RELIABLE, ENERGY-AWARE CROSS-LAYER PROTOCOL FOR WIRELESS SENSOR NETWORKS by Ahmed Badi A Dissertation Submitted to the Faculty of The College of Engineering and Computer Science in Partial Fulllment of the Requirements for the Degree of Doctor of Philosophy Florida Atlantic University Boca Raton, FL December 2009
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RELIABLE, ENERGY-AWARE CROSS-LAYER PROTOCOL FOR WIRELESS
SENSOR NETWORKS
by
Ahmed Badi
A Dissertation Submitted to the Faculty of
The College of Engineering and Computer Science
in Partial Fulfillment of the Requirements for the Degree of
Perhaps the first network protocol that is specifically designed for wireless sen-
sors is the LEACH protocol [35]. The main setting that this protocol addresses is
19
that of a large number of homogeneous, resource constrained nodes monitoring the
environment and periodically sending their readings to a base station located far away
from the field as shown in Figure 2.2. The protocol achieves its power saving goals
by allowing a small percentage of the nodes, called cluster heads, to collect data from
their surrounding neighbors, aggregate that data and send a report to the base station
representing the combined readings.
The protocol avoids depleting the cluster heads energies by selecting a new set
of cluster heads at the beginning of each round. The set up overhead is assumed to
be negligible since the setup time is small compared to the rounds duration. The
protocol uses a randomized routine for each node to elect itself as a cluster head. This
routine is run locally at each node to avoid the traffic overhead of a centralized routine.
Simulation results show that LEACH can increase the network lifetime by as much as
a factor of eight compared with direct transmission. The protocol suffers from few
shortcomings including the fact that the energy level and other node resources are not
taken into consideration in the election routine. Yet, LEACH is considered the first
energy efficient protocol targeting wireless sensors, and the benchmark against which
the performance of other protocols is compared.
2.2.3.2 Power-Efficient Gathering in Sensor Information Systems (PEGA-
SIS)
PEGASIS [52] is an improvement over the LEACH protocol by introducing the
following ideas:
1. Nodes transmit only for a short distance to the closest neighbor. Each node
20
Figure 2.2: LEACH protocol configuration.
defuses its data with the data it receives before transmitting as shown in Figure
2.3.
2. Only one node reports the collected data to the base station instead of a group
of cluster heads going through the expensive transmission.
3. The leader node receives at most two messages instead of an average of 20 mes-
sages in the case of the LEACH protocol with a 100 nodes network [52].
PEGASIS achieves 100-300% energy performance improvement over LEACH.
The protocol does not specify how the leader is selected. But since this is an enhance-
ment over LEACH, one can assume that it uses the same random equation by setting
the number of cluster heads to one. In which case, issues associated with LEACH
cluster heads selection routine can be assumed to be present in PEGASIS.
21
Figure 2.3: PEGASIS protocol configuration.
2.2.3.3 Threshold sensitive Energy efficient Sensor Network Protocol/ Adap-
tive Periodic Threshold-sensitive Energy Efficient Sensor Network
Protocol (TEEN/APTEEN)
In classifying the routing protocols for wireless sensor networks, two classes can
be identified, proactive and reactive protocols [58]. The LEACH and PEGASIS proto-
cols discussed above can be considered to be proactive protocols since they periodically
send reports to the base station. Reactive protocols, in which reporting is triggered by
the occurrence of the event of interest are more suitable for time critical applications
where immediate response to changes in the sensed parameter(s) is required.
The Threshold sensitive Energy Efficient sensor Network protocol (TEEN) [58]
and the Adaptive Periodic Threshold-sensitive Energy Efficient sensor Network Proto-
col (APTEEN) [59] fall under this reactive category. Similar to LEACH and PEGASIS,
TEEN is also a hierarchical protocol. The protocol defines and uses two parameters,
22
a hard threshold and a soft threshold. The sensors are assumed to monitor the en-
vironment continuously. If the value of the sensed parameter reaches or exceeds the
hard threshold value, the node will turn on its transmitter and send a report to its
cluster head. To prevent the nodes from flooding the network with reports once the
hard threshold is reached, the nodes will send a new report only if the value of the
sensed parameter exceeds the last reported value by an amount equals to at least the
soft threshold value. The TEEN protocol offers the following features:
1. Time critical data is reported immediately to the user.
2. Data transmission occurs only if the threshold value is reached, thus substantial
energy conservation is achieved.
3. By varying the values for the hard and the soft threshold parameters, the user
has control on the network reporting behavior. The soft threshold can also be
adjusted to trade off between accuracy and energy saving.
As already stated in [58], the main drawback of this protocol is that if the
threshold value is never reached, the user will get no reports at all and will not be aware
if all the nodes in the network are dead. The above limitation of the TEEN protocol
was removed by introducing a hybrid version of the protocol, the APTEEN protocol
[59]. APTEEN defines a new Count Time (CT ) parameter that is also under the
users control. The count time is defined as the maximum time between two successive
reports. By setting values for this count time APTEEN can act as a pure reactive, a
pure proactive, or a hybrid protocol.
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2.2.3.4 Directed Diffusion
Directed diffusion [37] is a data-centric protocol where data consists of an
attribute-value named pair. It can be considered as a reactive protocol where data
is requested by sending an interest in the named data. The protocol relies on local
communication between neighbors. To a node, a request arriving from a neighbor will
be treated as if it originated from that neighbor and no global routes between source
and sink exist. Initially, a node will flood its neighbors with its interest. Later it will
enforce the selection of minimum delay routes, or routes that have been constantly
delivering timely data. This protocol is applicable to surveillance and target tracking
applications.
2.2.3.5 Geographical and Energy Aware Routing Protocol (GEAR)
GEAR [103] is an energy aware geographical routing protocol for wireless sen-
sors. The GEAR protocol assumes that nodes are aware of their geographical location
for its operation. This can be achieved by using Global Positioning Systems (GPS) or
some localization algorithms. The GEAR protocol is suitable for applications where
the operator is interested in querying a specific geographical region. When there is a
neighbor closer to the destination, the protocol forwards the request to that neighbor.
When more than one neighbor exists that is closer to the destination, the GEAR picks
the one that minimizes some cost function. When all the neighbors are further away
from the destination, a hole is said to exist in the path and the GEAR protocol chooses
the neighbor that minimizes the cost function to forward the request.
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2.2.3.6 Sensor Protocols for Information via Negotiation (SPIN)
The SPIN protocol [34] is defined as an application-level approach to network
communication. It introduces the use of high-level data naming (metadata) for mes-
sage exchange. For the effectiveness of using metadata in this protocol, the metadata
messages are assumed to be much shorter that the actual data messages. SPIN uses
a simple Advertise-Request-Data handshake to enable a node to send its data to only
those nodes that are interested in obtaining it.
A variation of this protocol, named SPIN-2 achieves further energy conserva-
tion by requiring nodes to monitor their energy resources and participate in the data
exchange phase only if they have adequate amount of residual energy. Simulation of
the SPIN-2 protocol shows that 60% more data can be delivered using this setting
compared to basic flooding.
