Post on 05-Jan-2016
description
Kai Li
Division of Computer Science
University of Central Florida
Mobile Data Collection Networks for Wireless Sensor Networks*
Traditional Wireless Sensor Networks
Wireless Sensor Networks are composed of a large number of small devices (irreplaceable in many applications), called wireless sensors, which are normally distributed in an ad hoc manner. Wireless sensors gather information, such as
pressure, humidity, temperature, speed etc. Wireless sensors typically share some common
characteristics, such as small size, low power, low cost etc.
Applications
MilitaryBattle damage assessment, nuclear,
biological and chemical attack detection, etc. Environmental
Forest fire detection, habitat monitoring, etc. Health applications
Patients monitoring, drug administration, etc. Industry
Production temperature, humidity, pressure control
Data Collection in Traditional WSNs Data Collection is of paramount importance
sensing data needs to be routed to the sink or base-station (sometimes further transmitted over the Internet through them) for further analysis or applications.
Data transmission over the ad hoc network formed by resource-constrained sensorsdata generated at each sensor can only reach their
neighbors within communication range data go through multiple sensors on their way to the
sink
Data Collection Issues in traditional WSNsCommunication is a major energy consumer for those energy-constrained sensors Multi-hop communication
→ sensors assume dual roles: data source and data forwarder
Fixed routing path (towards static sink)→ sensors in the neighborhood of the sink will deplete their energy faster (“energy hole problem”), which render the WSN dysfunctional prematurely
WSNs with Mobile Elements
Adding Mobile Elements is considered to be a promising solution to the aforementioned problem. Existing approach includes
Mobile Sink Approach
Mobile Messenger Approach
Mobile Sink Approach
Mobile Sink
Sinks moves to different locations in the network field during WSN lifetime
Sojourn for a time interval at each location
When sojourning at each site, routing path of sensors are updated and traffic is redirected towards the current sink site
Mobile Sink Approach
AdvantagesThe “neighborhood” of the sink does not remain
unchanged any more, thus distributing the burden of those sensors over the whole network, preventing premature cessation of network operation.
LimitationsSensors still do multi-hop communicationMobile sinks are not feasible for some applications
(e.g. it’s not possible for them to have access to Internet in harsh environments)
Not scalable for large scale WSNs
Mobile Messenger Approach
Sink
Mobile Messenger
Sinks are static Mobile messengers start out
from sink site, following a path, to visit each sensor
Sensors upload their data to the messenger (in a single hop ) when they approach.
Mobile messengers go back to he sink to deliver the collected data
Mobile Messenger Approach
Advantages sensors transmit data in a single hop, and do not
forward data for other sensorslow communication overhead w.r.t. routing energy consumption at each sensor is greatly
reduced. Limitations
every sensor has to wait a long time for the messenger to approach, thus resulting in long or even unpredictable latency
long wait may result in sensor buffer overflows, thus reducing data delivery ratio
Our Approach—the MDCNet
We propose a new data collection paradigm—the Mobile Data Collection Network (MDCNet) for WSNs that featuresEnergy efficiency (single-hop
communication model)Short latency (compared with mobile
messenger approach)High data delivery ratio
MDCNet is a self-deployed mesh network
formed by Mobile Relay Nodes (MRNs) (e.g. mini robot, autonomous vehicles), each serving a certain number of sensors
with partial and intermittent connection among MRNs (MRN only communicate with other neighboring MRNs when it needs to transmit data)
through which data could be uploaded by sensors in a single hop and electronically transmitted towards the sink or base-station
MDCNet - A new data collection paradigm
MDCNet - A new data collection paradigm
The MDCNet is designed with the following three major considerations the number of sensors each MRN serves should
be balanced to reduce sensor contentionsensor’s data should be collected in a timely
manner to avoid data loss caused by sensor buffer overflows
data relay among MRNs should conform to a reliable protocol to guarantee safe arrival at the sink
Each of the above three requirements is satisfied by corresponding techniques
Load-balanced Area Partitioning Deterministic Area Partitioning (DAP) Adaptive Search and Conquer (ASC)
Local Data Collection Protocol
Data Relay Protocol
MDCNet - A new data collection paradigm
Load-balanced Area Partitioning: DAP approach
𝑅√2
𝑅
2𝑅
Assumption sensor locations are known
a priori Centralized administration
of MRN deployment is possible
Simple partition Evenly divide the region into
several parts Associate each partition
with a mobile relay node MRN moves back and forth
in a snake-like pattern to collect data from sensors
The Adaptive Search and Conquer (ASC) approach has the following characteristics
It assumes no knowledge of sensor locations
No centralized deployment (i.e. decentralized self-deployment) is required
MRNs cooperatively and incrementally search and conquer different regions until the whole WSN has been covered
Load-balanced Area Partitioning: ASC approach
2R𝐿
𝐿
1 2
3L+2R
L+2R
Load-balanced Area Partitioning: ASC approach These Target Areas will be explored by other MRNs, upon their receipt of the NOTICE message from the MRN that claimed the bottom left region as its Service Area4th Expansion by 2R
2nd Expansion by 2R
3rd Expansion by 2R1st Expansion by 2R
After conquer the area as its service area, the MRN will move in a snake-like pattern the service area to collect data from sensors
MRNs set a random timer in the beginning, the MRN whose timer expires first will be the first one to start out and at the same time send out a TIMEOUT message. Others will cancel their timer upon receipt of this message.
