A Chain Topology for Efﬁcient Monitoring of Food Grain Storage using

Smart Sensors

Alekha Kumar Mishra

, Asis Kumar Tripathy

, Mohammad S. Obaidat

2, ∗

, Zhiyuan Tan

Mukesh Prasad

, Balqies Sadoun

and Deepak Puthal

Vellore Institute of Technology, Vellore, India

ECE Department, Nazarbayev University, Astana, Kazakhstan

KASIT, University of Jordan, Jordan

Edinburgh Napier University, U.K.

University of Technology Sydney, Australia

Al Balqa’ Applied University, Jordan

Keywords:

Food Grain Storage Monitoring, Smart Sensor Devices, Sensor Networks, Network Topology, Chain

Topology.

Abstract:

Due to lack of an efﬁcient monitoring system to periodically record environmental parameters for food grain

storage, a huge loss of food grains in storage is reported every year in many developing countries, especially

south-Asian countries. Although Smart Sensor Networks have been successfully implemented in various ap-

plications such as health-care, military, and wildlife monitoring, there are still various issues to be addressed

in food grain storage monitoring applications. Due to the food grain storage infrastructure constraints, the

commonly practiced network topologies of sensor devices such as mesh, star, and grid cannot provide an ef-

fective monitoring environment. In this paper, we proposed a topology using smart sensors that can effectively

cover and monitor the food grain storage area. It uses a chained structure of sensor devices with directional

antennas to accurately sense and report the environmental data. The proposed topology works better than

common topologies due to its chain-based structure which remains unaffected by various hindrance imposed

due to food grain storage infrastructure. From the experimental results it is conclude that the proposed topol-

ogy has effective coverage percentage, detection accuracy, and message delivery over Cluster-based and Mesh

topologies in food grain storage environments.

1 INTRODUCTION

Due to climatic changes, irregularities were observed

in weather conditions in recent years. These changes

directly affect the longevity and utility period of food

grains. It is reported that the wheat and rice produc-

tion is passing the record level every year in south-

Asian countries. However, due to lack of adequate

monitoring infrastructure and process, the loss of

food grains in these years apprehend the productiv-

ity (Manay and M.Shadaksharaswamy, 2008; Sawant

et al., 2012). Irregular changes in weather condi-

tions provides favorable conditions for growth of in-

sect, pests, molds, rodents, fungi, and mycotoxins in

food grain storage. It is reported that around twenty

percent of food grains are wasted in food storage.

∗

Fellow of IEEE and Fellow of SCS

The above fact demands a monitoring and control

environment for food grain in food depot across the

agricultural zones of a country. In this environment,

the inﬂuential factors of the food storage depots are

continuously observed to maintain favorable scenario

for food grains. It is found that traditional or man-

ual food grain storage monitoring by store ofﬁcials

is inefﬁcient as they cannot reach to all locations on

regular basis. The smart sensor devices are playing

these years in the sensing domain these days where

these devices detect and collect relevant environmen-

tal data such as temperature, atmospheric pressure,

humidity, and light (Sahoo et al., 2012; Sharma et al.,

2017b). Securing such infrastructure is also playing

a vital role in current communication infrastructure

(Puthal et al., 2018). Therefore, a smart sensor net-

work can be effectively used for monitoring inﬂuenc-

Mishra, A., Tripathy, A., Obaidat, M., Tan, Z., Prasad, M., Sadoun, B. and Puthal, D.

A Chain Topology for Efﬁcient Monitoring of Food Grain Storage using Smart Sensors.

DOI: 10.5220/0006850600890098

In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications (ICETE 2018) - Volume 1: DCNET, ICE-B, OPTICS, SIGMAP and WINSYS, pages 89-98

ISBN: 978-989-758-319-3

ing parameters in food grain storage (Hadjidj et al.,

2013; Carlos-Mancilla et al., 2016). It is observed

from the literature that the smart sensor devices are

commonly deployed in free space. If more number

of smart sensors are involved for a particular monitor-

ing application, then it is found that there exists suf-

ﬁcient free space for direct communication between

these devices. These common deployment structure

is provided by various network topologies such as

tree, mesh, grid, star, and clustered. However, these

topologies may not be suitable for food grain storage

monitoring application, since the food grain storage

are often ﬁlled with grain bins and there is hardly

a clear line of sight inside the storage area of bins.

