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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 23, NO. 12, DECEMBER 2005 2305
Data Dissemination in Autonomic
Wireless Sensor Networks
Max do Val Machado, Olga Goussevskaia, Raquel A. F. Mini, Cristiano G. Rezende, Antonio A. F. Loureiro,
Geraldo Robson Mateus, and José Marcos S. Nogueira, Member, IEEE
Abstract—In this paper, a new data dissemination algorithm for
wireless sensor networks is presented. The key idea of the proposed
solution is to combine concepts presented in trajectory-based for-
warding with the information provided by the energy map of the
network to determine routes in a dynamic fashion, according to
the energy level of the sensor nodes. This is an important feature
of an autonomic system, which must have the capacity of adapting
its behavior according to its available resources.
Simulation results revealed that the energy spent with the data
dissemination activity can be concentrated on nodes with high-en-
ergy reserves, whereas low-energy nodes can use their energy only
to perform sensing activity or to receive information addressed to
them. In this manner, partitions of the network due to nodes that
ran out of energy can be significantly delayed and the network life-
time extended.
Index Terms—Data dissemination, energy map, trajectory-
based forwarding (TBF), wireless sensor networks (WSNs).
I. INTRODUCTION
ONE OF THE MOST important challenges in the design
of wireless sensor networks (WSNs) is to deal with the
dynamics of such networks. The physical world where the sen-
sors are embedded is dynamic. Over time, the operating condi-
tions and the associate tasks to be performed by the sensors can
change. Some of the causes that may trigger these changes are
the events occurring in the network, amount of resources avail-
able at nodes, particularly, energy and reconfiguration of nodes.
Furthermore, it is important that sensors adapt themselves to the
environment since manual configuration may be unfeasible or
even impossible. In summary, the kind of distributed system we
are dealing with calls for new data communication, coordina-
tion, and control algorithms for large scale, highly dynamic, and
unattended WSN.
“Autonomic computing is an approach to self-managed
computing systems with a minimum of human interference”
[6]. Given this definition, the challenge is to design WSNs
Manuscript received October 18, 2004; revised May 5, 2005. This work was
supported in part by CNPq, Brazil, under Process Number 55.2111/2002-3, Sen-
sornet Project (http://www.sensornet.dcc.ufmg.br), and in part by Scholarships
from CAPES, Ministry of Education, Brazil.
M. do Val Machado, O. Goussevskaia, C. G. Rezende, A. A. F. Loureiro,
G. R. Mateus, and J. M. S. Nogueira are with the Department of Computer Sci-
ence, Federal University of Minas Gerais, Belo Horizonte 30123-970, Brazil
(e-mail: maxm@dcc.ufmg.br; olga@dcc.ufmg.br; rezende@dcc.ufmg.br;
loureiro@dcc.ufmg.br; mateus@dcc.ufmg.br; jmarcos@dcc.ufmg.br).
R. A. F. Mini is with the Department of Computer Science, Pontifical
Catholic University of Minas Gerais, Belo Horizonte 30535-610, Brazil
(e-mail: raquel@dcc.ufmg.br).
Digital Object Identifier 10.1109/JSAC.2005.857209
that are self-managing, self-diagnostic, and transparent to the
monitoring entity. This new computing paradigm, when applied
to a WSN, means that the design of such a network should aim
to embed autonomic capabilities in sensor nodes.
In WSNs, data communication, from the point of view of
the communicating entities, can be divided into three cases, as
depicted in Fig. 1: from sensors to a monitoring node, among
neighboring sensors, and from a monitoring node to sensors.
The first is used to send the sensed data to a monitoring applica-
tion. The second often happens when some kind of cooperation
among nodes is needed. The last, called data dissemination, is
normally used to disseminate a piece of information that is im-
portant to sensor nodes. Reliable data dissemination is crucial to
WSN since a monitoring node has to perform some specific ac-
tivities, such as to change the operational mode of part or the en-
tire WSN, broadcast a new interest to the network, activate/de-
activate one or more sensors, and send queries to the network.
In this work, an energy-efficient data dissemination protocol
for WSNs, called trajectory and energy-based data dissemi-
nation (TEDD), is proposed. The key idea is to combine con-
cepts presented in trajectory-based forwarding (TBF)1[15] with
the information provided by the energy map2[12] to determine
routes in a dynamic fashion. TEDD is comprised of two main
parts. The first one is an algorithm for generating trajectories
that pass through regions with higher energy reserves and avoid
low-energy areas. The main idea is to select a set of nodes that
are most suitable for disseminating information and to find the
best set of curves passing through or near these selected points.
The second part of TEDD is a new packet forwarding mech-
anism which is a receiver-based approach. This characteristic
introduces two improvements to the TBF process. First, it elim-
inates the need of neighbor table maintenance, which is very
expensive in terms of radio transmissions. Second, it presents
a more robust behavior in dynamic topology scenarios, such as
WSNs.
The rest of this paper is organized as follows. Section II dis-
cusses the related work. The two parts of TEDD are described
in Sections III and IV, respectively. In Section V, we analyze the
experimental results. Finally, in Section VI, we present the con-
clusion and the future directions.
1Data dissemination technique in which packets are disseminated from a
monitoring node to a set of nodes along a predefined curve. The main idea is to
embed a trajectory in the packet, and then let the intermediate nodes forward it
in a unicast manner to those nodes that lie close to the trajectory.
2Energy map is the information about the amount of energy available at each
part of the network.
0733-8716/$20.00 © 2005 IEEE
2306 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 23, NO. 12, DECEMBER 2005
Fig. 1. Data communication schemes in WSNs. (a) Data communication from a sensor node to a monitoring node. (b) Data communication among neighboring
nodes. (c) Data communication from a monitoring node to sensor nodes.
