Mobile Data Collection in Wireless Sensor

Network

Vasaki Ponnusamy, Low Tang Jung Computer & Information Sciences Department

Universiti Teknologi PETRONAS

Bandar Seri Iskandar, 31750 Tronoh, Perak,

Malaysia

vasaki.ponnusamy@gmail.com,

lowtanjung@petronas.com.my

Anang Hudaya

Faculty of Information Science and Technology

(FIST),Multimedia University

Jalan Ayer Keroh Lama, 75450 Melaka,

Malaysia

anang.amin@mmu.edu.my

Abstract— Battery powered Wireless Sensor Networks

(WSNs) provide critical solutions to a wide range of

applications including environmental monitoring,

wildlife management, human and object tracking, and

surveillance systems. Recharging or replacing batteries

is often difficult since sensors are often placed in areas

which are difficult to access. Hence this technology

requires sensor nodes to be as autonomic as possible.

Moreover, multihop routing in WSN causes routing

holes and shorter network life time. Biologically-

inspired algorithms offer a new paradigm for naturally

inspired solutions to problems arising in WSN. Ant

routing, bee colonization and bee optimization

algorithms have shown outstanding performance for

WSN. Most of these bio-inspired algorithms are applied

into autonomous networking for self-organization, self-

healing, self-management, and others. In this paper,

data harvesting from sensor nodes and energy provision

in sensor nodes derived from the analogies of bee nectar

harvesting and pollination respectively, are proposed

with detailed mapping. Simulation and prototype

results reveal that the bio-inspired mechanism can be a

potential solution.

Keywords-bee analogy, energy-efficient, biologically-

inspired, multihop routing, mobile agent, wireless

sensor network

I. INTRODUCTION

Biological systems encompass the capability of

handling many challenges in system intelligence far

beyond their natural capabilities. Most natural

systems have been refined by life over many

centuries, generating living organisms that are able to

autonomously repair themselves when damaged,

produce emergent behavior, and survive even

following severe environmental changes.

Biologically inspired system is a concept of

understanding the physical, ecological and social

environment particularly reception of sensory

stimulation and behavioural actions such as

pheromone trail, colonization, feeding behaviour,

nectar collection etc. In dealing with behaviour,

ethology and psychology becomes prime importance.

One such example is the algorithm by Dorigo et al.

1996 which tracks pheromone trail to create route

from the source to the destination. So applying

animal behaviour into computing, more specifically

into the area of networking is not something recent.

This interesting capability of self-refining, self-

healing and self-organizing has created interest in the

area of bio-inspired computing by Dressler and

Carreras (2007).

The purpose of this paper is to propose a

system architecture that provides for a biologically

inspired mobile agent based sensor network

(BIMAS), which is based on BiSNET by Suzuki and

Suda (2005)(2007). In this work, autonomous and

self-healing behavior of bee colonies is applied to the

conceptualization of energy efficient self-healing

wireless sensor networks (WSNs). BIMAS

development contributes further to our understanding

of the use of bio-mimetic design approaches to

address problems of energy provisioning in systems.

The shorter transmission distance between nodes and

base reduces energy consumption and its self-healing

mechanism expands network lifetime.

II. RELATED WORKS

Members of the research team have conducted

several studies related to the proposed research

project. Summaries of those studies are highlighted.

SENMA by Tong et al. (2003) is a new mechanism of

using mobile agents together with low power sensor

nodes to reduce the complex tasks away from sensor

nodes. In SENMA, mobile agents (MA) are not

software units but are powerful hardware units in

terms of communication and processing capability.

Data MULE by Shah et al. (2003)(2006) explores the

idea of using mobile entities (MULE) to collect

sensor data from sparse sensor networks. MULEs

retrieve data from sensors when it reaches closer to

the sensors, buffer it and forward to the wired access

point

Mobile relay by Wang et al. (2008) focuses on

a heterogeneous architecture composed of a few

resource rich mobile relays and large number of static

sensors. The mobile relays are rich in energy that can

move around the network to relieve the burden of

high traffic sensors. This can help to extend the

lifetime of static sensors. Unlike other mobile

approach such as MULE (Shah et al. 2003) this wok

focuses more on the use of mobile relays to deliver

network resources such as energy, computational

2013 IEEE 11th Malaysia International Conference on Communications

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power, sensing and communication abilities instead

of delivering sensor data. Bari et al. (2010) presents a

hierarchical architecture for heterogeneous sensor

networks using mobile data collectors (MDC) at the

upper tier and relay nodes at the middle tier which

acts as cluster heads. The MDC uses a fixed

trajectory to visit all the relay nodes and the base

station. Zebranet by Juang et al. (2002) looks at the

decision and design tradeoffs in the context of

applying wireless peer-to-peer networking techniques

in mobile sensor network design. This is a biological

based research to support wildlife tracking focused on

monitoring of the zebras, carried out under large wild

area. ZebraNet is based on mobile sensor network in

which all the sensors (animal) are mobile and zebras

are dedicated as mobile relays to collect data from

sensors. BiSNET (Suzuki and Suda, 2005) is a

biologically-inspired architecture for sensor networks

that tries to address several issues such as autonomy,

scalability, adaptability, self-healing and simplicity.