This paper devises an effective topology of smart sen-

sors to monitor inﬂuential environmental parameters

in a food grain storage depot. It is efﬁcient and more

suitable for this monitoring environment compared to

mesh, tree, and clustered topology. It is shown that

the proposed topology can perform effectively with

sensor devices equipped with directional antenna and

sectored sensing disc. The performance of the pro-

posed topology is compared with mesh and clustered

topology using simulation experiments and found to

be better in terms of detection accuracy, energy efﬁ-

ciency, and message delivery ratio. The sections of

this contribution are organized in the following way.

Section 2 provides a detailed report on infrastructure

of food grain storage depot. Section 3 summaries the

characteristics along with pros and cons of existing

smart sensor network topologies. Section 4 provides

an analysis on unsuitability of existing topologies for

monitoring food grain storage depot. Section 5 elab-

orates on the structure and operational details of the

proposed topology for food grain storage monitor-

ing. Section 6 analyzes the coverage and energy ef-

ﬁciency of the proposed topology. Section 7 summa-

rizes simulation results of comparison between pro-

posed topology and existing competing schemes fol-

lowed by concluding remarks in Section 8.

2 SURVEY OF FOOD GRAIN

STORAGE INFRASTRUCTURE

Almost a common standard is followed throughout

the globe for constructing infrastructure for food grain

storage depot. The dimension and facilities may vary

up to some extent depending on the kind of food

grain. The most common shape of storage is pyramid

dome shaped as shown in Figure 1.

The standard storage area of the depot is 126 meters

× 22 meters. The height of the depot varies from 5.5

meters to 6.25 meters. The entire capacity of the de-

Figure 1: The dimension and shape of a food grain depot.

pot is divided into 3 compartments with each com-

partment of capacity 5000 metric ton (MT). The num-

ber of stacks for each compartment is twelve. The

ventilators are used on both top and bottom side of the

walls. The stack size is approximately 6 meters × 10

meters. The Galvanized Iron Corrugated (GIC) silos

storage are used for longterm storage in selected de-

pots where food grain may be stored for a longer pe-

riod (years). GIC silos are quite expensive compared

to standard storage depot (Deshpande et al., 2010).

Additionally, cover and plinth (CAP) storage struc-

tures are the most common among the farmers. Some-

times, cover and plinth structures are also practiced by

food grain depot ofﬁcials to store excess amount of

food grains with depot’s premise. Sufﬁcient space are

provided between food grain stacks inside a depot to

facilitate smooth movement of carriage vehicles. But,

during post-harvesting months the food grain depots

are often full occupied with food grain bins.

The essential parameters that are monitored regularly

in the food grain depot is temperature and air ﬂow

inside depot, and moisture content of the food grains.

Table 1 shows the drying temperature and maximum

moisture level maintained for different types of food

grains (Hellevang, 2010).

Table 1: Moisture content levels and drying temperature for

various food grains.

Food grain Drying Max. Moisture

Temperature (

◦

C) Content (%)

Paddy 60 17

Wheat 65 14.5

Oats 60 14

Barley 55 14.8

Maize 49 14

Flax 80 10

Peas 45 16

It is reported that these parameters are continuously

monitored and when these parameters reaches a level

beyond the mentioned range due to changes in envi-

ronment, it requires the manual attention of the food

ICE-B 2018 - International Conference on e-Business

grain store ofﬁcials for further action. Smart sensors

are a suitable options to replace the manual monitor-

ing process. A set of smart sensor devices may be de-

ployed to form a smart network to facilitate the mon-

itoring process. In the following section, a survey of

existing smart network topology is provided for food

grain storage monitoring.

3 SURVEY OF SMART SENSOR

NETWORK TOPOLOGY

It has always been a challenging task to plan a suit-

able network topology for a target application. It is

required that the designed network topology must sat-

isfy the desired coverage, connectivity, coverage, and

network lifetime (Fan and Jin, 2010). The topolo-

gies as reported in the literature are mostly designed

based on the target application requirements (Ceclio

and Furtado, 2014). A brief survey of these topolo-

gies is provided below.

