Data Gathering in Wireless Sensor Networks with Ferry ... - IEEE Xplore

5 downloads 238 Views 511KB Size Report
The advantage of using a ferry based approach is that, it eliminates the need for multi-hop forwarding of data, and as a result energy consumption at the nodes is ...
Proceedings of 2015 IEEE 12th International Conference on Networking, Sensing and Control Howard Civil Service International House, Taipei, Taiwan, April 9-11, 2015

Data Gathering in Wireless Sensor Networks with Ferry Nodes

1 1 Mariam Alnuaimi ,Khaled Shuaib \ Klaithem Alnuaimi and Mohammed Abed-Hafez l

2

College of Information Technology, UAEU P,O, Box 15551 Al Ain, UAE

e-mail: {mariam.aluuaimi.kshuaib.klaithem.nuaimi}@uaeu.ac.ae 2

College of Engineering, Electrical Engineering Department, UAEU P.O. Box 15551, AI Ain, UAE e-mail: [email protected]

Abstract-

improve the performance such as the life time of a WSN and

Mobile ferries are an alternative way for

the maintained coverage area. Ferries are mobile elements

collecting data from dispersed sensor nodes especially in large­ scale

networks.

Unlike

data

collection

via

that are used to carry data over distance to the base stations

multi-hop

or to a data center.

forwarding among the nodes, ferries travel across the sensing

They are also used to connect isolated

islands of WSNs. In addition, ferries can be used to resolve

field and collect data from the sensing nodes. The advantage of

the issue of coverage for holes in a WSN resulting from the

using a ferry based approach is that, it eliminates the need for

need for replacing deployed fixed sensor nodes which have

multi-hop

energy

run out of energy. Mobile elements can be attached to

consumption at the nodes is significantly reduced. However,

people, animals, vehicles, robots, unmanned aerial vehicles

this increases data delivery latency and as such might not be

or any movable object. .

forwarding

of

data,

and

as

a

result

suitable for all applications. In this paper we survey the recent

There are different types of ferries or mobile elements

progress of using mobile ferry nodes for data gathering in

that are used in WSNs [7]. They can be classified as in the

WSNs addressing two main areas, determining the path of the

following sub sections:

ferry and the scheduling when to dispatch the ferry to collect data from sensors.

We also highlight challenges facing the

1.

deployment of mobile ferries in wireless sensor networks.

Ordinary sensor nodes: Ordinary sensor nodes are the source nodes that perform the sensing task as shown in Figure 1. Mobile ferry can be used as a scale sensor that

Keywords- ferry protocol; wireless sensor networks; load balancing; routing protocols; energy efficiency protocols;

senses data from the surrounding environment and sends the data (e.g, temperature, light, gas) to the cluster head or a collector. The advantage of these nodes is that they are moving, so they can track a

I. INTRODUCTION

movable event like intruder detection in border

Recent improvement in hardware electronics technology

monitoring applications.

enabled manufacturers to develop low cost, low power and small size sensors [1, 2, 3]. Hundreds and thousands of these sensors are deployed as wireless sensor networks (WSN) serving many applications based on the specific requirements of each one [4, 5, 6]. Nowadays, there are many applications of

sensor

networks

agriculture, toys,

covering

medicine,

intrusion

military,

detection,

different

fields

environment

motion

such

as

monitoring,

tracking,

machine

malfunction, and many others [2]. Sensors can be deployed to continuously report environmental data for long periods of time. In general, a wireless sensor network is a collection of static

nodes

with

sensing,

computation,

and

wireless

Base station

communication capabilities [2]. However, due to the nature of some applications, mobile nodes are needed. Using

Figure 1 Mobile node is used as a part ofWSNs

mobile nodes to collect data from sensors in WSNs can

978-1-4799-8069-7/15/$3\.00 ©20151EEE

221

II.

Mobile Sink or Base Station

2.