BiSNET adopts certain biological properties from bee

colony such as decentralization, food gathering and

natural selection. Research by Atakan and Akan

(2007) discusses distributed node and rate selection

(DNRS) algorithms which are based on natural

immune system. The protocol creates a B-cell based

designated node (DN) to sense desired event. This

concept works similar to immune system in which

when the system detects a pathogen, B-cells are

stimulated to create some antibodies at different

densities to reduce the effect of the pathogen.

III. PROBLEMS TO BE SOLVED

Sensor networks are to be deployed randomly, form a

network by itself, and perceive the environment,

sense real world data and forward required details

back to the base station. These networks should be

able to operate autonomously even with limited

resources such as energy, memory and processing

capability. The nodes should co-ordinate themselves

by exploiting energy efficient communication without

outside intervention.

But this is not the actual fact in existing WSN

due to the resource limitations such as battery power,

buffer capacity and processing capability. And due to

these factors, sensor networks observe frequent

failure (Tong et al. 2003). In a multihop

configuration, nodes near the base station are heavily

utilized and cause tremendous energy depletion of

these nodes. This eventually leads to routing hole

near the base station. Whereas in a direct

communication, nodes directly forward data to the

base station and energy consumption is of the fourth

order of distance. Therefore nodes away from the

base station deplete energy quickly and lead to

routing hole further at the edge of the network. This

is known as hot spot phenomenon, so there exists

these hot spot at the region closer to the base station

due to multihop communication and at the outer

region due to direct communication as shown in Fig.

1.

In a clustering concept, the cluster head node

send data directly to the base station no matter of the

distance. So cluster head nodes further from the base

station (similar to direct transmission) consume

energy at the fourth power of distance. So these nodes

consume higher energy. And although cluster head

selection is on a rotation basis, the energy

consumption is still affected due to the direct

transmission and leads to hotspot (Fig. 1). In a mobile

relay approach, mobile relay communicates with the

entire sensor nodes deployed which is considered an

unnecessary waste of energy for communication

which leads to energy depletion of all the nodes. So

there exist hotspot phenomenon for all the four

routing mechanisms discussed above and our

proposed mobility based schemes produce much

better results by deploying mobile agent/relay to

collect data from sensor nodes by reducing the

communication distance.

Fig. 1 Problem State in Sensor Network

IV. BIOLOGICALLY-INSPIRED MOBILE

AGENT BASED SENSOR NETWORK (BIMAS)

BIMAS considers clustering mechanism so that only

the cluster head nodes communicate with the mobile

agent. To our understanding, this is considered a

novel contribution to the research of deploying

mobile agent/relay in WSN. Moreover, BIMAS

attempts to address enhancement features in BiSNET

(Boonma and Suzuki, 2007) by exploiting a mobile

agent as the shortest path based routing to the base

station. Rather than utilising all the nodes for

communication as in other mobile relay approaches,

BIMAS adopts some of the behaviors adopted in

BiSNET such as migration and pheromone emission.

The following are the objectives of the paper:

1. 1. To design animal behavior-inspired concepts for

energy efficient architecture in sensor networks.

This gives inspiration to devise a new theory

Missing

nodes

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using the bee nectar collection concept for

deploying mobile agents in the routing

architecture.

2. To propose energy provisioning techniques to top

up the energy of sensor nodes. This feasible by

looking into bee pollination for self-healing

architecture in sensor network and how mobile

agent can top up the energy of sensor nodes.

3. To simulate the proposed protocol and develop a

prototype to study the viability of the mobile

agent development and to measure energy

efficiency.

A. BIMAS Overview

BIMAS consists of three layers: sensors (cluster), a

mobile agent and a base station, which are analogous

respectively to flowers, a bee and a bee hive. Sensors

from the first layer collect sensor data (nectar) and

forward them to the mobile agent (the bee), and the

mobile agent carries them to the base station (bee

hive). The mapping in Table 1 shows how BIMAS is

mapped to the bee analogy and its biological

properties.

