1. Kai Li, Kien Hua Department of Computer Science University of Central Florida

2. Traditional WSN: Energy issues  Sensors are energy constrained: Typically powered by AA batteries  Communication consumes too much energy Data packet (self generated and other sensor’s) Control packet (e.g. routing, topology maintenance) Internet Sensors not only sense but also relay data Wireless sensor

3. The a MANET Approach Internet Sensors transmit only their own data Autonomous MANET nodes collects and forwards data to sink  The idea: Save sensor energy by separating sensing from communication

4.  a MANET is motivated by mobile connected robots research Multiple mobile nodes cooperate to achieve some common tasks (e.g. for energy-efficient data collection) Mobile nodes form a middle-layer network for data collection and electronic transmission  Our a MANET approach is different from existing mobile elements approaches such as: Mobile sinks. An a MANET node doesn’t have to be as advanced as mobile sinks (i.e. cost-effective). They don’t have to be connected to the internet. Data mules. Data mules travel physically to deliver data to the sink, resulting in unpredictable latency. a MANET, However, exploits electronic data transmission. The a MANET Approach

5. The a MANET Challenge Each aMANET node is responsible for a sensor group Need a clustering technique that can be performed in a distributed manner can save sensors energy to extended their lifetime Autonomous mobile node

6. Clustering in a MANET Clustering in a MANET is different from traditional sensor clustering algorithms (e.g. LEACH, HEED, etc.)  In traditional sensor clustering, the cluster head (CH) is chosen from normal sensors. CH roles are rotated to distribute energy consumption.  It’s straightforward to let the aMANET nodes to assume the CH role, which is a energy consuming task. Which one is more energy efficient ?

7. Some Numerical Analysis

8. Numerical Results Experiment Parameters: 5nJ/bit 75m

9. MADSEC: Problem Formulation

10. Problem Formulation

11. The K-means Algorithm Initialization: Iteration: Repeat: // Step 1: Assign sensor to the closest aMANET node (Cluster formation) // Step 2: Update MCHs positions (Cluster Update)

12. Challenges

13. Initialization: Iteration: Repeat: // Step 1: Assign sensor to the closest aMANET node (Cluster formation) // Step 2: Update MCHs positions (Cluster Update) The K-means algorithm (revisit) Step 1 can be approximated. Each mobile node sends out an invitation message. Sensor joins the one with the strongest received signal strength How can a mobile node reposition himself to the right location in Step 2 without location information?

14. MADSEC: The w AMRP metric How does a mobile node compute the w AMRP at its current location ?

15. MADSEC: Computing w AMRP  Each mobile node use the following protocol to compute w AMRP within its cluster. 1:ClusterInfo = {} 2:for power_level = 1 to MAX_POWER_LEVEL do 3: Set transmission power to Power(power_level) 4: Broadcast probe_msg(MyID) 5: for all received ask_msg(SensorID, ResEnergy, MyID) do 6: 7: Add SensorID to ClusterInfo, Compute weight according to ResEnergy, and Record weight and Power(power_level) 8: endif 9: endfor 10:endfor 11:Compute w AMRP

16. MADSEC: Relocation  How to locate the point where we get the minimum wARMP ? We do not assume location awareness Exhaustive search is not a feasible solution Not interesting!!!

17. MADSEC: Relocation Initial location Target location We actually could arrive at the optimal location with only three moves!

18. Formulation

20.  The only requirement for a valid solution of the equation array is simply Which gives us  The two random moves should not be collinear!

21. MADSEC: Data Collection  aMANET nodes schedule data aggregation after clustering is finished Each round of data collection is divided into a number of TDMA frames, in a similar way to LEACH Each sensor will be allocated one time frame for data transmission  aMANET nodes fuses data collected from sensor, sends them over the aMANET and the sink.

22. An overall review of MADSEC … Iteration 1 Iteration 2 Iteration 3 Cluster formation CH reposition Clustering Phase … Data Collection Phase … … Round 1 Round 2

23. NS2 Simulation Parameters TypeParameterValue NetworkNetwork size Number of Sensors (N)100 Sensor distributionRandom Sink Location(50, 0) ApplicationSensor initial energy1 Joule aMANET node speed2 m/s Data rate100 kb/s Radio transceiverMaximum transmission power-1.58dBm Receiver sensitivity-24dBm 5 nJ/bit MADSECRound length50s Clustering frequency5 rounds Number of power levels20

24. Simulation Results Random Mobility: each MCH makes a random move, sensors join an MCH with the minimum RSS C-LEACH: a centralized version of LEACH, assuming a centralized server holing information of the whole network Even random mobility can almost double sensor network lifetime. And MADSEC does even better! Comparison of different clustering techniques:

25. Simulation Results (Contd.) With smaller size clusters (more MCHs), the computation of wAMRP is less accurate More MCHs incurs more network overhead Comparison of variable number of MCHs Clusters becomes smaller with more MCHs, therefore sensors consumes less energy and live longer

26. Simulation Results (Contd.) Comparison of varying number of power levels With more discrete power levels, the relocation accuracy becomes higher, leading to closer results compared with optimal

27. Conclusions MADSEC is a clustering technique designed for an a MANET for energy-efficient data collection. Its desirable features are:  Energy-efficiency: sensor network lifetime are remarkably improved over conventional clustering techniques.  Distributed Computing: each a MANET node runs the clustering algorithm in a distributed manner.  Few assumptions: we only need adjustable power levels. a MANET nodes don’t need GPSs for clustering updates.

28. kaili@eecs.ucf.edu