Energy optimization technique for distributed

localized wireless sensor network

Sunil Kumar Department of Computer Science

ABES Engineering College Ghaziabad, India

sunil.kumar@abes.ac.in

Dr. A.L.N. Rao Department of IT Amity University

Noida, India alnrao@amity.edu

Dr. Radhakrishnan Ramaswani Department of Computer Science

ABES Engineering College Ghaziabad, India

radhakrishnanramaswami@gmail.com

Abstract-Several localization techniques have been proposed to identify the exact location of sensor nodes. Localization is important for computation of many applications such as routing in wireless sensor network. Energy preservation is a challenge in wireless sensor network and many algorithms have already been proposed. The localized node lifetime is maximized by using energy-efficient routing, use of minimum number of layers in communication and by minimizing their active time. In this paper a technique which will optimize the energy in routing algorithm by reducing the number of active sensors in communication is proposed. Major performance factors are network throughput, latency and message complexity. In energy optimization these factors are considered and an optimized solution for localization with high accuracy is proposed.

Keywords: Global positioning system (GPS); gateway node; beacon static node; beacon moving node; static node; active node; passive node; dead node; throughput; delay;

I. INTRODUCTION Wireless sensor networks (WSN) field is one of

the most investigated research area in recent times. Importance of this field is strongly linked to the advancements of new mobile devices and wireless sensor as they play a very important role where a human intervention is not possible.

A WSN is a set of many self configured wireless nodes where a node can be a sensor. These wireless nodes help in monitoring the area where they are deployed. Sensor nodes collect information where they are placed, process it and pass it to other sensor nodes in wireless sensor networks. Localization accuracy & availability depend on various factors such as characteristics of sensors, surrounding environment and distance from the base station.

Localization algorithm in WSN categorised as range based & range free. Range based depends on angle and distance measurements such as RSSI, TOA and TDOA while range free is based on neighbouring node information.

Many localization techniques have already been proposed but performance is not up to the level of acceptance when sensors are deployed

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in very unfriendly environment or in situation where some sensors are dynamic and others are static nodes.

Consider a forest scenario where the objective is to measure the temperature of a forest and in case of fire an alarm is to be generated at the base station. The sensors are deployed by dropping from an aircraft so that they are randomly deployed. The nodes are automatically configurable and start sending data on activation. If there is a fire and the base station gets the information on temperature as 98* C, then locating the place of high temperature becomes Vital, so that action could be taken in time. Same is depicted in fig.l.

In above explained example, sensor nodes dropped from aircraft may not deploy at exact location and some of the nodes keep moving, the exact location cannot be predicted. Identification of node position becomes difficult unless sensors themselves find their location and combined this information with collected data and forward this information to the gateway node which further forwards it to a base station.

Localization problem in WSN solved by GPS but GPS has several limitations. A sensor node identifies position after communicating with a moving GPS enabled node pass its coordinate information to a base station. Several algorithms have been proposed related to accuracy but here the aim of this paper to provide a solution which consumes low power because each sensor has power constraints.

Fig. I. Sensors deployment in forest

II. RELATED WORK In distTibuted sensor network every node has a

capability to compute the data and pass it to a gateway node (GN) of its cluster then GN forward it to a base station where user retrieves the data as

350

per requirement. In order to optimize the energy need to create clusters as shown in fig. 2, the proposed work addresses localization & effective routing information based on this cluster based architecture.

Many approaches propose increasing sensor routing lifetime by considering it as a linear programming problem or by calculating the energy expenditure as a function of initial energy and residual energy.

• • _.. a.t .............. St.,,,"

• O.' ....... yNo. C. ........ unk.U.n

.. S.n .... nod. _ .... _.".I •• U.n wh:h 'lhe •••• _.y a.t ...... v Co ........ nkMJon

Fig.2 Existing cluster network technique

III. PROBLEM STATEMENT In localization problem various issues and

challenges need to be dealt with while designing an energy optimization technique in WSN. Some of the issues of location verification and routing algorithm are given below:

• Localization issues with minimum energy consumption associated with the number of layers in communication for routing.

