2015/10/1 a color-theory-based energy efficient routing algorithm for mobile wireless sensor...

112/04/19

A color-theory-based energy A color-theory-based energy efficient routing algorithm for efficient routing algorithm for

mobile wireless sensor networksmobile wireless sensor networks

Tai-Jung Chang, Kuochen Wang, Yi-Ling HsiehDepartment of Computer Science

National Chiao Tung University

Hsinchu, Taiwan

February 2008 , Computer Networks(The International Journal of Computer and Telecommunications Networking)

A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 2112/04/19

This paper propose a Color-theory-based Energy Efficient Routing algorithm to prolong the life time of each sensor node.

This paper adopted a range-free Color-theory based Dynamic Localization to help identify each sensor node's location.

The uniqueness of our approach is that by comparing the associated RGB values among neighboring nodes.

Simulation results have shown that our routing algorithm can save 50% - 60% energy than ESDSR in mobile WSNs.

Abstract


Introduction1

Related Work (CDL)2

Proposed Approach (CEER)3

Simulation Result4

Outline

Conclusion5

Introduction1

Related Work (CDL)2


Simulation Result4

Conclusion5


Introduction It is a great challenge for routing in WSNs due to the following

reasons. First, since it is not easy to build a whole network topology, it is hard to

assign each routing path. Secondly, sensor nodes are tightly constrained in terms of energy,

processing, and storage capacities.

The proposed Color-theory-based Energy Efficient Routing algorithm (CEER) is based on CDL , in which the location of a sensor node is represented as a set of RGB values.

• CDL - Color-theory based Dynamic Localization

Color-theory

Color-theory based Dynamic Localization

Color-theory-based Energy Efficient Routing algorithm

Outline

Introduction1

Related Work (CDL)2


Simulation Result4

Conclusion5


Color-theory RGB HSV➠

Related Work(1/5)

➠

RGB►Red►Green►Blue

HSV►Hue (360)►Saturation ►Value

(1,0,0)

(0,1,0)

(1,1,0)

(0,0,1)

(1,1,1)

(0,0,0)

(1,0,1)

(0,1,1)


Related Work (2/5)

Each node i maintains an entry of (Rik ,Gik ,Bik) and Dik, where k represents the kth anchor. Davg is the average hop distance, which is based on DV-Hop. hij is the hops between nodes i and j. Dik represents the hop distance from anchor k to node i :

Dik = Davg × hij

Range represents the maximum distance that a color can propagate. (Rk , Gk , Bk) are the RGB values of anchor k. (Hik , Sik , Vik) are the HSV values of anchor k received by the ith node.

Dik (Rik,Gik,Bik)

kth … …

A

B

C

D

E

F

G

Notations that are defined in CDL

(RA , GA , BA)


Related Work (3/5) There are four phases involved in the CDL.

In the first phase:

The RGB values of anchors are assigned randomly from 0 to 1. Anchors broadcast RGB value.➠

➠The neighbors receiving the packet would record the number of hops to the anchor.➠

After completing the broadcast, each anchor receives a set of hop count values from all other anchors and is

able to calculate the distances and hops to all other anchors.

Upon obtaining the information from all anchors, each node computes the average hop distance.

DV-Hop


Related Work (4/5) In the second phase:

Once the nodes nearby the anchors receive the RGB values and location information from the anchors, they convert the RGB values to corresponding HSV values.➠➠

(Hk, Sk, Vk)= RGBtoHSV (Rk, Gk, Bk) (1)

kik

ikkikkik Range

DV)1(V,SS,HH (2

)

(Rik , Gik , Bik)= HSVtoRGB (Hik , Sik , Vik) (3)

n

kikikikiii n 1

)B,G,R(1

)B,G,R( (4)

➠

By the means of broadcasting, the RGB values would be received by the server eventually.

Sensor node

Anchor

where n is the number of anchors that node i received.

Sensor node

➠


Related Work (5/5)

In the third and fourth phase:

➠

The establishment of a location database is performed when the server obtains the RGB

values and coordinates (locations) of all anchor.

22kikiik yyxxd

(Xi, Yi) is the coordinate of location i, and (Xk ,Yk) is the location of anchor k.

(Hk, Sk, Vk)= RGBtoHSV (Rk, Gk, Bk) (6)

kik

ikkikkik Range

DV)1(V,SS,HH

(5)

(7)

(Rik , Gik , Bik)= HSVtoRGB (Hik , Sik , Vik) (8)

➠➠

N

kikikikiii N 1

)B,G,R(1

)B,G,R( (4)

where N is the number of anchors

➠


Introduction1

Related Work (CDL)2


Simulation Result4

Conclusion5


Proposed Approach (1/4) The network model is described as follows:

A server is used for building a location database, localization, and collecting sensed data.

The area is divided into clusters and a cluster head is selected by the server for each cluster.

There are four anchors, which are placed in the four corners of the area . Anchors collect aggregate data received from cluster heads.

All sensor nodes have a uniform energy at the beginning. Each node wakes up at a fixed rate to check if its RGB values have

changed. All sensor nodes are mobile. CSMA/CA is used to avoid collision of packets.


Setup Phase First, we define a grid size as R × R , where R is a sensor node’s radio range. All sensor

nodes in a cluster (grid) are within their cluster head’s radio range.

Secondly, each anchor floods its RGB values and average hop distance to each sensor node

Each sensor node can calculate its hop count to the anchor and adjust its RGB values.

By the means of broadcasting, the RGB values would be received by the server.

By looking up the location database, the server can calculate each sensor node’s location and choose a cluster head which is close to the center of the grid.

Figure 1. Setup phase 1 Figure 2. Setup phase 2

Proposed Approach (2/4)


Data Dissemination Phase After the setup phase, each cluster head will receive its cluster member’s

information. When a cluster head receives information from its cluster member, it will

setup a timer. When a cluster head wants to forward the aggregate data toward the server,

two steps are performed in CEER.• The sensor node selects its one hop neighbors that are closer to a nearby anchor

than itself.

• The sensor node with the highest energy level is selected as the next hop.

Figure 3. Data dissemination step 1

Proposed Approach (3/4)

Figure 4. Data dissemination step 2


Introduction1

Related Work (CDL)2


Simulation Result4

Conclusion5


Simulation Result (1/2)

Simulation Model

Table 2. Simulation parameters.

Parameter Value

Area size 500 × 500 m2

Node speed Randomly choose from [Vmin , Vmax]

Node transmission range (R) 50 m

Pause time 0

Measurement period 50 tu

Update interval 50 tu

Time slot length (time unit) tu


Refinement Phase

Figure 5. Energy consumption vs. number of nodes.

Simulation Result (2/2)

Figure 6. Latency per packet vs. number of nodes.

Without cluster heads


Introduction1

Related Work (CDL)2


Simulation Result4

Conclusion5


Conclusion

In this paper, we have presented a color-theory-based energy efficient routing algorithm (CEER) based on a color-theory-based dynamic localization algorithm (CDL).

Simulation results have shown that our routing algorithm can save 50% - 60% energy compared to ESDSR in mobile wireless sensor networks.

In addition, the latency per packet of CEER is 50% less than that of ESDSR.

2015/10/1 a color-theory-based energy efficient routing algorithm for mobile wireless sensor...

Documents

bikktha colortheory

abstracta colortheory

proposed colortheory

cdl color

rangefree colortheory

dvhopa colortheory

routing path

terms of energy