Energy Efficient Data Gathering with Mobile Element Path Planning and SDMA-MIMO in WSN

Energy Efficient Data Gathering with Mobile Element Path Planning and SDMA-MIMO in WSN

1G.R.Divya 2S.Rajkumar

M.E., Communication System Assistant Professor, Department of ECE

DMI College of engineering DMI College of engineering

Chennai, India Chennai, India

Abstract

Wireless Sensor Network (WSN) is a network which depends on the intermediate node to relay the data so the energy of the nodes near to the sink node is exhausted very quickly, as a result network gets disconnected. To overcome this problem and to prolong the lifetime of the network, we propose Wireless Sensor Network with multiple SenCar. It has high data gathering rate and shortest path with low time latency model which uses mobile nodes to collect the data. Also a mobility model of the nodes (SenCar) is proposed to move in the network and cover the whole network area. Further we compare the simulation results with existing protocols and find that the proposed model gives better results in terms of throughput, residual energy and lifetime of the networks. The lifetime of sensor network depends on the operation time of individual sensor nodes. Therefore, a model, which defines the amount of power consumed in each action of a sensor node, influences the lifetime of networks to a great degree. The multiple SenCars eliminates this problem and efficient data gathering is made possible with high channel capacity. The OQPSK (Offset Quadrature Phase Shift Keying) modulation and demodulation reduces the bit error rate as well as increase the signal strength at the nodes of the networks. This model is applicable to different networks which all are connected by the multiple SenCars.

1. Introduction

   In this paper, we consider a wireless sensor network that consists of a large number of sensors and a limited number of mobile data collectors called SenCars. In such a network, SenCars take over the burden of routing from sensors, roaming over the sensing area and collecting data from nearby sensors via short-range wireless communications. We present a series of efficient mobile data gathering schemes in such sensor networks, which aim to prolong network lifetime and shorten data gathering latency.

   Figure 1. Typical wireless sensor network

   1. Node neighborhood problem

      Previously, Sensor nodes used to communicate with sink node by multi-hop [2], [5] mechanism as in fig. 1. Various existing energy efficient techniques concern the delivery of the sensed data from the sensor to the node, which generally improves the burden to the nodes that are closer to the node. More specifically, when a node is statically placed, the sensor nodes that are the neighbor of node tend to deplete their energy faster than other nodes. They consume energy to communicate their own data as well as they relay the data of other node [8], [12] and the node gets isolated from the rest of the network due to early death of its neighbors when most of the sensor nodes are still fully operational. This problem, termed as Node Neighborhood Problem [11] which leads to a premature disconnection of the network. To overcome this problem we need to make the network dynamic. As sensor network deployed for risk management and disaster management, we cannot make all the sensor nodes as mobile node because we cannot access all sensors for the renewal of energy and it is also not possible to establish a path in order to collect the data.

   2. Energy efficient data collection

      In the proposed model we consider a large number of sensor nodes placed at uniformly in the service area, for collecting data or monitoring events. Data collected by a many SenCars travelling through proposed mobility model in the monitored region. Here we assumed that the node has no significant resource limitation, i.e. computational, memory, and communication capabilities. Node is able to directly communicate wirelessly with a subset of one hop

      reachable nodes. Each sensor node in the network is equipped with a given buffer space that is utilized to store data for later retrieval by the mobile node. We use OFDM technology to enhance secure and energy efficient communication. SenCar changes its position randomly according to the proposed mobility model. Before changing the position, SenCar halts for some fixed amount of time called a pause time to collect the data from the corresponding node within its range. Before changes its position it broadcast another beacon frame to reset the sensors. We follow the last step to reduce the packet drop. Mobile node needs the energy efficient and relative motion of the node. Our proposed model provides the relative random motion of the node. We need mobile node because it resolves the energy efficient data collection in sensor network and overcome the node neighborhood problem. It also avoids the multi hop communications [5] and the threats arise in the multi hop communications.

