The Hybrid Mobile Wireless Sensor Networks


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The Hybrid Mobile Wireless Sensor Networks

  1. 1. The Hybrid Mobile Wireless Sensor Networks for Data Gathering Biao Ren, Jian Ma, Canfeng Chen Proceeding of the 2006 international conference on Communications and mobile computing IWCMC '06 July 2006
  2. 2. Outline <ul><li>Introduction </li></ul><ul><li>Models and Related works </li></ul><ul><li>Analyses </li></ul><ul><li>Simulations </li></ul><ul><li>Conclusions </li></ul>
  3. 3. Introduction <ul><li>Traditional wireless sensor networks extension based on pure ad-hoc networks ,where the dense distribution of sensor nodes and multi-hop transmission over the whole network are their outstanding characteristics. </li></ul><ul><li>Some disadvantages still exist, such as poor scalability, weak energy balance, as well as low network lifetime. </li></ul><ul><li>Hybrid wireless sensor network is usually comprised of some kinds of heterogeneous devices, which mainly act as sinks responsible for gathering and forwarding data from sensor nodes. </li></ul>
  4. 4. Introduction(cont.) <ul><li>Some of them are energy-rich or rechargeable, some are capable of communication with better capability and some are mobility enabled. These features can not only improve the network performance, but also extend the potential applications and make commercial implementation easy. </li></ul><ul><li>The paper investigates the impacts of the number, velocity, transmission radius and gathering mode of mobile sinks on large-scale and sparse wireless sensor networks. </li></ul>
  5. 5. Models and Related works <ul><li>Network Model </li></ul><ul><li>Mobility Model </li></ul><ul><li>Traffic Model </li></ul>
  6. 6. Network Model <ul><li>The hybrid mobile sensor network consists of numerous static sensor nodes and some number of mobile sinks. The location of the static nodes are fixed and distributed uniformly at random. </li></ul><ul><li>The mobile sinks are randomly distributed at initial time. At later time their position and velocities are given by the mobility model. </li></ul><ul><li>In order to comparison, we also consider the fixed sinks where the velocity of mobile sinks is set to zero. Each fixed sink is arranged at a grid point to optimize the performance. </li></ul>
  7. 7. Mobility Model <ul><li>Each of the total m mobile sinks pick a direction uniformly at random from (0, 2π ] and moves in that direction for a distance d at speed v , where d is a exponentially distribution. </li></ul><ul><li>If the sink hits the boundary of the sink, it is reflected at the boundary (The positions of mobile nodes are independent of each other). The direction of the mobile node is also uniformly distributed in (0, 2π ] all the time. </li></ul>
  8. 8. Traffic Model <ul><li>In dense sensor network, data gathered from environment is forwarded to sink node in multi-hops fashion. Although it can provide low data delivery delay, the energy consumption per bit is much higher due to the multi-hop forwarding of one packet. </li></ul><ul><li>We adopt the limited k-hop scheme for data gathering from sensor nodes. That is, the data transmission of a sensor node will not happen until at least one mobile sink approach to it within at most k-hop distance. </li></ul>
  9. 10. Analyses <ul><li>Delay Analysis </li></ul><ul><li>Velocity Impact on Delay </li></ul>
  10. 11. Delay Analysis(/) <ul><li>The end to end data delivery delay is dominated by the duration during the sensor nodes are waiting for a sink to approach. So waiting duration is our primary concern. </li></ul><ul><li>It is related to the number and the velocity of mobile sinks as well as the transmission radius of sensor nodes. </li></ul>
  11. 12. Delay Analysis(/) <ul><li>Theorem Given a sensor node S . Let m denotes the number of mobile sinks, r is the range of transmission and v is the velocity of mobile sinks. With high probability, the average duration D until which a mobile sink first enters the field of sensor node S is </li></ul>
  12. 14. Delay Analysis(/) <ul><li>M: some mobile sink at a distance l from the static sensor node S. </li></ul><ul><li>The probability that M enters the neighborhood of S is equal to the angle subtended by the neighborhood of S at M. </li></ul><ul><li>cr : scaling factor angle: </li></ul><ul><li>R i :rings with width m -1/2 (consists of points which are at a distance between i - 1/ m -1/2 and i / m -1/2 from S ) </li></ul><ul><li>Let X i,j be a random variable such that X i,j =1 if the i th mobile sink is in R j and it will enter neighborhood of S . </li></ul><ul><li>X i,j = 0, otherwise. (The event X i,j ≈2cr/m ) </li></ul><ul><li>The probability that M i ∈ R j is ≈ 2j m -1/2 </li></ul>
  13. 15. Delay Analysis(/) <ul><li>Define: </li></ul><ul><li>d :the number of rings far from S. </li></ul><ul><li>Use the second Chernoff bound Equation </li></ul><ul><li>=> d =4 f log m / cr </li></ul><ul><li>a sensor node can take time less than </li></ul><ul><li>to wait for a mobile sink reaching it with high probability. </li></ul><ul><li>The service probability that a sensor is within the coverage of at least one mobile sink </li></ul>
  14. 16. Velocity Impact on Delay(1/3) <ul><li>High velocity can increase the probability for the sensor and mobile sink meet with each other, but mobile sinks passing through the effective region of a sensor node so fast that there is no adequate time to perform continuous transmission. </li></ul><ul><li>Increasing velocity will increase the service probability whereas decrease the service duration of each time. </li></ul>
  15. 17. Velocity Impact on Delay(2/3) <ul><li>The average travel distance through the region is equal to </li></ul><ul><li>The available time for message transmission is proportional to </li></ul><ul><li>L: message length w: channel bandwidth </li></ul><ul><li>=>L/w: the number of time slots required to transmit a </li></ul><ul><li>message </li></ul><ul><li>p: service probability </li></ul><ul><li>service time μ : </li></ul>
  16. 18. Velocity Impact on Delay(3/3) <ul><li>The average message delivery delay: </li></ul><ul><li>λ =1 => </li></ul>λ : average arrival rate
  17. 19. Simulations <ul><li>Evaluate three different performance metrics which are crucial to improve the QoS and prolong the lifetime of sensor network. </li></ul><ul><li>1.The average data delivery delay </li></ul><ul><li>- duration from data generation to data reception </li></ul><ul><li>2.The data success rate </li></ul><ul><li>- the ratio of the number of message generated by sensor </li></ul><ul><li>nodes to it received by mobile sinks </li></ul><ul><li>3.The lifetime of the network </li></ul><ul><li>- minimal remaining energy (1000) </li></ul>
  18. 20. Simulations(cont.) <ul><li>Totally 1500 sensor nodes are deployed uniformly at random in the determined area. </li></ul><ul><li>Varying transmission radius is chosen properly to assure some degree of connectivity of network. </li></ul><ul><li>Mobile sinks move according to mobility model. </li></ul><ul><li>The data generation of each sensor nodes follows a Poisson process with an average arrival interval of 1 s. </li></ul>
  19. 29. Conclusions <ul><li>Choosing appropriate number, transmission range, velocity as well as gathering fashion of mobile sinks can significantly guarantee lower end-to-end data delivery delay and achieve better energy conservation. </li></ul><ul><li>A promising direction for future work is to explore the use of cooperative mobility, find an effective data dissemination protocol, and improve the throughput capacity of hybrid sensor network. </li></ul>