TABLE 1. MAPPING BEE BIOLOGY TO A MOBILE AGENT

BASED SENSOR NETWORK (BIMAS)

Bee Sensor Network Symbols

Bee Mobile Agent MAi

Flower Active Sensor

Node/Cluster head

CH(Si)

Flower buds Sleeping nodes Si or

Plant Cluster of sensor

nodes

Tid CH(Si)

Nectar Sensor Data Mdata

Pheromone

Trail

Path visited list Plist

Pollen Energy E(Sn)

Pheromone Mobile Agent

Messages

Mhello,

Menergy

Honey Aggregated data Maggr

Bee Hive Base Station BS

BIMAS architecture is classified into three main

entities namely nectar (sensor data) production, bee

(mobile agent) visitation and nectar (data) collection.

Each entity performs its delegated duty and

communicates with each other for collective tasks.

The following section briefly describes each entity

and its duty.

Nectar production takes place in floral

nectarines, glands within flowers of the plant. In the

bio-mimetic analogy on which BIMAS is designed,

the flowers, CH(Si), are the nodes or other agents that

sense the environment and collect sensor data

(analogous to nectar; Mdata) for bees (MAi) to gather

later. A given cluster of nodes in the sensor network

is analogous to one single plant and the entire sensor

network corresponds to a colony of plants. The

analogue of the most prominent, colorful, attractive

and resource rich flower is the sensor node which is

elected to function as the cluster head. This

prominent node attracts a mobile agent, analogous to

a bee, within its proximity to relay sensor data.

Within the analogy, bee visitation is a process between sensor node and mobile agent for data collection, energy provisioning and communication. This process can be further classified into five subclasses as nectar collection, honey digestion, pheromone emission, pollination and migration. In nectar collection subclass, a mobile agent migrates to collect sensor data (nectar) from one sensor node to another. The mobile agent detects sensor node, or cluster, through three different modes: i) a random walk, whereby the agent performs a random walk to the nearest sensor node without having a path visited list (P

list), analogous to a pheromone trace, ii) a

chemotaxis walk, in which the agent uses the transmitted P

list in the same manner that bees follow

the trail of earlier pheromone emissions, and iii) a random waypoint, where the mobile agent changes its P

list such that it follows a particular trail during a

given walk sequence and, when a specified time interval expires, it uses other trails to continue its walk. Collected data is then aggregated into data fusion (honey; M

aggr) and sent to the base station.

The mobile agent emits a P

list pheromone upon

leaving the cluster of sensor node, in much the same way as a bee might release a pheromone marker. This pheromone is used to trace its route back to previously visited clusters. This is also useful for other mobile agents to trace the direct route to the sensor clusters, much like other members of a hive will follow pheromone trails back to sources of nectar. The mobile agent emits this pheromone and dances around the cluster floor to maintain the route trace. The mobile agent collects energy (pollen; E(S

n)

from one cluster and drops it at other clusters. This behaviour allows sensor nodes to equally distribute their energy levels. BIMAS provides self-healing capabilities by balancing the energy level at each sensor node. This pollination analogue exhibits self-

healing characteristics by preventing nodes from

dying due to energy depletion. Migration by the

mobile agent is performed for two reasons: a) energy provisioning (pollination) and b) sensor data collection (nectar collection). The mobile agent uses the P

list (pheromone trail) to trace its route back to

other destinations. Honey (data) collection is the

phase where digested data is transmitted to the base

station. The mobile agent performs a random walk,

chemotaxis or random waypoint walk to the base

station in the same way that a bee uses a pheromone

trail.

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V. SIMULATION

In order to evaluate the performance of our proposed

work we have implemented simulation in NS-2. The

experiment is based on the simplified energy model

without considering the MAC or physical layer. All

the data generated were forwarded to the cluster head

first before being dedicated to the mobile agent. Each

source node generated constant bit rate (CBR) traffic

during the entire simulation.

Single mobile agent approach does not scale well

when the density of the network increases, due to an

increase in the number of nodes. Since the mobile

agent mobility is fixed with a round trip across the

network region, more mobile agents are needed to

collect the huge number of data generated from

increasing number of nodes. This indicates that our

network is scalable with further validation of the

results presented in Fig. 2.

Fig. 2 Number of mobile agents

Fig. 3 Mobile agent distance

Fig. 3 shows remaining energy per node for routing data between mobile agent and sensor nodes. The results were collected by placing mobile agent at two different distances from the sensor nodes and energy value is measured as the mobile agent is moving closer to the sensor node.

