Mobilityassisted Distributed Sensor Clustering for Energyefficient Wireless Sensor

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

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

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

The a. MANET Approach � 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 travel physically to deliver data to the sink, resulting in unpredictable latency. a. MANET, However, exploits electronic data transmission.

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

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 Which one is rotated to distribute energy consumption. more energy efficient ? � It’s straightforward to let the a. MANET nodes to assume the CH role, which is a energy consuming task.

Some Numerical Analysis �

Numerical Results Experiment Parameters: 5 n. J/bit 75 m

MADSEC: Problem Formulation �

Problem Formulation �

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

Challenges �

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

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

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

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

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

Formulation �

Formulation �

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!

MADSEC: Data Collection � a. MANET 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 � a. MANET nodes fuses data collected from sensor, sends them over the a. MANET and the sink.

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

NS 2 Simulation Parameters Type Network Application Radio transceiver Parameter Value Network size Number of Sensors (N) 100 Sensor distribution Random Sink Location (50, 0) Sensor initial energy 1 Joule a. MANET node speed 2 m/s Data rate 100 kb/s Maximum transmission power -1. 58 d. Bm Receiver sensitivity -24 d. Bm 5 n. J/bit MADSEC Round length 50 s Clustering frequency 5 rounds Number of power levels 20

Simulation Results Comparison of different clustering techniques: 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!

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

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

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.

Thank you! kaili@eecs. ucf. edu