Energy Optimisation using Distance and Hop-based Transmission

(DHBT) in Wireless Sensor Networks

Scheme and Simulation Analysis

T. S. PradeepKumar

, P. V. Krishna

, M. S. Obaidat

, V. Saritha

and K. F. Hsiao

VIT University, Vellore, India

Sri Padmavati Mahila University, Tirupati, India

Fellow of IEEE, Fordham Univeristy, U.S.A.

Sri VIdyanikethan Engineering College, Tirupathi, India

University of Jordan, Jordan, and Ming-Chuan University, Taiwan

Keywords: Sensor Node, Simulation, Distance, Multi-Hop, Power, Lifetime.

Abstract: Wireless Sensor networks operate in a low energy mode and consume less power. The ultimate challenge of

a sensor node is to make the lifetime of the node increase by which the energy consumption is so minimal.

This paper addresses a mechanism by which the distance between any source and destination nodes is used

by the source nodes to decide whether transmission of the message to destination node must be multi-hop or

direct transmission by simply boosting the node power. The network size and the topology are considered

for determining the threshold value for the distance based on which the decision is made. The simulation

results have shown that the proposed method, DHBT, can increase the lifetime of the sensor network by at

least 130% when compared to the legacy systems.

1 INTRODUCTION

Wireless sensor networks play a major role in

applications like surveillance, animal habitat,

agricultural monitoring and observation, nuclear

radiation observation, location tracking, smart

homes and industrial automation (Obaidat and

Misra, 2014). Usually small sized nodes are

deployed in an environment. These nodes are used to

monitor the environment, collect the data and report

to the gateway node or base station nodes. All this

work is done autonomously (Lee et al., 2009).

One of the greatest challenges is to increase the

lifetime of sensor network due to adversarial

conditions of deployment environments that limits

battery power of sensor nodes, as they are not

rechargeable easily.

As of now, many routing algorithms, data

aggregation, location tracking, and clustering have

been proposed and most of these minimize energy

consumption due to delay in routing; optimize the

nodes to go to sleep mode and thereby increasing the

network lifetime. The nodes in a network will

consume more power if a single node dies that

determines the lifetime of a network (Obaidat and

Misra, 2014, Jurdak et al., 2010).

Metrics like energy consumption, and latency,

routing issues are being directly affected by the

number of hops and distance of hops. If the hop

count is too large, the energy consumption can be

minimized by adopting multi-hop transmission, but

at the cost of increase in end-to-end delay and

overhead. If the hop count is small (in case of direct

transmission), the latency and end-to-end delay will

be very small and hence the energy consumption is

high at the source node as the hoping is avoided.

Having said this, both methods should exist in a

network to increase the lifetime of the sensor

network (Anastasi et al., 2009). An optimal schedule

should be formulated to optimize energy, thereby

increasing the lifetime of the network of dynamic

and static topology.

This paper addresses this issue to handle both the

multi-hop and direct transmissions. Based on the

hop count number and hop distance, the source node

either bypasses intermediate nodes or uses direct

transmission. Since either of the techniques is

selected at runtime, the lifetime of the network will

be increased at least by 130%. A strategy is used to

PradeepKumar, T., Krishna, P., Obaidat, M., Saritha, V. and Hsiao, K.

Energy Optimisation using Distance and Hop-based Transmission (DHBT) in Wireless Sensor Networks - Scheme and Simulation Analysis.

DOI: 10.5220/0006483400170023

In Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2017), pages 17-23

ISBN: 978-989-758-265-3

find the Hop count threshold and distance of the

hops in the case of multi-hop transmission.

The objective of this paper is to increase the

lifetime of the sensor network via distance based

transmission. The contributions of this paper are

listed below:

1. The source node determines all the possible

paths along with their corresponding hop count

and distance before starting the transmission.

2. If the destination is within a threshold distance

or minimum hop count threshold, then the

source node bypasses the hops and sends the

information to destination node by slightly

increasing its power. With this, the lifetime of

the network is increased as source node uses

minimal power rather than awakening the

intermediate nodes. This method will not be

effective large-scale deployments of sensor

nodes with huge number of intermediate nodes

exist between source and destination nodes.

The rest of the paper is organized as follows.

Section 2 presents the previous related research and

introduces the problem statement. In Section 3,

relevant network, traffic and energy models are

presented. Performance evaluation using simulation

analysis and comparison are given in Section 5 and

Section 6 concludes the paper.

2 RELATED WORK

The researchers have proposed many methods to

reduce the energy utilization of node in sensors.

