An Efficient Event Handling Protocol for Wireless
Sensor and Actor Networks
Charalampos Konstantopoulos
Grammati Pantziou
Department of Informatics
University of Piraeus, Greece
Email:konstant@unipi.gr
Department of Informatics
Technological Educational Institution of Athens, Greece
Email:pantziou@teiath.gr
Ioannis E. Venetis
Damianos Gavalas
Department of Informatics
Technological Educational Institution of Athens, Greece
Email:ivenetis@teiath.gr
Department of Cultural Technology and Communication
University of the Aegean, Greece
Email:dgavalas@aegean.gr
Abstract—A critical issue dealt with in Wireless Sensor and
Actor Networks (WSANs) is the real time response of actors
to events occurring in the network area. The fast notification
of actors from Sensor Nodes (SNs) about the events as well as
the effective coordination of actors for prompt event handling is
most important in these networks. In this paper, we introduce
a distributed protocol for effective event handling in a WSAN.
Existing approaches mainly favoring actors near the current
event for handling it, may lead to highly ineffective solutions for
certain worst-case scenarios. Through a randomized approach,
our solution also selects distant actors for handling events, and
guarantees fast average responsiveness to events and a balanced
energy distribution among actors. In addition, it features efficient
distributed algorithms for sensor to actor and actor to actor
coordination which are of independent interest.
I.
I NTRODUCTION
WSANs is an important enhancement over the classical
wireless sensor networks (WSNs) featuring a number of active
elements (actors) that can directly act on events sensed by
the SNs of the network. The most common situation arising
in WSANs deployments, is the occurrence of critical events
demanding quick response from actors. Thus, SNs triggered
by events should be able to send prompt notification to one
or more actors; thereafter the actors should quickly decide on
the actor that will handle the current event. The most common
practice is for nearby actors to handle the current event. Such
a greedy reaction may be the most natural way of handling
events but in some scenarios, this approach may not be the
optimal one in terms of the total distance traveled by the
actors. The total travel distance of actors is critical for their
energy supply since the energy consumption due to physical
movement is typically much higher than the energy consumed
for communication with other actors or SNs. Also, this metric
divided by the number of events occurred in a time interval
is proportional to the average delay for handling these events,
given a constant speed for actor movement.
This research has been co-financed by the European Union (European Social
Fund ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework
(NSRF) Research Funding Program: Archimedes III. Investing in knowledge
society through the European Social Fund.
In this paper, we differentiate from existing approaches [1]
where actors close to the current event are mainly selected for
handling it, and we propose a solution where distant actors
might be selected. This approach handles worst-case scenarios
where the greedy approach of always selecting an actor close to
the current event, may lead to excessive actor movement and
hence, to solutions with high energy consumption and high
event handling delay. A useful algorithmic paradigm for our
problem is the on-line k-server problem [2]. This problem
considers a metric space (e.g. euclidian) and the existence of
k servers which can move and handle requests occurring in
this space. After each request, a server is selected to relocate
and serve the request. The on-line algorithm for the k-server
problem should make decisions without knowing any of the
future requests. The main objective is the minimization of
the total distance traveled by servers handling a sequence of
requests. Clearly, there is a direct analogy between the k-server
problem and that of the on-line event handling in a WSAN.
