster Based BeamStar[13] and proposed architecture is illustrated in Fig.5.

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Fig. 4: Novel network partitioning mechanism

IV.

S IMULATION R ESULTS

A WSN with 100 nodes deployed randomly in 120m radius from BS is considered here. The base station equipped with power controlled capability directional antenna is located at one corner of the sensing field. The network is divided into 9 zones out of 3 rings according to the proposed network partition model. We also consider, CBS architecture and a plain network, for comparison. It is assumed that, the network has data to send in regular intervals of time continuously. The simulation has been done for 500secs with 50secs round length. The results are given by the average value of 10 simulation runs. Here, three routing protocols are considered, to evaluate the performance of different network architectures discussed above. 1)

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StandardLEACHRouting: This is the original LEACH[14] routing protocol used as a basis for

(c) CBS Data Transmission

(d) Proposed Transmission

Architecture

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Fig. 5: Data Transmission Process

From Fig.6, it is inferred that, the proposed novel network partitioning scheme produces consistent number of nodes in each zone of the network, whereas others are failed to do so. It is observed from Fig.7 that, the uniform sensor node distribution of proposed technique helps the ResidualEnergyLEACHRouting to rise the packet delivery ratio. The consistency in the distribution of number of nodes in each zone, helps the proposed network architecture to distribute the load uniformly across the network. From Fig.8, it is noted that, the ResidualEnergyLEACHRouting consumes less energy when compared to StandardLEACHRouting. This is because, the uniform load distribution helps ResidualEnergyLEACHRouting to utilize the network resources optimally. Fig.9 illustrates that, the proposed architecture elevates the average nodes lifetime with uniform load distribution

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Sensor Node Distribution BeamStar

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Fig. 6: Node Distribution by BeamStar, CBS and Proposed network partitioning scheme V. Packet Delivery Ratio 40 ClusterBasedBeamStarRouting ResidualEnergyLEACHRouting StandardLEACHRouting

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Fig. 7: Packet Delivery Ratio Consumed Energy

C ONCLUSION

Day by day enhancements in the technology leads to discover many things to make human comfortable. WSN is one such development and is back bone for establishing smart environments. The range of requirements support for variety of applications, made WSN most popular technology in the wireless networking. There has been much research carried out to meet the requirements of applications. But, the consistent increase in WSN applications, introducing more challenges for researchers every day. So, here an attempt has been made to establish a energy efficient WSN by dividing the network into smaller regions to distribute the load uniformly. It is observed form the simulation results that, the proposed network partitioning scheme distributes the sensor nodes uniformly across the sensing field. By doing this, it is successful to distribute the load uniformly at various parts of the network and helps to rise in packet delivery ratio. Also, it is noted that, the proposed model consumes less energy and helps the network bodies live longer. By uniform load distribution, the proposed method is successful in building up an energy efficient WSN.

13 ClusterBasedBeamStarRouting ResidualEnergyLEACHRouting StandardLEACHRouting

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Fig. 8: Energy Consumption

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