II. RELATED WORK
Several different routing protocols for WSNs have been pro-
posed in the literature [1], [3], [7], [9]. Among all algorithms
already proposed in the literature, the closest to the one pre-
sented in this work is the TBF [15], that is a technique to dis-
seminate messages in dense wireless networks. The key idea is
to embed a curve (trajectory) in the packet to be disseminated
from a monitoring node to sensor nodes [Fig. 1(c)], and then let
the intermediate nodes forward it in a unicast manner to those
nodes that lie close to the curve. TBF is a sender-based algo-
rithm since the current node systematically chooses the next hop
of the route. This forwarding decision is based on the curve and a
neighboring table. To update this table, nodes exchange beacon
packets periodically. TBF is a source routing protocol since the
entire trajectory is defined by the data dissemination source. In
traditional source routing protocols for mobile ad hoc networks
[8], the source node inserts all nodes of the path into the packet
as a discrete set of points, generating a considerable overhead
and making impracticable its use in WSNs. Two main advan-
tages of TBF are compact representation of a route, since curves
can be described using few parameters, and node independence,
since no particular node address is specified in the trajectory.
Algorithm 1 shows the basic operation of TBF.3When a node
receives a beacon packet, it updates its neighbor table. If the
received packet is not a beacon, but a data packet, this node
checks if it is the node elected to forward this packet. If it is
not the case, the node drops the packet, otherwise it chooses the
next node in the trajectory defined by the curve. This choice is
made based on its neighbor table and a predefined forwarding
policy (e.g., minimum deviation). After choosing the next node,
the current node transmits the packet.
Algorithm 1: TBF—Receiving packet
input: the received packet
if the packet is a beacon then
Update my neighbor table
else
/The received packet is a data
packet /
if I am the node elected to forward
this received data packet then
Choose the next node in the
trajectory
3Since TBF is a source routing protocol, its basic operation is similar to the
traditional source routing protocols. The main difference to them is that TBF
defines the routes as curves.
Insert the chosen node as the next
hop
Transmit the packet
else
Drop the packet
endif
endif
Despite its advantages, TBF has two main drawbacks. First,
the overhead required to update the neighbor tables increases
the number of transmitted packets, and consequently, the total
energy spent. In dynamic topology environments, such as WSNs
in which nodes frequently enter a sleep mode to save energy,
mechanisms for neighbor table maintenance have a prohibitive
cost. Second, TBF is not fault tolerant in scenarios in which
topological changes are faster than the neighbor table updates.
In this case, broken trajectories happen when the selected node
is unavailable (e.g., the node is sleeping). Therefore, we note
a tradeoff between the neighbor table update overhead and the
protocol robustness.
III. DYNAMIC TRAJECTORY GENERATION
In this section, we discuss the problem of generating trajec-
tories for data dissemination. First, the problem is defined and,
afterward, the proposed solution is presented.
A. Problem Definition
As input to this problem, we have a data forwarding protocol,
a set of nodes distributed in an ad hoc manner over the WSN,
and a monitoring node that disseminates data. The problem of
generating routing trajectories asks for the ideal number of
trajectories and the parameters (we suppose a continuous
model, describing a curve using parameters), such that the
objectives of the routing protocol are achieved or maximized.
Despite providing several insights into the problems that
might arise during the process of specifying a forwarding
trajectory, the authors of TBF [15] do not present solutions
to the curve generation problem, specially to the problem of
how and based on what kind of information the trajectories
should be generated for the routing. In Cartesian routing [5],
the route is defined as a straight line between the router and the
destination. In source-based routing [8], the route is specified
as a discrete set of points. In other protocols [3], [7], the route
is “discovered”instead of being defined. Therefore, to the best
of our knowledge, the solution proposed in this work is pioneer.
DO VAL MACHADO et al.: DATA DISSEMINATION IN AUTONOMIC WIRELESS SENSOR NETWORKS 2307
Fig. 2. Process of trajectory generation.
The problem of generating trajectories can be divided into
several subproblems. In the following, we discuss and propose
solutions to each subproblem.
B. Input: The Energy Map
The first question that arises when solving the problem of
curve generation is: what kind of information should the pro-
cedure be based on. In this work, we use the energy map as our
input, since energy is an important constraint in WSNs. In [12],
the authors analyze the cost of obtaining this map using a pre-
diction-based approach and show that it is viable in WSNs. It is
worth mentioning that the cost of obtaining the energy map can
be amortized among different network applications, and, thus,
neither of them has to pay for this information itself.
C. Point/Node Selection
Having defined the input to the procedure (i.e., the energy
map), a subset of points4from the WSN has to be selected to
serve as input data for the fitting process. Several strategies can
be used to select this subset. The main criterion for this selec-
tion is the energy available at each of these points. The idea is
to force the trajectories to pass through points with greater en-
ergy reserves, in order to avoid nodes with little energy to par-
ticipate in the forwarding process. Another criterion is the node
density in each part of the network. The denser the region the
trajectory passes, the greater the network connectivity is, and
the better the chances of the packet to be delivered successfully.
This occurs because nodes are frequently programmed to turn
off their radios in order to save energy. Therefore, there is al-
ways a possibility of the trajectory to break, in case there are no
nodes “awake”to propagate the packet.
In this work, the points are selected using a combination of
energy and density criteria. For every node, the sum of energies
of all its neighbors, together with its own is calculated. After-
wards, the nodes are sorted in decreasing order of this factor,
4We can study a WSN as a set of points where each point can be a node in
the network graph or a pixel in the image of the energy map.
and the first half of the nodes is selected to be the input to the
fitting procedure.5In this manner, the trajectories are “forced”
to pass through regions of higher densities and energy reserves.
The main concern here is to avoid that low-energy nodes partic-
ipate in forwarding activities and to minimize packet losses due
to broken trajectories.
D. Curve Representation and Curve Fitting
The second question that arises when solving the problem of
trajectory generation is: which model should be used to repre-
sent the trajectories. In this work, we use a polynomial repre-
sentation that offers a compact encoding, allowing to control
the number of parameters by limiting the degree of the polyno-
mial. Moreover, the value of the dependent variable is directly
computed for any value of the independent variable . Based on
experiments, we could observe that this type of representation
is flexible and expressive enough to generate trajectories that
avoid low-energy and low-density areas.
Having defined the model to represent the trajectories, a
curve-fitting algorithm has to be determined. Due mainly to its
simplicity, we use multiple linear regression [10], [14] to fit the
curves into a set of points and, in particular, we use the LSQR
algorithm6[17].