We present a distributed solution for effective event handling in a WSAN which employs a randomized approach for
selecting actors to handle events. Specifically, our approach
proposes an efficient implementation of the harmonic algorithm (HA) [2] on WSANs. Note that the HA is an inherently
centralized approach for solving the k-server problem, and
not easily amenable to distributed implementation. Although,
methods for emulating centralized on-line algorithms in a
distributed setting exist (e.g. [3]), they are mainly of theoretical interest and their implementation on WSANs incurs
high energy cost. Our solution guarantees fast event handling
on average, and fairly distributes the workload among actors
resulting in balanced energy consumption across actors. The
experimental results confirm the above advantages of our approach over other approaches in the literature. In addition, our
protocol features efficient distributed algorithms for actor to
actor coordination which are of independent interest. Although,
the actor coordination problem in WSANs has already been
studied in the literature, most previous works provide a rather
high level description of coordination protocols without many
details about the distributed implementation on WSANs [4],
[5]. In this work, we analytically describe the actor coordination protocol, highlighting the inherent difficulties that such
an implementation commonly has. Specifically, our techniques
are based on geometric characteristics of the event area for
achieving effective arbitration among actors. In addition, due
to the real-time constraints of event handling in WSANs, we
mainly focus on the fast implementation of the proposed protocols. Since faithfully implementing the harmonic algorithm
may incur high message (energy) cost and long execution delay
or may not be feasible in some cases (e.g. disconnected actor
network), we opted for fast protocols which, in some cases,
may deviate from the typical harmonic algorithm. Fortunately,
these situations are not very likely to appear as explained
later, and when they do occur, the degradation in the protocol
efficacy is acceptable.
The rest of the paper is organized as follows. In Section II,
relevant work to our research is presented. In Section III,
the proposed distributed protocol is described. In Section IV
experimental results for the performance of the protocol are
presented, while Section V concludes our work.
II.
R ELATED W ORK
The most critical issue in WSANs is the rapid response of
actors to events occuring in the network area with minimum
energy consumption on SNs and actors [1]. First, SNs sense
the event and then should send their reports to an actor
(sensor-actor commumication) with low delay and energy cost.
However, the actor receiving event reports may not be the best
actor to act for this event; thus, the actors should cooperate
for deciding which actor will eventually handle the on-going
event (actor-actor coordination). These two steps are the main
focus of all works on WSANs [4]. In [6], a scheme for
sensor-actor and actor-actor communication is presented. The
actors are stationary and SNs in an event area are partitioned
in different groups, each reporting to a different actor. The
actor-actor coordination problem is formulated as a Mixed
Integer Non-Linear Program and a localized auction protocol
runs for deciding the handling actors for the current event.
The same authors in [7] also present a scheme with mobile
actors. Each SN predicts the current position of the closest
actor using Kalman filtering. The actor coordination problem
is again formulated as a Mixed Integer Non-Linear Program. In
addition, the algorithm may postpone some low priority events
in favour of a new high-priority event.
The authors in [8] study the assignment of actors to events
such that the overall distance traveled by actors for handling
events is minimized. The solution should also guarantee response time under a certain threshold and that each event is
handled by a sufficient number of actors. In [9], SNs sensing
an event are clustered around the node first detecting the
event. Each of these nodes forms an event map from the
reports received from their cluster members and then send the
map to their closest actor. Then, actors positioned nearby the
event decide on which of the actors will eventually handle the
event. The authors in [10] propose a cluster-based approach
for sensor-actor communication. Each cluster head gathers the
reports from its members and then, based on these data, it
selects the handling actors for the on-going event so that the
action range of the actors involved has minimal overlap.
In [11], a dominating set is found in the sensor network
such that SNs are at most k hops away from at least one
Fig. 1.
Failure of greedy technique for handling events.
dominator and any two dominators are at least 1-hop away of
each other. Then, actors are positioned at the locations of these
dominators. In [12], SNs are organized in clusters and a distributed protocol based on the Galey Shapley (GS) algorithm
from stable matching theory is proposed for matching actors to
cluster heads with minimum message overhead and minimum
actor relocation. In [13], a sensor-actor communication scheme
with reliable event-reporting is proposed. The event reports
from SNs are sent to actors within a certain time bound
and with different priorities according to the importance of
each event. In [14], an auction based coordination protocol
is proposed. The actor first notified for an event broadcasts
a request for bids from nearby actors and the latter return
their bids to that actor. The actor incurring the lowest cost
for handling the event is selected for event handling. In [15],
a framework for sensor-actor and actor-actor coordination
is proposed where a number of deadlines are imposed for
completing the different phases of event handling. Each SN
sensing an event decides on the actor being notified based on
the remaining energy, the distance from the SN and the load
of nearby actors. Similar criteria are considered for deciding
the actors which will handle the on-going events. In [5], a
localization service is used for a SN sensing an event to find its
nearest actor and then the actually nearest actor to the current
event is found by a localized auction protocol from [14].