ApPLICATIONS OF USING MOBILE FERRIES INWSNs

The Sink or the Base Station (BS): destination where

Due to the nature of some applications of WSNs, mobile

all data are gathered to be used by data centers or

ferries are needed. Using ferries to collect data from sensors

outside applications. Sink can be mobile and visit all

in WSNs can improve the performance of WSNs such as the

nodes to collect data from them directly or through

life

intermediate nodes as shown in Figure 2. Mobile

applications that can utilize the advantages of using ferries.

sinks can increase the network lifetime, decrease delay

and

decrease

traffic.

However,

having

time

1.

a

and

the

coverage.

Below

are

some

of

the

Border monitoring

Mobile node can be used in intrusion detection and

mobile sink requires a full knowledge or control

border surveillance to collect data from sensors scattered

over its movement and schedule.

along the border line. BorderSense is an example of such an application, where mobile ferry such as Unmanned Aerial Vehicles (UAVs) are used to collect data from static sensors

Mobile Sink

[19]. Mobile nodes can also be used as sensor nodes to provide additional coverage in case needed. In addition to that, mobile nodes can track intruders based on information from static sensors and help in catching the intruders. 2.

Disaster Recovery

During times of a disaster, such as an earthquake or a tsunami

the

communication

infrastructures

usually

are

destroyed, which made rescue and recovery efforts hard. Therefore, there is a need for mobile nodes to be used in collecting information from the surrounding environment and help in rescuing. In [20] multiple mobile sensors carried on vehicles are used across large distances with minimal need for wired infrastructures to provide communication coverage for disaster recovery. In addition, static nodes can Figure 2 Mobile Sink collects data from nodes

be deployed to monitor the disaster area and information can either be disseminated through multi-hop forwarding or by

Mobile support nodes

3.

using mobile ferries carried by robots or other means.

Support nodes: the intennediate nodes that help the data to

be

transferred

from

the

source

(sensing

nodes)

3.

to

Environment Monitoring

destination (Sink) as shown in Figure 3. A WSN might

Mobile elements can be attached to people, animals,

become partitioned into several islands for many reasons,

vehicles or any movable object to continuously report

which makes the communication in the network impossible.

environmental data for long periods of time. They can be

In this case, mobile support nodes can be used to connect

used in

portioned WSN. Mobile support node can also be used to

detection flood detection. CitiSense

detecting

air and water

pollutions,

forest

fire

[21] uses wearable devices and mobile phones carried by users to collect

replace dead critical nodes in case of emergency. An example of it is sending a robot to cover a certain area when

environmental parameters (CO, N02 and 03, temperature,

nodes in duty are dead. This strategy will help in providing

humidity and barometric pressure) from static sensors to

more coverage and increase the lifetime of the network.

monitor air pollution in certain areas and associate them with other events or aspects. 4.



NOde

��

.•

Node

battlefield Nod�._.....-�

�.

•.

No!

•..

..

\

.d

Military Applications

Military

applications surveillance,

involve

intrusion

monitoring

friendly

detection, forces,

battlefield damage assessments, information gathering and

�'V"'�'

smart logistics support in an unknown deployed area. In [22] static sensor nodes deployed on the ground with a job to detect and track vehicles passing through the area over a dirt



road. The vehicle tracking information was collected from

NO

the sensors using the UAV in a flyover maneuver and then sent the data to an observer at the base camp.

Base station

5.

Intelligent Road Transportation

Applications of Intelligent Road Transportation (lRT)

Figure 3 Mobile support node used to transfer data in

usually fall under navigation,

WSNs

222

traffic flow control

(e.g.

changing traffic lights) and the need to plan and build new

collect sensed data whenever they are in the communication

infrastructures.

range of the static sensor nodes. In [8] mobile entities called mules were deployed in the environment. Mules picked up

Vehicles on the roads equipped with sensors could act as

data from sensors when they were in close range, buffered it,

mobile nodes on the road network providing a rich source of

and dropped it off when within a communication range of

data about the traffic, environment and road conditions. This

wired access points. They used two-dimensional random

information

traffic

walk to model the mobility of mules. Both mules and sensors

effectively in order to maintain a good traffic flow and

required to have memory capacities as they were buffering

minimize the risk of accidents and roads congestions [26]. In addition, data can be disseminated by these mobile nodes to

data. In [9] mobile nodes were used in the sensor field as forwarding agents. When a mobile node moved in close

assists

traffic

managers

to

regulate

subscribed drivers to avoid congestion and get road and

proximity to sensors, data is transferred to the mobile node

weather conditions in real-time at a minimum cost.

for later depositing at the destination. They used analytical models to understand the key performance metrics such as

6.