E. Architecture
Having discussed the previous subproblems, some questions
related to the architecture of the curve generation process have
to be answered. The architecture proposed in this work is illus-
trated in Fig. 2 and is described in the following.
The process has some variations depending on the dissemina-
tion type. As showed in Fig. 2 (Point A), the first step is to select
points from the dissemination target area. If the dissemination is
5The reason to select only 50% of nodes is purely empirical. Experiments
were made with different percentages, and better results were obtained by elim-
inating 50% of nodes from the fitting procedure.
6The computational requirements of LSQR are: storage
(
n
+2
p
)
, and number
of floating-point multiplications per iteration
(3
n
+5
p
)
. The maximum number
of iterations was set to
4
np
.
2308 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 23, NO. 12, DECEMBER 2005
Fig. 3. Maximum number of sectors depends on the position of the monitoring node. (a) Network sectoring with monitoring node in the center. (b) Network
sectoring with monitoring node at the corner.
a broadcast, the points are selected from the whole map, which
is the target area. If the dissemination is a multicast (the target
area is a subset of the map), two sets of points are selected: one
inside the area between the monitoring node and the target area,
and the other inside the target area. The former set is used to
fit a special curve, called delivery curve [Fig. 2 (Point B)], that
is used as a tunnel between the monitoring node and the target
area. The latter set is used to fit curves inside the target area. The
delivery curve must intersect the monitoring node at one end,
and the target area at another. To not overload the nodes located
at the point of intersection between the delivery curve and the
target area, this point must be defined dynamically, based on en-
ergy/density. It means that, given all points located on the target
area boundary, the intersection point is the one with greater en-
ergy/density factor. Inside the target area, the origin of the gener-
ated curves is the intersection point. A procedure called network
sectoring is used to define the ideal number of curves inside the
target area [Fig. 2 (Point C)], as explained in this next section.
1) Network Sectors: Given a set of points that we would
like to force to participate in the forwarding process and given
the curve type (polynomial), we have to decide how many
curves/trajectories would be sufficient to achieve a certain goal.
The goal could be to disseminate information to a particular
area of the network or just perform a broadcast to all nodes.
By introducing the concept of network sectors, which divide
the network area in identical angular sectors centered at the
monitoring node, the problem of determining the best number of
curves can be viewed as the problem of finding the best number
of network sectors and placing a unique trajectory at each net-
work sector. The curve corresponding to each network sector is
fitted based solely on the points located inside that sector [Fig. 2
(Point D)].
An arbitrary number of network sectors could be used. How-
ever, it is not reasonable to have a large value, since this would
result in an unacceptably high number of parameters to be trans-
mitted with each packet and an unacceptably low number of
points at each sector, compromising the quality of the fitting
procedure. A maximum limit can, therefore, be defined for the
number of network sectors. This limit depends on the position
of the monitoring node. If it is located at one of the corners of
the target area, the sectoring is made within a 90 angle. If it is
located at the center, the sectoring is made within a 360 angle,
allowing the greatest possible number of sectors. These situa-
tions are illustrated in Fig. 3.
Besides the number of sectors, the degree of the polynomial
also has influence on the quality of the fitting procedure. There-
fore, the curve fitting is not only performed for different num-
bers of network sectors, but also for different polynomial de-
grees. The maximum polynomial degree used in this work is
four, since the higher the degree of the polynomial, the greater
the complexity of calculating the distance between each node
and the curve.
Given a maximum number of network sectors and a max-
imum polynomial degree, all possible curve sets are generated.
By selecting the curve set with the “best”average quality,we
determine the boundaries and the angles of the sectors, as is ex-
plained in the next section.
2) Best Curve Set Selection: The last step in the curve gen-
eration process is the selection of the best curve set, as shown
in Fig. 2 (Point E). This selection can be made by calculating
the average quality for each set and simply choosing the one
with the best average quality. The average quality of one set
can be calculated as the sum of the qualities of each curve
participating in the set, divided by the number of network
sectors in the set. The quality of one curve can be calculated
based on different criteria, depending on the application re-
quirements. In this work, the following fit evaluation criteria
were used: maximum average energy, which maximizes the
average energy of the nodes within the covering range of the
curve , and
maximum coverage, which maximizes the total number of
nodes within the covering range of the curve. Finally, the “best”
fit quality is determined by calculating the average criteria
of each set, and selecting the one with the highest average fit
quality.
In the following section, we provide examples of network
sectoring and curve fitting in different network scenarios.
3) Examples of Network Sectoring: Fig. 4(a) and (b) illus-
trates two sets of broadcast curves, selected for two different
energy maps, using the maximum average energy criterion.
Fig. 4(c) and (d) illustrates the same scenarios, however, using
the maximum coverage criterion. It can be observed that when
DO VAL MACHADO et al.: DATA DISSEMINATION IN AUTONOMIC WIRELESS SENSOR NETWORKS 2309
Fig. 4. Curve sets to perform broadcasts. Energy map of an area of
35
2
35
mwith one and two low-energy areas. (a) Maximum energy. (b) Maximum energy.
(c) Maximum coverage. (d) Maximum coverage.
Fig. 5. Curve sets to perform multicasts. Energy map
35
2
35
mwith one and two low-energy areas.
Target area = (20
;
20)
0
(35
;
35)
. (a) Maximum energy.
(b) Maximum energy. (c) Maximum coverage. (d) Maximum coverage.
the first criterion is used, a set with fewer sectors is selected,
and the curves avoid the low-energy areas. When the second
criterion is applied, the maximum number of sectors is selected,
and the curve inside each sector is fitted closer to the nodes
with greater energy reserves.
Fig. 5(a) and (b) illustrates two sets of curves generated to per-
form a multicast, using the maximum average energy criterion.
The target area is determined as a rectangle with coordinates
(20,20)–(35,35). Fig. 5(c) and (d) illustrates the same scenarios,
however using the maximum coverage criterion. It can be ob-
served that the delivery curve avoids low-energy areas. When
the first criterion is used, less sectors are used inside the target
area. When second criterion is applied, more sectors are used.