A common feature of the works above is that they act
myopically, considering only the current events. However, low
cost handling of the current event may subsequently lead to
high handling cost for all future events. Usually, an event
triggers additional events in the local area and a solution which
gradually gathers more actors in the eventful area will surely
pay off finally. So, in our solution, besides the actors near
the current event, distant actors may also be selected with
comparatively lower probability. This less greedy approach
achieves lower event handling delay on average, as well
as lower energy consumption due to actor movements. A
main contribution of this work is the implementation of the
inherently centralized HA on a WSAN with low energy and
communication cost. Also, as mentioned in the introduction ,
we also give all the necessary details of this implementation.
III.
T HE D ISTRIBUTED E VENT H ANDLING P ROTOCOL
Let N (S, A) be a WSAN with a set of sensors S and a set
of actors A (|A| = k). Each time an event occurs, one of the
k actors should be selected for handling the event. Then, the
selected actor moves toward the location of the event, handles
the event and remains at the new position. The objective is
to minimize the total distance traveled by all actors over a
sequence of events. The important constraint in the problem is
that the future events are not known in advance, and therefore,
the actor selection is made without such knowledge.
Note that the greedy approach where the actor closest to
the current event is always selected, is not necessarily optimal.
In some cases, selecting a more distant actor is more efficient
in terms of the total distance travelled by actors in a series of
events. For instance, in Fig. 1 there are two actors and events
are happening alternatively at points X and Y . Then a greedy
solution continuously moves actor 1 while actor 2 remains still
at point Z. A non-greedy algorithm would bring the actor 1 to
point X and actor 2 to Y . So, no actor movement would be
needed for handling the events happening at points X and Y .
Now, we present a distributed algorithm for event handling
which employs the HA for selecting the actors which will handle events. The HA is a well-known approach for solving the
k-server problem [2]. Upon event occurrence, this algorithm
selects a server for handling the event with a certain probability. Specifically, assume that di is the distance of the i-th server
(i = 1 . . . n) from the on-going event. ThenP
the probability of
n
the i-th server being selected is pi = d1α / j=1 d1α where α
i
j
is a small positive constant (α ≥ 1). Clearly, the servers close
to the event are favored against servers located away from it.
In this way, the average travel distance of servers is relatively
small. Indeed,
average travel distance for handling an event
Pthe
n
is equal
Pn to1 i=1 pi di . For α = 1, this sum is written as
n/ j=1 dα , that is the harmonic mean of actor distances.
j
Now, it is well known fact that the harmonic mean strongly
tends to the lowest of di . This is more so in the case when
α > 1. At the same time, servers located far from the event
may still be selected even with a small probability; hence, the
algorithm is not trapped in scenarios similar to that of Fig. 1.
Note that in the original description of HA, α = 1. However, in
a practical setting, by properly setting the value of a, we can
leverage the greedy behaviour of the algorithm since higher
values of a increases the probability of selecting an actor near
the event.
The implicit assumption in the HA is that there is a central
entity being constantly aware of the server locations and able
to instantly know the exact location of the on-going event.
Based on this information it decides which server will handle
the event. In WSANs, such an assumption is not applicable
because it would entail high communication cost. Therefore,
the proposed distributed implementation should cope with the
absence of the above central entity and should also provide
for inaccuracies in actor and event locations as well as for the
inherent delay incurred for actor location updates.