Fig.4 Energy consumption using pheromone list

Fig. 5 Comparison for sensor node and cluster head

Pheromone emission is useful for mobile agent

communication in which a mobile agent creates path

visited list after the first random visit to a cluster.. As

seen in Fig.4, energy consumption without

pheromone is much higher as compared to the

presence of pheromone list. This is because, with the

path visited list, mobile agent does not simply

perform random walk to the cluster and thus reduces

unnecessary communication with the sensor nodes.

Fig. 5 presents energy consumption for sensor nodes

and cluster head under pheromone emission. Cluster

head consumes more energy as only these nodes are

responsible for communication with the mobile agent.

Sensor nodes are dedicated to sensing the event and

reporting data to the cluster head. Therefore our

clustering and communication using mobile agent

proves to be increasing lifetime of sensor nodes.

Fig. 6 Effect of mobility models on energy consumption

Fig. 7 Effect of self-healing

Simulation was carried out using three different

mobility models. Based on the simulation results in

Fig. 6, controlled mobility consumes more energy

with single mobile agent as compared to random

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walk. The results presented in Fig. 7 shows that self-

healing can be achieved by energy as proposed using

bee analogy. Cluster head nodes that deplete energy,

send urgent message to the mobile agent for energy

provisioning. Mobile agent moves to the particular

cluster, harvest from energy rich cluster head and

send energy wirelessly to energy depleting cluster

head. The rotation of cluster head election ensure that

all sensor nodes being recharged. Results show that

through self-healing remaining energy of nodes

increases over time and remaining energy drops over

time for system without self-healing.

We compare the proposed BIMAS system with other

routing mechanisms as multihop (flooding) (Suzuki

and Suda, 2005), clustering (LEACH) by Heinzelman

et al. (2002), mobile agent without cluster head

(SENMA) by Tong et al. (2003) and mobile sensor

nodes(ZEBRANET) by Juang et al. (2002). In Fig. 8

and Fig. 9, we study the energy consumption under

different node scalability. The simulation

environment is the same as mentioned at the

beginning of the chapter where nodes are randomly

deployed in a 670X670 m2. Based on the results,

BIMAS consume the least average energy as

compared to ZEBRANET, SENMA and LEACH.

Fig. 8 Average Energy Consumption underNumber of Nodes

Fig. 9 Average Energy Consumption under Number of Nodes

From Fig. 8 and Fig. 9 we find a similar energy

distribution for different routing algorithms. LEACH

(clustering), ZEBRANET (mobility with flooding)

and SENMA (Mobile agent) consume more energy as

compared to BIMAS. BIMAS consumes the least

energy even with the scalability of the network. This

is due to the combination of clustering and mobile

agent based concept integrated into our protocol.

VI. PROTOTYPE IMPLEMENTATION

The proposed BIMAS framework is extended into a

prototype to further validate our argument that mobile

agent distance plays an important factor in energy

consumption. The prototype was developed for a

specific case on intrusion detection within buildings

using the standard PIR motion sensor with other

components such as microcontroller, XBee as

communication module between the sensor node and

mobile agent (laptop in this case). The PIR motion

sensor detects any intruder to the building through

motion sensing and sends the results via ZigBee

(XBee) to the laptop carried by security personnel. X-

CTU software within the laptop collects the results

for motion detection data and energy scan per channel

as shown in Fig. 10. The testbed model developed for

our research is shown in Fig. 11 and Fig. 12. Energy

consumption by sensor nodes is shown in Figure 13.

Mote E with energy provisioning (self-healing)

capability shows better remaining energy level

compared to nodes without self-healing. Moreover

Node E shows lesser energy drop compared to other

nodes.

Fig. 10 Effect of Distance

Fig. 11 System view of remote XBee with PIR Sensor Microcontroll

PIR Sensor

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Fig. 12 The Master Mode (Mobile Agent) Communicating with the

remote XBEEs

Fig. 13 Energy Consumption of Sensor Nodes

VII. CONCLUSION

A biologically inspired mobile agent based routing

mechanism has been proposed. Initial concepts

underlying this work were drawn from our earlier

research into three-tier architecture (Ponnusamy et al.

2010, 2012). The proposed model allows a sensor

network to perform autonomous self-healing from

energy depletion. Through simulation results, it is

proven that BIMAS performs better than multihop

routing, direct transmission, clustering and mobility

without clustering. In this architecture, the mobile

agent shifts the multihop routing burden away from

sensor nodes, sending inactive sensor nodes into sleep

mode. This allows sensor nodes to preserve energy

purely for event sensing..

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XCTU software

XBee/ZigBee

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