Star topology is a single-hop system, where all sen-

sors communicate directly to the gateway. This is

simple and efﬁcient for small networks (Yang et al.,

2017). Here, a single node failure is not an issue since

it does not affect the communication process of other

nodes. There can be more network segments branch-

ing out from the central gateway node. The energy

consumption for communication is limited to the dis-

tance between a node and its gateway. This topology

is suitable for network of smaller size limited to ge-

ographical area of approximately 100 meters. When

it is required to be deployed in large scale, the star

topology is applied with hierarchical schemes. In a

tree topology, the sink node also known as base sta-

tion (BS) is considered as the root of the network tree.

The root node is connected with relay nodes; those

are responsible for forwarding sensed data to the root.

The relaying nodes forms the network with multiple

levels with root as sink node (Sharma et al., 2017a).

Sometime the relay nodes are equipped with sensors

in order to perform both sensing and relaying task.

The tree topology can be scalable to desired size with

a minimum effort. The overall energy consumption of

the network is lower because of the small range node-

to-node communication. The data transmission from

sensing nodes to the sink node requires the coordina-

tion of intermediate relaying nodes. A mesh topol-

ogy mostly resembles a mathematical graph struc-

ture, where each node is in connection with all other

nodes located with its communication range. An ef-

ﬁcient multi-hop communication scheme is essential

for this topology. Since, intermediate nodes in the

path to sink node play the role of forwarding node,

it is necessary to ensure that the path to sink node is

connected before sending data. Mesh topology based

networks are excellent for fault tolerance due to ex-

istence of multiple path to sink nodes. The charac-

teristics of existing multi-hop communication scheme

can be easily modiﬁed to support larger network. In

a Grid topology, the network is divided into a rectan-

gular grid, where sensor devices are deployed at each

grid point(Roy et al., 2018). The commonly adapted

grid layout are unit square, equilateral triangle, and

hexagon. The square grid topology is more popu-

lar compared to its counterparts due to having natu-

ral placement characteristics. A grid topology is an

ideal condition of topology in terms of coverage of

network. Since, it is really difﬁcult to achieve opti-

mal performance with basic grid topology, often more

than one node are placed on a square grid. Clus-

tered topology is most adaptive and energy-efﬁcient

for most common applications. As it is named, the

topology consists of a number of clusters, where each

cluster comprises a group of nodes located geograph-

ically closer to each other. A designated node called

cluster-head (CH) is responsible for coordination of

nodes within a cluster and forwarding data of the

nodes to the sink node. The task of non-CH nodes is

limited to sensing and sending data to CH. There may

be bridge to connect CHs and facilitate inter-cluster

communication. The bridge nodes signiﬁcantly re-

duce the energy consumption of inter-cluster com-

munications. The clustered topology has the lowest

communication overhead among all topologies due

to minimum number of message communications per

round. Moreover, hierarchical structure simpliﬁes the

process of routing messages.

Some applications demand the combination of more

than one previously discussed topologies to improve

the performance (Aziz et al., 2013). The hybrid ap-

proaches are scalable up to thousands of nodes with-

out deteriorating the network operations. Clustered-

tree topology forms tree among the clusters in the

network (Hong et al., 2016). It uses multilevel het-

erogeneous features to deﬁne network structure. It

takes the hierarchical structure advantage from tree,

and utilizes optimal network communication scheme

of clusters. Cluster-star topology is a hybrid of star

and clustered topology. It uses the simpliﬁed structure

of star topology at the lower end of the network while

utilizing cluster based communication at the higher

level. Cluster-mesh topology divides the deployment

area into number of cells. Here, the term cells is re-

ferred to rectangular areas of equal size (Alsemairi

and Younis, 2016). Each cell forms a cluster among

the nodes in the cell. Thereafter, an inter-CH mesh

network is formed for inter-cluster communication.

A Chain Topology for Efﬁcient Monitoring of Food Grain Storage using Smart Sensors

The sink node participates in the topology as a cluster

head and does not appear just as the end point of data

routes. Table 2 summaries the strengths and limita-

tions of the above discussed topologies.

4 ANALYSIS OF EXISTING

TOPOLOGIES FOR FOOD

GRAIN STORAGE

MONITORING

The ﬁrst and foremost requirement of a food grain

storage structure is that network deployment must not

utilize the open space available within the depot. It

is reported that placing sensing devices in this space

would hinder the process of bulk loading and unload-

ing of food grains. Secondly, the sensing devices

cannot be deployed near the food grain bins as the

bins stacks are displaced frequently from one place to

another depending upon the space requirements. In

this case, devices deployment locations are required

to change every time; the bins are relocated or dis-

placed. Moreover, sensing devices placed in these lo-

cations may be prone to physical damage. Finally,

there is no additional support such as pillars near the

food grain bins to ﬁx sensing devices permanently.