A high level description of the proposed distributed event
handling protocol is given in Fig. 2. Note that at the initialization phase of the protocol, each SN learns its nearest actor by
having actors broadcast hello messages and SNs choose the
actor being minimum number of hops away. Assuming dense
sensor networks, the actor with minimum hop distance from a
SN is also the geometrically closest to that node. In the sequel,
the basic steps are described in detail.
Step A: Event notification of actors. Each SN around
an event notifies its nearest actor possibly through multihop
communication. Thus, actors are notified with low delay and
energy cost. Essentially, the SN-actor communication is an
anycast where SNs just need to contact any of the nearby
actors. Should there be a concern about network congestion
or reduced reliability, our shortest path based communication
A. Each SN sensing an event sends a report to its nearest actor
B. The actors notified about the event decide which of them will
execute the HA
C. The selected actor runs the HA and then notifies the actor that
should handle the event
D. The actor which receives the notification:
i. broadcasts a ‘leave’ message to all SNs nearest to that actor.
Each of these SNs finds the nearest of the remaining actors in
the local area and thus all future event reports from these SNs
are sent to the newly selected actors.
ii. moves to the location of the on-going event.
iii. updates all actors by broadcasting its new position.
iv. asks all neighboring actors at its new position to send it their
local cache with actor locations and then updates its own local
cache
v. sends a ‘hello’ message to all SNs around its new position so
that SNs nearest to it will send their future event reports toward
the newly arrived actor.
vi. handles the on-going event and then stays at its new position
until it is selected again for handling a new event.
Fig. 2.
The Distributed Event Handling Algorithm
E
D
D
E
C
A
B
F
H
K
G
C
I
A
B
(a) Single HA invocation.
Fig. 3.
(b) Multiple HA invocations.
Different actors notified about an event.
between SNs and actors could be easily replaced with other
anycast protocols (e.g. those in [7], [13]) which consider these
issues.
Step B: Actor selection for the HA execution.
In large-scale events, the SNs inside the event area may
notify more than one nearby actors. Next, if all these actors
run the HA, each of them may assign a different actor for
event handling. Apart from the likely inconsistencies in actor
locations held by each actor, the different decisions are mainly
due to the probabilistic nature of the algorithm. In that case,
more than one actors will relocate to handle the on-going
event. Thus, a coordination procedure among these actors is
necessary to ensure that only one of the them will run the
HA. The coordination protocol is based on the geometric
information that each actor acquires from the SNs that notified
it about the event. For instance, Fig. 3(a) depicts an event
spread over the grey area, with five actors located within this
area. The illustrated cells of the Voronoi diagram partition all
SNs in the grey area according to their nearest actor. Note that
each actor can approximately compose the event area inside
its Voronoi cell from the location information received from
its SNs.
Now, an easily implemented criterion for choosing the actor
that will run the HA, is to select the actor whose area includes
the southmost point of the event area. In a straightforward
approach, each actor should exchange its southmost point
(local minimum) with all actors located within the event
area for finding the actor with the southmost point. This
is essentially a leader election protocol with relatively high
message (energy) cost and possibly long execution delay. Note
also that when the ad-hoc network of actors is disconnected
in the event area, the selection of more than one actors for
running the HA is unavoidable regardless of the leader election
protocol in use.
However, in most cases we can avoid the execution of a
“heavy” leader election protocol if we can afford the selection
of more than one actors for running the HA with small
probability. Specifically, if the event area is convex, each actor
needs to exchange its local minimum only with its neighboring
actors (being in the transmission range of each other) since
for convex areas, a local minimum point in the area is also a
global minimum point for the whole area. Even in the case of
a non convex event area, the above technique may as well be
successful. Since the actors are typically far less than the SNs,
the area supervised by each actor is relatively large. As a result,
ignoring local concave segments, the whole event area may
again be viewed as convex on the large scale. For instance, the
event area of Fig. 3(a) is not convex. Nevertheless, in order to
find the southmost point, the actors should only compare their
local minimum against those of their neighboring actors since
the small ‘gulfs’ existing in the area do not affect convexity
on a large scale.