They have taken solar power and combined many

routing protocols to increase the life span of battery

and this technique is named as Enhancement of

energy conservation technology (EECT). This EECT

method is used in large organization to monitor the

air conditioners, which use heat energy and sunlight.

So, heat energy is utilized to make the environment

pollutant free and the battery usage is reduced

(Thayananthan and Alzranhi, 2014). The

performance is also affected by latency, energy and

reliability. Data aggregation approach is used to

reduce the redundancy and cost. ACO (Ant Colony

Optimization) is used to reduce the energy

utilization and also help to improve the life of sensor

node. This is only used when the channel is secured

(Ye and Mohamadian, 2014). The MEB (Minimum

Energy Broadcast) is used to reduce the

consumption of energy. POS based hybrid algorithm

and local search is used in this method. To decrease

the transmission power, POS is applied. The r-shrink

method is modified to re-establish the network again

(Hsiao et al., 2013).

The other methods using POS is either non-linear

programming or linear programming in order to

minimize the battery usage (Akyildiz et al., 2002;

Zeng and Pei, 2009). Development of routing

protocol is done using multi-objective fitness

function and encoding. These all use clustering

algorithm to minimize the energy consumption.

Physical to network layer optimization is done to

increase lifespan of network and reduce battery

consumption. The important applications and its

requirements are gathered to overcome this problem

(Pazzi and Boukerche, 2008).

Clustering is used to improve scalability, lifetime of

network and to reduce traffic load (Alla et al., 2012).

Due to extra load of receiving and transmitting data

to base station, cluster heads utilize more energy.

When overhead is more, cluster heads are destroyed

and stop working. Thus, this will reduce the overall

performance of a network. To overcome this

problem, Differential Evaluation based algorithm is

used which will reduce the load and enhance the

lifetime of network. This will also increase the

efficiency of data transfer from source sensor node

to its destination (Kuila and Jana, 2014).

These all are the techniques, which were used to

optimize the energy utilization during transferring

data from a particular source sensor node to the

destination sensor node (Akkaya and Younis, 2005;

Beyme and Leung, 2014; Saleem et al., 2012). These

techniques when applied to any protocol in the

sensor network will increase the lifespan of our

network and the battery lifetime. But it is not

optimized more than 20%. Many other methods are

there on which researchers work to reduce more and

more power consumption during the data transfer in

sensor networks.

Jeong-Hun Lee designs a network through an energy

efficient protocol based on the resource constraints

available between the BS and the source node (Lee

and Moon, 2014).

3 RADIO MODEL AND

PROBLEM STATEMENT

The energy consumption model of a typical sensor

network is shown in Figure 1. Energy is being

consumed by the radio of the sending and receiving

node through its processor, transceiver and the

amplifier. Amplifier consumes energy only when

SIMULTECH 2017 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

Figure 1: Energy Model of Typical Sensor network.

transmitting a packet while the radio consumes

power during the reception of a packet.

The aim of this paper is to design an algorithm

that uses distance and hop as a metric that optimizes

the energy based on the dynamic selection of any of

the metric during the data transmission and also to

reduce the battery consumption, which increases the

lifetime of the sensor nodes.

In the proposed algorithm, DHBT, the distance

and hop are two metrics that are selected

dynamically during each data transmission. If a

source node decides to send a packet or data to a

destination node, the source node computes the path

to the destination node. The number of hops in each

path, the distance along each path and the residual

energy of all the nodes within the path are

determined. Firstly, the source node selects the path

that requires minimum energy. Based on the hop

count and the path length the packet transmission

between the source and destination nodes happens

either through multi-hop transmission or direct

transmission. The Hop count threshold will be

computed based on an algorithm, which also

depends on the network size and the topology.

3.1 Assumptions

1. The nodes are static or stationary

2. All the nodes are homogenous (same energy

level while transmitting or receiving a packet)

3. The distance between the nodes is not

constant.

4. Nodes are placed on a plane (X and Y axis)

5. Power usage during sleep mode is negligible

6. A boundary is considered while evaluating

the sensor energy model. (This model may

not work for a larger boundary with huge

distance constraints).

7. Minimal number of intermediate nodes (δ)

Table 1: Variables Used.

Variables and their purpose

Paths, i ε 1….m, n<m

Energy Consumption while transmitting a

message, Joules

Energy consumed while receiving a

message, joules

Energy consumed while forwarding a

message, joules

Number of Hops

Hop count threshold

Minimum distance threshold by which the

source node send info to destination node

directly, m

Energy consumed by the radio, joules

Message length

Distance threshold, m

D[n]

Distance matrix

C[n]

Energy Matrix

E[n][m]

Stores all the paths and its optimal energy

value

3.2 DHBT Algorithm

Initialize number of nodes → Sn.

