APPARATUS AND METHOD FOR A BIOLOGY INSPIRED TOPOLOGICAL PHASE TRANSITION FOR WIRELESS SENSOR NETWORK

• US 20130235757A1
• Filed: 03/07/2012
• Published: 09/12/2013
• Est. Priority Date: 03/07/2012
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: WANG, Shu
• Application Number:
  US13/414,049
• Publication Number:
  US 20130235757A1
• Time in Patent Office: Days
• US Class Current: 370/254
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/70 Services for machine-to-machine communication [M2M] or machine type communication [MTC]
• CPC Class Codes: H04W 40/00 Communication routing or communication path finding
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks
• CPC Class Codes: Y02D 30/70 in wireless communication networks

Abstract

An apparatus and a method for a topological phase transition of a Wireless Sensor Network (WSN) are provided. The method includes determining an optimal topological phase of the WSN, and at each wireless sensor node, establishing connections to other wireless sensor nodes in the WSN in accordance with the determined optimal topological phase.

Claims

1. A method for a topological phase transition of a Wireless Sensor Network (WSN) comprising a plurality of wireless sensor nodes, the method comprising:

determining an optimal topological phase of the WSN; and

at each wireless sensor node, establishing connections to other wireless sensor nodes in the WSN in accordance with the determined optimal topological phase.

2. The method according to claim 1, further comprising:

at each wireless sensor node, dynamically determining the optimal topological phase;

at each wireless sensor node, establishing connections to other nodes according to the determined optimal topological phase.

3. The method according to claim 1, wherein the optimal topological phase is determined in accordance with at least one of an internal state of each wireless sensor node, a detected sensor data of each wireless sensor node, and a connectivity state of each wireless node.

4. The method according to claim 3, wherein the internal state comprises at least one of a time of a clock, a remaining power level of a power supply, a charge/discharge rate of the power supply, an available communication bandwidth of each wireless sensor node, a rate of sensor data collection of each wireless sensor node, and a rate of data relaying of each wireless sensor node.

5. The method according to claim 3, wherein the connectivity state comprises at least one of a connectivity status of each wireless sensor node, a connectivity history of each wireless sensor node, and a connectivity aggregation weight of each wireless sensor node.

6. The method according to claim 1, wherein the optimal topological phase comprises one of a random graph network, a scale-free network, and a star network.

7. The method according to claim 1, further comprising:

at each wireless sensor node, aggregating sensor data;

at each wireless sensor node, transmitting activity diffusion messages to next hop neighbor wireless sensor nodes;

at wireless sensor nodes, receiving the activity diffusion messages, accumulating the activity diffusion messages and, when an activity diffusion weight of received activity diffusion messages exceeds a threshold value, transmitting a message indicating it is an aggregation candidate node; and

at each wireless sensor node, when a data aggregation is complete, selecting an aggregation candidate node and transmitting the aggregated sensor data to the aggregation candidate node,

wherein the determined optimal topological phase comprises a star network.

8. The method according to claim 7, wherein the selecting of the aggregation candidate node comprises selecting an aggregation candidate node comprising a highest activity diffusion weight at the time of the selecting.

9. The method according to claim 1, further comprising:

at each wireless sensor node, choosing K most preferred neighbor wireless sensor nodes, based on the wireless sensor node'"'"'s neighbor choice history, and using the K most preferred neighbor wireless sensor nodes as next hop candidates; and

at each wireless sensor node, sending activity diffusion messages to only the K most preferred neighbor wireless sensor nodes during an aggregation interval and, when the aggregation interval elapses, selecting one of the K most preferred neighbor wireless sensor nodes as a next hop;

wherein the optimal topological phase comprises a scale free network.

10. The method according to claim 9, wherein the selecting of the next hop comprises selecting a preferred neighbor wireless sensor node comprising a highest aggregation weight.

11. The method according to claim 1, further comprising:

at each wireless sensor node, storing a history of next hop choices; and

if the next hop choice history shows a neighbor wireless sensor node is chosen as the next hop as frequently as at least a predetermined threshold indicating stability, choosing the stable next hop as a fixed next hop,

wherein the optimal topological phase comprises a star network.