2. Block diagram

   The block diagram in figure 2 given below explains various steps included in the proposed method.

   problem is the use of OQPSK. OQPSK modulation is such that phase transitions about the origin are avoided. In OQPSK, the phase transitions take place every Tb seconds. In QPSK the transitions take place every 2Tb seconds.

   Next capacity calculation for every node is done

   for power allocation, using water filling algorithm. Then node localization is done which means, determining the geographical location of each node in the system. Based on the capacity of the nodes polling points are selected. The SenCar gathers data only at these polling points. Very few nodes are selected as polling points so the SenCar need not travel to all nodes. Thus the travelling distance known as tours of the SenCars are reduced. Further, with the use of multiple SenCars provides division of entire sensing region into sub-regions, with small tours for each SenCar used within each sub-region to obtain energy and time efficiencies. Each node will have its own coverage area within which it could transmit data in a single hop. When the SenCar comes into this coverage zone, it sends a beacon to alert the node so that it comes to active state and starts transmitting data. Very little time is needed for the SenCar to pause at the polling point because, data transmission occurs at very high speed for data gathering. After that, next

   Data Generation

   Encoding

   OQPSK

   modulation

   Node distance & Node tracking

   Shortest data path mobility

   Water filling algorithm

   Random node model

   Energy efficient path

   Wireless receiver

   beacon is sent so that the node stops sending data packets and goes to sleep state. Still sensing function of the sensor nodes continues. It comes to active state only during data transmission which saves lot of energy.

3. Proposed model

   1. Space Division Multiple Access

      SDMA 2.0 supports several advanced multi- antenna techniques including single and multi-user MIMO (spatial multiplexing and beam forming) as well as a number of transmit diversity schemes. In single-user MIMO (SU-MIMO) scheme only one user

      Figure 2. Block diagram of the proposed system

      The efficient data gathering with mobile element data path planning in a Wireless Sensor Network involves a series of steps. Initially the raw data generated by the sensors are encoded and modulated. We proposed OQPSK (Offset Quadrature Phase Shift Keying) modulation scheme so as to avoid non-linearity. Because QPSK or Quadrature Phase Shift Keying used in previous methods involves the splitting of a data streammk(t)=m0,m1,m2,m3,m4,m5, into an in- phase stream mI(t)=m0,m2,m4, and a quadrature stream mQ(t)=m1,m3,m5, Both the streams have half the bit rate of the data stream mk(t), and modulate the cosine and sine functions of a carrier wave simultaneously. As a result, phase changes across intervals of 2Tb, where Tb is the time interval of a single bit of the mk(t). This may make us susceptible to non-linearities, which may be prevented using linear amplifiers but they are more expensive and power consuming. A solution to the above mentioned

      can be scheduled over one resource unit, while in multi-user MIMO (MU-MIMO), multiple users can be scheduled in one resource unit. Therefore we can take Space Division Multiple Access (SDMA) = allocating an angle direction sector to each user.

      Figure 3. Allocating an angle direction sector to each

      user

      y

      y

      n

      n

      The Rayleigh-distribution is a well known estimation of the PDF (Probability Density Function)

      Combining the equations at time slot 1 and 2,

      n

      n

      of the fading statistics in a radio channel; Since the MIMO system architecture uses the independent

      1 p1 p2 1

      1

      1

      1

      1

      1

      1

      fading between different antenna-elements. All

      1 p1 p2

      y

      y

      =

      =

      n

      n

      1

      1

      2

      2

      h h

      x1 + 1

      2

      2

      2

      2

      signals are transmitted from all elements once using

      y2 12

      11 2 2

      x

      x

      2

      2

      single-hop and the receiver solves a linear equation system to demodulate the message as below.

