International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
Lifetime Maximization in Wireless Sensor Networks
Using Residual Energy Sleep Scheduling
Ronald Rygan. S, PG Scholar,
Department of Computer Science and Engg,
Regional Centre, Anna University: Tirunelveli.
Abstract — Lifetime maximization is a fundamental concern in
wireless sensor networks (WSNs) owing to the limited energy of
each sensor. Random deployment of sensors in wireless sensor
network provides some coverage redundancy problem. There are
several methods to avoid such redundancy for extending the
overall lifetime of the networks. Wireless sensor nodes are
equipped with limited lifetime batteries and redundantly cover
the target area. To extend lifetime of the WSN, the objective is to
minimize energy consumption while maintaining the full sensing
coverage. Residual energy sleep scheduling (RESS) algorithm is a
node self-scheduling scheme to decide which sensor nodes have to
switch to the sleep state. A low remaining energy node has high
priority over its neighbour nodes to enter sleep state. Based on
the local neighbourhood knowledge periodically adjust sleep and
awake times for sensors based on their relative energy difference.
This scheme tries to prolong the sleeping periods of sensors with
relatively low energy and compensate for their absence by
shortening sleeping periods of sensors with relatively high energy.
Keywords: redundancy, sensing coverage, scheduling, remaining
energy.
I. INTRODUCTION
A wireless sensor network (WSN) consists of spatially
distributed autonomous sensors to monitor physical or
environmental conditions, such as temperature, sound,
pressure, etc. and to cooperatively pass their data through the
network to a main location. The more modern networks are
bi-directional, also enabling control of sensor activity. The
development of wireless sensor networks was motivated by
military applications such as battlefield surveillance; today
such networks are used in many industrial and consumer
applications, such as industrial process monitoring and
control, machine health monitoring, and so on.
A sensor node can only be equipped with a limited energy
supply in all application scenarios. Energy is consumed during
computation and communication among the nodes. The
Sensor node lifetime shows a very strong dependency on
battery lifetime. Selecting the optimum sensors and wireless
communications link requires knowledge of the application
and problem definition. Battery life, sensor update rates, and
size are all major design considerations. Examples of low data
rate sensors include temperature, humidity, and peak strain
captured passively. Examples of high data rate sensors include
strain, acceleration, and vibration.
Most of the energy conservation protocols ensure only one
of the fundamental requirements of the wireless sensor
ISSN: 2231-5381
Mrs. J.Roselin, Asst. Professor,
Department of Computer Science and Engg,
Regional Centre, Anna University: Tirunelveli
network is sensing coverage. The sensing coverage issue
demands that target area or points are perfectly covered by the
sensing range of devices.
One of the major challenges in the design of WSNs is the
fact that energy resources are very limited. Recharging or
replacing the battery of the sensors in the network may be
difficult or impossible, causing severe limitations in the
communication and processing time between all sensors in the
network. Therefore, the key parameter to optimize for is
network lifetime.
In area coverage main objective of the sensor network is to
cover or monitor the region, i.e., the collection of all space
points within the sensor field and each point of the region to
be monitored.
Target coverage covers a set of point with known location
that need to be monitored. The point coverage scheme focus
on determining sensor nodes exact positions, where guarantee
efficient coverage application for a limited number of
immobile points.
Path coverage is one of the applications of wireless sensor
networks where the network is responsible for monitoring a
path and detecting any object that crosses it. The goal of path
coverage is minimize the probability of undetected penetration
through the region.
K-coverage is an area that can be covered by at least k out
of n sensors randomly placed in a bounded region. This is
referred to as k-coverage. The generalized version of the
coverage-preserving problem requires a point to be covered by
at least K sensors called the K-coverage problem.
Energy efficient coverage should be considered as the key
design objective for implementing WSN. Since, a sensor node
can only be equipped with a limited energy supply in all
application scenarios. Sensor node lifetime shows a very
strong dependency on battery lifetime. To solve the Energy
Efficient Coverage problem, a solution to the device coverage
problem needs to address the issue of sensing coverage.