2.2.3.7 Cost-effective Maximum Lifetime Routing (CMLR)
The CMLR protocol [36] identifies a cost function and a maximum lifetime
function and attempt to select a route that minimizes the first function and maximizes
the second one. The authors argue that while the path selected will not be the one
with the least cost function or the maximum lifetime one, it will be the route that will
attempt to optimize both.
2.2.4 MAC Layer
As stated in the previous section, the networking layer is responsible for the
end to end routing and delivery of data messages. Designing energy efficient protocols
25
in the routing layer is important since this will affect the route selected, number of
hops per message, the distance covered per transmission, and the load placed on nodes
participating in the relaying of the data. At the other end, the MAC layer is responsible
for per hop transmission between neighboring nodes. For this reason, and similar to
the network layer, the MAC layer attracted significant attention.
2.2.4.1 Sensor MAC (SMAC)
The first protocol that addresses the energy problem at the MAC layer is the
SMAC [102]. SMAC identifies the sources of energy waste at the MAC layer as being
due to the following four factors:
1. collision
2. overhearing
3. control messages overhead
4. idle listening.
To reduce the effect of idle listening, SMAC introduces the concept of periodic
listen and sleep cycles as shown in Figure 2.4. Nodes follow a sleep and listen schedule
that synchronizes them together. SMAC also attempts to address the problem of con-
trol messages overhead by reducing the number of control messages needed for data
exchange between any sender and receiver pairs. For overhearing and collision issues,
SMAC borrows from the IEEE 802.11 [1] medium access standard. The standard de-
fines a pair of control messages, Request-To-Send and Clear-To-Send (RTS/CTS) for
26
initiating communication between sender and receiver. SMAC requires all nodes hear-
ing either or both RTS/CTS messages to refrain from accessing the medium to avoid
collision. For overhearing, the nodes use the network allocation vector (NAV) concept
introduced in the IEEE 802.11 standard. In this vector, a node will store the duration
of time that a communication between its neighbors will take. This time duration can
be obtained from the RTS or CTS messages that the node overhears. The node can
then switch off its radio and go to sleep for the duration of time while its neighbors are
using the channel. To achieve these energy savings the trade-offs introduced by SMAC
are increased delays, and compromised per-node fairness.
Figure 2.4: S-MAC periodic listen and sleep schedule.
2.2.4.2 Delay MAC (DMAC)
The delay problem introduced by SMAC is partially solved in the D-MAC [1]
protocol by exploiting the structure of data gathering trees. It solves the message
forward interruption by adding an offset to each nodes schedule. This offset depends
on the nodes depth within the forwarding tree as shown in Figure 2.5.
2.2.4.3 TMAC
S-MAC is further improved by using a variable length active period in the TMAC
protocol [91]. TMAC reduces idle listening by transmitting all queued messages in
27
bursts of variable length and going to sleep directly afterwards. During active time
the node will keep listening or transmitting and will go to sleep before the end of the
active period if no further activation events are heard within a defined Activation Time
period (TA).
Figure 2.5: DMAC staggered listen and sleep schedule.
2.2.4.4 WiseMAC
In the WiseMAC protocol [26], a node will wake up regularly for a very short
period to sense the medium. If no activity is detected in the channel, the node will
go back to sleep immediately until the next sampling time. The nodes sampling times
are not synchronized together. If a node finds the medium busy, it stays awake to
receive the transmitted data. If a node has data, it will precede its transmission with
a preamble of length equal to or greater than the network sampling period. The
advantage of using this scheme is that at low traffic levels, nodes will only wake up for
very short time at each sampling period. The disadvantages are the high transmission
cost of the preamble signal, and that all nodes hearing the preamble must stay awake
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to hear the data transmission even if it was not meant for them. To minimize this
transmission cost, WiseMAC requires the nodes to keep a list of their neighbors and
their next wakeup times. Then a transmitting node can start the preamble signal
just ahead of the receivers wakeup schedule keeping the preamble transmission to a
minimum.
2.2.4.5 Group-based Medium Access Control (GMAC)
GMAC [14] is a cluster-centric, reservation based MAC protocol. Each frame
cycle is divided into a contention period and a contention-free period. A gateway
node collects all transmission requests from its members in the form of a Future-
Request-To-Send (FRTS) control messages. The gateway then schedules the nodes that
submitted requests for transmission during the contention-free period. The gateway
node is responsible for storing the transmission request, schedule the transmission time
slots, collecting the data messages from its members, and forwarding all the traffic out
of the cluster.
To avoid the depletion of the gateways battery, the GMAC protocol uses the
Resource Adaptive Voluntary Election (RAVE) scheme to periodically elect a new
gateway node. The RAVE is a self election contention back-off algorithm that takes
into consideration the nodes available levels of energy and other resources.
The algorithm is based on the batterys voltage range, which can be used as an
indication of residual energy. The voltage ranges are divided into four different levels
high, medium, low, and minimum as shown in Table 2.1. The node will set a Battery
Power Level parameter according to its battery level. This parameter is then used to
29
calculate a ’Resource Level’ (RL) for the node that can have one of four possible values
0, 1, 2, and 3 as shown in Table 2.2. Based on the nodes RL value, its cluster head
contention back-off can be dynamically adjusted using the following equation:
ElectionBackoff = Random(27) + (RL ∗ 128) (2.1)
The main advantage of using the above settings and equation is that nodes with better
energy resource have higher chance of becoming cluster heads due to shorter contention
period.
A potential limitation of the GMAC protocol is that a node may be forced to
miss its allocated time slot and contend using FRTS message in the next contention
round. This will happen if another node belonging to a different gateway and within
interference distance from the node uses the channel during that time slot for its
own transmission. This scenario can result in excessive delays and high probability of
collisions in densely deployed networks or in applications characterized by high network
traffic.
Table 2.1: RAVE battery resource level.
Battery power Power level Voltage rangelevel nomenclature00 HIGH 2.6 < power ≤ (3.0-3.6)01 MEDIUM 2.4 < power < 2.610 LOW 2.1 < power < 2.411 MINIMUM power ≤ 2.1
0 HIGH 0 to 127 slots (0 ms to 2 ms )1 MEDIUM 128 to 255 slots (2 ms to 4 ms)2 LOW 256 to 383 slots (4 ms to 6 ms)3 MINIMUM 384 to 511 slots (6 ms to 8 ms)
2.2.4.6 Traffic-Adaptive Medium Access Protocol (TRAMA)
The TRAMA protocol [14] is similar to GMAC in that the communication
channel is divided into frame cycles. Each frame is divided into a random access (con-
tention) period and a scheduled access (contention-free) period. The scheduled period
is divided into time slots. Nodes compete during the random access period to reserve
slots for their data transmission during the scheduled access period. To guarantee a
collision-free transmission, the TRAMA protocol uses the Neighbor Protocol (NP), the
Schedule Exchange Protocol (SEP), and the Adaptive Election Algorithm (AEA) to
obtain and exchange one and two hop information and schedules. Nodes with no data
to send will switch off their radios and go to sleep to conserve energy.