   2. MIMO linear equation system

      Also,

      y2

      p2

      p1

      (3)

      p1 p2

      n2

      H =

      H =

      p1 p2

      h h

      12 11

      h h

      22 21

      (4)

      Figure 4. MIMOtwo transmitting antennas and two receiving antennas (Tx=2, Rx=2)

      The received signal in the first time slot is,

      To solve for x1 , we know that we need to find the

      x

      x

      2

      inverse of H.We know, for a general m x n matrix, the

      pseudo inverse is defined as,

      H+ = (HHH)1HH

      (5)

      The Term,

      = || + || + || + ||

      1 1 | | + | | + |

      | + |

      |

      y1 = p1 p2 x1 + n1

      y

      y

      n

      n

      2

      2

      2

      2

      1 p1 p2 x2 1

      (1)

      Assuming that the channel remains constant for the second time slot, the received signal is in the second time slot is,

      2 2

      (6)

      Since this is a diagonal matrix, the inverse is just the inverse of the diagonal elements, i.e

      y

      y

      x

      x

      n

      n

      2

      2

      1 2

      1 2

      y1 = p1 p2 x2 + n1

      2 p1 p2 2

      = || + || + || + ||

      (2)

      || + || + || + ||

      Where,

      y

      y

      1

      1 y1

      (7)

      2

      is the received information at time slot 1 on receive

      The estimate of the transmitted symbol is,

      antenna 1, 2 respectively,

      1

      1

      y2

      y

      y

      2

      2

      x1

      1

      y

      y

      1

      y

      y

      1

      = HH H 1HH 2

      is the received information at time slot 2 on receive antenna 1, 2 respectively,

      x2

      (8)

      y2

      1

      1

      2

      2

      y2

      hij is the channel from ith receive antenna to jth transmit antenna, x1, x2 are the transmitted symbols,

      n

      n

      1

      n

      n

      1

      1

      1 is the noise at time slot 1 on receive antenna 1, 2

      2

      respectively, and

      n2

      In order to separate the data flows from different sensors, the Mobile sink needs to make the receive beam forming If there exists beam forming vector u1=[u11,u12] and u2=[u21,u22] that makes u1p*=0 and u2p*=0 we finally obtain,

      u y = u h *d +u h *d +u n = u h *d +u n,

      1 is the noise at time slot 2 on receive antenna 1, 2

      1 1 1 1

      1 2 2 1

      1 1 1 1

      n

      n

      2

      2

      respectively.

      u2y = u2p*d1+u2p*d2+u2n = u2p*d2+u2n

      (9)

      According to the channel state, the data from different sensors are intelligently separated by the mobile sink without introducing co-channel interference. The u1 can be any vector lying in V1 which is the space orthogonal to p.

      However, to maximize the received signal

      strength, u1 should lie in the same direction as the projection of p onto V1. u2 should be similarly chosen. In practice, u1 and u2 can be unit vectors because increasing the length of them will not increase the signal-to-noise ratio. Based on these selection criteria, the normalized beam forming vectors can be expressed as follows

      u = (h -<p ,p> h )/| h -<p,p > h |,

      Figure 6. Eight element SDMA smart array antenna

      1 1 <p ,p > 2 1 <p,p > 2

      u = (h -<p,p> h )/| h -<p,p > h |

      2 2 <p,p > 2

      (10)

      2 <p,p > 1

      To ensure that the mobile sink can successfully receive the data simultaneously transmitted by the two sensors, the following criteria must be satisfied

      SNR1=

      |u1 h |2.Pt

      1

      1

      |u1 |2N0

      0, SNR2= (11)

      |u2h |2.Pt

      2

      2

      |u2 |2 N0

      0

      Where SNR1 and SNR2 are the signal-to-noise ratio (SNR) of the received data from the two sensors, respectively, Pt is denoted as the transmitting power of each sensor, N0 is the variance of the background noise, and 0 is the SNR threshold for the mobile sink to correctly decode the received data.

      Thus the SDMA-MIMO combined concept could be further extended for more number of transmitting and receiving antennas (Tx=n, Rx=n, where, n=3,4,) as shown in below figures.

      Figure 5. Four element SDMA smart array antenna

      Figure 7. Sixteen element SDMA smart array antenna

      This way, the concept of MIMO combined with SDMA concept (SDMA-MIMO) can be extended to any number of input and output antennas. This achieves good energy efficient data gathering in the region of WSN considered.

