An Efficient Load Balanced Clustering Method for Mobile Data

An Efficient Load Balanced Clustering Method for Mobile Data Gathering

in WSN

Soumya Gyanapnor

PG Scholar,CSE, Lingraj Appa Enginnering

College Bidar Email: [email protected]

Abstract

In WSN applications, sensors are generally densely deployed and randomly scattered over a sensing field and left unattended after being deployed, which make it difficult to recharge or replace their batteries. After sensors form into autonomous organizations, those sensors near the data sink typically deplete their batteries much faster than others due to more relaying traffic. When sensors around the data sink deplete their energy, network connectivity and coverage may not be guaranteed. Due to these constraints, it is crucial to design an energy-efficient data collection scheme that consumes energy uniformly across the sensing field to achieve long network lifetime. Here, we propose a three-layer framework LBC-DDU for mobile data collection in wireless sensor networks, which includes the sensor layer, cluster head layer, and mobile collector (called SenCar) layer.

Keywords: Clustering, Load Balanced Clustering, SenCar, Wireless Sensor Networks

1. Introduction

The Wireless Sensor Networks which consists of sensor nodes which is useful for sensing the information of environmental, industrial, healthcare etc. The wireless sensor network is infrastructure less. The main work of sensor node is to collect the data and then transform into digital signals and finally send the data to the base station. The base

Swathi C Assistant Professor, CSE, Lingraj Appa

Engineering College Bidar Email: [email protected]

station is used for collecting the sensor information like node id and other information.

Figure 1. WSN Architecture

Wireless Sensor Networks is composed of sensor nodes which is used in the practices just as environmental, machine-made manufacturing and all. To acquisition of the facts or data is one of the rapidly propagating and also demanding field in today’s universe.

In few appliances or functions, the humans cannot go forward to the zones which are threatening . For example, to conflict the forest fire, the sensor nodes

Soumya Gyanapnor et al, International Journal of Computer Technology & Applications,Vol 7(3),432-437

IJCTA | May-June 2016 Available [email protected]

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mailto:[email protected]�

mailto:[email protected]�

are placed to perception the surroundings. These appliances usually yield enormous number of readings within a short interval of time, people cannot able to move near to that to accumulate the information. So a mobile collector will be sent out to assemble the facts.

There is a capability called data collection which is used to post the facts or information from all the sensor nodes to the sink node. The profit of this data collection technique is to diminish the delay and to expand the lifetime of the network. The main acute thing in wireless sensor network is the Energy adequacy. To decrease the energy consumption the technique called Clustering is used. The clustering technique is important to cut down the crashes of data.

In the network geography, the nodes which are alike are grouped as one cluster. Cluster is a family which is taken as a unique system. Each cluster will select one or more cluster heads, the cluster heads are picked out based on the residual energy. After allocation of the energy, the energy which is remained is called as residual energy. All the information from the sensor node is redirected to the cluster head and then the mobile collector named SenCar is bring to share the aggregated data from cluster head to SenCar. Then the SenCar will collects those data from the cluster head and finally offloads the data to the base station.

SenCar is fixed with the two antennas which is useful for simultaneously forward the two data at a time to base station. The inter-cluster communication is used to dispatch the things from cluster head to SenCar. In this project abstraction, there will be more than one cluster heads inward a cluster. So it will balance the work load in dispersion of the sensors

2. Related Work

E. Lee, S. Park, F. Yu, and S.-H. Kim, “Data gathering mechanism with local sink in geographic routing for wireless sensor networks[1]”

Most existing geographic routing protocols on sensor networks concentrates on finding ways to guarantee data forwarding from the source to the destination, and not many protocols have been done on gathering and aggregating data of sources in a local and adjacent region. However, data generated from the sources in the region are often redundant and highly correlated. Accordingly, gathering and aggregating data from the region in the sensor networks is important and necessary to save the energy and wireless resources of sensor nodes. We introduce the concept of a local sink to address this issue in geographic routing. The local sink is a sensor node in the region, in which the sensor node is temporarily

selected by a global sink for gathering and aggregating data from sources in the region and delivering the aggregated data to the global sink. We next design a Single Local Sink Model for determining optimal location of single local sink. Because the buffer size of a local sink is limited and the deadline of data is constrained, single local sink is capable of carrying out many sources in a large-scale local and adjacent region. Hence, we also extend the Single Local Sink Model to a Multiple Local Sinks Model. We next propose a data gathering mechanism that gathers data in the region through the local sink and delivers the aggregated data to the global sink. Simulation results show that the proposed mechanism is more efficient in terms of the energy consumption, the data delivery ratio, and the deadline miss ratio than the existing mechanisms.