Now, Fig. 3(b) depicts a scenario where there may be
multiple invocations of HA. Here, we assume that actors A and
G cannot communicate. As a result, actor G falsely assume
that holds the southmost point and thus the HA will run on
actor G in addition to actor A. However, due to relatively
large transmission range of actors, most actors in the local
event area can communicate directly with one another and
thus the case of two actors not able to communicate directly
is rather uncommon. Also, large-scale irregularities as those
in Fig. 3(b) mainly arise when the event has spread over
a large area. However, the SNs continuously monitor their
area and the actors are notified in time before the event is
spread over a large area. Thus, long segments in the event area
are not common. The following lemma precisely provides the
necessary conditions for multiple HA invocations:
Lemma 1: Let Ai , i = 1, . . . , m, be the actors notified
about the current event, pi be the southmost point of the area
supervised by Ai , i = 1, . . . , m, and p be the southmost of all
points pi , i = 1, . . . , m. Consider the digraph G(P, E) where
P = {pi : i = 1, . . . , m} and (pi , pj ) ∈ E if Ai and Aj can
directly communicate, and pi is south of pj . Then the number
of HA invocations equals the number of components created
by a Depth First Search (DFS) traversal of G rooted at p.
Proof: The fact that DFS from p cannot reach all nodes of
G implies that the G is disconnected. Each time DFS restarts,
we select again the southmost of all remaining points of the
graph and run the DFS from this point. Each of these points
from which DFS restarts dominates over all other points in
its connected component. Also, these points are not connected
with one another in G. As a result, the corresponding actors
falsely assume that have the southmost point of the entire event
area. Therefore, each of these actors will run the HA.
Note that we can easily reduce the probability of multiple
HA invocations, by having each actor exchange its southmost
point with all actors within k hops of it instead of exchanging
only with its immediate neighbors. Apparently, by increasing
k, the above probability is reducing and for large value of k
with finally get the general leader election protocol discussed
above.
Step C: Notification of the event handling actor. For convenience, assume that HA runs on a single actor, say, actor A.
The HA will decide the handling actor, say, actor B, and then
A should send a notification to actor B about this selection.
For routing the notification from A to B, a geographical
routing protocol [16] is employed over the ad-hoc network
of actors. In this protocol, at each step, the next node to send
the message is the neighbor closest to the final destination. We
have adopted the basic greedy approach without the relatively
complex fallback technique of perimeter routing in the case of
network voids. Thus, when the greedy approach gets stuck at
a void, the last node holding the notification packet takes over
the event handling from the actor B. Note that the positions
of B and its substitute are expected to be close together. This
is because the node A running the HA is close to the event,
the HA mostly favours the actor located near the event and
the substitute of B is met on the way to B. So, due to the
relatively nearby positions of these two actors, this change in
the handling actor only marginally deviates from the “typical”
execution of the HA.
Finally, if we consider the ad hoc network N of the actors
where an arc between two actors exists when both are within
transmission range of each other, then we can easily see that
for deciding the handling actor, the actor A needs to consider
only the actors belonging to the same connected component of
N as A. Also, as discussed in the following step, each actor
is at least aware of the locations of all actors in the same
connected component.
Step D: Event handling and actor location update. Apparently, the successful implementation of the HA depends
on the accuracy of actor positions stored in the actor running
the algorithm. Since the only change in the network is the
movement of a single actor toward the current event, the
network of actors can be considered relatively stationary.
Furthermore due to the relatively small number of actors in
a WSAN, a simple broadcasting protocol is employed for
updating the actors of the network with the new location
of the handling actor. Specifically, before moving to the
event location, the handling actor checks if it is going to be
disconnected from its current connected component by the
completion of its relocation1. In that case, it broadcasts its
new location before leaving its current position so that all
actors in its former component are informed about its new
position. After its movement, the handling actor broadcasts its
new location and asks from all neighboring actors at its new
location to send it their local cache of other actors’ positions.