Based on the above requirements and constraints it is

found that the only option available to deploy the net-

work is to utilize the inner walls and ceiling of the

depot. The topology like star, tree, mesh, grid, and

clusters cannot be used efﬁciently in food grain depot.

This is because, all the above mentioned topologies

and the hybrids requires the deployment of the nodes

in the region of interest. Additionally, these topolo-

gies need open space between nodes for communica-

tion in all direction because of the omni-directional

antenna equipped with these devices. The star topol-

ogy is feasible up to some extent; however, it is dif-

ﬁcult to ﬁnd a suitable location for gateway node to

stay in the communication range of all the devices.

All clustered topologies require the sensing devices

to be deployed near the food grains, therefore are not

suitable for food grain storage. The grid topology is

suitable for food grain depot if the grid is deployed on

the inner walls. However, the sensing range may be

limited due to omni-directional antenna. Moreover it

requires signiﬁcantly more number of sensor devices.

The tree based topology may also require signiﬁcantly

large number of nodes when deployed on the inner

walls of the depot. Table 3 summarizes the unsuit-

ability of existing topologies for food grain storage

monitoring. It is observed from the above discussion

that the sensing devices with directional antenna and

sectored sensing disc are more suitable for indoor ap-

plications compared to their counterparts (Yu et al.,

2011).

5 PROPOSED WORK

In this section, the proposed topology of smart sensor

devices is presented for efﬁciently monitoring food

grain storages. This topology is a double ended chain

structure and it is named as two-tail chain topology.

It consists of smart sensors that form a chain structure

with the following additional features. The node de-

ployed on the top of the chain is the head node also

known as the gateway node. It is responsible for col-

lecting data from all nodes and forwarding it to the

relaying node of the network. An additional relaying

node is connected to the gateway node. It is respon-

sible for forwarding the data collected from gateway

node to the sink node. It is assumed that the relaying

node can communicate to sink node either directly or

via multi-hop communication. The sensing devices

are equipped with directional communication antenna

and sensors with sectored sensing disc. Each node in

the network can sense and communicate data except

gateway and relaying node. The process of sending

sensed data begins from both ends (also known as

tails) of the chain. It is then forwarded to the next

node toward the location of gateway in the chain until

it reaches gateway node. In the following subsections,

the structure and functions of proposed topology is

discussed in details.

5.1 Structural Details of Proposed

Topology

Figure 2 shows the deployment structure of the pro-

posed topology. Two nodes at the end of the chain

are called tail nodes. The tail nodes and the gateway

nodes are placed at the opposite ends across the length

of the storage depot. The inner walls across the length

of the storage depot are used to deploy the chain of

sensing devices. The simplest structure of the chain

consists of only one chain on each of the walls. More

than one chain can be deployed on each wall and ceil-

ing connecting to gateway depending on the coverage

requirements.

The above topology of sensing devices is effec-

tive only if the following type of device positioning

structure is followed. Figure 3 shows the direction of

communicating radio antenna of sensing devices in

the chain. Since the major task of these devices is to

sense temperature and moisture data and send them to

gateway node, the communicating sector range of the

ICE-B 2018 - International Conference on e-Business

Table 2: Summary of pros and cons of smart sensor network topologies.

Topology Strengths Limitations

Star Overall lower power consumption Limited communication

Small area network (100 meters)

Tree Lower power consumption Nodes are time synchronized

Scalable Requires coordination among the nodes

Mesh Fault tolerant Redundancy

Scalable Higher energy consumption

Grid Ideal condition for coverage Not practical

Cluster Minimized no. of messages Cluster-head selection overhead

Simpliﬁed routing

Cluster-Tree Lower power consumption Computational overheads

Improved network lifetime

Cluster-Star Lower energy Additional hardwares

Scalable

Cluster-Mesh Lower energy Higher node density

Table 3: Unsuitability of smart sensor network topologies.

Topology The reason for unsuitability

Star Difﬁculty in positioning the gateway

node

Tree Requires open space

Mesh Requires signiﬁcantly higher num-

ber of nodes

Grid Limited sensing range

Cluster Requires open space for inter-

cluster communication

head node

Relaying node

Tail node

6 meters

126 meters

22 meters

Tail node

sensing nodes

Figure 2: The position of sensing devices in the chained

structure.

directional antenna are positioned towards the upper

level (gateway end) of the chain.