• Message complexity for location verification.

• Number of active sensors between sensor node and gateway node.

• Distance between gateway node and base station.

• Mobile beacon trajectory path. • Cost metrics (Prorogation delay,

processing delay, and queuing delay) in communication between sensors.

• Interoperability capability between the sensor nodes.

• Effective resource utilization.

IV. ENERGY OPTIMIZATION

ROUTING

A. Energy parameters: Message Complexity: Number of rounds of message is needed to identify the sensor node position. Higher numbers of message give higher accuracy. But to preserve the energy we require minimum 3 messages to calculate the position of a node.

Number of layers: Basically five layers are used in communication between two sensors. Number of layers can be reduced based on wireless sensor application to optimize the energy III

communication.

Distance: Power consumption is based on distance of communication. To reduce the distance between sensor and gateway & to optimize energy we created clusters (hierarchical approach).

Mobile beacon trajectory path: In our approach we used mobile beacon which continuously move in deployment area and help the sensor nodes to find out their location. When a node identifies its position then it plays an important role in routing. Mobile beacon trajectory is the shortest path to cover the deployment area to save energy.

Interoperability capability: Use of light weighted middleware layer, CORBA, Java RMI is proposed to provide compatibility.

Effective Resource utilization: No need to include passive & dead node in routing algorithm.

Parameters for energy calculation:

There are two phases for energy calculation in wireless sensor network: network establishment & data dissemination.

TABLE 1. ENERGY PARAMETERS IN ROUTING ALGORITHM.

Symbols Parameter N Number of sensor nodes

K Number of clusters

D Distance

M Number of bits

Erx(M) Transmission energy for M bits data

ERX(M) Reception energy for M bits data

fs Energy consumed by the circuit to launch 1-

bits of infonnation

Er(N) Total energy consumed in transmission by

node N

ER(N) Total energy consumed in reception by node N

E(N) Total energy consumed by node N

EOA (N) Energy consumed in data aggregation by node

N

E,,,�,(N) Energy consumed by node N in sensing

1. Energy consumed establishment:

in network

During network establishment clusters are formed. Each node only transmits and receives data from its neighbour nodes. The energy consumed by a node in transmission is given below:

ETCN) = ETXCM) + {sCM)d2 The first part of the above equation shows the

transmission energy for M bits and second part shows the energy consumed in transmitting M bits to d meter distance in free space. Energy consumed by node in reception is as follows:

ERCN) = ERXCM)

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Total energy consumed by the node is the sum of energy consumed in transmission of packets to the neighbour nodes and energy consumed in reception of Mbits packets from neighbour nodes.

2. Data dissemination phase:

In data dissemination phase, each node can sense, transmit and receive packets. The consumption of energy is different for different types of nodes. Since gateway node send the request locally in their clusters, the energy consumed by the gateway node in transmission of M bits (as size of request packets) to all the nodes belonging to that cluster is given as:

The first part of the equation shows the transmission energy for M bits. Second part shows the energy consumed in transmitting M bits to d meter distance and the third part shows the energy consumed in the data aggregation at the gateway node. Gateway node receives the packets from all the nodes belonging to that cluster, energy consumed by the gateway node in receiving M bits packets from any node is given as:

The above equation is for receiving Mbits from N (- - 1) nodes. Total energy consumed by the K

mobile beacon is the sum of energy consumed in sensing, transmission and reception. It is equal to:

E(GN) = Esense(M) + ETX(M) + ts(M)d2 + EDA(M) + (�-l)ERx(M)

Since gateway nodes receive packets from gateway nodes of neighbouring clusters and transmit them to the gateway nodes of other Clusters, the energy consumed by GN in transmission is as follows:

