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Fast Data Collection in Tree-Based Wireless Sensor Networks.

Platform : MOBILE COMPUTING

IEEE Projects Years : 2012

Fast Data Collection in Tree-Based Wireless Sensor Networks.

 

Abstract:

 

 

 

We investigate the following fundamental question - how fast can information be collected from a wireless sensor network organized as tree? To address this, we explore and evaluate a number of different techniques using realistic simulation models under the many-to-one communication paradigm known as converge cast. We first consider time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a converge cast. Next, we combine scheduling with transmission power control to mitigate the effects of interference, and show that while power control helps in reducing the schedule length under a single frequency, scheduling transmissions using multiple frequencies is more efficient. We give lower bounds on the schedule length when interference is completely eliminated, and propose algorithms that achieve these bounds. We also evaluate the performance of various channel assignment methods and find empirically that for moderate size networks of about 100 nodes, the use of multi-frequency scheduling can suffice to eliminate most of the interference. Then, the data collection rate no longer remains limited by interference but by the topology of the routing tree. To this end, we construct degree-constrained spanning trees and capacitated minimal spanning trees, and show significant improvement in scheduling performance over different deployment densities. Lastly, we evaluate the impact of different interference and channel models on the schedule length.

 

 

 

Algorithm used:

 

 

 

1. BFSTIMESLOTASSIGNMENT.

 

2. LOCAL-TIMESLOTASSIGNMENT

 

 

 

Algorithm 1 BFS-TIMESLOTASSIGNMENT

 

1. Input: T = (V, ET )

 

2. While ET _= φ do

 

3. e ← next edge from ET in BFS order

 

4. Assign minimum time slot t to edge e respecting adjacency and interfering constraints

 

5. ET ← ET \ {e}

 

6. end while

 

 

 

Algorithm 2 LOCAL-TIMESLOTASSIGNMENT

 

1. node.buffer = full

 

2. if {node is sink} then

 

3. Among the eligible top-subtrees, choose the one with the largest

 

number of total (remaining) packets, say top-subtree i

 

4. Schedule link (root(i), s) respecting interfering constraint

 

5. else

 

6. if {node.buffer == empty} then

 

7. Choose a random child c of node whose buffer is full

 

8. Schedule link (c, node) respecting interfering constraint

 

9. c.buffer = empty

 

10. node.buffer = full

 

11. end if

 

12. end if

 

 

 

 Architecture

 

 

 

 

 

Existing System:

 

 

 

Existing work had the objective of minimizing the completion time of converge casts. However, none of the previous work discussed the effect of multi-channel scheduling together with the comparisons of different channel assignment techniques and the impact of routing trees and none considered the problems of aggregated and raw converge cast, which represent two extreme cases of data collection,

 

 

 

Proposed System:

 

Fast data collection with the goal to minimize the schedule length for aggregated converge cast has been studied by us in, and also by others in, we experimentally investigated the impact of transmission power control and multiple frequency channels on the schedule length Our present work is different from the above in that we evaluate transmission power control under realistic settings and compute lower bounds on the schedule length for tree networks with algorithms to achieve these bounds. We also compare the efficiency of different channel assignment methods and interference models, and propose schemes for constructing specific routing tree topologies that enhance the data collection rate for both aggregated and raw-data converge cast.

 

 

 

Modules:

 

 

 

  1. 1.    Periodic Aggregated Converge cast.
  2. 2.    Transmission Power Control
  3. 3.    Aggregated Data Collection
  4. 4.    Raw Data Collection
  5. 5.    Tree-Based Multi-Channel Protocol (TMCP)

 

 

 

Module Description:

 

 

 

1. Periodic Aggregated Converge cast.

 

Data aggregation is a commonly used technique in WSN that can eliminate redundancy and minimize the number of transmissions, thus saving energy and improving network lifetime. Aggregation can be performed in many ways, such as by suppressing duplicate messages; using data compression and packet merging techniques; or taking advantage of the correlation in the sensor readings

 

 

 

We consider continuous monitoring applications where perfect aggregation is possible, i.e., each node is capable of aggregating all the packets received from its children as well as that generated by itself into a single packet before transmitting to its parent. The size of aggregated data transmitted by each node is constant and does not depend on the size of the raw sensor readings.

 

 

 

2. Transmission Power Control

 

 

 

We evaluate the impact of transmission power control, multiple channels, and routing trees on the scheduling performance for both aggregated and raw-data converge cast.. Although the techniques of transmission power control and multi-channel scheduling have been well studied for eliminating interference in general wireless networks, their performances for bounding the completion of data collection in WSNs have not been explored in detail in the previous studies. The fundamental novelty of our approach lies in the extensive exploration of the efficiency of transmission power control and multichannel communication on achieving fast converge cast operations in WSNs.

 

 

 

 3. Aggregated Data Collection

 

 

 

We augment their scheme with a new set of rules and grow the tree hop by hop outwards from the sink. We assume that the nodes know their minimum-hop counts to sink.

 

 

 

4. Raw Data Collection

 

 

 

The data collection rate often no longer remains limited by interference but by the topology of the network. Thus, in the final step, we construct network topologies with specific properties that help in further enhancing the rate. Our primary conclusion is that, combining these different techniques can provide an order of magnitude improvement for aggregated converge cast, and a factor of two improvement for raw-data converge cast, compared to single-channel TDMA scheduling on minimum-hop routing trees.

 

 

 

5. Tree-Based Multi-Channel Protocol (TMCP)

 

 

 

Fig: Schedule generated with TMCP

 

 

 

TMCP is a greedy, tree-based, multi-channel protocol for data collection applications. It partitions the network into multiple sub trees and minimizes the intra tree interference by assigning different channels to the nodes residing on different branches starting from the top to the bottom of the tree. Figure shows the same tree given in Fig. which is scheduled according to TMCP for aggregated data collection. Here, the nodes on the leftmost branch is assigned frequency F1, second branch is assigned frequency F2 and the last branch is assigned frequency F3 and after the channel assignments, time slots are assigned to the nodes with the BFSTimeSlotAssignment algorithm.

 

 

 

Advantage

 

 

 

 Advantage of TMCP is that it is designed to support converge cast traffic and does not require channel switching. However, contention inside the branches is not resolved since all the nodes on the same branch communicate on the same channel

 

 

 

 

 

System Requirements:

 

 

 

Hardware Requirements

 

 

 

  • SYSTEM            : Pentium IV 2.4 GHz
  • HARD DISK      : 40 GB
  • FLOPPY DRIVE : 1.44 MB
  • MONITOR         : 15 VGA colour
  • MOUSE              : Logitech.
  • RAM                   : 256 MB
  • KEYBOARD      : 110 keys enhanced.

 

 

 

Software Requirements

 

 

 

  • Operating system :-  Windows XP Professional

 

 

 

  • Front End  :JAVA, Swing(JFC) ,J2ME
    • Tool                    :Eclipse 3.3

 

 



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