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

1. Mobility-assisted Distributed Sensor Clustering for Energy-efficient Wireless Sensor Networks Kai Li, Kien Hua Department of Computer Science University of Central Florida

2. Traditional WSN: Energy issues Internet • 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) Sensors not only sense but also relay data Sink Wireless sensor

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

4. The aMANET Approach • aMANET 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 aMANET approach is different from existing mobile elements approaches such as: • Mobile sinks. An aMANET 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. aMANET, However, exploits electronic data transmission.

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

6. Clustering in aMANET Clustering in aMANET 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 and can be estimated as And using the Two-ray Ground radio model. is the number of bits transmitted per round Denote the average energy consumption of a non-CH sensor in each round as , and that of a CH as . Let denote the ratio of CH. Compare the following two paradigms • Static Cluster Head (SCH). In SCH, sensors are chosen from normal sensors and CH roles are periodically rotated among sensors • Sensor’s average energy consumption in each round • Mobile Cluster Head (MCH). In MCH, a number of mobile aMANET nodes assume CH role • Sensor’s average energy consumption in each round • According to the Two-ray Ground radio model, the energy consumption of transmitting and receiving one bit data over distance are given as • , • and • , • Where is the energy dissipation of the transceiver circuit, and is that of amplifier. Both and are dependent on distance: when is smaller than a threshold and otherwise

8. Numerical Results Experiment Parameters:

9. MADSEC: Problem Formulation • Sensors communicate with aMANETnode in single hop, using adjustable transmission power • Each sensor has an initial energy of Joules and transits -bit data in each time-slot • Find a configuration to optimize some energy-oriented objective function.

10. Problem Formulation When setting =1 this function measures the energy consumption for the whole network in each time-slot. The objective function • is the energy sensor n consumed by transmitting data to aMANET node k (located at ) in a round. • could be 1 or 0 depending on if sensor n is clustered to MCH k • is a function of the sensor’s state • can be seen as a weight to each sensorbased on sensor’s state (e.g. residual energy).

11. The K-means Algorithm • The minimization of with respect to and , could be solved using the standard K-means algorithm

12. Challenges • The K-means algorithm is centralized • Can we make it distributed? • The algorithm requires location information , , which is generally not available in WSN, especially (i.e. the location of sensors) • Can we make the least assumption about location awareness?

13. 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 wAMRP metric • The weighted Average Minimum Reachability Power (wAMRP) • : number of sensors in a cluster • : the weight assigned to a sensor • : the minimum power for sensor to reach its Cluster Head • Radio propagation model dictates that How does a mobile node compute the wAMRP at its current location ? NEXT

15. MADSEC: Computing wAMRP • Each mobile node use the following protocol to compute wAMRP within its cluster.

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 We actually could arrive at the optimal location with only three moves! Random move Random move We don’t know yet! Initial location Directional move Target location

18. Formulation • Let wAMRP = be the metric measured at location , then • We have the following equation array =

19. Formulation • We need to get to , but we only need to know . Substituting this into the equation array, we get Where and

20. Formulation • 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 nodesschedule 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 nodesfuses data collected from sensor, sends them over the aMANETand the sink.

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

23. NS2 Simulation Parameters

24. Simulation Results Comparison of different clustering techniques: Random Mobility: each MCH makes a random move, sensors join an MCH with the minimum RSS Unequal weights MADSEC, sets compute the weight of a sensor as a function of its residual energy: 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!

25. Simulation Results (Contd.) Comparison of variable number of MCHs More MCHs incurs more network overhead With smaller size clusters (more MCHs), the computation of wAMRP is less accurate 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 aMANET for energy-efficient data collection. Its desirable features are: • Energy-efficiency: sensor network lifetime are remarkably improved over conventional clustering techniques. • Distributed Computing: each aMANET node runs the clustering algorithm in a distributed manner. • Few assumptions: we only need adjustable power levels. aMANET nodes don’t need GPSs for clustering updates.

28. Thank you! kaili@eecs.ucf.edu