ET(GN) = ETX(M) + ts(M)d2 The first part of the equation shows the

transmission energy for M bits packets and second part shows the energy consumed in transmitting M bits to d meter distance. The energy consumed by the gateway node in receiving is as follows:

Total energy consumed by the gateway node is the sum of energy consumed in transmission of packets to the gateway nodes of another cluster and energy consumed in receiving M bits packets from the gateway node of first cluster. Total energy consumed by the gateway node is as follow:

E(GN) = ETX(M) + ts(M)d2 + ERX(M) Since all the sensor nodes send their packets to

the beacon node and perform sensing in their region the energy consumed by sensor node in transmission of data to the beacon and performing sensing is as follows:

ET(SN) = ETX(M) + ts(M)d2 + Esense(M)

The first part shows the energy consumed in the transmission and second part is for amplifying while third part shows energy consumed in sensing. Since SNs also listen to the neighbouring nodes' packets, the energy consumed by the SN s in receiving is as follows:

Total energy consumed by a sensor node is the sum of energy consumed in its transmission and energy consumed in receiving the neighbourhood nodes' packets.

E(SN) = ETX(M) + ts(M)d2 + Esense(M) + ERX(M)

B. Cost calculation

Average Energy Consumption (EavgJ:

It can be defined as the average amount of energy consumed by each node for a particular task. A verage energy consumption Eavg is :

E = E[':.l[Eini(O-Eres(O] avg n*T

Where Eini(i) and Eres(i) are initial energy and residual energy of node i respectively. 'N' is the number of nodes and T is number of tasks.

Average Delay/latency (TavgJ:

It can be defined as the time required for receiving the first data after the query generation at the base station for a particular task. Average delay Tavg is:

T -ET=l[tV(O-tQ(O] avg - T Where tD(i) is the time of receiving first data

packet for particular task i, tQ(i) is time of sending query by sink for a particular task i and T is the total number of task.

Average packet transmission (PavgJ:

It can be defined as average number of packet transmissions per node for a particular task. A verage packet transmission P avg is:

p =

E[':.1[Prec(O-Ptrans(O]/2 avg N*T Where Prec(i) and Ptrans(i) are the number of

packets received and transmitted by node i respectively. 'N' is the number of nodes and T is number of task.

C. Routing algorithm Among the number of sensors in deployment

area, some of sensor nodes work as active nodes and others work as passive nodes or dead nodes. Distribution of these nodes depends on various factors such as deployment environment and mobility of nodes. GPS enable node which know their position and those in range of GPS enable node and within the cluster will act as active node while others behave as passive nodes.

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Steps in routing algorithm:

• GPS enabled active nodes behave as beacon nodes which are moving and some of beacon nodes can be static.

• Beacon nodes forward their location information to base station.

• If a beacon node is not able to forward information due to any reason then other beacon nodes in that cluster must be present to take over the charge.

• Beacon nodes broadcast their information in the cluster area. Based on beacon nodes broadcast information other active nodes calculate their location.

•

•

•

After certain interval of time all other active nodes pass their information to base station. Base station maintains all the information in a routing table and broadcast this table information to all active nodes. Finally active sensors use DV hop algorithm for shortest path based on received routing information.

• • • •

• • •

• •

- . . • •

•

• •.. ___ :-_.N-; •.. ____ .

Fig. 3 Routing network

•

....... -

D. Routing algorithm based on energy parameters:

In this proposed paper idea is to activate few sensors among the largely deployed sensors that survived after the fall from aircraft. The logic behind this idea is to optimize the energy in WSN. The number of deployed sensor nodes in quantity is large. The remaining passive sensor nodes become active later when active nodes lose their energy and reach a predetermined threshold point. When work can be completed by few sensors, then there is no need to activate all the sensors simultaneously. By using this technique many nodes are idle in the beginning and participate later, thus we are optimizing energy and making wireless sensor network active for long time.