F. Some Remarks
It is important to point out that the trajectory generation
strategy proposed here is not restricted to the illustrated net-
work scenarios. An energy map of a network with an arbitrary
shape and an arbitrary number of randomly distributed mon-
itoring nodes can be used as input to this procedure. In this
situation, each node would be able to participate in more than
one trajectory, possibly forwarding packets originated by dif-
ferent monitoring nodes. This solution presents two important
features of an autonomic system: flexibility and adaptability.
Another relevant consideration is about the process of en-
coding the trajectories. Curve parameters can be embedded in
the packet header or can be preconfigured in the nodes before
delivering them. However, in the latter case, the monitoring
node should be able to update those values periodically.
IV. PACKET FORWARDING POLICY
In this section, we present the second part of our solution that
consists of the data dissemination model of TEDD, whose goal
is to discover the best energy-efficient routes.
A. Proposed Improvements
TEDD extends the principles of TBF by incorporating the
usage of the energy map. The proposed protocol defines a re-
ceiver-based data dissemination policy, i.e., each node upon re-
ceiving a packet decides itself whether to relay it or not, as op-
posed to TBF that is a sender-based data dissemination policy.
In TEDD, the decision to forward a packet or not is based on
the node geographical location and the packet information. The
forwarding decision process uses a temporization policy: before
relaying a packet, the current node waits a small time interval.
After this time, if no neighbor has relayed the packet, the node
transmits it. The key idea of this technique is how to estimate
the delay time, based only on the distance between the current
node and a point ahead on the curve called reference point.
Using the temporization policy, TEDD overcomes the draw-
backs of TBF. First, TEDD avoids both the necessity of neighbor
tables and beacon transmissions, and consequently, spends less
energy in the forwarding process. In TBF, the neighbor table is
fundamental to the process of choosing the next hop of the tra-
jectory. Second, TEDD is more robust than TBF because nodes
are not selected by the previous elected node. TEDD is a re-
ceiver-based protocol and, thus, avoids situations where the for-
warding process is interrupted because the selected node is un-
2310 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 23, NO. 12, DECEMBER 2005
Fig. 6. Reference points.
available. Another important advantage of TEDD is the possi-
bility of disseminating data only to a target area, as shown in
Fig. 5. In this case, the protocol avoids nodes not interested in
this data (outside the target area).
B. Temporization Policy and Forwarding Modes
The proposed temporization policy is based on the distance
from the current node to a point ahead on the curve, called ref-
erence point. In particular, the reference point is the point (not
necessarily a node) closest to the curve localized at the circum-
ference with center at the current node and radius equals to the
node communication radius (Fig. 6). In each relay, the selection
of the reference point is determined by the previous hop of the
trajectory. Each node that receives a packet adjusts its delay time
using its distance to the reference point sent in the packet.
Based on this policy, TEDD defines two different forwarding
modes: one data flow and two data flows. In the first one, the
data is disseminated using only one flow, in a way that only one
node decides to forward the packet. In the second forwarding
mode, two flows are used to disseminate the data, in a way that
two nodes end up forwarding the packet. As illustrated in Fig. 6,
when one flow is used, the node closer to reference point B
relays the packet; and when two flows are used, the node closer
to reference point A and the one closer to reference point C relay
the packet. As an example, in Fig. 5(a)–(d), TEDD uses one flow
in the delivery curve and two flows in the target area.
The choice between one or two flows depends on the goal
of the dissemination. In data dissemination protocols, there is a
tradeoff between minimizing the number of transmissions and
maximizing the coverage. In situations in which the first goal
is the most important requirement, only one data flow should
be used. On the other hand, when maximizing the coverage is
the main goal, two data flows should be used. Its important to
point out that independently of the number of data dissemina-
tion flows, the previous trajectory node always selects only one
reference point. In the two flows forwarding mode, nodes that
receive a packet calculate the two reference points also using the
reference point present in the packet.
C. Basic Operation
Algorithm 2 presents the basic operation of TEDD. When a
node receives a packet, it verifies whether it is inside the re-
ceived network sector. If it is not, it drops the packet. If it is
inside the network sector, the node verifies if its distance to the
reference point (calculated by the previous hop) is higher than
the communication range. When the forwarding mode with two
flows is used, the node calculates the two new reference points
and its distance to both points, and then selects the closest point
as the reference point. If the calculated distance is higher than
the communication range, the node drops the packet. If it is not,
the node waits a delay time that is calculated according to its
distance to the reference point. The smaller the distance, the
smaller the delay. After the node waits the delay time, it verifies
whether any of its neighbors retransmitted the packet. If this is
the case, the node drops the packet. Otherwise, the node selects
the reference point and, then, forwards the packet. The process
of selecting the reference point is presented in the next section.
Algorithm 2: TEDD—Receiving packet
input: the received packet
if I am inside the received network
sector then
Calculate my distance to the
reference point
if this distance vale is less or
equal to the communication range then
Calculate the delay time
Wait the delay time
if any of my neighbors retransmitted
this packet then
Drop the packet
else
Calculate the reference point
(Algorithm 3)
Forward the packet
endif
else
Drop the packet
endif
else
Drop the packet
endif
D. Selecting the Reference Point
The process of selecting a reference point is described in Al-
gorithm 3 and it is determined by the previous hop in the tra-
jectory. Before forwarding the packet, the node calculates two
special points on the curve, and , where
and . In this
case, and are, respectively, the
-coordinate and the communication range of the node that is
selecting the reference point. Similarly, the same holds for the
values of and . If the data dissemination is from left to
right, , otherwise, if it is
from right to left, . In the
second step, the algorithm defines the straight line
DO VAL MACHADO et al.: DATA DISSEMINATION IN AUTONOMIC WIRELESS SENSOR NETWORKS 2311
TABLE I
DEFAULT VALUES USED IN SIMULATIONS
Fig. 7. Possible reference points.
that passes through and . This equation is used by the
proposed algorithm instead of the curve equation because the
algorithm has to calculate the distance from a curve to a point.