2.2.5 Physical and Radio Layer
At the physical and hardware level the focus in Complementary metaloxidesemi-
conductor (CMOS) circuit design and optimization is shifting from faster switching cir-
cuits to ones that are optimized for power consumption. The work in [70] summarizes
the challenges facing low power design for WSNs as:
1. Design of low power low cost transceiver.
31
2. Low power sensing and processing unit design.
3. Energy efficient modulation schemes and strategies to overcome signal propaga-
tion effects.
Several projects focused on the radio component and the design of energy effi-
The tool selected for our work is the JiST/SWANS wireless network simulator
[9],[10] because of its scalability. JiST (Java in Simulation Time) [9] is comprised of
four components: A compiler, a byte code rewriter, a simulation kernel and a vir-
tual machine. The simulation programs are written in plain Java. SWANS (Scalable
Wireless Ad hoc Network Simulator) [10] is built on top of the JiST platform. The
SWANS software is designed as separate independent software modules, which could be
106
combined to form one wireless network. The SWANS architecture is shown in Figure
5.1. Every SWANS component is encapsulated as a JiST entity, i.e. it stores its own
local state and interacts with other components via interfaces. Each SWANS wireless
device (node) is an entity. The entities within the node are the OSI stack application,
transport, network, routing, MAC, and physical layers. There are also mobility and
routing entities within the node component.
Table 5.2: Comparison between JiST/SWANS, GloMoSim and NS-2 memory andexecution time performance. Reproduced from [10].
Number Performance JiST/SWANS GLoMoSim NS-2of nodes parameter
Execution time 43 s 82 s 7136 s500Memory 1,101 KB 5,759 KB 5,8761 KB
Execution time 430 s 6191 s –5,000Memory 5,284 KB 27,570 KB –
Execution time 4377 s – –50,000Memory 49,262 KB – –
Execution time – – –1000,000Memory 933 MB – –
Figure 5.1: SWANS system architecture with energy model added. Reproduced from[10].
107
5.2.3.1 Upgrading JiST/SWANS to a Wireless Sensor Networks Simulator
The JiST/SWANS is a highly scalable ad-hoc wireless network simulator with
reported results for a million nodes network. However, the tool lacked many com-
ponents needed for wireless sensor networks simulation. In previous work, an energy
model and the S-MAC protocol for JiST/SWANS has been developed [89],[90]. Also,
the radio component has been upgraded to include several radio signal fading and path
loss models. This is discussed further in Section 5.4.5. The network layer has also been
upgraded with the ability to select between the default, no packet drop queue, and
a new queue model that drops packets after a certain number of packets are queued
(buffered). The number of packets the network layer can hold is under the user’s con-
trol. A predefined default setting will be activated, if the user does not set the node’s
buffer queue size.
Event creation and event detection is important to simulating wireless sensor
networks monitoring the physical environment. The ability to simulate physical event
taking place in the environment, and the nodes being equipped to sense the occurrence
of these event was not available. To solve this tool limitation, an architectural mod-
ification was implemented to add these capabilities to the JiST/SWANS. Figure 5.2
shows the new sensor, event and radio fading components that were added to transform
the tool to a wireless sensor network simulator.
The JiST/SWANS had a MAC layer implementation of the CSAM protocol
IEEE 802.11 [1] that was incomplete. A working CSMA protocol is needed for the im-
plementation and performance evaluation of the proposed MAC Dynamic Backoff algo-
rithms. Through several efforts, the JiST/SWANS IEEE 802.11 MAC implementation
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Figure 5.2: JiST/WANS additional components for WSNs simulation.
was fixed and upgraded to be used in the proposed protocol’s simulation experiments.
To obtain top-level simulation results, the tool’s architecture has also been up-
dated with a global statistical component that monitors the simulation parameters. Ex-
amples of these parameters are: Total system energy consumed; Average node residual
energy; Average message queues size; Number of messages sent classified by messages
type; Number of messages dropped by the network layers, classified by messages type;
Number of messages dropped by MAC layers, classified by messages type; Number of
messages received at destinations, classified by messages type.
5.2.3.2 Validating the JiST/SWANS for WSNs Simulation
To validate the upgraded simulator, we implemented LEACH [35] protocol on
JiST/SWANS as a representative of WSNs routing protocols. This LEACH imple-
mentation is also used as a foundation for implementing APTEEN, which is used as
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the reference protocol in the performance evaluation. LEACH energy performance re-
sults obtained using JiST/SWANs were in close agreement with the protocol published
Matlab results.
5.3 Proposed Protocol Evaluation Scenarios
In this section, we present the simulation scenarios used to validate, and to
evaluate the performance of the proposed protocol. The reference protocol used in
the evaluation is APTEEN. Each message the proposed protocol generates will have
a field indicating its reliability level. This field will be set by the application layer
for messages created by the application, or by the network layer, for network control
messages originating from the network layer. Details of the proposed protocol messages
were given in Chapter 4, Section 4.3.1.
At every node in the network, the application layer will be sending periodic
reports. From the discussion in the previous chapter, these reports will have the least
reliability demands. Their reliability flag will indicate low reliability setting. Events
are assigned an event radius as discussed in [71]. Events are generated with some
predefined probability at random geographical locations. All nodes with coordinates
within the event radius, upon detecting the event, will send an event message reporting
the occurrence of the event. This type of messages has high reliability requirements.
The routing control messages, generated by the network layer, will have a medium
reliability setting. These messages are cluster head advertisement messages and the
join messages sent by new cluster members.
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5.3.1 Routing and Cluster Head Selection Evaluation Scenarios
In the initial cluster head selection phase, and since the nodes have no informa-
tion about the link qualities for their one hop neighbors, the cluster head selection will
follow the random election algorithm similar to the one used by LEACH and APTEEN
protocols. Once the network is operational, nodes will start building statistics about
their one hop links using periodic ‘Hello’ messages and monitoring the signal levels.
This will be used for the calculation of link ratings that will influence the joining of
future cluster heads. A node will elect to join the cluster head that has the best av-
erage of link rating ratios. The link statistics will also be used to set the transmission
powers used to communicate with the cluster head. A member node will have three
transmission power settings, one for each of the message types.
For the routing layer, a comparison will be conducted between cluster-head
selection (CH) based on our proposed link rating scheme against the random APTEEN
selection criteria. This comparison will also include individualized link power setting
against APTEEN unified minimum intra-cluster transmission power. The performance
parameters measured here will be power consumption and reliability.
5.3.2 MAC layer Evaluation Scenarios
The performance of our protocol at the MAC layer will be tested against APTEEN
that uses SMAC protocol. In this setting, the APTEEN MAC layer has a pre-set re-
transmission limit that applies to all messages regardless of their type. The back-off
timer is also fixed and independent of the message type. For the proposed protocol,
111
the MAC layer will have different retransmission limits based on the message reliabil-
ity settings. Messages with high reliability requirements will have higher retry limits.