4. Travelling salesman problem (TSP)

   So far we have explained how SDMA with the linear decorrelator works. In this section, we formally formulate the problem of mobile data gathering with SDMA-MIMO technique. The various objectives of the proposed system include:

   • SenCar moving path planning with relays.

   • Single-hop data gathering.

   • Mobile data gathering with controlled mobility and SDMA technique.

   • Bounded relay hop mobile data gathering scheme.

     The user input data tells the number of nodes, the connections between these nodes (and its distance). Then we need to find the shortest distance that the SenCar need to travel to visit all nodes. But there are some restrictions:

   • The travel must start and end at the same node.

   • We must visit each node only once.

   • We must visit at least 2 nodes.

   • The connections are unidimentional so we can only travel at one direction on each connection.

   To overcome the TSP problem, in the proposed model we assume that the Sensor nodes have enough energy, memory and processing power.

   For the data gathering in WSN we find the best mobile element path which will visit all the polling points and returns to the starting point. During pause time at the polling points SenCar communicates with the neighbors in three step process.

   • In initial step it broadcasts a beacon frame to

   Figure 8. Flow chart of the proposed system

   1. Proposed system architecture

      Based on all the above discussions the final proposed system architecture can be given as in figure 9 below.

      Selected

      alert the neighbor nodes in the range to transmit data packet. Every node sets to send the packet to the sink.

      • In the second step every node that have set, they send their data packets to sink with one hop.

      • In the third step sink broadcasts a beacon frame to its neighbors to stop sending the data packet, which reduces the packet drop.

   4.1. Flow chart

   Polling point SenCar path Sub regions

   Sink

   Sink

   Sink

   INPUT DATA FROM SENSORS

   INPUT DATA FROM SENSORS

   SenCar Sensors

   Unselected Polling point

   SenCar Sensors

   Unselected Polling point

   Sink

   Sink

   ENCODED & MODULATED

   ENCODED & MODULATED

   Figure 9. Architecture of the proposed system

   RELAY FOR HIGH SIGNAL STRENGTH

   NO LOW

   BER

   YES

5. Performance evaluation

   Extensive MATLAB simulations were conducted to evaluate the performance of the proposed algorithms. In this section, the simulation results presented as below.

   CALCULATE BEST POSSIBLE ROUTE

   CALCULATE BEST POSSIBLE ROUTE

   COMPATIBLE

   NO PAIRS &

   POLLING

   POINTS SELECTION

   YES

   ENERGY EFFICIENT PATH SELECTED

   ENERGY EFFICIENT PATH SELECTED

   ALL

   NODES NO

   ARE COVERED

   YES

   DATA GATHERED BY SENCAR

   DATA GATHERED BY SENCAR

   1

   0.9

   Transmitted Data

   0 SDMA Performance

   10

   AWGN Channel Raleigh Channel

   0.8 -1

   10

   0.7

   Amplitude

   Amplitude

   Bit Error Rate

   Bit Error Rate

   0.6

   -2

   10

   0.5

   0.4

   -3

   0.3 10

   0.2

   0.1

   0

   0 5 10 15 20 25 30 35 40 45 50

   Number of Samples

   Real Part of Modulated Data (First 50 Samples

   1

   -4

   10

   5 10 15 20 25 30

   SNR in dB

   0 OQPSK with Rayleigh

   10

   Simulation Theory

   0.8

   0.6

   -1

   10

   BER Vs SNR

   BER Vs SNR

   0.4

   -2

   10

   Amplitude

   Amplitude

   0.2

   0

   -0.2

   -3

   10

   -0.4

   -0.6

   -0.8

   -1

   0 5 10 15 20 25 30 35 40 45 50

   Number of Samples

   -4

   10

   0 5 10 15 20 25 30 35

   Eb/No in (dB)