12. The method according to claim 11, further comprising, if a wireless sensor node has a fixed next hop, refraining from sending activity diffusion messages during an aggregation interval and instead directly using the fixed next hop for data aggregation.

13. The method according to claim 11, further comprising:

notifying the fixed next hop that it is chosen as a fixed next hop; and

at the next hop, storing an identity and an aggregation interval of the choosing wireless sensor node.

14. A wireless sensor node for a Wireless Sensor Network (WSN) comprising a plurality of the wireless sensor nodes, the node comprising:

a controller for controlling operations of the node;

a wireless transmitter for transmitting communications from the node;

a wireless receiver for receiving communications;

a sensor unit for sensing sensor data; and

a memory unit for storing the sensor data,

wherein the controller controls the node to connect to other nodes or to an external WSN access point in accordance with a determined optimal topological phase of the WSN.

15. The node according to claim 14, wherein the controller dynamically determines the optimal topological phase.

16. The node according to claim 14, wherein the optimal topological phase is determined in accordance with at least one of an internal state of the node, a detected sensor data from the sensor unit, and a connectivity state of the node.

17. The node according to claim 16, wherein the internal state comprises at least one of a time of a clock, a remaining power level of a power supply, a charge/discharge rate of the power supply, an available communication bandwidth of the node, a rate of sensor data collection of the sensor unit, and a rate of data relaying of the node.

18. The node according to claim 16, wherein the connectivity state comprises at least one of a connectivity status of the node, a connectivity history of the node, and a connectivity aggregation weight of the node.

19. The node according to claim 14, wherein the optimal topological phase comprises one of a random graph network, a scale-free network, and a star network.

20. The node according to claim 14, wherein:

the node aggregates sensor data and transmits activity diffusion messages to next hop neighbor nodes,

if the node receives the activity diffusion messages, the node accumulates the activity diffusion messages and, when an activity diffusion weight of received activity diffusion messages exceeds a threshold value, transmits a message indicating it is an aggregation candidate node,

when a data aggregation is complete, the node selects an aggregation candidate node and transmits the aggregated sensor data to the aggregation candidate node, and

the determined optimal topological phase comprises a star network.

21. The node according to claim 20, wherein the selecting of the aggregation candidate node comprises selecting an aggregation candidate node comprising a highest activity diffusion weight at the time of the selecting.

22. The node according to claim 14, wherein:

the node chooses K most preferred neighbor nodes, based on the node'"'"'s neighbor choice history, and uses the K most preferred neighbor nodes as next hop candidates,

the node sends activity diffusion messages to only the K most preferred neighbor nodes during an aggregation interval and, when the aggregation interval elapses, selecting one of the K most preferred neighbor nodes as a next hop, and

the optimal topological phase comprises a scale free network.

23. The node according to claim 22, wherein the selecting of the next hop comprises selecting a preferred neighbor node comprising a highest aggregation weight.

24. The method according to claim 14, wherein:

the node stores a history of next hop choices, and

if the next hop choice history shows a neighbor node is chosen as the next hop as frequently as at least a predetermined threshold indicating stability, the node chooses the stable next hop as a fixed next hop,

wherein the optimal topological phase comprises a star network.

25. The node according to claim 24, wherein if the node has a fixed next hop, the node refrains from sending activity diffusion messages during an aggregation interval and instead directly uses the fixed next hop for data aggregation.

26. The node according to claim 24, wherein:

the node notifies the fixed next hop that it is chosen as a fixed next hop, and

the next hop stores an identity and an aggregation interval of the choosing node.

27. A Wireless Sensor Network (WSN), the WSN comprising:

a plurality of wireless sensor nodes,

wherein each wireless sensor node dynamically connects to other wireless sensor nodes in accordance with a determined optimal topological phase.

28. The WSN according to claim 27, wherein each wireless sensor node separately dynamically determines its optimal topological phase.