Identify two corner nodes of the

topology and compute all the paths

between the two nodes

fori=1 to n

Find all path→[A]

D[i] → distance(Pm)

Discard N = 1

Compute (fix) Nt, L

Return(Pm)

If N >Nt: Goto HOP

Elseif N <Nt: Transmit with N=1

Elseif N<Nt and distance > d

Goto

ENERGY

end for

HOP: fori=1 to m

C[i]→hop(Pm) //find number of hop

in all paths and store in array C

Return Pk

end for

ENERGY: fori=1 to m

for j=I to m

E[i][1] →min(C[ i])

E[i][2] →min(D[i])

Return(E[i][j])

end for

endfor

The algorithm explains the computation of energy of

the path, hop count and paths between the corner

nodes. Then, the average path length L or the

characteristic path length of a graph G, denoted L, is

Energy Optimisation using Distance and Hop-based Transmission (DHBT) in Wireless Sensor Networks - Scheme and Simulation Analysis

the average distance between vertices, where the

average is taken over all pairs of distinct vertices. In

any graph of order n, there are |E(Kn)| distinct pairs

of vertices. So, for a graph G of order n,









)(2,1

)2,1(

)1(

GVvv

vvd

(1)

Complexity analysis:

a. The problem belongs to NP-Hard class to

compute the output of this problem as it takes

infinite time complexity. Mean time

complexity is O (n^n).

b. This NP-hard problem is reduced to NP

complete problem, which can be solvable in real

time.

c. Brute force attack is used to compute the path as

all the paths between a source and destination

has to be computed. Also, the intermediate

nodes should be minimal, otherwise it will take

infinite time to compute optimal path and the

algorithm becomes complex.

4 MATHEMATICAL MODEL

The energy consumed during the transmission and

reception of packets is:

....

...

ddif,dεk+Ek

d<ifd,dεk+Ek



(2)

= k . E

(3)

= E

(4)

For 1 bit message the total energy consumed by the

N hop is given as:

















dε+)E(=E(N)

.12N

(5)

If N <N

, there will be a direct transmission, in that

case the equation boils down to:

E(N )= 2E

+ε

(6)

Since the E (N) here depends on the distanced,

which is to the power of 4, it proves that when the

distance is huge, the energy occupation will also be

more. Here in this case, the intermediate nodes are

not transmitting anything and they may be in sleep

mode as the source node decides to use direct

transmission.

However, DHBT algorithm also uses distance as one

of the metrics in reducing the energy level.

If N <N

and if the distance is greater than the

threshold distance, then normal multi hop

transmission will be occurring, thereby minimizes

the energy that will be wasted in direct transmission.

4.1 Analysis of Graph Model

Let the network shown in Figure 2 is as a graph G.

The figure shows only one edge, but the graph we

consider is a multi-graph (A loopless graph that has

multiple edges between two nodes). Here, we model

this as multi-graph as our algorithm proposes three

metrics: distance, hop and energy. In each edge, hop,

energy and distance will be considered as weight.

The multi-graph will be solved based on the

algorithm and is shown in Table. 2

Let G = {V, E, r} where:

 V is the vertices

 E is the number of edges

 r:E; Assigning to each edge an unordered pair

of end nodes

Figure 2: Path taken by DHBT.

Let Aij will be the adjacency matrix and Iij will be

the incident matrix to find whether the given nodes

are incident on the edges or not.

Iij = 1 if Ej is incident with Vi; else 0.

Let Eij represents the weighted matrix for Energy,

Dij represents the weighted matrix for distance and

Hij be the weighted matrix for Hop count.

ENMENEN

MEEE

Eij

...21

.............

2...2221

1...1211



SIMULTECH 2017 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

DNMDNDN

MDDD

Dij

...21

.............

2...2221

1...1211



2. Find path using brute force from matrix Dij

All path -> [Dij]

check all the combinations v0-v1, v0-v2,…… ,

v0-v1,……..,v0-v1-v2-….v10.

3. Find path having shortest path using maximum

[Eij]

Min path-> [Eij]

4. Take the path having minmum distance and min

energy.

5 IMPLEMENTATION AND

RESULTS

The network is tested using Network Simulator 2

(NS2) and mannasim where the simulations were

carried out. Here are the parameters that were used

during the simulation as shown in Table 2. For

comparison of DHBT protocol, the nearer

resemblence protocol is DSR, so DSR is taken for

comparison. DSR also computes all the paths

between the source and destination. The path taken

by the DHBT and DSR protocol is shown in Figures

2 and 3, respectively.