1 Provided that the transmission range of all actors is the same and if no
obstacles prevent the communication of nearby actors, this can be easily done
based on the locations of actors and checking if the new actor location is out
of transmission range of all actors in the current connected component.
5000
0,20
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Percentage"of"square"sides"(15%"to"30%)"and
events"per"square"(2%"to"10%)
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1600
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1200
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400
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Fig. 4.
Stdandard"D
Deviation"of"total"distance"
travelled
d"by"one"actor"(Me
eters)
10000
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Average"distance"ttravelled
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by"actors"(Metters)
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Percentage"of"square"sides"(15%"to"30%)"and
events"per"square"(2%"to"10%)
0,30
0,15 Greedy
0,15"Greedy
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0,15"K
Server
0,20 Greedy
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Average distance travelled by each actor to serve events.
After receiving that information, the handling actor keeps the
most recent information in its local cache. Obviously, when
the handling actor is still connected to its original connected
component, the first broadcast and the cache update protocol
are not actually necessary and only the second broadcast needs
to be performed. Thus, the above update scheme not only
ensures that the new location of the event-handling actor is
made known to the maximum possible number of actors, but
the handling actor is made aware of the most recent locations
of other actors in the network too.
Besides the actors, SNs should also be notified about the
relocation of the event-handling actor. Thus, when this actor
leaves an area (step D.i), it broadcasts a leave message to all
SNs in its surrounding area. Each SN receiving this message
first checks if the departing actor is the nearest one. In that
case, it discards all local information kept for that actor and
then relays the message to all its neighbors except the one
it received the message from. When a SN receives a leave
message not originated from its nearest actor, this SN discards
the received message and replies back to the message sender
with the ID of its nearest actor and the number of hops for
reaching that actor. Each time, an “orphan” SN receives such a
reply, it checks whether the new route to an actor is the shortest
so far. In that case, it updates its local information and then
forwards the newly updated data to its neighbors except the one
it received the reply from. Thus, the SNs formerly closest to
the departing actor are now associated with the nearest among
the remaining actors in the local area. When the event-handling
actor arrives at the event area, it brodcasts a ‘hello’ message
to the SNs of the area (step D.v). Each recipient SN updates
its local information if the newly arrived actor is the nearest
one in the area. Each SN which associates with the new actor,
it then sends the updated information to its neighbors.
Finally, the newly arrived actor at the event area handles
the on-going event. However, due to possible multiple HA
invocations, more than one actors may arrive at that area. Each
of these actors sends beacons to nearby actors at the event
location and the actor with the smallest ID handles the event
eventually. Thus, apart from the unnecessary actor movement,
the selection of more than one actors for event handling does
not affect the efficacy and the correctness of the protocol.
IV.
E XPERIMENTAL R ESULTS
In order to evaluate the performance of our algorithm we
have implemented it in the Castalia simulator [17]. We also
implemented the greedy algorithm, where every event is served
by the actor nearest to the event. As has been mentioned,
Fig. 5.
events.
Standard deviation of the distance travelled by each actor to serve
the involvement only of actors near the event is typical of
other works on WSANs. The field where events occur has
been defined to be a square 500m × 500m. Inside the square,
500 sensors have been randomly deployed. The number of
actors used to serve the events has been set to 20. At the
beginning of each simulation run, the actors are positioned on
a 5 × 4 grid. A total number of 5000 events were simulated,
with events occuring at a 500 second time interval. After a
number of experiments, we also set the parameter α of HA
to 3, as HA achieves the best overall performance for this
particular value. Also, at step B (Fig. 2) we implemented the
simple scheme where each actor contact only its neighbors
for finding out whether it holds the southmost point of the
event area. We conducted a number of experiments where the
employed scenarios follow a certain pattern. Specifically, we
consider that the sequence of events is partitioned into 1/p
subsequences of 5000 · p events each, where p is a percentage
value varying in the range of 2% to 10%, in steps of 2%.