One of the key challenges in wireless sensor networks is to
design an energy efficient communication protocol. Wake-up
scheduling scheme is used to minimize energy consumption
caused by idle listening. In random deployment sensor nodes
are deployed with some redundancy. Using this scheduling
protocol scheduled some sensor nodes to active stage and
others to power saving stage.
The rest of the paper is organized as follows. In Section II,
we review the related literature. In Section III the Energy
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
Remaining Sleep Scheduling algorithm is detailed. In Section
IV, the simulation results of the algorithm are presented.
Finally, we conclude the paper in Section V.
process carefully to conserve energy and extend network
lifetime and decreasing the load on the central base station.
Gaurav S. Kasbekar, Yigal Bejerano, and Saswati Sarkar
[8] In wireless sensor networks (WSNs), a large number of
II. REVIEW OF LITERATURE
sensors perform distributed sensing of a target field. A sensor
Numerous algorithms and scheduling approaches to cover is a subset of the set of all sensors that covers the target
maximize the life time of the wireless sensor network. The field. Design a distributed coordinate-free algorithm for
methods and approaches can be discussed by many authors in attaining high lifetimes in sensor networks, subject to ensuring
the k -coverage of the target field during the network lifetime.
wireless sensor network.
Jiming Chen, Junkun Li, Shibo He, Youxian Sun, and Also prove that the lifetime attained by our algorithm
Hsiao-Hwa Chen [1] proposed to find the sensor set with approximates the maximum possible lifetime within a
maximum residual energy to cover all point of interest. This logarithmic approximation factor.
Bo-Chao Cheng, Hsi-Hsun Yeh, and Ping-Hai Hsu [9]
issue was named as minimum weight sensor coverage
problem (MWSCP). Ant colony optimization (ACO) and Network lifetime predictability is an essential system
particle swarm optimization (PSO) intelligent algorithms are requirement for the type of wireless sensor network used in
used to solve this MWSCP to extend the lifetime of the safety-critical and highly-reliable applications. The High
Energy First (HEF) algorithm is proven to be an optimal
wireless sensor network.
Yanli Cai, Wei Lou, Minglu Li, Xiang-Yang [2] Address cluster head selection algorithm that maximizes a hard N-of-N
the Multiple directional cover sets (MDCS) problem of lifetime for HC-WSNs under the ICOH condition. Then, we
organizing the directions of sensors into a group of non provide theoretical bounds on the feasibility test for the hard
disjoint cover sets to extend the network lifetime. Yang Xiao , network lifetime for the HEF algorithm.
Mark Perillo and Wendi Heinzelman [10] proposed that,
Hui Chen , Kui Wu, Bo Sun, Ying Zhang, Xinyu Sun and
Chong Liu [3] proposed that, the optimal k-search approach is an integrated route discovery and sensor selection protocol
a special case mode for (k = 2) of a randomized scheduling called DAPR that further lengthens network lifetime by jointly
algorithm, in which k subsets of sensors work alternatively. selecting routers and active sensors, again with the goal of
Analyze a problem of maximizing network lifetime under minimizing the use of sensors in sparsely covered areas.
Quality of Service constraints such as bounded detection delay, Hongseok Yoo, Moonjoo Shim, and Dongkyun Kim [12]
proposed DSR and DSP schemes allow sensor nodes to adjust
detection probability, and network coverage intensity.
Sung-Yeop Pyun , and Dong-Ho Cho [4] solve the major their duty-cycle according to their residual energy for The
issue in wireless sensor network is Multiple -target coverage purpose of reducing the sleep latency and balancing energy
problem (MTCP). Heuristic and Optimal algorithms are used consumption among sensor nodes.