Figure 4 shows the sensing rage of the devices.

Since devices with sectored sensors have long range

sensing ability compared to omni-directional sensors,

the sensing discs are positioned facing the opposite

wall of the depot to achieve accurate sensing from the

mid area of the depot.

head node

sensing nodes

Relaying node

Tail node

22 meters

Tail node

Figure 3: Communication range of sensors with directional

antenna.

126 meters

22 meters

6 meters

Figure 4: Sensing range of the sensors.

5.2 Energy Efﬁcient Data Aggregation

Here, it is assumed that the sensing devices are time

synchronized for data collection and aggregation pro-

cess. The data collection begins at tail nodes. In each

round of data collection, tail nodes send data to the

next node towards gateway in the chain. The interme-

diate nodes in the chain aggregate the received data

from tail end with its own data and forward them to

A Chain Topology for Efﬁcient Monitoring of Food Grain Storage using Smart Sensors

the next node towards gateway. Algorithm 1 shows

this process of aggregation. The node in the commu-

nication range of gateway can directly send the data

to gateway.

Algorithm 1: Algorithm for OnReceive().

OnReceive(Message *msg){

prev node ←− msg.sender

data

←− msg.data

data

new

←−Aggregate(data

, data

sensed

)

msg.status[prev node] ←− 1

msg.data ←− data

new

msg.sender ←− id

sel f

send(msg)

}

5.3 Communication Between Relaying

Node and Sink Node

Upon receiving the aggregated data from both ends

of the chain, gateway node sends it to the relaying

node. The number of relaying nodes in the network

depends on the number of food grain depots under

the processing of monitoring. The following existing

topologies can be adapted depending on the location

of sink and number of relaying nodes:

• Star topology: If it is possible to locate the sink

node around the mid point of the area of the food

grain depot center, then star topology would be

more suitable for this part of the communication.

In this case, all relaying nodes can directly com-

municate with the sink node. Thus, the process

of forwarding aggregated data by a relaying node

is independent of other relaying nodes in the net-

work.

• Tree topology: When all relaying nodes are not

in the communication range of the sink node and

number of relaying nodes is large, then it is appro-

priate to use tree topology of relaying nodes with

sink node as root. A simple multi-level routing

scheme (Zin et al., 2014; Tanwar et al., 2015) can

be used to forward the aggregated data to the sink

node using this topology.

5.4 Communication Under Node

Failure

To synchronize the process of the data aggregation,

each node waits for a speciﬁc period of time before

initiating its data transfer. When the data arrives from

the tail end before the timer expires, then the node

follows the normal aggregation and forwards process

as discussed before. However, if the data does not

arrives before the timer expires, then node initiates a

fresh data transfer along with a failure status bit for

the previous node in the chain. Algorithm 2 repre-

sents this communication process.

Algorithm 2: Algorithm for OnTimerExpire().

OnTimerExpire(){

msg = createMessage()

msg.data ←− data

sensed

msg.status[id→predecessor()] ←− 0

msg.sender ←− id

sel f

send(msg)

}

Failure of a node leads to a break of a link in the

chain; only if each node has exactly one node in its

communication range. The tolerance of connectiv-

ity can be improved by increasing the node coverage

across the chain to greater than or equal to 2. This im-

plies that the connectivity failure tolerance is directly

proportional to the node density across the chain.

6 ANALYSIS

6.1 Network Size and Coverage

The proposed two-tail chain topology can collect data

efﬁciently with minimum number of nodes compared

to existing topologies. This is possible due to the

chain structure of sensor devices, and positioning of

their directional sensing disc. Since devices also

uses directional antenna for communication and po-

sitioned across the line of the chain, the proposed

topology is able to provide a clear space for commu-

nication. Whereas, the topologies such as mesh, tree,

and cluster-based ﬁnd it difﬁcult to get a clear range

of communication in the food depot with minimum

number of nodes. Here, an analysis of the proposed

topology is provided to establish a relationship be-

tween network size and required coverage. The sens-

ing range of sensing devices lies between 25-30 me-

ters (Ammari and Das, 2012). The width of standard

depot is approximately 22 meters. Therefore, a sensor

deployed on either walls can sense the area in its sec-

tored disc range till the opposite wall. The depot can

be divided by sensing zone of each sensor as shown

in Figure 5. It can be observed that by placing sens-

ing devices at the center of the sensing disc (shown

by dark spots) in the ﬁgure, the entire depot can be

covered.