The evaluation of the curve/point distance is not trivial, mainly
considering the limited resources of sensor nodes. On the other
side, the distance from a straight line to a point is easier to be
calculated. This heuristic presents good results, since the gen-
erated curves do not present a great variation inside the node
communication radius. In the third step, the node determines
the quadrant where the reference point is localized. The quad-
rant of a reference point is an important concept because it re-
duces the amount of possible reference points to examine. The
communication radius has four quadrants, the first one is lo-
cated at the northeast and the others are, respectively, located
at the northwest, the southwest, and the southeast. Moreover,
this concept is the same one used in the plain geometry for the
Cartesian plan, except that the source is the current node. The
quadrant of a reference point is obtained using Algorithm 4 and
it is detailed below. In the last step, the node selects the refer-
ence point among some points located in the selected quadrant.
In Fig. 7, the points of each quadrant are, respectively: (N, NNE,
NE, ENE, E); (N, NNW, NW, ENW, W); (W, WSW, SW, SSW,
S); and (E, ESE, SE, SSE, S).
Algorithm 3: TEDD—Selecting the reference
point
input: the received packet
Calculate points and where
and
Calculate the straight line that
passes through points and
Call the procedure to discover the quad-
rant of the reference point (Algorithm
4)
Select the reference point using the quad-
rant and the straight line .
Return the reference point
The process of discovering the quadrant of the reference
point is described in Algorithm 4 and it is the third step of
Algorithm 3. Using quadrants, TEDD reduces the amount
of possible reference points, and consequently, the cost of
selecting this point. As illustrated in Fig. 7, the use of quadrants
reduces this value from sixteen to five. The proposed process
considers two scenarios: 1) the curve intercepts the “commu-
nication circle”and 2) it does not intercept. In the former, the
desired quadrant is the one that contains the reference point; in
the latter, the desired quadrant is the one nearest to the curve.
To identify whether the curve intercepts the communication
radius, TEDD uses the following procedure. The node verifies
if the point is inside its communication radius. If it is, the
selected quadrant is the one where is inside. In this case,
TEDD knows that the circumference is intercepted by the line
and is inside the quadrant of the reference
point. On the other hand, if is outside the communication
circle, TEDD evaluates the inclination of the straight line
, and the values of (the -coordinate of point
) and (the -coordinate of the node that is selecting
the reference point). TEDD selects the first quadrant when
and . The other quadrants are, respectively,
selected when: and (second); and
(third); and and (fourth).
Algorithm 4: TEDD—Discover the quadrant of
the reference point
input points and , and straight
line ;
if is localized inside my
communication circle then
I select the quadrant where point
is localized.
else
if then
if then
I select the first quadrant.
else
I select the third quadrant.
2312 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 23, NO. 12, DECEMBER 2005
Fig. 8. Energy map and network coverage evolutions for one instance of each protocol evaluated (TBF, TEDD with one flow forwarding mode and flooding)in
a broadcast scenario. (
T
=
time,
C
=
coverage,
E
=
mean energy). (a) TBF,
T
=0
s:
C
= 100%
,
E
= 100%
. (b) TBF,
T
= 500
s:
C
=39%
,
E
=48%
.
(c) TBF,
T
= 1000
s:
C
=0%
,
E
=1
:
3%
. (d) TEDDc(1F),
T
=0
s:
C
= 100%
,
E
= 100%
. (e) TEDD(1F),
T
= 500
s:
C
= 51%
,
E
= 60%
.
(f) TEDD(1F),
T
= 1000
s:
C
= 46%
,
E
= 23%
. (g) Flooding,
T
=0
s:
C
= 100%
,
E
= 100%
. (h) Flooding,
T
= 500
s:
C
= 79%
,
E
= 38%
.
(i) Flooding,
T
= 1000
s:
C
=0%
,
E
=0%
.
end if
else
if then
I select the second quadrant.
else
I select the fourth quadrant.
end if
end if
end if
E. Some Remarks
The goal of TEDD is to reduce the number of transmitted
packets so that only nodes closer to the reference point relay
packets. Moreover, TEDD maintains a good network coverage
since nodes closer to the reference point are exactly those that
reach the highest number of yet unreached nodes. Using this al-
gorithm, we are able to reach our goals and, thus, increase the
received/transmitted ratio, which is an important metric to in-
dicate the quality of a data dissemination technique. One draw-
back of this approach is a higher latency to deliver data. This
DO VAL MACHADO et al.: DATA DISSEMINATION IN AUTONOMIC WIRELESS SENSOR NETWORKS 2313
Fig. 9. Performance parameters (TEDD, TBF, and flooding) in a broadcast scenario. (a) Percentage of reached nodes. (b) Number of transmitted packets. (c) Mean
energy. (d) Percentage of dead nodes. (e) Received/transmitted ratio. (f) Latency.
and other metrics are evaluated using simulations, as discussed
in the next section.
V. S IMULATION RESULTS
In this section, we show the behavior of TEDD in two sce-
narios of data dissemination in a WSN. In the first one, the
monitoring node disseminates data to the entire network. In
the second one, it disseminates data to a target area located at
the right top corner of the sensing field, which has an initial
low-energy area in the center of it. The remainder of this section
is organized as follows. Section V-A presents the simulation pa-
rameters. Sections V-B and V-C show our protocol performance
in both data dissemination scenarios.
2314 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 23, NO. 12, DECEMBER 2005
TABLE II
AVERAGE NUMBER OF OPERATIONS,T
RANSMISSIONS,AND NETWORK COVERAGE AT EACH DAT A
DISSEMINATION FOR TEDD, TBF, AND GOSSIPING IN A MULTICAST SCENARIO
A. Scenarios
In this section, we present the scenarios used throughout the
simulations. We consider a dynamic topology, where nodes are
static but periodically go into a sleeping mode to save energy,
which leads frequently to topology changes. In [4], Hill et al.
state that a WSN should embrace the philosophy of getting the
work done as quick as possible and going to sleep. The best
way to save energy is to turn off parts of the sensor that are not
needed, as modeled by a state-based energy dissipation model
(SEDM) presented in [13]. In order to analyze the performance
of TEDD, we use the ns-2 simulator [16].