The sensor is not served within a predefined time frame . (This is to make sure that a sensor does not get repetitive service when MRN is within its communication range )
Local Data Collection Protocol
Mobile Relay Node Sensor
time
4. Start sending data packet
...
1. HELLO message
2. ACK message
If satisfy service requirement, then stop and set a wait timer
3. START message
5. FINISH message
Cancel timer &receive data
Move& broadcast
timeout
End Session
Data Relay ProtocolData relay hierarchy of DAP
O(Sink Location)
1 2 3
654
87 9
1
54 2
7 8 6 6 3
Sink
Data Relay ProtocolIn the ASC approach, the data relay hierarchy is automatically established as MRNs cooperatively search the sensor field.
1
42 3
8 5 6 7 9
Sink
O(Sink Location)
128673954
Data Relay ProtocolMRN (child)
time
...
HELP message
Ready messagestop and set a wait timer
start sending data packet Cancel timer &receive data
stop serving & seek help
MRN (parent)
FINISH messageResume serving sensors Session end
transmit data
Serving sensors
timeout
Simulation Environment
Simulation Environment: NS2 Network Topology: 100m by 100m Sensor Nodes
data generation rate:10bit very 0.1 secondsbuffer capacity: 10KBcommunication radius: 7mrandom distribution
Mobile Relay Nodes:moving speed: 2m/scommunication radius: 40m
Performance Metrics
Data delivery ratioratio of the data packets delivered to the sink and the data packets generated by the sensors
Latencysensors’ average service interval by mobile relay nodes
Deployment time (for ASC approach)average searching time of mobile relay nodes
Effect of Sensor Density
DAP cannot dynamically adjust the size of its service area with the increase of number of sensors (i.e. its load keep increasing)
ASC tries to keep given workload (number of sensors to serve), and adjust its service area size accordingly
When there are less than 300 sensors, MRN has not reach full load. Thus, in our setting 300
is the full load point
When sensors are very sparse, the 1st MRN takes a long time to conquer a
service area, which dominates the deployment
timeAs sensors density
increases, more MRNs are dispatched, which
contributes to the gradual increase in
deployment time
Effect of Load factor (ASC approach)
When load factor exceeds 60, the number of partitions can not decrease any more (i.e. at least 4 parts)
Taking all factors (latency, data delivery ratio, cost in terms of number of MRNs needed) into account, the optimal load factor in our setting should be between 40 to 50.
performance does not degrade
anymore, because partition
number has reached
minimum
Too many sensors per
MRN. They are overloaded
Latency shows rapid increase
around 50, as a result of overload
Conclusion and Future Work A Mobile Data Collection Network has many
advantages: Single-hop communication saves sensors energy to the
largest extent Electronic transmission of sensing data over MDCNet
contributes to shorter latency Self-deployment and distributed cooperation of MRNs is fit for
large scale WSNs
Future Work Further reduce assumptions such as location awareness (i.e.
GPSs) Generalization to irregular-shaped service areas Consideration of obstacles and/or constrained path