Yanwei Wu, Xiang-Yang Li,YunHao Liu and Wei Lou
to solve this problem that considers both the transmitting
energy for collected data and overlapped targets. These [11] designing energy-efficient protocols for low-data-rate
sensor-scheduling algorithms are considers the transmitting WSNs, where sensors consume different energy in different
energy according to the number of targets covered by the radio states (transmitting, receiving, listening, sleeping, and
sensor and removes the redundancy of overlapped targets. The being idle) and also consume energy for state transition. They
optimal sensor-scheduling algorithm as an integer use TDMA as the MAC layer protocol and schedule the
programming that is proved to be NP complete. Heuristic sensor nodes with consecutive time slots at different radio
algorithm is used to reduce the complexity of the optimal states while reducing the number of state transitions. They
also prove that the energy consumption by our scheduling for
algorithm.
K. Ramachandran and B. Sikdar [5] analyzing the network homogeneous network is at most twice of the optimum and
lifetime as a function of time and energy consumption. the timespan of our scheduling is at most a constant time of
Models are developed for sensors with and without battery the optimum. The energy consumption by our scheduling for
recharging and expressions are derived for the network heterogeneous network is at most (log (Rmax/Rmin)) times of
lifetime as well as the distribution and moments of random the optimum.
variables describing the number of sensors with different
III. RESIDUAL ENERGY SLEEP SCHEDULING
levels of residual energy as a function of time. The model is
also extended to the case where new sensors are periodically
The RESS is the self scheduling algorithm which is used
added to the network to substitute older sensors that have to maximizing the lifetime of the wireless sensor network
expended their energy.
using remaining energy. Scheduling the sensor nodes in active
Santosh Kumar, Ten H. Lai, Marc E. Posner, and Prasun or sleep mode while maintaining the full coverage of a target
[6] proposed optimal solutions to the sleep wake-up problems area using minimum active nodes. Increasing the sleep period
for the model of barrier coverage for both the homogeneous of sensor based on balanced energy budget. Also monitoring
and heterogeneous lifetime cases.Yan Wu, Zhoujia Mao, the task and report to the sink node using reactive tasking
Sonia Fahmy, and Ness B. Shroff [7] designed Data-gathering
ISSN: 2231-5381
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
model. It is a localized self-scheduling algorithm which
considers only one-hop neighbourhood knowledge.
The RESS algorithm is divided into rounds, where each
one begins by a self scheduling phase and ends by a sleep
scheduling phase. In self scheduling phase, the nodes verify
the coverage of their Sensing area. Eligible nodes (those
whose Sensing areas are covered by a subset of their
neighbours sensing area) turn off their communication and
sensing units to save energy. Ineligible nodes will perform
sensing tasks and report the sensory data to the sink node.
The self-scheduling phase includes two steps. First, each
node obtains the neighbouring nodes‟ information. Second,
each node checks the eligibility according to eligibility rules.
Fig.1 represents the structure of the RESS round.
Thus, a blind point occurs after turning off both nodes c and d,
as in Figure2.
To avoid such a blind point without the need to
exchange additional messages, introduce priority between the
nodes based on local information of each node. This priority is
assigned to each node with the usage of residual energy. Each
node compares its residual energy with their neighbours and
decided which one has highest priority and which nodes have
lowest priority.
b
c
a
ROUND T
b
c
a
d
d
e
e
f
e
f
(1)
(2)
Blind point
b
R
1
R2
c
a
R3
b
d
a
e
f
Selfscheduling
phase
Sleepscheduling
phase
e
f
(3)
(4)
Fig. 2 Occurrence of a blind point
1. Original sensing area covered by nodes a – f
2. Node d turns off itself by the eligibility rules
3. Node c turns off itself by the eligibility rules
4. Occurence of a blind point
Fig. 1 Representation of the RESS round
The second phase is sleep scheduling phase. In this
phase the sink node is placed at the origin and responsible for
assigning tasks. Also adjusting the sleep and awake periods
based on the residual energy budget.