Animal Tracking

data

Sensors can be attached to animals to track them in

transfer,

latency

to

the

destination,

and

power

consumption.

support of wildlife research or simply to locate them. When

Due to the random mobility of the ferry it is difficult to

sensors are attached to animals they became mobile sensor

gather sensed data from all deployed nodes. Unlike the

nodes. ZebraNet system is a WSNs tracking system carried

random path approach, in the planned path approach a path is

by animals under study across a large area. Sensors carry

determined before the dispatching of the ferry and thus the

logged data to researchers such as the animal positions, their

ferry is sent to cover a certain area of deployed sensors to

temperature, heart rate and frequency of feeding and send

collect data. In [10], an architecture of a wireless sensor network for a traffic surveillance application with mobile

them to the base center to be used by wildlife researchers

sinks was proposed. All sensor nodes in this architecture

[27]. These mobile sensors can also be used to collect data from scattered static sensors deployed in the farm area for

were assumed to be located within the direct communication

various applications and communicate such data effectively

range of the mobile sink. All multi-hop transmissions of high

to a base station for further transmission to a central database

volume data over the network are converted into single-hop

and processing.

transmissions to preserve the energy of the network further. Therefore; nodes will transmit only in a single-hop fashion to

7.

the mobile sink.

Pipeline Monitoring

There

are

many

applications

of WSNs

in

pipeline

monitoring

and

oil

nodes

used

in

In Mobi-Route [11], a routing protocol where the sink moves on a planned path to prolong the network lifetime in

pipelines monitoring because pipelines are stretched along a

WSNs was proposed. In this protocol, the sink moved and

large area and it is more costly to deploy static nodes across

stopped at certain points of interest. The stopping time

monitoring pipeline

such

as

monitoring.

water

pipeline

Mobile

sensor

are

it. TriopusNet is a mobile wireless sensor network system for

periods were designed to be long enough to allow for the

autonomous sensor deployment in pipeline monitoring. It

collection of data. All deployed static sensor nodes needed to

releases sensor nodes from a centralized repository located at

be aware of the sink movement and the location and time of

the source of the water pipeline building a wireless network

stops to send the sensed data to it.

of interconnected sensor nodes. When a node dies or has a

The authors in [12] used a single ferry to collect data from a circular dense sensors network. They showed that the

low battery level TriopusNet system sends a new node from

optimal mobility strategy of the ferry achieved when moving

the repository to replace the dead node [28].

at the border of the sensing area. They divided the area into III.

circles starting from the source. The inner circles forward the SURVEYING PREVIOUS RESEARCH WORK

data to the outer ones; until the border was reached where the ferry was used to collect the sensed data.

Using mobile ferry in WSNs is relatively a new area of research which is gaining the attention of many researchers.

2.

Incorporating ferries in WSNs helps in eliminating the need

The scheduling of when exactly to send the ferry to

for multi-hop forwarding of data. It also reduces the energy

collect sensed data from nodes is a quiet complicated task. In

consumption at the nodes. However, using ferries might add

[13, 14] authors studied the scheduling problem where the path of the mobile sink was optimized to visit each node in

delay in collecting, disseminating and processing data and thus might not be suitable for all applications. Existing

the WSN before its buffer was full. Buffer overflow was

research in this field can be classified in two main areas or

used as a trigger to send the ferry to collect data to avoid data

categories as listed in the below subsections. 1.

Scheduling the dispatch of the ferry

loss.