We consider a WSN with static and homogeneous nodes
with a finite amount of energy. Nodes are deployed randomly,
forming a high-density flat topology. It is assumed that each
node knows its own location and the monitoring node knows
the coordinates of all nodes. One monitoring node with no
energy, memory or processor restrictions is placed at the
bottom left corner of the network and performs a series of data
disseminations. In each data dissemination, a new set of curves
is recalculated based on the current energy map that is obtained
using a prediction-based approach [12]. The cost of obtaining
this map is not considered in the results since it is expected
to be distributed among different network activities. The cost
of generating the curves is also not considered since they are
generated in the monitoring node. The numerical values chosen
for the simulations can be seen in Table I.
B. Dissemination to the Entire Network
In this section, a scenario where the monitoring node dissemi-
nates information to the entire network is studied. The behavior
of TEDD is analyzed using both forwarding modes: one and
two flows, presented in Section IV. Its performance is compared
to the TBF and to the flooding-based dissemination schemes.
Both TBF and TEDD use the same trajectory generation proce-
dure, described in Section III. The maximum coverage criterion
was used to select the best set of curves. In Fig. 8, we show the
network energy map evolution during the network lifetime. To-
gether with the energy available at each node, the network cov-
erage is shown. White squares represent nodes that receive the
disseminated packets and the black ones indicate nodes that do
not receive packets at that particular moment. Since the max-
imum number of network sectors was set to five, this was the
number of network sectors selected to maximize the network
coverage.7
7In this work, the term network coverage is used to designate the number of
nodes that receive the disseminated data.
When we compare a flooding-based dissemination scheme
to TEDD, we can see that its energy consumption is signifi-
cantly higher [Fig. 8(d)–(i)]. Although flooding starts with a
better network coverage, after approximately 750 s of simu-
lation, the average node energy becomes insufficient to guar-
antee network connectivity. As result, network coverage drops
to zero and no more packets are transmitted. This behavior is il-
lustrated in more detail in Fig. 9(a)–(d), which show the number
of reached nodes, the number of transmitted packets, the av-
erage node energy, and the number of dead nodes, respectively.
The number of transmitted packets by flooding remains constant
after 800 s, since no packets can be transmitted in a disconnected
network. Moreover, in Fig. 9(a), we observe that flooding covers
only about 80% of the network. It happens because of the dy-
namic topology, i.e., nodes periodically go into sleeping mode
to save energy.
Comparing the energy consumption of TBF and TEDD
(both forwarding modes: one and two flows) [Fig. 8(a)–(f) and
Fig. 9(c)], the cost of neighbor table maintenance becomes
evident. In average, TEDD consumes 22% less energy than
TBF. In this scenario, after approximately 600 s, if TBF is
used, nodes located near the monitoring node begin to die.
After 950 s, TBF is not able to perform broadcasts anymore,
since the monitoring node becomes disconnected from the
network. When TEDD is used, however, more than 98% (one
flow) and 82% (two flows) of nodes remain alive, with more
than 21% (one flow) and 16% (two flows) of their initial energy
[Fig. 9(c)–(d)] and more than 45% of network coverage, even
after 1000 s of simulation [Fig. 9(a)]. The number of trans-
mitted packets by the TBF does not remain constant after 950 s
in Fig. 9(b), since beacon packets continue to be transmitted
even in a disconnected network.
When one flow and two flow modes of TEDD are compared,
we observe that one flow minimizes the number of transmis-
sions and two flows maximize the coverage. It can be seen that,
when two flows are used, TEDD achieves a 10% greater network
coverage [Fig. 9(a)]. On the other side, when one flow mode is
used, TEDD sends twice less packets and spends 9% less energy
[Fig. 9(b) and 9(c)].
In Fig. 9(c), the received/transmitted ratio of all four ap-
proaches is shown. It can be seen that TEDD using one flow
mode achieves the best result, followed by TEDD using two
flows. Flooding-based dissemination presents a ratio equal to
one, which was already expected, since every packet that is
received by a node is forwarded with probability one. TBF
presents the worst received/transmitted ratio, due to the over-
head of beacon transmission.
DO VAL MACHADO et al.: DATA DISSEMINATION IN AUTONOMIC WIRELESS SENSOR NETWORKS 2315
Fig. 10. Energy map and network coverage evolutions for one instance of each protocol evaluated (TBF, TEDD, and flooding) in a multicast scenario. (
T
=
time,
Ct
=
coverage inside the target area,
Elea
=
mean energy inside the low-energy area, and
En
=
mean energy in the entire network). (a) TEDD,
T
=0
s:
Ct
=78%
,
Elea
= 44%
,
En
=93%
. (b) TEDD,
T
= 500
s:
Ct
=46%
,
Elea
= 11%
,
En
= 58%
. (c) TEDD,
T
= 1000
s:
Ct
= 36%
,
Elea
=0%
,
En
= 27%
. (d) TBF,
T
=0
s:
Ct
= 70%
,
Elea
= 44%
,
En
= 93%
. (e) TBF,
T
= 500
s:
Ct
= 30%
,
Elea
=0%
,
En
= 43%
. (f) TBF,
T
= 1000
s:
Ct
=2%
,
Elea
=0%
,
En
=2%
. (g) Gossiping dynamic,
T
=0
s:
Ct
= 89%
,
Elea
= 44%
,
En
= 93%
. (h) Gossiping dynamic,
T
= 500
s:
Ct
= 34%
,
Elea
=6%
,
En
= 53%
. (i) Gossiping dynamic,
T
= 1000
s:
Ct
= 23%
,
Elea
=0%
,
En
= 21%
.
In Fig. 9(f), the latency for TEDD, TBF and flooding are an-
alyzed. The latency is calculated as the time elapsed for each
packet sent by the monitoring node until it reaches nodes lo-
cated at different distances from the monitoring node. It can be
seen that TEDD presents a significantly greater latency than the
other approaches. This is due to its timing mechanism, which
established delays for nodes to forward packets. The delays, as
described in Section IV, are used in order to guarantee that only
the nearest nodes to the reference points of each dissemination
curve forward the packets. This might be the main drawback of
TEDD, what means that it has to be adapted for environments
where latency is crucial.
