1. Distributed Algorithms for Mobile Sensor Networks
CS5802 – Nathan Loika

2. What is a Mobile Sensor Network?
Network of nodes
Capable of point-to-multi-point communication
Performs the collection of data
Flexible hardware
Sensing
Computation
Communication
Locomotion
Discrete or embedded
Possibly disposable

3. Physical Microcontroller / Processor Sensors Power Communications
Tailored to application
Power
Solar, Battery
Communications
Radio
Antenna
Movement

4. Current Applications Machine learning Data mining Automation
Autonomous vehicles
Data mining
Fitness trackers
Environmental trackers
Automation
Home security
Manufacturing optimization

5. Mobile Network Obstacles
Communication / Security
Energy usage
Locomotion / Distribution
Cost

6. Communication / Security
Cluster based communication
Eases routing and energy use
Wireless communication
Radio / Bluetooth
Broadcast
Security
Extremely limited resources
Custom Protocols
SPINS

7. Energy Management Each node needs energy production and storage
Primary energy usage is communication
Clustering – distributed algorithm
Homogenous vs Heterogenous
Homogenous – LEACH, PEGASIS, HEED
Heterogenous – SEP, DEEC

8. Homogenous Clustering
Low-Energy Adaptive Clustering Hierarchy (LEACH)
Rotating cluster heads, even energy drain
Stochastic selection based on number of nodes
Set-up, Stead-state
Hybrid, Energy-Efficient, Distributed Clustering (HEED)
Linear time (with respect to number of nodes)
Stochastic selection based on percent of energy
Transmission power levels adjusted to cluster
HEED:
This algorithm has four main goals:
Prolonging network lifetime by distributing energy consumption
Terminating clustering process by constant number of iterations
Minimizing control overload
Providing appropriate distributed cluster heads and compressed clusters.
HEED Dependent on minimum power selected

9. Low-Energy Adaptive Clustering Hierarchy (LEACH)
Assumes equal energy and communication
Round based selection
Each cluster head is randomly selected
Steady state communication is slotted

10. Heterogenous Clustering
Stable Election Protocol (SEP)
Two energy levels
Weight the odds of becoming cluster head
Distributed Energy-Efficient Clustering (DEEC)
Multiple energy levels
Selection on residual energy

11. Heterogenous Clustering

12. Locomotion / Localization
Three major cases
Distribution
Redistribution
Location feedback
Typically passive locomotion
Movement only by outside forces
Distribution one time
Location awareness is costly
GPS is expensive
DV-HOP, MCL
DV-HOP -> Hop counting, good for low density networks
MCL -> Monte Carlo Localization, Weighted position set, Predict then update applying a small movement each time to gain information

13. Distribution Algorithm Example
Given N mobile nodes with isotropic radial sensors of range Rs and isotropic radio communication of range Rc how should they deploy themselves so that the resulting configuration maximizes the net sensor coverage of the network with the constraint that each node has at least K neighbors?
Attraction and repulsion forces
Repel until node has degree K
Communicate to critical nodes, continue to repel others
Increase attraction force until equilibrium

14. Distribution Algorithm Example

15. Citations
Heinzelman, Wendi B., Anantha P. Chandrakasan, and Hari Balakrishnan. "An application- specific protocol architecture for wireless microsensor networks." IEEE Transactions on wireless communications 1.4 (2002):
Younis, Ossama, and Sonia Fahmy. "HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks." IEEE Transactions on mobile computing 3.4 (2004):
Smaragdakis, Georgios, Azer Bestavros, and Ibrahim Matta. SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. Boston University Computer Science Department, 2004.
Qing, Li, Qingxin Zhu, and Mingwen Wang. "Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks." Computer communications (2006):
Perrig, Adrian, et al. "SPINS: Security protocols for sensor networks." Wireless networks 8.5 (2002):
Hu, Lingxuan, and David Evans. "Localization for mobile sensor networks." Proceedings of the 10th annual international conference on Mobile computing and networking. ACM, 2004.
Howard, Andrew, Maja J. Matarić, and Gaurav S. Sukhatme. "An incremental self-deployment algorithm for mobile sensor networks." Autonomous Robots 13.2 (2002):