To be able to test the effectiveness of our proposed MAC layer algorithm, the data-
reporting rate will have to be set to a level that creates a heavily utilized or congested
network conditions. A congested network can be detected when intermediate nodes
start dropping packets because of full buffers.
The first set of experiments will test the effect of setting the retransmit limits
to values according to the following condition:
i < j < k (5.1)
Where i is report messages retry limit, j is control messages retry limit, and k is event
messages retry limit. The parameters i, j and k are integers.
The retransmit limit for the reference scenario will be set to be equivalent to
medium reliability messages. This way, report messages are given below average trans-
mission buffer lifetime and event messages are given better than average lifetime. The
performance parameters are energy consumption, average message latency for each of
the message types, and the reliability of data delivery for each of the message types.
For the periodic reports and the network control messages, the reliability of the mes-
sages will be calculated as the ratio of the number of messages sent to the number of
messages received. For event detection messages, the reliability will be calculated as
the ratio of the number of events detected by nodes to the number of events received
112
by the Base Station. This set of experiments will be repeated to test the above perfor-
mance in heavily congested network settings. The goal here is to obtain performance
results for the reliability of event reporting under unfavorable network conditions.
In the second set of experiments, the performance of varying the back-off timers
on reliability will be tested. This will be tested in isolation from the effect of the other
proposed elements. Here again the reference test case is a MAC protocol with a single
back-off timer that has a random duration equivalent to messages of medium reliability
requirements. The dynamic back-off timer has two components as shown below:
Backofftimer = C ∗ Random(x) + K ∗ (RL) (5.2)
Where Random(x) generate a random number in the range 0 to x, RL is the reliability
level required for the current message, k and C are constants.
The performance parameters for these experiments will be average message delay
for each of the three message types, power consumption, and data delivery reliability
for each of the message types.
5.3.3 Radio and Physical Layers Evaluation Scenarios
The radio and physical layer are not evaluated separately. Their evaluation
is part of the routing layer evaluation scenario. The radio for the reference scenario
will have constant radio power setting. This will be compared against the proposed
protocol radio component that sets the transmission power according to the message’s
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power setting specified by the routing layer. The evaluation parameters used are power
consumption and event reporting reliability.
5.3.4 Optimizing the Hello Messages Exchange
As previously stated in Chapter 4, the Hello messages exchange introduces a
non-negligible energy overhead. This overhead may reduce or cancel the energy savings
gained by the Link Rating and the Individualized Link Power Setting algorithms. Below
are the techniques added to the Hello messages exchange algorithm to minimize the
impact of the energy overhead:
Limiting the Number of Hello Messages: Through exhaustive and ex-
tensive simulation experiments, graphs similar to the one shown in Figure 5.3 were
obtained. The graph shows the minimum link power that will result in a commu-
nication that meets the required reliabilities. These values for the reliabilities were
set to 90% for event reporting, 80% for control messages and 70% for periodic report
messages. The graph shows that the power percentages for the three message types
were stable after 20 rounds. In the worst case, the power was stable after 30 rounds.
Any Hello exchange beyond that will not change the power setting values and will only
waste energy and bandwidth resources. Limiting the Hello exchange to a number in
the 20 to 30 messages range will save energy while fulfilling its mission.
Multicasting Instead of Unicasting the Hello Messages: Considerable
energy savings can also be obtained by limiting the number of Hello messages nodes
send in each round. This optimization is achieved by multicasting the Hello and the
Hello-reply messages. Each node will send a single Hello message that contains a list of
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intended recipients. Upon receiving a Hello message, a node will add the sender to its
reply list. After receiving Hello messages from its neighbors, a node will send a single
reply message containing a list of the intended recipients and the relevant information
for each recipient.
Dynamic Minimum Exchange Power Settings: The Hello messages will be
exchanged at different power levels. At the beginning, the messages will be sent at the
initial maximum power setting. After the predefined number of messages for the power
level are sent, the exchange power will be reduced by an amount equals to a predefined
step size. At each level, once the targeted number of messages is reached, the power
will be reduced, until the minimum power level is reached. In some situations, the link
connectivity can be superior. The minimum exchange power can give high reliability
levels. This may be due to the nodes close proximity to its cluster head. Such situation
provides a chance for extra energy savings. The minimum setting can be readjusted. If
for a given link the report messages power setting happens to be equal to the minimum
power level setting, This raises the possibility that a power level below the minimum
level may also produce a satisfactory performance. A new lower minimum power level
setting is selected from a predefined minimum power levels list. This list is stored as
part of the node’s firmware. A new Hello messages exchange will be triggered to test
the reliability performance of the power levels all the way down to the new minimum.
It is important to point out that this new Hello exchange will be triggered only if
the report messages power setting happens to be equal to the minimum setting. This
condition is required in order to avoid unnecessary and meaningless Hello exchange.
Several minimum power settings can be defined up to an absolute minimum. Figure
115
5.3.4 shows an example of such settings. Figure 5.5 is a flowchart for the optimized
Hello messages exchange algorithm. It summarizes the optimizations added to the
Hello messages exchange algorithms. These algorithms were discussed in Chapter 4,
Section 4.3.2.2.
Figure 5.3: Optimized Hello messages exchange.
Transmit power levels setting Percentage of inter-clustertransmit power
maximum power level setting 100%1st minimum power level setting 65%2nd minimum power level setting 45%3rd minimum power level setting 20%... ...absolute minimum power level setting 5%
Figure 5.4: Hello messages optimization, Variable minimum link power setting.
5.4 Radio Model for Simulation
Wireless communication link quality is dependent on the radio signal propa-
gation. Link reliability is a measure of the link quality. Modeling the wireless radio
channel is a challenging task [85]. Several factors affect the link quality, some of which
116
Figure 5.5: Optimized Hello messages exchange.
are time-varying [101], [49]. No deterministic models exist for the wireless channel. Ex-
isting models either approximate or ignore the channel’s probabilistic nature. Below
are examples of some well-known radio channel models.
5.4.1 Disk Radio Model
This is the simplest signal propagation model. It assumes perfect reception
within a certain distance, and zero reception beyond that. It is a workable solution
when the simulation model needs to be kept simple, and the wireless channel behavior
117
has negligible effect on the experiment’s outcome.
5.4.2 Rayleigh fading Radio Model
Rayleigh is a reasonable model when there are many objects in the environment
that scatter the radio signal before it arrives at the receiver. Rayleigh fading is most
applicable when there is no dominant propagation along a line of sight between the
transmitter and receiver. Rayleigh fading models assume that the magnitude of a signal
that has passed through such a channel will vary randomly according to a Rayleigh
distribution. Several mathematical models exist for generating this distribution.