Table II compares the number of operations and radio
transmissions performed by TEDD, TBF and flooding. It can
be seen that TEDD (one flow) covers less nodes than TEDD
(two flows), however, TEDD (one flow) performs significantly
less computational operations and transmits twice less packets
than the others. Comparing TEDD (both flows) and TBF,
the former performs more arithmetic operations, although
it realizes less comparisons and assignments, transmits less
2316 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 23, NO. 12, DECEMBER 2005
Fig. 11. Performance parameters (TEDD, TBF, and gossiping) in a multicast scenario. (a) Percentage of reached nodes inside the target area. (b) Numberof
packets transmitted in the entire network. (c) Received (target area)/transmitted (entire network) ratio. (d) Number of packets transmitted inside the low-energy
area. (e) Mean energy in the entire network. (f) Mean energy inside the low-energy area.
packets and covers more nodes. Comparing TEDD (both
flows) and flooding, the protocol proposed in this work per-
forms more arithmetic operations and assignments, and covers
less nodes, however, it transmits less packets and performs less
comparisons. This happens because when a node transmits a
packet, each one of its neighbors has to process the packet
(e.g., in flooding, each neighbor evaluates whether it has
already received the packet). The flooding protocol transmits
significantly more packets than TEDD, and performs more
comparisons. Finally, considering that the cost of a radio
transmission is higher than the cost of a processor operation,
TEDD makes an excellent tradeoff.
DO VAL MACHADO et al.: DATA DISSEMINATION IN AUTONOMIC WIRELESS SENSOR NETWORKS 2317
TABLE III
AVERAGE NUMBER OF OPERATIONS,T
RANSMISSIONS AND NETWORK COVERAGE AT EACH
DATA DISSEMINATION FOR TEDD, TBF, AND GOSSIPING IN A MULTICAST SCENARIO
It can be concluded that by avoiding packet transmission by
nodes with little energy and establishing trajectories that avoid
low-energy nodes, TEDD prolongs the lifetime of these nodes,
still guaranteeing that they receive the data disseminated by
the monitoring node. When compared with a flooding-based
dissemination approach, it can be seen that, despite providing a
better network coverage at first, flooding-based scheme imposes
extremely high costs in terms of energy consumption. This
fact compromises, first, the low-energy nodes and, eventually,
the entire network. When compared to the TBF forwarding
technique, it is important to point out that TEDD is a protocol
that does not use neighbor tables, spends much less energy
and presents a more adaptive behavior in a dynamic topology
scenario.
C. Dissemination to the Target Area
In this section, we analyze a scenario that contains an ini-
tial low-energy area and the monitoring node disseminates in-
formation to a target area. The low-energy area is located at the
middle of the network, and the target area is located at the upper
right corner. In this section, we consider three main goals for
the data dissemination, all of them having the same relevance:
to have the best coverage inside the target area; to transmit the
smallest amount of packets in the entire network; and to pro-
long the lifetime of the nodes located at the low-energy area.
The performance of TEDD is compared with both TBF and gos-
siping,8aflooding-based dissemination scheme with probability
[2]. Both TBF and TEDD use the same trajectory generation
procedure, described in Section III. Outside the target area, a
delivery curve connecting the monitoring node to the dissemi-
nation target area is generated. The one flow mode is used by
TEDD to forward packets along the delivery curve. Inside the
target area, the maximum coverage criterion was used to gen-
erate the dissemination curves, and two flows are used to for-
ward packets. In Fig. 10(a)–(i), we show the network energy
map and the network coverage evolutions during the network
lifetime. White squares represent nodes that receive the dissem-
inated packets and the black ones indicate nodes that do not re-
ceive any packet at that particular moment. Moreover, we ob-
serve in Fig. 10(a)–(i) the low-energy area and the target area.
Fig. 11(a) illustrates the network coverage inside the target
area. We observe that TEDD reaches more nodes, followed
by gossiping. TEDD reaches approximately 1.3 times more
8The gossiping protocol works as follows. If a node is outside the target area,
the node relays packets with probability of 0.4, otherwise, when a node is inside,
it always relays the packets. In this case, the probability is one and the gossiping
is equal to flooding.
nodes than gossiping and 1.5 times more nodes than TBF.
In Fig. 11(b), the number of transmitted packets in the entire
network is shown. In this case, due to the cost of neighbor
table maintenance, TBF sends more packets than the other two
approaches. Moreover, gossiping sends 2.6 times more packets
than TEDD. Fig. 11(c) shows the ratio between the number of
nodes covered inside the target area and the number of packets
transmitted in the entire network area. Even though the ratio
achieved by TEDD is approximately 1.4, it is significantly
above the ratios achieved by the other two approaches. This
apparently poor result is due to the fact that a long path has to
be traveled before the packets reach the dissemination target
area. In Fig. 11(d), we verify that nodes located inside the
low-energy area are not used in the data communication when
TEDD is used. Gossiping sends 7.7 times more packets than
TEDD, and the TBF sends 53 times more packets than TEDD.
The traffic is not completely excluded inside the low-energy
area because this region has an intersection with the target area.
A comparison between the energy consumption by the proto-
cols in the entire network and in the low-energy area is shown
in Fig. 11(e) and (f), respectively. In both cases, TEDD presents
the least consumption and TBF, the greatest. The first result oc-
curs because TEDD has a better selection mechanism of nodes
that relay data packets, and the second result is a consequence
of the TBF neighbor table maintenance cost. As depicted in
Fig. 11(f), TEDD was able to extend the lifetime of nodes in-
side the low-energy area.
Table III compares the number of operations and radio trans-
missions performed by TEDD, TBF, and gossiping. Comparing
TEDD and TBF, TEDD performs more arithmetic operations,
although it realizes less comparisons and assignments, transmits
less packets and covers more nodes. Comparing TEDD and gos-
siping, our protocol performs more arithmetic operations and
assignments, however, it realizes less comparisons, transmits
less packets, and covers more nodes. TEDD makes an excellent
tradeoff between radio transmissions and amount of processing
to be done.