A. Occurence of a blind point
If all nodes simultaneously make decisions (that is
nodes checks its sensing area is fully covered by the
neighbouring nodes sensing area) blind points may appear, as
shown in figure2. Node d finds that its sensing area can be
covered by nodes f–c-b-e. According to the eligibility rule,
node d turns itself off. While at the same time, node c also
finds that its sensing area can be covered by nodes d, b, a, and
f. Believing node d is still working, node c turns itself off too.
ISSN: 2231-5381
A problem can still occur when two neighbours have
the same energy level. To avoid such case and to ensure a
unique order between nodes, the order operator was finally
defined in the Cartesian product energy identity. Thus, even if
two neighbour nodes have the same energy level they may be
distinguished based on their unique identity. The low residual
energy node has higher priority to enter into a sleep state.
B. HGS and LGS list computation
Neighbour nodes:
The neighbouring nodes of a node are the nodes which
are present within its communication range. Here SN
represents the sensor node.
ci, cj є SN are neighbours iff d(ci, cj) < Rc
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
General Sensing area nodes to the node „c‟ (GS(c)):
Nodes whose sensing areas (SA) intersects with the
sensing area of the node „c‟.
GS(c) = {ci є SN: SA(c) ∩ SA (ci) ≠ ϕ}
Neighbours and General Sensing area nodes to the node „c‟
(NGS(c)):
Nodes belonging to GS(c) that are directly
communicate with „c‟.
NGS(c) = {ci є GS(c): d(c, ci) < Rc}
Send an ADV message
HGS(c) = {ci є NGS(c): c ≺ ci}
Lower general sensing area nodes to the node „c‟ (LGS(c)):
Nodes belonging to NGS(c) that have lower order
than „c‟
LGS(c) = {ci NGS(c): ci≺ c}
Figure 3 represents the computation of the higher
general sensing area node (HGS) list and lower general
sensing area node (LGS) list. Each node transmits to its
neighbour nodes an advertisement message (ADV), including
its ID and its current remaining energy. The receiver node will
compare itself to the transmitter node that is the Sender node
covers the receiver nodes sensing area. Based on the
comparison result the transmitter node will be added to one
of the two lists: HGS (Receiver) list, LGS (Receiver) list. The
nodes belonging to the HGS (Receiver) list have less priority
than the receiver node to be deactivated. The nodes belonging
to the LGS (Receiver) list have more priority than the receiver
node to be deactivated.
C. Eligibility rule verification
Reception of an ADV message
Calculation of HGS and LGS list
Testing the eligibility rule of HGS
list
no
Sender covers
part
of
the
receiver’s SA
yes
Eligible
node
no
Ignore
message
Waiting active message
yes
Tp has
expired
no
yes
Sender has
lower order
than receiver
yes
Testing the eligibility rule of
i.
HGS list
ii.
LGS list that have
already sent awake
message
no
yes
no
Eligible
node
Add sender node
to LGS list
Add sender node
to HGS list
Sending active message
Fig. 3: HGS, LGS list computation
Higher general sensing area nodes to the node „c‟ (HGS(c)):
Nodes belonging to NGS(c) that have higher order
than „c‟
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Deactive state
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Active state
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
energy sleep scheduling scheme. Initially sensor nodes are
randomly deployed in a 100m*100m square area. The
Fig 4: Eligibility rule verification
powerful sink node is placed at the origin and that node is
responsible for assigning tasks using the position (x, y). Here
Figure 4 represents the verification of eligibility rules. the task assignment is totally based on reactive task model.
If SA (Receiver) is covered by the union of the SA of the
nodes belonging to its HGS list, then it enters directly to the
TABLE 1
RESS SCHEDULING PARAMETERS
sleep state.
If SA (Receiver) is not covered by the union of the
PARAMETERS
VALUE
SA of the nodes belonging to its HGS list, then it checks
verification with LGS (Receiver) list.
Deployment area size
100m*100m
If the receiver node is an eligible node then select
that node for deactivation. Otherwise send active messages to
Transmission range
25m
its neighbour nodes and select that node for activation.