Y. Wu, Z. Mao, S. Fahmy, and N. Shroff, “Constructing maximum lifetime data-gathering forests in sensor networks [2]”

Energy efficiency is critical for wireless sensor networks. The data-gathering process must be carefully designed to conserve energy and extend network lifetime. For applications where each sensor continuously monitors the environment and periodically reports to a base station, a tree-based topology is often used to collect data from sensor nodes. In this work, we first study the construction of a data-gathering tree when there is a single base station in the network. The objective is to maximize the network lifetime, which is defined as the time until the first node depletes its energy. The problem is shown to be NP-complete. We design an algorithm that starts from an arbitrary tree and iteratively reduces the load on bottleneck nodes (nodes likely to soon deplete their energy due to high degree or low remaining energy). We then extend our work to the case when there are multiple base stations and study the construction of a maximum-lifetime data-gathering forest. We show that both the tree and forest construction algorithms terminate in polynomial time and are provably near optimal. We then verify the efficacy of our algorithms via numerical comparisons.

X. Tang and J. Xu, “Adaptive data collection strategies for lifetime constrained wireless sensor networks [3]”

Communication is a primary source of energy consumption in wireless sensor networks. Due to resource constraints, the sensor nodes may not have enough energy to report every reading to the base station over a required network lifetime. This paper investigates data collection strategies in lifetime-constrained wireless sensor networks. Our objective is to maximize the accuracy of data collected by the base station over the network lifetime. Instead of sending sensor readings periodically, the relative



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importance of the readings is considered in data collection: the sensor nodes send data updates to the base station when the new readings differ more substantially from the previous ones.

M. Ma and Y. Yang, “SenCar: An energy-efficient data gathering mechanism for large-scale multihop sensor networks [4]”

In this paper, the authors proposed a new data gathering mechanism for large-scale multi-hop sensor networks. A mobile data observer, called SenCar, which could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, works like a mobile base station in the network. SenCar starts the data gathering tour periodically from the static data processing center, traverses the entire sensor network, gathers the data from sensors while moving, returns to the starting point, and, finally, uploads data to the data processing center. Unlike SenCar, sensors in the network are static and can be made very simple and inexpensive. They upload sensed data to SenCar when SenCar moves close to them. Since sensors can only communicate with others within a very limited range, packets from some sensors may need multi-hop relays to reach SenCar. We first show that the moving path of SenCar can greatly affect network lifetime. We then present heuristic algorithms for planning the moving path/circle of SenCar and balancing traffic load in the network. We show that, by driving SenCar along a better path and balancing the traffic load from sensors to SenCar, network lifetime can be prolonged significantly.

M. Zhao, M. Ma, and Y. Yang ”Mobile Data Gathering with Space-Division Multiple Access in Wireless Sensor Networks” [5]

Recent years have witnessed a surge of interest in efficient data gathering schemes in wireless sensor networks (WSNs). In this paper, we address this important issue in WSNs by adopting mobility and space-division multiple access (SDMA) technique to optimize system performance. Specifically, a mobile data collector, for convenience, called SenCar in this paper, is deployed in a WSN. It works like a mobile base station and polls each sensor while traversing its transmission range. Each sensor directly sends data to the SenCar without any relay so that the lifetime of sensors can be prolonged. We also consider applying SDMA technique to data gathering by equipping the SenCar with two antennas. With SDMA, two distinct compatible sensors may successfully make concurrent data uploading to the SenCar. Intuitively, if the SenCar can always simultaneously communicate with two compatible sensors, data uploading time can be cut into half in the ideal case..

SDMA: Linear Decorrelator Strategy

In this section, we briefly explain the SDMA technique. In the literature the use of multiple receive antennas in the uplink is often called SDMA. In the application of mobile data gathering, the SenCar is the receiver equipped with multiple antennas and sensors are the senders each having a single antenna to upload sensing data to the SenCar. We will mainly consider the case when the SenCar is equipped with two antennas, because it is not hard to mount two antennas on the SenCar, while it will likely become difficult and even infeasible to mount more antennas due to the constraint on the distances between antennas to ensure independent fading. There are some transceiver architectures that can be used as SDMA strategies. For example, each of the sensor’s signal can be demodulated by using a linear decorrelator or a minimum mean square error (MMSE) receiver at the SenCar The tradeoff between the shortest moving path and full utilization of SDMA is done using the optimal solution .