Determining the path

In [15] the authors suggested the mobile sink to visit locations (rendezvous points) based-on a

exact

The path that the ferry takes to collect sensed data from

predetermined schedule to collect data. The rendezvous

sensors can be classified into two categories, random path

points buffer and aggregate data originated from the source

and planned path. Usually in case of the random path the ferry is attached to people or animals moving randomly to

223

nodes through multi-hopping and transfer it to the mobile

2.

sink upon its arrival. In[16] a ferry is used to help in collecting data in partitioned

wireless

sensor

networks

and

transfer

carry data over distances to the base stations. Their mobility

the

can be an issue when collecting the data from sensors.

collected data stored locally back to the base station. Authors

Therefore, presence detection, speed and direction of the

classified scheduling of ferry visit into three categories, time

mobile ferry to enable nodes to send data and the ferry to

based scheduling, location based scheduling and dynamic

collect data from the nodes in an efficient way while

based scheduling. Time based scheduling happened when a

preserving as much energy of the network nodes as possible

node dies and its death leads to portioned WSNs. This node

is an important challenge that needs to be investigated.

will have a higher priority for ferry visits. The location based

3.

scheduling assigns the nodes closer to the base station a higher priority for the ferry visit. The dynamic based

visited by the ferry yet, and selects the shortest distance for

In this research, sensor nodes broadcast data collection

have to be defmed to improve the performance of the network. This will allow nodes to sleep and awake based on

In [18] a mobile node was used to help in disseminating data to the sink. It was used to move back and forth along the

the ferry schedule and proximity. As a result, energy of the network will be preserved and the life time of the network

linear network, and collect data from the individual sensors

will increase. If the path and schedule of the ferry is known

when it comes within their communication range. The

or can be predicted, sensors can be awakened only when they

mobile node will then transfer the collected data to a base

expect the ferry to be in their communication range. This

station. The mobile node was also used to perform other

will further preserve the energy of the network and it is still

functions, such as data processing, and aggregation, and can

an open research challenge to optimize the motion of the

also transport messages from the sink to the sensor nodes.

ferry in a controlled manner and thus efficiently manage the

Table 1 shows a summary literature work of using ferries

duty cycles of sensing nodes depending on the deployed

in WSNs. The research work is classified into either using

application.

single-hop or multi-hop in forwarding data. Furthermore, the

4.

research is classified into two categories either all nodes in the network are visited by the ferry or only a subset of nodes.

[15], [17], [11], [12]

IV.

Optimum data transfer

The communication time between static nodes and the mobile ferry might be short while a significant amount of

re ate d work

[9]

data might need to be collected. Therefore, there is a need to provide coverage to the entire network and maximize the number of reliable sent massages to the ferry. In [25] authors investigated how to efficiently collect data from stationary sensor nodes using multiple robotic vehicles such as data ferries under different circumstances. They proved that

CHALLENGES IN USING FERRY IN WSNS

finding an optimum ferry path is an np-hard problem. Therefore, finding a reliable and efficient path for the ferry

Based on the previous background and literature review

to take to provide coverage for the entire network using least

discussed several challenges can be identified in deploying

energy and minimum latency is one of the hardest challenges

ferries inWSNs. Below are some of these challenges that are

that needs to be investigated and addressed.

still open research areas to be tackled in the future by researchers. 1.

all

when the motion of the ferry is controlled. As an example,

collect data, and transfer the data to the sink.

Multi-hop

in

the path, the speed and the stopping time periods of the ferry

such requests, the ferry moves toward the sensor nodes to

[16], [8]

issue

mobile ferry to visit sensors in a WSN should be defined

requests when their buffers are about to be full. On receiving

[13], [14], [18], [10]

important

the awake mode consumes energy. A predefmed policy for a

In [17] the authors considered on demand data collection.

single-hop

an

static and mobile nodes. They found that keeping nodes in

next visit.

Visits subset of nodes

is

network life time ofWSNs. Authors in [23, 24] surveyed the existing energy management schemes in literature for both

partitioned wireless sensor networks that have not been

0f

management

networks. Efficient management can lead to prolonging the

current location of the ferry and the locations of the

Visits whole nodes

Ef f icient Energy Management Strategies

Energy

scheduling is based on calculating the distances between the

Table 1 Summary

Mobility of the ferry

Ferries are rechargeable mobile elements that are used to

V.