Table 2: Simulation Parameters.

Parameter

Description

MAC layer

802.11

Protocol

DSR, DHBT, LEACH

Propogation method

TwoRayGround

Transport Agents

TCP with 1500 bytes

Number of nodes

11,50,100

(LEACH protocol handles

the Cluster head

automatically, whereas DSR

and DHBT handles clutser

head separately)

Topology

Random with X=776 and

Y=612)

Tranmission Power

18mW

Forwarding Power

17mW

Receving Power

19.7mW

Idle Power

5 µA/0.005mW

Sleep Power

2µA/0.002mW (Negligible)

Sensed parameters

Temperature and pressure

Data dissemination interval

0.1 seconds

Sensing type

On Demand/Programmed

Figure 3: Path taken by DSR Protocol.

Experimental Setup:

The nodes were deployed as shown in the Fig 3 with

x and y locations so that for all the protocols under

comparison we will have the same topology. Since a

simulator is used for validating the protocol, the

sensing happens through Gaussian distribution.

Pressure and temperature sensors were modelled and

sensed.

Energy Consumption:

In DHBT, the path taken by the protocol is 0->1->9-

>10, whereas the DSR handles it 0->5->7->9->10.

So DHBT is optimising the power consumed at least

by one hop and hence the energy occupied by DHBT

is less compared to DSR. The number of nodes is

tested from small to huge nodes (11, 50,100). In

most of the cases the energy compared by the DHBT

is lower by at least 25% compared to DSR protocol.

Since DHBT directly computes all the paths related

to hop, distance and energy, AODV is not preferred

for comparison as AODV selects the path based on

the network topology on an on-demand basis.

Throughput:

When we take the case of transmission of data from

source to destination, then the average rate of

successful transmission is taken as throughput.

Hence, we have analysed throughput of DSR and

DHBT protocols. Throughput of DHBT protocol is

comparatively high as compared to DSR.

Route Selection:

DSR protocol considers hop count to find the path

for forwarding packets and also takes another path to

send the acknowledgment of the packet received.

Energy Optimisation using Distance and Hop-based Transmission (DHBT) in Wireless Sensor Networks - Scheme and Simulation Analysis

Figure 4: Throughput vs. time.

But DHBT protocol will first consider the paths

having less number of hops then will consider the

distance and energy. And the acknowledgment of

the data received is also sent by the same path.

Therefore, DHBT is more effective if we consider

all the conditions as compared to DSR protocol.

Figure 4 shows this difference between them.

Figure 5: Average packet delivery Ratio vs. number of

cluster heads.

The packet delivery ratio, and the lifetime of the

network are shown in Figures 5 and Figure 6,

respectively. Both are normalized and the PDR is

relatively constant for various bandwidth increases.

This predicts the stability of the network during the

increase in the bandwidth. Moreover, the lifetime of

the network is good for LEACH and DHBT, but

DHBT has slightly 10% more lifetime over LEACH

and network with DSR lifetime is too low compared

to other protocols.

Figures 7 and 8 show the energy consumed and

dissipated by DSR and DHBT protocols for 50

nodes by varying the number of cluster heads.

Figure 9 shows the dissipation of energy for a long

time by DHBT whereas the energy depleted fast by

the other protocols.

Figure 6: Network Lifetime vs. nuber of cluster heads.

Figure 7: Energy consumption vs. number of cluster

heads.

Figure 8: Energy dissipation vs. time.

6 CONCLUSIONS

In this paper, we derived a new algorithm called

DHBT by which the distance between any source

and destination nodes is used by the source nodes to

decide whether transmission of the message to

destination node must be multi-hop or direct

SIMULTECH 2017 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

transmission by simply boosting the node power.

DHBT scheme has reduced the overall energy

utilization for each transfer of data in a sensor

network. Energy and hop count is working well with

DHBT whereas the distance calculation depends on

the transmitter and the receiver, so this work does

not handles distance calculation. However, distance

can be accurately calculated in the future work.

Also, distance can be computed using a localization

algorithm for sensor networks and thus the nearest

location of the sensor node could be found out and

can be solved for energy calculation. Simulation

analysis was used to predict the performance of our

proposed schema and to compare its performance

with competing schemes. We found out that DHBT

has excellent performance. As future work, we plan

to conduct more simulation experiments on DHBT

under different scenarios in order to check further

the performance under different conditions and

environments.

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Energy Optimisation using Distance and Hop-based Transmission (DHBT) in Wireless Sensor Networks - Scheme and Simulation Analysis