Consider a depot of dimension l ×b m

. Let r and

θ are the sensing range and angle of sensing sector for

ICE-B 2018 - International Conference on e-Business

a sensor respectively. Let d be the length of the chord

as shown in Figure 5.

Figure 5: Coverage of depot using sectored sensing disc.

Theorem 1: The number of nodes required by two-

tail chain topology to achieve 1-coverage is:

r × sin





+ 2

. (1)

Proof. From Figure 5, it can be inferred that the total

number of sectors required to cover depot of dimen-

sion l × b is the sum of number of sectors facing left

wall and number of sectors facing right walls plus 2

additional partial sectors at both ends of the depot.

No. of sectors in depot = 2 ×





+ 2 (2)

The value of d can be represented in terms of r and

angle θ as:

d = 2 × r × sin





(3)

Using Equation 3 in 2 we get:

No. of sectors in depot = 2 ×





2 × r × sin









+ 2





r × sin









+ 2

(4)

From the above Equation, 1-coverage of the depot can

be achieved by placing a sensor at each center of the

sectors as shown by dark spot in Figure 5. There-

fore, number of nodes required to achieve 1-coverage

is equal to:

r × sin





+ 2

(5)

Theorem 2: Chain density for 1-coverage topol-

ogy is equal to:

2 × r × sin





. (6)

Proof. Considering Figure 5, the dimension of the de-

pot is given by l × b. Then, the total length of the

chain across the wall is 2 × l. Using Equation 5, the

number of nodes required for 1-coverage is given by:

N =

r × sin





+ 2

. (7)

Therefore, the chain density to achieve 1-coverage is

given by:

chain density =

2 × l



r×sin

(

)

+ 2



2 × l

2 × r × sin





. (8)

k-coverage of depot is achieved by increasing

chain density for 1-coverage topology by a factor of

Lemma 1: Chain density for k-coverage topology is

equal to:

k ×

2 × r × sin





. (9)

6.2 Energy Consumption

This paper follow energy consumption model pro-

vided in (Mishra and Samantara, 2016), which is con-

sidered for evaluating energy consumption of sensing

devices used in proposed topology. Let us assume

that the size of data message is m bits. To achieve

1-coverage the number of nodes required by two-tail

topology is given by Equation 5. Since the chain

length is taken as 2 × l, then the average distance be-

tween two successive sensing devices in the chain is

given by:

d =

2 × l

N − 1

2 × l



r×sin

(

)

+ 2



− 1

2 × l × r × sin





l + r × sin





(10)

A Chain Topology for Efﬁcient Monitoring of Food Grain Storage using Smart Sensors

The total energy consumed by all devices in the chain

in a single round of data transmission is given by:

E = E

T X

+ E

(11)

where, E

T X

, E

, and E

are the total energy con-

sumed during data transmission, reception, and aggre-

gation respectively. The energy consumed to transmit

a message of m bit over a distance of d is given by:

elec

× m + ε

amp

× m × d

(12)

where, E

elec

and ε

amp

is the energy required to trans-

mit a single bit by the transmitter electronics and the

ampliﬁer respectively. Then, E

T X

is given by:

T X

= (E

elec

× m + ε

amp

× m × d

) × N



elec

× m + ε

amp

× m × d



r × sin





+ 2

Similarly, E

is given by

= (E

elec

× m ×

r × sin





+ 2

(13)

Let E

be the energy spent for aggregation of m-bit

data, then

= E

r × sin





+ 2

(14)

7 EXPERIMENTAL RESULTS

The performance of the proposed two-tail chain topol-

ogy is evaluated by simulation analysis. The param-

eters of interest are temperature and moisture content

(Hu, 2013). Both of these values are varied using

physical process module. The proposed topology is

compared with mesh and clustered topology using the

following metrics: detection accuracy, message deliv-

ery ratio, energy consumption, delay, and number of

alive nodes in the network.