Ferry Presence detection

CONCLUSIONS

In this paper we have surveyed the recent progress of

Detecting the presence of the ferry in the communication

using mobile ferries for data gathering in WSNs addressing

range is a very challenging issue especially if the presence

two areas,

was for a very short time and the path of the ferry is

scheduling when to dispatch the ferry to collect data from

uncontrollable. Therefore, sensors need to be awake and in

static sensors. We presented a classification of mobile

detection mode all the time to detect the presence of the

ferries, based on the role they play in addition to carrying

ferry.

determining

the path of the ferry and the

information. Furthermore, we surveyed the existing work on path planning and scheduling of ferry dispatching in the literature.

224

In

addition

to

that,

common

challenges

in

[16] Chen, Tzung-Cheng, Tzung-Shi Chen, and Ping-Wen Wu.

deploying mobile ferries inWSNs were discussed along with

"Data collection in wireless sensor networks assisted by mobile collector." Wireless Days, 2008. WD'08. 1st IFIP. IEEE, 2008.

many of their possible applications.

[17] He, Liang, et al. "Evaluating on-demand data collection with

REFERENCES

mobile elements in wireless sensor networks." Vehicular technology conference Fall (VTC 201O-Fall), 2010 IEEE 72nd. IEEE, 2010.

[I]

I.Akyildiz, W.Su , Y.Sankarasubramaniam, E. Cayirci, "Wireless sensor networks: a survey", Computer networks 38, no. 4 (2002): 393-422. Jan,2002.

[2]

[18] Imad Jawhar, Mostafa Ammar, Sheng Zhang, Jie Wu, Nader

P.Baronti, P. Pillai, V.Chook"V. Chessa, AGotta, "Wireless sensor networks: a survey on the state of the art and the 802.15.4 and ZigBee standards". Computer communications 30, no. 7 (2007): 1655-1695, 2007.

Mohamed, "Ferry-Based Linear WirelessWireless Sensor Networks", accepted in the IEEE Global Communications Conference Exhibition & Industry Forum (IEEE Globecom 2013), Atlanta, Georgia, USA, December 9-13, 2013.

[3]

M. Alnuaimi, F. Sallabi, and K. Shuaib, "A Survey of Wireless Multimedia Sensor Networks Challenges and Solutions", IEEE International Conference on Innovations in Information Technology (lIT' 11), pp. 191-196, Abu Dhabi, UAE, April 2011.

[19] Z. Sun, P. Wang, M.C. Vuran, M. AI-Rodhaan, A AI­

[4]

M. Alnuaimi, K. Shuaib, I. Jawhar, "Performance Evaluation of IEEE 802.15.4 Physical Layer Using MatLab/Simulink", IEEE International Conference on Innovations in Information Technology, 2006 , pp.I-5, Nov. 19-21, 2006.

"Communications support for disaster recovery operations using hybrid mobile ad-hoc networks," in Proceedings of the 32nd IEEE Conference on Local Computer Networks, 2007.

[5]

[21] Nikzad, N., Verma, N., Ziftci, C., Bales, E., Quick, N.'vZappi,

K. Alnuaimi, M. Alnuaimi, N. Mohamed, I. Jawhar, K. Shuaib, "Web-based wireless sensor networks: a survey of architectures and applications. The 6th International Conference on Ubiquitous Information Management and Communication, Feb. 20-22, Malaysia, 2012.

P., Patrick, K., Dasgupta, S.,5Krueger, I., Rosing, T.S., and Griswold, W. G.: "CitiSense: Improving Geospatial Environmental Assessment of Air Quality Using a Wireless Personal Exposure Monitoring System", Wireless Health, 2012.

[6]

Garcia Villalba, Luis Javier, et al. "Routing protocols in wireless sensor networks" Sensors 9, no. 11 (2009): 83998421, Oct 2009.

[7]

Di Francesco, Mario, Sajal K. Das, and Giuseppe Anastasi. "Data collection in wireless sensor networks with mobile elements: A survey." ACM Transactions on Sensor Networks (TOSN) 8.1, 20II.

[8]

[9]

Dhelaan, I.F. Akyildiz, BorderSense: border patrol through advanced wireless sensor networks, Ad Hoc Networks 9, 2011.