5.4.3 Rician fading Radio Model
Rician fading is a stochastic model for radio propagation caused by partial
cancellation of a radio signal by itself. The signal arrives at the receiver through two
or more different paths. Rician fading occurs when one of the paths, typically a line of
sight signal, is much stronger than the others. The amplitude gain is characterized by
a Rician distribution.
5.4.4 First Order Radio Model
In the first order radio model, the signal strength is assumed to attenuate pro-
portional to some power of the distance traveled. The relationship between the trans-
mitted power and the received power can be expressed mathematically by the following
equation:
PTx = PRx + λ ∗ dn (5.3)
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Where PTx is the transmission power, PRx is the received power, λ is the power-distance
propagation coefficient, d is the distance between the transmitter and the receiver, and
n is a power factor that depends on several environmental factors.
5.4.5 Radio Model Used for Performance Analysis
Several WSNs protocol studies have suggested and used the first order radio
model [35], [58], [59]. Our performance analysis will be carried out against one of these
protocols, APTEEN [59]. For these reasons, the first order radio is the model selected
and implemented in JiST/SWANS for the proposed protocol performance evaluation.
The value for the exponent variable n is known to vary between 2 for outdoors appli-
cations to slightly above 5 for some indoors settings. The exponent value is set to 2 in
all the simulation experiments in this work. The first order radio model, as given by
Equation 5.3, is deterministic. To capture the random nature of the radio channel, a
probabilistic component is added. This component will alter the received signal ran-
domly before delivering it to the destination. This is illustrated by the gray triangular
area in Figure 5.6.
Figure 5.6: Simulation analysis radio model.
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5.5 Performance Analyses
In this section, the results of the simulation experiments will be presented. The
first set will show performance results for the reliable protocol versus APTEEN using
the default parameters settings. These settings are given in Table 5.3. Results for this
first set of experiments are presented in Section 5.5.1.
The Hello message exchange will produce a non-negligible energy impact on the
proposed protocol. At the same time, the proposed protocol techniques will not be
activated until a certain number of Hello messages have been exchanged. A node will
send out one Hello message and one Hello-reply message per report period. To test the
protocol’s sensitivity to this Hello overhead, the second set of experiments will vary the
number of reports per round. This will affect the number of Hello messages sent per
round. This will ultimately affect how soon the energy saving techniques are activated.
Results from this set of experiments are presented in Section 5.5.2.
In Section 5.3.4, we presented optimizations to the Hello messages exchange.
One of the proposed optimizations is setting a limit to the number of Hello messages
exchanged per link. In the third set of experiments, we vary the number of Hello
messages and analyze its impact on the proposed protocol’s performance. Results for
these experiments set are presented in Section 5.5.3.
The default packet size used in all experiments is 300 Bytes, as shown in Table
5.3. This should be adequate for most applications. In the fourth set of experiments,
we test the proposed protocol’s performance when varying the packet size. This will
indirectly test the protocol’s scalability in handling heavy traffic levels. Another scal-
ability test we conduct is increasing the network size. Varying the packet size analysis
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is presented in Section 5.5.4.
Radio transceiver chip technology for wireless sensor networks is constantly im-
proving [17], [65], [21], [27], [22], [45]. Technical data sheets from various manufacturers
show different transmit to receive (Tx:Rx) power ratios. Earlier technologies show a
Tx:Rx ratio of around 2:1. Improvements in transceiver chip technology are towards
a smaller ratio. The simulation experiments default setting used for Tx:Rx ratio is
1:1, as shown in Table 5.3. In the fifth set of experiments, we test the effect of the
improvements in radio chip technology on the proposed protocol performance. In the
experiments of this set, three Tx:Rx ratios were compared, 2:1, 1:1 and 1:2. The results
of this set are given in Section 5.5.5.
Increasing the network size is the second scalability test. The first scalability
test is increasing the packet size. As shown in Table 5.3, the default number of nodes
is 100. In the sixth set of experiments, we increase the network size to 1600 nodes.
Results for this scalability test are presented in Section 5.5.6.
To test the MAC layer’s proposed Dynamic Backoff algorithm, the last set of
experiments create congested bandwidth conditions forcing the network to drop packets
due to full buffers. The default messages inter-arrival time is given in Table 5.3 as
1000 ∗ 106 nano seconds. This value was obtained through trial and error. It was
found to produce non-congested network utilization level. Message queues will hold
few messages, but no packets get dropped. For the MAC layer experiments, this value
was reduced to 1 ∗ 106 nano seconds. This creates a situation where all the nodes
buffers are full and the bandwidth is over-utilized. The network is forced to drop
packets, therefore, testing the effectiveness of the Dynamic Backoff algorithm at the
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MAC layer. The results of this set are given in Section 5.5.7.
Table 5.3: Simulation default parameter values.Parameter Value
Number of nodes 100
Number of cluster heads (CH) 5 (5%)
Number of simulation rounds 100
Number of reports per round 50
Packet size 300 Bytes
Messages inter-arrival time 1000 * 106 nano sec
Required Report messages reliability 70%
Reliability Required Control messages reliability 80%
Required Event messages reliability 90%
Minimum Hello exchange before using 10
the Link Rating parameter
Maximum number of Hello messages per link per 10
power setting
Initial (maximum) transmit power -77 dB
Hello messages Minimum transmit power (1) a -81 dB
Minimum transmit power (2) -83 dB
Minimum transmit power (3) -86 dB
Absolute minimum transmit power -90 dB
Power step size 0.5 dB
Retry limit for Report messages 2
MAC retry limit Retry limit for Control messages 3
Retry limit for Event messages 4
Retry limit for APTEEN messages 3
Transmit:Receive ratio (Tx:Rx) 1:1
Radio Node default transmit power -77 dB
Cluster head to base station (BS) 10 dB
transmit power
a the use of several values for the minimum transmit power is explained in Section 5.3.4
5.5.1 Proposed Protocol performance using Default Parameters
These are the main simulation experiments that measure the impact and effec-
tiveness of the proposed protocol. In this set, the simulation parameters were set as
given in Table 5.3. The performance parameters monitored are energy performance,
reliability and latency.
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5.5.1.1 Energy Performance
Figure 5.7 shows the energy performance of the proposed reliable protocol versus
APTEEN. The proposed protocol shows better energy performance before the 5th
round starts. From the graph, the gap between the two curves widens at around the
35th round. The reason is that the energy overhead due to the Hello messages exchange
the proposed protocol has incurred has been completely offset. After this point, the
energy savings that the protocol can achieve are clearly visible.
5.5.1.2 Reliability Performance
The reliability graphs for the report messages and the control messages are
shown in Figure 5.8 and Figure 5.9. These graphs show that the proposed protocol
has always exceeded the reliability requirements for the two message types. It is by
design that the proposed protocol has a lower reliability for these two message types.
It is this reliability relaxation that the proposed protocol uses to achieve the energy
savings shown in Figure 5.7.