The plot for detection accuracy vs. number of

nodes deployed in the network is depicted in Figure

6. It is observed that the detection accuracy of pro-

posed topology raise to hundred percent when number

of nodes is greater than or equal to twenty. This accu-

racy is better than the existing ones. This is because

of optimal positioning of sensing devices to sense data

uniformly over the food grain storage depot.

The comparison of energy consumption over

number of rounds is shown in Figure 7. It is ob-

served from the ﬁgure that the energy consumption

per node of the proposed topology over number of

rounds remains almost constant. A marginally lesser

100

10 15 20 25 30 35 40 45 50

Detection Accuracy(in %)

Number of nodes deployed

Proposed

Mesh

Cluster-based

Figure 6: Detection Accuracy vs. Number of nodes de-

ployed in the chain

0.1

0.2

0.3

0.4

0.5

50 100 150 200

Average Energy Cosumed per node (mJ)

Rounds

Proposed

Mesh

Cluster-based

Figure 7: Energy Consumed per node vs. Number of

rounds.

100

10 15 20 25 30 35 40 45 50

Message Delivery Ratio(in %)

Number of nodes deployed

Proposed

Cluster-based

Mesh

Figure 8: Message delivery ratio with food grain bins inside

the depot.

100

10 15 20 25 30 35 40 45 50

Message Delivery Ratio(in %)

Number of nodes deployed

Proposed

Cluster-based

Mesh

Figure 9: Message delivery ratio without food grain bins

inside the depot.

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consumption is achieved compared to mesh and clus-

tered topology due to use of directional antenna for

communication.

Figures 8 and 9 show the comparison of mes-

sage delivery ratio over number of nodes deployed in

the network with and without foog grain bins inside

the depot, respectively. For the proposed topology,

the message delivery ratio is computed with relaying

node as the destination node. It is observed that the

proposed topology is able to achieve higher message

delivery ratio with food grain bins in the depot than

mesh and cluster-based topology. This is because,

when food grains are placed in the depot, they hinder

the communication between the nodes of mesh and

clustered topology. However, two-tail chain topology

uses routing of messages along the chain and remains

unaffected by existence of food grain bins in the de-

pot.

1800 1900 2000 2100 2200 2300 2400 2500

Number of Alive Nodes

Rounds

Proposed

Mesh

Cluster-based

Figure 10: Number of alive nodes vs. Number of rounds.

Figure 10 compares the number of alive nodes in

the network vs. number of rounds. It is observed that

the rate of decreasing in the number of alive nodes

in proposed topology is marginally higher than mesh

topology and lower than clustered topology. This is

because, the proposed topology does not use multi-

path routing like mesh topology to balance the re-

maining energy of the nodes. The higher rate of de-

crease in the clustered topology is due to signiﬁcant

consumption of energy for inter-CH communication

inside food grain depot.

Finally, the comparison of average delay vs. num-

ber of rounds are depicted in the Figure 11. It is ob-

served that the proposed topology incurs higher delay

compared to mesh and clustered topology and it re-

mains almost constant over number of rounds. This

delay is mostly due to additional waiting time of each

node before processing and forwarding the data to the

next node.

400

450

500

550

600

20 40 60 80 100 120 140 160 180 200

Average Delay per Node(msec)

Rounds

Proposed

Mesh

Cluster-based

Figure 11: Average delay per node vs. Number of rounds.

8 CONCLUSION

The proposed two-tail chain topology aims to provide

an efﬁcient monitoring process for food grain depot.

The proposed topology is designed based on the stan-

dard infrastructure of food grain depots and other en-

vironmental constraints. Due to open space constraint

the proposed topology use a chain structure to deploy

sensing devices on the inner walls of the depot. To

achieve efﬁcient sensing and communication in this

environment, the sensing devices are equipped with

directional communication antenna instead of omni-

directional antenna. This helps to achieve a clear line

of sight for sensing and communicating data inside

the food grain depot. The performance of the pro-

posed topology is compared with that for mesh and

clustered topology. The results of detection accuracy,

message delivery ratio, and energy consumption in-

fers the suitability of the proposed topology over ex-

isting ones for monitoring food grains.

Further, we are aiming to deploy proposed two-tail

chain topology in real-time testbed for better evalua-

tion our topology.

ACKNOWLEDGEMENTS

An initial short version of this work has been pub-

lished in the proceedings of 5th International Con-

ference on Advanced Computing, Networking, and

Informatics (ICACNI), June 2017 (Tripathy et al.,

2017).

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