VI. CONCLUSION AND FUTURE WORK
In this paper, we proposed TEDD, a new data dissemination
scheme for autonomic WSNs. The key idea is to combine con-
cepts presented in TBF with the information provided by the en-
ergy map. We proposed a method for specifying the curves dy-
namically based on the energy map. We also presented a scheme
for dealing with data dissemination to a specific target area. In
the original TBF, nodes use a forwarding technique based on
neighbors table that consumes more energy. TEDD replaces this
2318 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 23, NO. 12, DECEMBER 2005
mechanism with a new forwarding technique: when a node re-
ceives a packet, it decides whether it should forward the packet
based solely on its own location and the equation embedded in
the packet. All these features, when put together, present an au-
tonomic solution to data dissemination in a WSN.
The simulations showed that when TEDD is used, the routing
process becomes more adaptive to topology changes. Moreover,
the energy spent with the routing activity can be concentrated on
those nodes that have high-energy reserves, whereas low-energy
nodes can be left to use their energy only to perform the sensing
activity or to receive information addressed to them, showing in
this way the autonomic characteristics of our solution.
There are several improvements that we are planning to in-
troduce. One aspect to be explored is the way of interpreting the
network. Currently, we are representing the network as a set of
sensors, whose coordinates are used as input to the curve fitting
procedure. Another interesting manner of performing the map-
ping is by viewing the network as a set of geographic points,
whose energy reserves are calculated as an interpolation of the
energy of those sensor nodes that cover each point. Another fu-
ture work is to introduce other techniques to avoid transmissions
inside the low-energy region. For example, we can use a energy
threshold to allow nodes that have less energy than a certain pre-
defined amount to not forward data.
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1982.
Max do Val Machado received the B.S. degree
in computer science from the Pontifical Catholic
University of Minas Gerais, Belo Horizonte, Brazil,
in 2002, and the M.S. degree in computer sci-
ence from the Federal University of Minas Gerais
(UFMG), Belo Horizonte, Brazil, in 2005. Currently,
he is working towards the Ph.D. degree in computer
science at UFMG.
His research interests are routing and cross-layer
design in wireless sensor networks and mobile ad hoc
networks.
Olga Goussevskaia received the M.S. degree in com-
puter science from the Federal University of Minas
Gerais (UFMG), Belo Horizonte, Brazil, in 2005.
Her current research interests are primarily in
routing and control protocols for wireless sensor
networks. Her other interests include optimization
models and algorithms for mobile telecommunica-
tion systems.
Raquel A. F. Mini received the B.Sc., M.Sc., and
Ph.D. degrees in computer science from Federal
University of Minas Gerais (UFMG), Belo Hori-
zonte, Brazil.
Currently, she is an Associate Professor of Com-
puter Science at the Pontifical Catholic University of
Minas Gerais, Belo Horizonte, Brazil. Her main re-
search areas are sensor networks, distributed algo-
rithms, and mobile computing.
Cristiano G. Rezende received the B.Sc. degree
in computer science from the Federal University of
Minas Gerais, Belo Horizonte, Brazil.
His research areas area sensor networks and mo-
bile computing.
Antonio A. F. Loureiro received the B.Sc. and
M.Sc. degrees in computer science from the Fed-
eral University of Minas Gerais (UFMG), Belo
Horizonte, Brazil, and the Ph.D. degree in computer
science from the University of British Columbia,
Vancouver, BC, Canada.
Currently, he is an Associate Professor of Com-
puter Science at UFMG. His main research areas
are wireless sensor networks, mobile computing,
distributed algorithms, and network management.
DO VAL MACHADO et al.: DATA DISSEMINATION IN AUTONOMIC WIRELESS SENSOR NETWORKS 2319
Geraldo Robson Mateus received the M.S. and
Ph.D. degrees in computer science from the Federal
University of Rio de Janeiro, Rio de Janeiro, Brazil,
in 1980 and 1986, respectively.
He is a Full Professor of Computer Science at the
Federal University of Minas Gerais, Belo Horizonte,
Brazil. He spent 1991 and 1992 at the University
of Ottawa, Ottawa, ON, Canada, as a Visiting
Researcher. He has published over 100 scientific
papers and is a leader of several national and interna-
tional projects. His research interests span network
optimization, combinatorial optimization, algorithms, and telecommunications.
Dr. Mateus is a member of the Institute for Operations Research and the
Management Sciences (INFORMS), the International Federation of Operational
Research Societies (IFORS), and the Society for Industrial and Applied Math-
ematics (SIAM).
JoséMarcos S. Nogueira (M’94) received the B.S.
degree in electrical engineering and the M.S. degree
in computer science from the Federal University of
Minas Gerais (UFMG), Belo Horizonte, Brazil, in
1979, and the Ph.D. degree in electrical engineering
from the University of Campinas, Campinas, Brazil,
in 1985.
He is an Associate Professor of Computer Science
at UFMG. He held a Postdoctoral position at the Uni-
versity of British Columbia, Vancouver, BC, Canada
(1988–1989), and currently is on a sabbatical year at
the universities of Evry and Paris VI/LIP 6. He headed the Department of Com-
puter Science, UFMG from 1998 to 2000. Currently, he heads the Computer
Network Group, UFMG. He has supervised a number of Ph.D. and Master’s
students, He was the Technical Coordinator of the System for the Integration
of Supervision (SIS) Project, where a complex and distributed system for the
management of telecommunications networks was developed. He has served in
various roles, including General Chair (1985) and TPC Chair (1999 and 2004) of
the Brazilian Symposium on Computer Networks (SBRC), and General Chair of
LANOMS 2001. He publishes regularly in international conferences and jour-
nals. His areas of interest and research include computer networks, wireless
sensor networks, telecommunications and network management, and software
development.
Dr. Nogueira has been a TPC member in IEEE/IFIP NOMS (2000, 2002, and
2004), IEEE/IFIP IM 2003, IEEE LANOMS (1999, 2001, 2003, and 2005),
IEEE/IFIP MMNS (2000, 2001, 2002, and 2003), IPOM 2002, SBRC (from
1990 to 2005), and IEEE/IFIP DSOM (2003, 2004, and 2005). He is a member of
the Brazilian Computer Society (SBC) and Brazilian Telecommunications So-
ciety (SBrT). He also participates in the IEEE ComSoc CNOM Interest Group.
Currently, he is a Secretary of the IEEE ComSoc TCII.