The reliability graph for the event messages is shown in Figure 5.10. It shows the
proposed protocol has performed better than APTEEN. The proposed protocol takes
conservative approach to reliability. It does so by treating the application’s required
reliability values as constraints. The proposed protocol will use power settings that
are guaranteed to meet or exceed these values. As a result, it pursues event message
reliability for each event message reported. This coupled with higher retry limit at the
MAC layer has boosted the proposed protocol’s event reporting success rate. This is
while achieving better energy performance than APTEEN. This can be considered as
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Figure 5.7: Reliable protocol energy performance.
Figure 5.8: Report messages reliability.
key accomplishment of this work.
5.5.1.3 Latency Performance
The latency performance results are shown in Figure 5.11, Figure 5.12 and
Figure 5.13 for report messages, control messages and event messages, respectively.
Improving latency was not an objective for the proposed protocol. Nevertheless, the
effect of the proposed protocol on latency warrant studying to verify whether there
are any effects on latency. From the graphs, it is clear that under normal bandwidth
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Figure 5.9: Control messages reliability.
Figure 5.10: Event messages reliability.
utilization, the proposed protocol did not impact the latency performance. The latency
performance is revisited again later for congested network settings, and when the num-
ber of nodes in the network is increased. In these scenarios, the latency performance
is expected to degrade.
Figure 5.11: Report messages latency.
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Figure 5.12: Control messages latency.
Figure 5.13: Event messages latency.
5.5.2 Varying Number of Reports per Round
In these experiments, the number of simulation rounds is set to 40. The rest
of the simulation parameters are kept as given in Table 5.3, with the exception of
the number of reports per round, which is the parameter varied in this set. The aim
in conducting this set of experiment is to measure the impact of the Hello messages
overhead.
5.5.2.1 Energy Performance
The energy performance results are shown in Figure 5.14. The graph shows that
for 10 Hello messages per round, the simulation was over before any energy savings can
be obtained. The two protocols have the same energy performance at around 20 reports
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per round. Energy performance gains are obtained when the number of reports was 30
reports per round or more. It is important to state here that if these experiments were
carried out for 100 rounds, the outcome would have been favorable for the proposed
protocol. In conduction this set of experiments using 40 simulation rounds, we show
the sensitivities of the proposed protocol. There is an overhead associated with the
Hello messages exchange. To benefit from using the proposed protocol, the network
must to be expected to function for a long time. Since this is the type of applications
the protocol is targeting, there is a substantial energy gain on the long run. The results
also indicate that the proposed protocol is not suitable for short-lived networks. In such
settings, the network operations will be over before the proposed energy optimization
techniques have a chance to absorb the Hello messages overhead.
5.5.2.2 Reliability Performance
Figure 5.15 and Figure 5.16 show the reliability performance of the proposed
protocol vs. APTEEN for report messages and control messages, respectively. Similar
to the default performance, the protocol did meet or exceeded the reliability constraints
for the report messages and for the control message. The event messages reliability
results are shown in Figure 5.17. The protocol’s conservative approach to reliability
resulted in better performance for the event messages.
5.5.3 Varying Maximum Number of Hello Messages
In this set, the variable parameter is the number of Hello messages per link for
each of the power levels. This was varied from 10 Hello messages to 50 Hello messages.
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Figure 5.14: Varying number of reports per round energy performance.
Figure 5.15: Report messages reliability.
The number of simulation rounds was set to 40. The rest of the simulation parameters
were kept as given in Table 5.3.
5.5.3.1 Energy Performance
The aim in conducting this set of experiments is to measure the impact of
the Hello messages overhead. The energy performance results are given in Figure
5.18. From the graph, an interesting result is obtained. Increasing the number of
Hello messages has a positive impact on the proposed protocol’s performance. The
expectations were that sending more Hellos will be taxing on the node’s energy and
therefore, will negatively impact the performance. The explanation was found after
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Figure 5.16: Control messages reliability.
Figure 5.17: Event messages reliability.
careful study of the neighborhood tables in several nodes. The conservative approach to
link reliability meant that the communication power will be reduced only when enough
data justified dropping the power to a lower level. More Hello messages provided
the needed data for the power adjustment. Form the graph, it can be seen that the
energy performance improves in the range 20-30 Hello messages. It stays stable after
30 messages. This is consistent with the results obtained in separate experiments to
find the minimum number of Hello messages required before the link power settings
are stable. The results of that set were discussed in Section 5.3.4.
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Figure 5.18: Impact of maximum number of Hello messages on energy performance.
5.5.3.2 Reliability Performance
The reliability results for the report, control and event messages are given in
Figures 5.19, 5.20 and 5.21, respectively. The reliability results were similar to the
default setting where the report and control messages did meet or exceed the reliability
constraints set by the application. The reliability results graph in Figure 5.21 shows
that the proposed protocol outperformed APTEEN for the event notification messages.
Figure 5.19: Report Messages Reliability.
5.5.4 Varying Packet Size
This is the first of two scalability experiments. The aim in conducting this set
is to evaluate how the proposed protocol scales to more data transfer. In this set,
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Figure 5.20: Control Messages Reliability.
Figure 5.21: Event Messages Reliability.
the variable parameter is the packet size. Two sets of simulation experiments were
conducted. In the first set, normal packet sizes in the range 100 to 500 bytes were
used. WSNs communication is typically reporting sensed readings. This will typically
use small packet sizes. In the second set, larger packet sizes in the range 700 to 1100
Bytes were used. This set is aimed at showing the performance for a wide range of
message sizes. The number of simulation rounds was set to 40 rounds. The rest of the
simulation parameters were kept as given in Table 5.3.
5.5.4.1 Energy Performance
The energy performance results are shown in Figure 5.22 for normal packet
sizes and in Figure 5.23 for large packet sizes. From the graphs, the performance of
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the proposed protocol improved with increasing packet size. This results show that the
proposed protocol scales well and performs better with the increased amount of data
transferred.
Figure 5.22: Varying packet size energy performance, normal packet size.
Figure 5.23: Varying packet size energy performance, large packet size.
5.5.4.2 Reliability Performance
The reliability results for report messages, control messages and event messages
are shown in Figures 5.24 to 5.29 for different packet sizes . These results are for
both normal and large packet sizes. These results are consistent with the results
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obtained in the previous sets. These graphs show that the proposed protocol has
satisfied the reliability constraints for the report messages and the control messages.
The protocol exceeded the reliability requirement for the report and control messages
and outperformed APTEEN for the event reporting messages.
Figure 5.24: Report messages reliability using normal packet sizes.
Figure 5.25: Report messages reliability using large packet sizes.
Figure 5.26: Control messages reliability using normal packet sizes.
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Figure 5.27: Control messages reliability using large packet sizes.
Figure 5.28: Event messages reliability using normal packet sizes.
5.5.5 Varying Transmit-Receive Energy Ratio
Improvements to radio transceiver for wireless sensor networks is an area of on-
going research [17], [65], [21], [27], [22], [45]. A few years ago, a review of technical data
sheets from several wireless sensor hardware manufacturers indicated that transmit to
receive power ratio is roughly 2:1. The trend has been towards smaller transmission
Figure 5.29: Event messages reliability using large packet sizes.