3. Proposed Work

There are 3 layers: • Sensor layer • Cluster-head Layer • SenCar layer

The sensor layer is the bottom and basic layer. For generality, we do not make any assumptions on sensor distribution or node capability, such as location-awareness. Each sensor is assumed to be able to communicate only with its neighbours, i.e., the nodes within its transmission range. During initialization, sensors are self-organized into clusters. Each sensor decides to be either a cluster head or a cluster member in a distributed manner. To avoid collisions during data aggregation, the CHG adopts time-division-multiple-access (TDMA) based technique to coordinate communications between sensor nodes.



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Figure 2. System Architecture

The cluster head layer consists of all the cluster heads. As aforementioned, inter-cluster forwarding is only used to send the CHG information of each cluster to SenCar, which contains an identification list of multiple cluster heads in a CHG. Such information must be sent before SenCar departs for its data collection tour. Upon receiving this information, SenCar utilizes it to determine where to stop within each cluster to collect data from its CHG. The top layer is the SenCar layer, which mainly manages mobility of SenCar. There are two issues to be addressed at this layer. First, we need to determine the positions where SenCar would stop to communicate with cluster heads when it arrives at a cluster. 4. System Implementation 4.1 Modules

4.1.1 WSN implementation and data collection& communication

In this module, a wireless sensor network is created. Sensor nodes are created along with base station nodes. All the sensor nodes are connected using wireless links. The sensor nodes send the sensed data directly to the base station node. A routing protocol is applied in the network. A communication is enabled between sensor nodes and the base station node.

4.1.2. Performance analysis

In this module, the performance is analyzed. Based on the analyzed results X-graphs are plotted. Throughput, delay, energy consumption are the basic

parameters considered here and X-graphs are plotted for these parameters.

4.1.3.Implementation of LBC-DDU algorithm

In this module, LBC-DDU protocol is implemented in the network. LBC-DDU algorithm first organizes sensors into clusters, where each cluster has multiple cluster-heads. Second, multiple cluster heads within a cluster can collaborate with each other to perform energy-efficient inter-cluster transmissions. Third, a mobile collector with two antennas to allow concurrent uploading from two cluster heads by using MU-MIMO communication is deployed. The SenCar collects data from the cluster heads by visiting each cluster. It chooses the stop locations inside each cluster and determines the sequence to visit them, such that data collection can be done in minimum time.

The node is designed to move in a three dimensional topology. However the third dimension (Z) is not used. That is the node is assumed to move always on a flat terrain with Z always equal to 0. Thus the node has X, Y, Z(=0) co-ordinates that is continually adjusted as the node moves.

There are two mechanisms to induce movement in nodes. In the first method, starting position of the node and its future destinations may be set explicitly. The second method employs random movement of the node.

Select efficient cluster heads for transmit data from source to destination using Routing Algorithm.

Data Transmission is established between nodes using UDP agent and CBR traffic. Cluster-head maintains routing and passes it to other nodes.

Clustering by exchanging information nodes communicate with each other to select cluster-head.

The proposed Algorithm takes parameters for selecting cluster-head namely, degree of the node, battery power, transmission power, and stability of the node. Performance of the algorithm which is based on the NS2 simulator. To find cluster-heads and their cluster members. Analysis of no of cluster-head changes HEED. The performance of DSR routing algorithms which is based on node based cluster algorithm is evaluate in term of congestion. The algorithm is simulated for 40 nodes spread randomly in a 600m * 400m (real time) area network; transmission range for each node is 100 meters. Mobiles nodes are positioned randomly on



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the plane. Nodes start its travel from a random location to a random direction with a random speed. The network nodes that are involved in clusters and nodes that are isolated from the network, along with those nodes which can hear broadcast from other clusters and serves as gateway node.

4.1.4. Performance analysis and Result Comparison, Conclusion

In this module, the performance of the proposed method is analyzed. Based on the analyzed results X-graphs are plotted. Throughput, delay, energy consumption are the basic parameters considered here and X-graphs are plotted for these parameters. Finally, the results obtained from this module is compared with third module results and comparison X-graphs are plotted. Form the comparison result, final RESULT is concluded.

4.2.Use case Diagram

What is UML? UML remains for Unified Modeling Language. UML is a method for picturing a product project utilizing an accumulation of outlines. The documentation has developed from the work of Grady Booch, James Rumbaugh, Ivar Jacobson, and the Rational Software Corporation to be utilized for item situated configuration; however it has subsequent to been reached out to cover a more extensive mixed bag of programming building activities. Today, UML is acknowledged by the Object Management Group (OMG) as the standard for displaying programming improvement.