The events of a specific subsequence all take place randomly
within a smaller square region of the deployment area and
these squares are different for each subsequence. The position
of each square is also selected randomly. This kind of scenarios
is common since when an initial event occurs in a region a lot
of other related events will likely occur at nearby locations.
We have focused on both the energy required by SNs
and actors for communication as well as on the mechanical
energy required for moving actors to handle events. The first
is measured directly by Castalia while mechanical energy
consumption is inferred from the travelling distance covered
by the actors. Fig. 4 presents the average distance travelled
by each actor to serve the events. Besides being an indicator
of the mechanical energy spent by each actor, the average
distance, if multiplied by the factor (number of actors/number
of events), also indicates the average time required to serve an
event, assuming that actors move with a constant speed. Note
also that the delay due to actor movement is much higher than
the delay due to network communication. Figure 4 shows that
our algorithm performs better when considering smaller square
areas. For larger square sides, our algorithm is worse for small
numbers of events in each square, but outperforms again the
greedy algorithm for more events. This behavior is expected.
As more events happen in a specific area, the cost of moving
more actors initially further away into that area pays off
eventually. These actors now have to travel shorter distances
to handle future events. Next, we study the standard deviation
of the distance travelled by each actor (Fig. 5). This metric
shows how balanced the load of actors for serving events is.
Th
housands
Average"Consumed"Energyy"(Joules)
16
the current event for event handling. Thus, in practice, our
protocol guarantees low handling delay and fair distribution
of actor workload, resulting in balanced energy consumption
across actors. As a future work, we will study a different actor
location update protocol where only actors near the trajectory
of the moving actor will receive location updates. That change
will not much affect the overall performance of our approach.
14
12
10
8
6
4
2
0
Time"(Seconds)
Greedy
KどServer
Standard"Deviation"of"Consumed"Energy
Fig. 6. Average consumed energy per sensor. The side of the event square
is 20% of the field side. The number of events per square is 10% of the total
number of events.
0,012
0,010
0 008
0,008
0,006
0,004
0,002
0,000
Time"(Seconds)
Greedy
KどServer
Fig. 7. Standard Deviation of average consumed energy per sensor. Same
parameters as in Fig. 6.
Our algorithm clearly outperforms the greedy algorithm in all
cases. This is due to the random nature of our algorithm, which
might select an actor to serve an event, even if that actor is
further away from the event location. However, this brings
one more actor to the neighbourhood where events happen
and improves the behaviour of our algorithm when serving the
subsequent events. Note also that the graphs for our algorithm
are smoother compared to the ones of the greedy algorithm.
This is due to the tendency of the greedy algorithm to leave
many actors inactive. That depends on the initial position of the
actors and the coordinates of the first events in each square;
an actor may move to a specific area to serve an event and
the next events may be closer to that actor. In such case, this
actor will serve more events, covering a much larger distance
compared to other actors. Once again the random nature of
our algorithm prevents most of these cases.
Regarding the energy consumption of SNs due to communication, the algorithms have similar performance (Fig. 6),
since both algorithms use the same method for sensor-actor
communication, for determining which actor will run the
selection algorithm, for routing the notification packet to the
selected actor and for actor location update. Fig. 7 presents the
standard deviation of the average consumed energy per SN.
Clearly, both algorithms have comparable performance and
follow the same trend. Summing up, our algorithm requires
much less energy than the greedy approach, when considering
both the communication and the mechanical energy.
V.
C ONCLUSIONS
AND
F UTURE W ORK
We presented a protocol for event handling in WSANs. By
randomization, the protocol efficiently handles worst cases in
the spatial distribution of future events. In contrast, most previous works act greedily and consider only local actors around
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