[20] W. Lu, W. K. G. Seah, E. W. C. Peh, and Y. Ge,

[22] Arora,

Anish, Prabal Dutta, Sandip Bapat, Vinod Kulathumani, Hongwei Zhang, Vinayak Naik, Vineet Mittal et al. "A line in the sand: a wireless sensor network for target detection, classification, and tracking." Computer Networks 46, no. 5, 2004.

[23] G. Anastasi, M. Conti, M. Di Francesco, A Passarella, "Energy Conservation in Wireless Sensor Networks", Ad Hoc Networks, to appear(available at http://info.iet.unipi.it/�anastasi/papers/adhoc08.pdf).

R.C. Shah, S. Roy, S. Jain, and W. Brunette, "Data MULEs: Modeling a Three-Tier Architecture for Sparse Sensor Networks,"Proc. First IEEE Int'l Workshop Sensor Network Protocols and Applications, pp. 30-41, 2003.

[24] V. Raghunathan, S. Ganeriwal, M. Srivastava, "Emerging Techniques for Long Lived Wireless Sensor Networks", IEEE Communications Magazine, Vol. 44, N. 4, April 2006.

S. Jain, R.C. Shah, W. Brunette, G. Borriello, and S. Roy,"Exploiting Mobility for Energy Efficient Data Collection in Sensor Networks," Mobile Networks and Applications, vol. 11, no. 3, pp. 327-339, 2006.

[25] Xue, Lirong, Donghyun Kim, Yuqing Zhu, Deying Li, Wei Wang, and Alade O. Tokuta. "MUltiple Heterogeneous Data Ferry Trajectory Planning in Wireless Sensor Networks." , INFOCOM, May, 2014.

[10] L. Song and D. Hatzinakos, "Architecture of Wireless Sensor

[26] Z. Koppanyi, T. Lovas, A Barsi, H. Demeter, A Beeharee,

Networks with Mobile Sinks: Sparsely Deployed Sensors," IEEE Trans. Vehicular Technology, vol. 56, no. 4, pp. 18261836, July 2007.

and A Berenyi. "Tracking vehicle in gsm network to support intelligent transportation systems." Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B2:139-144, 2012. ISPRS Archives.

[II] J. Luo, J. Panchard, M. Piorkowski, M. Grossglauser, and J. Hubaux, "MobiRoute: Routing towards a Mobile Sink for Improving Lifetime in Sensor Networks," Proc. Second IEEEIACM Int'l Conf. Distributed Computing in Sensor Systems (DCOSS), pp. 480-497, 2006.

[27] P. Juang, H. Oki, Y. Wang, M. Maronosi, L. Peh, D. Rubenstein, "Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with ZebraNet", Proc. ASPLOS, Oct 2002.

[12] Luo, J. and Hubaux, J.-P. 2005. "Joint mobility and routing

[28] T. T.-T. Lai, W.-J. Chen, K.-H. Li, P. Huang, and H.H. Chu.

for lifetime elongation in wireless sensor networks". Proceedings of the 24th IEEE Conference on Computer Communications (INFOCOM 2005). Vol. 3. 1735, 2005

Triopusnet: automating wireless sensor network deployment and replacement in pipeline monitoring. In Proc. of ACM/IEEE IPSN. ACM, 2012

[13] A Somasundara, A Ramamoorthy, and M. Srivastava, "Mobile Element Scheduling for Efficient Data Collection in Wireless Sensor Networks with Dynamic Deadlines," Proc. 25th IEEE Int'l Real-Time Systems Symp. (RTSS), pp. 296305, 2004.

[14] Y. Gu, D. Bozdag, R. Brewer, and E. Ekici, "Data Harvesting with Mobile Elements in Wireless Sensor Networks," Computer Networks, vol. 50, no. 17, pp. 3449-3465, 2006.

[15] G. Xing, T. Wang, Z. Xie, and W. Jia, "Rendezvous Design Algorithms for Wireless Sensor Networks with a Mobile Base Station," Proc. ACM MobiHoc, pp. 231-240, 2008.

225