Sensing range
15m
Initial energy
50 joules
Number of nodes
Arbitrary
Maximum number of tasks
1000
The eligibility rules are defined as follows
RULE 1: Nodes belonging to HGS list of receiver is fully
covered by the receivers sensing area then select that receiver
node to sleep state.
RULE 2: Check the nodes belonging to the HGS list and LGS
list that have already sent awake messages are fully covered
by the receivers SA then select that receiver node to sleep
state.
D. Sleep-scheduling phase
According to the reactive tasking model the powerful
sink node at the origin. The sink node is assigning tasks to
sensors. A task is defined using its position (x, y). In that
position the events are monitored and the monitored data is
send back to the sink node. Initially assume that a sensor
sleeps for a random amount of time uniformly distributed in
[Ts, TS] and is awake for a random amount of time uniformly
distributed in [Ta, TA].
Based on the initial timing perform the task
monitoring. After performing each task, calculate the value of
the relative difference between sensor energy ei, the maximum
energy (emax) and the minimum energy (emin) among the
neighbouring sensor and adjust the upper bounds of the ranges
from which they select their sleep and awake times. The
adjustment is based on the following formulas
Fig. 5: Lifetime analysis
TSnew = Ts + 2 ((emax- ei) / (emax- emin)) (TS – Ts)
TAnew = Ta+ 2 ((ei - emin) / (emax- emin)) (TA– Ta)
Based on the new sleep and awake time scheduling each
sensor nodes in sleep mode or awake mode for performing the
task and pass the data to the sink node.
IV. PERFORMANCE EVALUTION
To verify the scheduling algorithm of proposed
system, it uses network simulator. The following table 1
illustrate the parameters which are used to execute the residual
ISSN: 2231-5381
Fig. 6: Energy consumption rate analysis
Figure 5 represent the lifetime maximization of the
following algorithms
1) Randomized scheduling.
2) Residual energy sleep-scheduling.
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
for the different number of nodes. Figure 6 represents the
energy consumption rate for the above algorithms with
different number of nodes.
[9] Bo-Chao Cheng, Hsi-Hsun Yeh, and Ping-Hai Hsu, “Schedulability
Analysis for Hard Network Lifetime Wireless Sensor Networks With High
Energy First Clustering” IEEE transactions on reliability, vol. 60, sep 2011.
[10] Mark Perillo and Wendi Heinzelman,“An Integrated approach to sensor
role selection” IEEE transactions on mobile computing, vol. 8, May 2009.
V. CONCLUSION
This paper contains the problems of energy conservation
and full sensing coverage in large wireless sensor networks.
The Residual energy sleep scheduling algorithm, has been
introduced based on a wake-up scheduling concept allowing
one to extend the lifetime of the WSN. The RESS algorithm
relies on the novel idea of exploiting the remaining energy in
making decision on which node has to enter sleep state. The
first main feature of the RESS algorithm consists in applying
an equity principle by balancing the remaining energy of
nodes. This has contributed to extend the WSN lifetime. The
second main feature consists in avoiding negotiation phases,
as decision to enter sleep state uses a computed priority based
on one-hop neighbourhood knowledge. This contributes not
only to extend WSN lifetime as message exchanges are
reduced, but also to avoid blind points and then to preserve the
full coverage of the target area. The third main feature is to
continuously adjust sleep and awake times for sensors based
on their relative energy difference.
[11] Yanwei Wu, Xiang-Yang Li,YunHao Liu and Wei Lou, “EnergyEfficient Wake-Up Scheduling for Data Collection and Aggregation” IEEE
transactions on parallel and distributed systems, vol. 21, Feb 2010.
[12] Hongseok Yoo, Moonjoo Shim, and Dongkyun Kim,“Dynamic DutyCycle Scheduling Schemes for Energy-Harvesting Wireless Sensor Networks”
IEEE communications letters, vol. 16, Feb 2012.
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