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cost compared to receiving. To test the performance of the proposed protocol under
different radio hardware technologies, a set of experiments is conducted using different
transmit-to-receive power ratios. This set of experiments aims at testing the suitability
of the proposed protocol for future improved radio transceivers hardware. As shown in
Table 5.3, the default transmit:receive (Tx:Rx) ratio used throughout the simulations
is 1:1. In this set of experiments, transmit: receive (Tx:Rx) ratio of 1:2 and 2:1 were
tested.
5.5.5.1 Energy Performance
The results in Figure 5.30 show the energy performance of the proposed protocol
vs. APTEEN, for Tx:Rx power ratio 1:2. The proposed protocol shows a clear energy
performance improvement compared to APTEEN. This energy improvement is even
better when the ratio is on the other extreme 2:1, as shown in Figure 5.31. Figure
5.32 shows a comparison between the three energy ratios 2:1, 1:1 and 1:2, at the end of
40 simulation rounds for each of the settings. The proposed protocol has consistently
shown better energy performance than APTEEN.
Figure 5.30: Energy performance using Tx:Rx ratio = 1:2.
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Figure 5.31: Energy performance using Tx:Rx ratio = 2:1.
Figure 5.32: Energy performance using Tx:Rx ratio = 1:2 vs. 1:1 vs. 2:1.
5.5.5.2 Reliability Performance
The reliability results for the report and control messages for Tx:Rx ratios 1:2
and 2:1 are given in Figure 5.33, Figure 5.34, Figure 5.35 and Figure 5.36. These
results show that the proposed protocol has met or exceeded the required reliability
levels. The reliability results using the defaults parameters for these two message types
were given earlier in Figure 5.8 and Figure 5.9. The Tx:Rx ratio used there is 1:1. From
these results, we conclude that the proposed protocol has always met or exceeded the
reliability constraints for the report and control messages.
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For event reporting messages, the results for Tx:Rx ratios 1:2 and 2:1 are given
in Figure 5.37 and Figure 5.38, respectively. Adding these to the results for event
message’s performance for the ratio 1:1, given in Figure 5.10, we show that the proposed
protocol has outperformed APTEEN for event reporting, regardless of the Tx:Rx ratio.
Figure 5.33: Report messages reliability, Tx:Rx ratio = 1:2.
Figure 5.34: Report messages reliability for Tx:Rx ratio = 2:1.
5.5.6 Varying Network size
The first scalability test was performed by increasing the packet size. This was
discussed in Section 5.5.4. The second scalability test was performed by increasing the
network size. In these experiments, the number of nodes in the network is increased to
1600. As a result, the number of cluster heads (CH) is also increased to 80, to maintain
the 5% number of nodes to number of cluster heads ratio.
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Figure 5.35: Control messages reliability for Tx:Rx ratio = 1:2.
Figure 5.36: Control messages reliability for Tx:Rx ratio = 2:1.
5.5.6.1 Energy Performance
The energy performance graph is shown in Figure 5.39. The proposed proto-
col shows improved energy performance results compared to APTEEN. The gap in
energy performance widened between the 2nd and 3rd rounds. This is due to energy
performance gains offsetting energy loss caused by the Hello messages exchange. This
exchange stopped by the end of the first round and the overhead was completely offset
Figure 5.37: Event messages reliability for Tx:Rx ratio = 1:2.
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Figure 5.38: Event messages reliability for Tx:Rx ratio = 2:1.
by the 3rd round. This shows that the proposed protocol scales well and gives even
better energy savings for bigger networks and for larger packet sizes.
Figure 5.39: Energy performance for large network (1600 nodes).
5.5.6.2 Reliability Performance
The reliability graphs for the report and control messages are shown in Fig-
ure 5.40 and Figure 5.41, respectively. The graphs show similar results to the cases
presented previously. When the network size is increased, the proposed protocol still
meets and exceeds the reliability constraint specified. The event messages reliability
results were similar or better than APTEEN’s as shown in Figure 5.42.
proposed protocol achieves energy savings and met the reliability constraints. This has
been the case regardless of the underlying radio hardware technology assumed.
The effectiveness of the proposed MAC layer Dynamic Backoff algorithms is
tested by creating congested network conditions. While the proposed techniques at the
routing and network layer were effective in improving energy performance, the MAC
layer proposed algorithms assured reliable delivery for important data. The proposed
protocol demonstrates the effectiveness of cross-layer design by ensuring important
data reliability, while delivering improved energy performance.
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Figure 5.67: Low reliability vs. high reliability messages performance, messages retrylimit= 5.
Figure 5.68: Low reliability vs. high reliability messages performance, messages retrylimit= 10.
The proposed protocol shows better energy performance than the reference pro-
tocol APTEEN. This energy performance has been consistent regardless of the exper-
iment settings. Under normal network bandwidth utilization, the proposed protocol
has always met the reliability constraints, with no visible impact on latency. Also,
regardless of the experiment setting, the protocol has better reliability performance
than the reference protocol when handling important data.
Studying the effectiveness of assigning different backoff timers in JiST/SWANS
proved to be a substantial research on its own. This is due to the full-fledge simula-
tion environment and the host of variables the JiST/SWANS simulator imposes. This
153
Figure 5.69: Low reliability vs. high reliability messages performance, low messagesretry limits= 5 and 10.
Figure 5.70: Maximum number of message retries reached for both high and lowimportance messages.
coupled with the algorithm’s many sensitivities call for a separate study. A setup us-
ing a stand-alone simulation will isolate and control the unwanted effect of the many
variables existing in the JiST/SWANS environment. These variables can then be intro-
duced one at a time to study their impact of the random backoff timers. This also will
provide a chance to experiment with several possible variations of the backoff equation.
This is a possible and valid extension to the work carried out in this research.
154
Chapter 6
CONCLUSIONS AND FUTURE WORK
This chapter concludes the dissertation and considers future extensions to the
efforts presented.
6.1 Conclusions
Wireless sensor networks (WSNs) are one of the fastest developing new technolo-
gies. The availability of small, cheap low power embedded processors, radio transceivers
and sensors, integrated on a single chip is leading to the use of sensing, computing and
wireless communication for monitoring and interacting with the physical world.
A wireless sensor network (WSN) is a telecommunication network consisting
of spatially distributed sensors to monitor physical or environmental conditions in
a cooperative manner. Military applications such as monitoring of troop movement
and target tracking originally motivated the development of wireless sensor networks.
However, currently, wireless sensor networks are found in many civilian applications as
well.
As the wireless sensor networks research matures, it needs to move beyond
studies that are focused on studies that address the challenges of energy conservation
and resource constraints. To build trust in using these systems, more emphasis should
155
be placed on studying and analyzing the reliability and dependability of these systems.
So far, wireless sensor networks energy efficiency research has not taken reliability into
consideration as a performance parameter or as a design constraint. Two focus areas
in wireless sensor networks research can be identified. One area is concerned with
optimizing the energy performance and improving network lifetime. The second area
is focused on studying the WSNs reliability problem independent of the networking
and energy performance issues.