   Figure 10c. Performance comparison

   Signal through the Channel

   2

   1.5

   1

   0.5

   0

   -0.5

   -1

   -1.5

   -2

   0 5 10 15 20 25 30 35 40 45 50

   1

   0.9

   0.8

   0.7

   Amplitude

   Amplitude

   0.6

   0.5

   0.4

   Figure 10a. Transmission results

   Real Part of De-Modulated Data (First 50 Samples

   Data sent through the Nodes – Throughput

   14

   12

   10

   8

   6

   4

   0.3

   0.2

   0.1

   0

   0 5 10 15 20 25 30 35 40 45 50

   Number of Samples

   Received Data

   100

   90

   2

   8 10 12 14 16 18 20 22

   Energy Efficient Nodes

   1

   80

   0.9

   70

   Distance

   Distance

   0.8

   0.7 60

   Amplitude

   Amplitude

   0.6 50

   0.5

   0.4

   0.3

   0.2

   0.1

   0

   0 5 10 15 20 25 30 35 40 45 50

   Number of Samples

   40

   30

   20

   0 2 4 6 8 10 12 14 16

   Nodes

   Figure 10d. Throughput calculation

   Figure 10b. Received output results

   14

   12

   10

   8

   6

   4

   2

   8

   80

   70

   60

   Distance

   Distance

   50

   40

   30

   20

   10

   0

   0

   150

   100

   50

   0

   Figure 10e. Energy efficient Path

   Coverage Area of Nodes

   Coverage Area of Nodes

   11

   11

   7

   1

   4

   7

   1

   4

   9

   3

   5

   15

   9

   3

   5

   15

   6

   10

   14

   6

   10

   14

   0

   50

   100

   0

   50

   100

   16

   16

   13

   13

   2 8

   2 8

   12

   12

   150

   150

   Figure 10f. Final coverage area

   Dissemination in Distributed Environments, IEEE Trans. Parallel and Distributed Systems, vol. 18, no. 5, pp. 608- 620, May 2007.

   Energy efficient and shortest path Efficiency
   10		12	14 16 Distance between Nodes		18	20	22
   			5 10 Number of Nodes				15

   Energy efficient and shortest path Efficiency
   10		12	14 16 Distance between Nodes		18	20	22
   			5 10 Number of Nodes				15

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   8. Biographies

6. Conclusion

   Deployment of many SenCars in the sensing fields to find optimal data gathering. The concept of multiple point to multiple point, enables free communications between any two nodes in network to fulfill quicker, convenient and economical data transmission which involves the automatic mapping and improved power saving. Minimizing the maximum data gathering time among different regions to prolong the network lifetime and shorten data gathering latency among different regions is obtained using proposed method. The simulation results demonstrate that the proposed algorithms can achieve very high energy efficiency and much shorter data gathering time than other compared schemes.

7. References

1. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, A Survey on Sensor Networks, IEEE Comm. Magazine, pp. 102-114, Aug. 2002.

2. A. Scaglione and S.D. Servetto, On the Interdependence of Routing and Data Compression in Multi-Hop Sensor Networks, Proc. MobiCom, 2002.

3. D. Marco, E.J. Duarte-Melo, M. Liu, and D. Neuhoff, On the Many-to-One Transport Capacity of a Dense Wireless Sensor Network and the Compressibility of its Data, Proc. Intl Conf. Information Processing in Sensor Networks (IPSN), Apr. 2003.

4. D. England, B. Veeravalli, and J. Weissman, A Robust Spanning Tree Topology for Data Collection and

G.R.Divya received her bachelor degree in Electronics and Communication Engineering from DMI College of Engineering, Chennai in the year 2011. She is currently pursuing her M.E degree in communication systems from DMI College of Engineering, Chennai. Her area of interest includes wireless sensor networks, WI-FI, WI-MAX and other wireless communication.

Mr.S.Rajakumar is presently working as Assistant Professor in the department of Electronics and Communication Engineering in DMI College of Engineering. He completed his B.E degree in Sun College of Engineering, Nagercoil and M.E in Sathyabama University, Chennai. He is currently, doing his PhD work on Pattern Recognition in Sathyabama University, Chenai. His area of interest includes VLSI, Image Processing and Signal Processing.