In this proposed work a GN within the cluster is introduced to take the advantages of hierarchical approach. GN will active for particular defined time T. Time T defined based on number of parameters in deployment area. It varies from ° to RTT!2, where RTT is the time elapsed between communication of active node and GN. Here in distribution environment gateway node is robust for estimated time T. in case of GN failure, a second beacon node takes up charge of GN. In summarized, minimum three beacon nodes are GPS enabled. The number of beacon node can be increased based on the deployment area of sensors.

In hazardous environment, more than three beacon nodes need to make intelligent to improve accuracy & avoid multiple active nodes failure. For every cluster of sensors, it is handled by mobile beacon nodes initially. Where a sensor node fails, information in each sensor node table requires updation. Further messages increase and a lot of energy is consumed in rerouting the network. These activities cause time delay and energy consumption.

•

•

•

, •

• •

Fig. 4. Active sensor communicating with gateway node

To optimize energy in routing algorithm there is no needs to consider those sensors which do not belong to any cluster. These sensors are set as passive nodes. When they come in range of any mobile beacon & continuously receive 3 messages within a specified time they become active. If not received then they remain passive for time 2', where r=O,I, .. n. We use this binary exponential technique to optimize the energy .

Gateway nodes communicate with base station, locate their positions and broadcast alert signals to all the nodes which comes in its range. Active nodes know about their gateway node and calculate their cost factors related to gateway node. Active nodes transfer their position information to a gateway node in reply message. The gateway node prepares a routing table and again broadcast its routing table to all the active nodes in cluster as depicted in table II based on fig. 5. Using this technique, each active node knows the cost of other active nodes with respect to the GN in a cluster. After receiving table from GN each active node updates its table as shown in table III based on fig. 5.

TABLE II. LOCALlZATION INFORMATION BROADCASTED BY AC TIVE NODES IN REPLY

Active Node Location Cost metric A 2468.33N 6628.31 E 230 micro sec. B 303 I .23N 577 I. lO W 332 micro sec.

TABLE III. NODE B's ROUTING TABLE BASED ON

FIG 5 Destination Cost metric Route Node

A 242 micro sec. B·A

C 340 micro sec. B·C D 610 micro sec. B-C-D

E 670 micro sec. B-C-D-E

F 630 micro sec. B-Gateway-G-F

G 870 micro sec. B-Gateway-G H 460 micro sec. B-I-H

I 230 micro sec. B-1

Total cost of the path is computed by the sum mati on of the individual link costs on the path.

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Proposed algorithm implemented by distance vector hop method used to calculate shortest path. Sensor node use least cost path to forward information to the gateway node .

• •

•

Figure 5.Shortest routing path network

When battery of active node falls below threshold value, it broadcast alert message to other nodes. These alert messages inform other nodes to not include these dead nodes in their routing table. Active nodes recalculate there routing table.

Fig. 7. System architecture in localization routing algorithm

Some active nodes may get heavily loaded if many other active sensors start communicating through these active nodes. When load increases beyond the predetermined threshhold value then

congested active nodes immediately broadcast alert message to other active nodes to select another route for communication.

•

•

Figure 6. Modified network in the presence of a dead node l.

To minimize such load in network instead of sending broadcast signals, it is necessary to only forward those messages to only active node which are communicating through that overloaded active node. When an overloaded active node becomes congestion free, then the active node again send alert message.

v. CONCLUSION

WORK

AND FUTURE

In WSN every node has limited battery energy so optimization of energy is important. T he power consumption is closely related to the route selection in WSN. In this research proposal various energy parameters with routing algorithm explained to optimize the energy in distributed localized WSN. This research proposal optimizes energy in localized WSN by selecting a routing algorithm where shortest path is a least cost path. A flow chart of proposed work is depicted in fig. 7. In future, a researcher can plan to enhance proposed work in a robust way with high accuracy. Challenges of obstacles in the deployment area & dynamic environment can increase in delay, affects on the cost factor. In future any researcher can try to reduce the waiting time of the sensor nodes which will reduce delay.

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