This work addresses communication reliability in the highly constrained wire-
less sensor networks environment. We propose a cross-layer, energy-efficient reliable
wireless sensor protocol design. The protocol benefits from the body of research in
the two areas of wireless sensors reliability and wireless sensors energy conservation.
The proposed protocol optimizes energy consumption while providing a reliable data
delivery network. The protocol introduces new energy saving techniques that consider
reliability as a design parameter and as a performance constraint. The protocol also
introduces a new medium access control layer (MAC) dynamic retry limit and dynamic
transmission power setting that are based on the messages reliability requirements.
Cross-layer design is defined as the interaction between the different stack layers
and the sharing of information with the goal of improving the overall system perfor-
mance. It has been used in ad hoc wireless systems to improve throughput, latency,
and quality of service (QoS). Due to the severe energy constraints that are common
to wireless sensor networks operations, several publications have proposed cross-layer
design as an optimization technique. It has been argued that cross-layer designs can
surpass the performance of the best-optimized protocol whose techniques target a single
156
layer of the network stack. The improvements gained in performance come at a price.
This includes decreased architecture modularity and loss of the decoupling between
design and development. Also, cross-layer designs may be hard to debug, maintain
or upgrade. The interdependencies introduced need to be carefully considered and
evaluated to avoid the non-trivial problem of system’s instability.
Our proposed protocol uses cross-layer design as a performance and energy op-
timization technique. Nevertheless, the protocol avoids introducing layer interdepen-
dencies by preserving the stack architecture and optimizes the overall system energy
and reliability performance by information sharing. The information is embedded as
flags in the data and control messages that are moving through the stack. Each layer
reads these flags and adjusts its performance and handling of the message accordingly.
The performance of the proposed cross-layer protocol is evaluated using simula-
tion. An ad-hoc simulation tool is upgraded by adding wireless sensor networks mod-
eling capabilities and used in the evaluation. The performance is compared against the
APTEEN protocol. Results show that the proposed protocol produced better energy
performance, met reliability requirements and performed better than the reference pro-
tocol in the reliable delivery of the class of messages that are tagged as important or
critical data.
Several simulation tests are developed to evaluate the performance of the pro-
posed protocol. Experiments covering a host of conditions and parameters are con-
ducted to measure their effect. These conditions included a default setting in which we
tried as much as possible to set the simulation parameters to typical WSNs operating
conditions.
157
The proposed protocol introduces a Hello message exchange to gather statistics
for the communication link quality for the node’s one hop neighbors. This Hello mes-
sages exchange introduces an energy overhead. To minimize the effect of this overhead,
several optimization techniques are employed.
The Hello messages impact is measured through varying the maximum number of Hello
messages exchanged. Another test to the Hello messages impact is varying the num-
ber of Hello messages per round. This affects how soon the proposed protocol energy
optimization techniques will be activated. Results in this part show that some en-
ergy overhead is introduced. Nevertheless, this will be completely neutralized by the
proposed protocol’s energy optimization techniques. In the long run, better energy
performance is obtained. Results in this set also demonstrated that, based on our
approach, sending more Hello messages will result in better energy performance. This
is until all the communication power settings are stabilized. Beyond that point, any
additional Hello exchange will be ineffective in improving the energy performance, and
will only waste energy.
To test the proposed protocol scalability, two sets of experiments are conducted.
In the first set, the packet size is varied. This is to test the protocol’s response to
more data traffic. In the second scalability set, the network size (number of nodes) is
increased. This test shows how the energy saving techniques scale to more communi-
cating entities in the network. The results obtained for these two sets revealed that
the proposed techniques scale well and produce better results for larger networks.
Variations in the radio transceiver technologies used in wireless sensor hardware
warrant testing the proposed protocol’s behavior under these different technologies.
158
Another set of experiments tested the proposed protocol for different transmit: receive
(Tx:Rx) power ratios. The conclusion reached in this set of experiments is that the
proposed protocol achieves energy savings and met the reliability constraints. This has
been the case regardless of the underlying radio hardware technology assumed.
The effectiveness of the proposed MAC layer Dynamic Backoff algorithms is
tested by creating congested network conditions. The improvements that the cross-
layer design approach can provide are evident in the proposed protocol. While the
proposed techniques at the network layer proved very effective in improving energy
performance, the MAC layer proposed algorithms assured reliable delivery for impor-
tant data. The proposed protocol demonstrates the effectiveness of cross-layer design
by ensuring important data reliability, while delivering improved energy performance.
The proposed protocol shows better energy performance than the reference pro-
tocol APTEEN. This energy performance has been consistent regardless of the experi-
ment settings. Under normal network bandwidth utilization, the proposed protocol has
always met the reliability constraints, with no visible impact on latency. Also, regard-
less of the experiment setting, the proposed protocol has better reliability performance
than the reference protocol when handling important data.
6.2 Future Work
This work is a first effort into combining multiple research proposals into one
deployable and practical solution. A great deal of work is still ahead and needed in
this direction. The following lists few opportunities:
• The Hello messages are instrumental in achieving the energy savings that the
159
proposed protocol enjoys. These energy savings are successfully in neutralizing
the overhead that the Hello messages produced. This Hello exchange gives the
nodes a local view of its neighbors. This view can be expanded as needed to
make the node aware of neighbors few hops away. This is possible by increasing
the amount and type of information that the Hello messages carry. Nodes will
then become aware of their expanded surroundings. This fact can be used to
assist with other networking challenges. There are many possibilities where this
exchange can provide a solution, an example is obtaining a balanced distribution
of cluster head (CH) nodes through the network. As it stands, all hierarchical
wireless sensor networks routing protocol use distributed cluster head selection
algorithms. These algorithms target, but do not guarantee uniform cluster heads
distribution.
• The results of the proposed MAC layer retry limit performance show some of the
proposed protocol ratios had better energy performance than others. This fact
needs more investigation into the exact behavior so more energy savings can be
achieved while ensuring that the reliability gains are not affected.
• Throughout the experimentation modeling and simulation phases, nodes are
given enough initial energy to stay alive till the end of the simulation rounds.
An investigation can be carried out for the effect of nodes consuming all their
energy resources, being removed from the network and fresh nodes being added.
Possible modifications to the proposed protocol to fit this more realistic view of
the network can be investigated.
160
• Studying the effectiveness of assigning different backoff timers in JiST/SWANS
proved to be a substantial research on its own. This is due to the full-fledge
simulation environment and the host of variables in the JiST/SWANS simulator.
This coupled with the algorithm’s many dependencies call for a separate study.
A setup using a stand-alone simulation will isolate and control the unwanted
effect of the many variables existing in the JiST/SWANS environment. These
variables can then be introduced one at a time to study their impact on the
random backoff timers. This also will provide a chance to experiment with several
possible variations of the backoff equation. This is a possible and valid extension
to the work carried out in this dissertation.
161
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