Figure 3. Use Case Diagram

4.3. Project Contribution

4.3.1Energy-efficient Multi-sink Clustering Algorithm

(EMCA)

The entire network is divided into several clusters, as depicted in Fig. 4. In each cluster, there is one Cluster Head (CH) for data collection and the rest of the sensors are called ordinary nodes. The CH is determined by the residual energy among sensors and the CH sends aggregated data to the relevant sink. By adopting clustering or hierarchical routing technique, network scalability and easier management can be guaranteed. If the clustering algorithm is well designed with CHs located in a geographically more uniform way, energy consumption can be well balanced and reduced, causing a much prolonged network lifetime.

Figure 4. Cluster Formation in EMCA

5. Performance Evaluation

The performance of our framework is compared with other schemes. Here the proposed system is compared with other scheme like is with the Energy-efficient Multi-sink Clustering Algorithm (EMCA).

Figure 5. Throughput

The figure 5 shows the throughput analysis. Here the throughput is compared with the EMCA system. The green line indicates the EMCA scheme which shows the higher throughput.



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Figure 6. Delay

The figure 6 shows the delay occurred in the scheme. The green line shows the less delay occurred in the scheme.

Figure 7. Energy Consumption

The figure 7 shows the how much energy is consumed to transfer the messages between one node to another node.

6. Conclusion and Future Work

In this paper, we have proposed the Load Balanced Clustering-Dual Data Uploading framework for mobile data collection in a WSN. It consists of sensor layer, cluster head layer and SenCar layer. It employs distributed load balanced clustering for sensor self-organization, adopts collaborative inter-cluster communication for energy-efficient transmissions among Cluster Head Groups, uses dual data uploading for fast data collection, and optimizes

SenCar‟s mobility to fully enjoy the benefits of MU-MIMO. Our performance study demonstrates the effectiveness of the proposed framework. The results show that LBC-DDU can greatly reduce energy consumptions by alleviating routing burdens on nodes and balancing workload among cluster heads. The results show that LBC-DDU can greatly reduce energy consumptions by alleviating routing burdens on nodes and balancing workload among cluster heads, which achieves 20% less data collection time compared to SISO mobile data gathering and over 60% energy saving on cluster heads. We have also justified the energy overhead and explored the results with different numbers of cluster heads in the framework. Finally, we would like to point out that there are some interesting problems that may be studied in our future work. The first problem is how to find polling points and compatible pairs for each cluster. A discretization scheme should be developed to partition the continuous space to locate the optimal polling point for each cluster. Then finding the compatible pairs becomes a matching problem to achieve optimal overall spatial diversity. The second problem is how to schedule MIMO uploading from multiple clusters. An algorithm that adapts to the current MIMO-based transmission scheduling algorithms should be studied in future.

References

[1] E. Lee, S. Park, F. Yu, and S.-H. Kim, “Data gathering mechanism with local sink in geographic routing for wireless sensor networks,” IEEE Trans. Consum. Electron., vol. 56, no. 3, pp. 1433– 1441, Aug. 2010. [2] Y. Wu, Z. Mao, S. Fahmy, and N. Shroff, “Constructing maximum- lifetime data-gathering forests in sensor networks,” IEEE/ ACM Trans. Netw., vol. 18, no. 5, pp. 1571–1584, Oct. 2010. [3] X. Tang and J. Xu, “Adaptive data collection strategies for lifetime- constrained wireless sensor networks,” IEEE Trans. Parallel Distrib. Syst., vol. 19, no. 6, pp. 721–7314, Jun. 2008. [4] M. Ma and Y. Yang, “SenCar: An energy-efficient data gathering mechanism for large-scale multihop sensor networks,” IEEE Trans. Parallel Distrib. Syst., vol. 18, no. 10, pp. 1476–1488, Oct. 2007. [5] M. Zhao, M. Ma, and Y. Yang, “Mobile data gathering with spacedivision multiple access in wireless sensor networks,” in Proc. IEEE Conf. Comput. Commun., 2008, pp. 1283–1291. [6] B. Krishnamachari, Networking Wireless Sensors. Cambridge, U.K.: Cambridge Univ. Press, Dec. 2005. [7] R. Shorey, A. Ananda, M. C. Chan, and W. T. Ooi, Mobile, Wireless, Sensor Networks. Piscataway, NJ, USA: IEEE Press, Mar. 2000



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An Efficient Load Balanced Clustering Method for Mobile Data