Building Programming Abstractions for Wireless Sensor Networks Using Watershed Segmentation

Building Programming Abstractions for Wireless
Sensor Networks Using Watershed Segmentation
Mohammad Hammoudeh1 and Tariq A. A. Alsbou'i1
Manchester Metropolitan University, Manchester, UK,
m.hammoudeh@mmu.ac.uk

Abstract The availability and quality of information extracted from
Wireless Sensor Networks (WSNs) revolutionised a wide range of appli-
cation areas. The success of any WSN application is, nonetheless, deter-
mined by the ability to retrieve information with the required level of
accuracy, within specied time constraints, and with minimum resource
utilisation. This paper presents a new approach to localised information
extraction that utilises the Watershed segmentation algorithm to dyna-
mically group nodes into segments, which can be used as programming
abstractions upon which dierent query operations can be performed.
Watershed results in a set of well delimited areas, such that the number
of necessary operations (communication and computation) to answer a
query are minimised. This paper presents a fully asynchronous Water-
shed implementation, where nodes can compute their local data in paral-
lel and independently from one another. The preliminary experimental
results demonstrate that the proposed approach is able to signicantly
reduce the query processing cost and time without involving any loss of
eciency.

1 Introduction
Wireless Sensor Networks (WSNs) are currently being employed in a variety of
applications ranging from home to industry, and from health to military. These
applications have a number of elements in common: (1) The request for informa-
tion; (2) The answer to this request is usually present in a set of unstructured
data streams; (3) WSNs generate large amount of data that is `imperfect' in na-
ture and contains considerable redundancy. Resource constraints on nodes in the
network coupled with the characteristics of returned data means that applica-
tions have to be developed with the primary design goal of minimising resource
utilisation. Distributed information extraction has been advocated to solve this
kind of problems. Information extraction is the sub-discipline of articial intel-
ligence that selectively structures, lters, and merges data generated by one or
more sensor nodes. It adds meaning to unstructured raw data; therefore, the
data become structured or quasi-structured making it more suitable for infor-
mation processing tasks. This denition is concise and covers exactly in what
sense the term information extraction will be used throughout this paper.

This paper presents an in-network information extraction system that nds
and links relevant information while ignoring irrelevant and extraneous infor-
mation. The nal output of the extraction process varies based on user queries;
however, it commonly involves the extraction of fragments of information from
various nodes within the same segment and linking of these fragments into a
coherent answer. We propose the utilisation of Watershed segmentation algo-
rithm [20] that result in a set of well delimited segments of homogenous sensed
regions based on nodes location and their corresponding sensor readings. Wa-
tershed algorithm is suitable for dynamically changing environments because it
uses no thresholding, instead the best option is chosen at each decision stage. We
also propose a parallel asynchronous Watershed algorithm implementation that
complies with sensor node constraints, i.e. real-time computing, and low power
consumption. This in-network information extraction does not return the entire
collected data, but it extracts sense data units from one or more network seg-
ments, typically simple or multi-modal information of spatio-temporal nature.
The goal of node segmentation is to provide a high-level programming model for
sensor networks that abstracts away the details of individual sensor nodes. In
this paper we examine query-based systems that utilise in-network processing
for query response. However, the produced abstractions can be utilised in by
other information extraction systems, i.e. event based and time-driven.

Query-based information extraction is a request-response interaction between
the sensor nodes and end-user or application component. The end user issues
a query in an appropriate language, and then the query is disseminated to the
network to retrieve the desired data from the sensors based on the description in
the query. Most query-based systems provide a high level interface to the sensor
network while hiding the network topology as well as radio communication. The
end user does not need to know how the data is collected or processed.

User controlled, query-based, information extraction is usually applied in
situations where it is known in advance what type of semantic information is to
be extracted from the network. For example, it might be necessary to identify
what type of events are happening in a certain part of the monitored environment
and at what time these events took place. Depending on the information needs,
dierent queries can be constructed to dierentiate various types of events at
dierent levels of semantic granularity. In some applications, for instance, it
will be adequate to specify that a part of a query is a temporal expression,
while in others it might be necessary to dierentiate between dierent temporal
classes, for example between expressions indicating past, present and future. In
other applications, not only the semantic nature of the target information is
predened, but also the unit and scope of the event to be extracted. The unit of
extraction refers to the granularity of individual information portions that are
lifted out of the sensor node. The scope of extraction refers to the granularity of
the extraction space for every individual information request. In order to decide
which portion of information is supposed to contribute in the answer of a query,
an information extraction application uses a set of extraction conditions. These

conditions state what formal properties a particular portion of information must
possess to belong to a particular semantic class.
The Watershed transformation is a popular image segmentation algorithm
for grey scale images [20,12,7,3,2,5,17,14,19,21]. Its basic concept comes from the
eld of topography, referring to the partitioning of a landscape to a number of
basins or water catchment areas. The authors in [12] use the following analogy to
explain how the Watershed algorithm works: The USA can be divided into two
main segments, one associated with the Atlantic Ocean and another associated
with the Pacic Ocean. All the rain falling on the east segment will ow into
the Atlantic Ocean, while the rain falling on the west segment will ow into the
other ocean. The water will reach the ocean given that it is not trapped in a local
minimum along the way. Both segments are usually named catchment basins,
and each one has an associated minimum (the ocean). The boundary line that
separates both basins is called the watershed line, corresponding to the conti-
nental divide in the example. Therefore, the image is viewed as a topographic
surface where each pixel is a point situated at some altitude as a function of its
grey level. The grey levels correspond to the altitude associated to the image
between 0 and 255.
After the original Watershed algorithm was published, several modications
and variations of this algorithm was published to suit various applications. Wa-
tershed algorithms can be classied into two conceptually distinct techniques:
immersion and raining.
1. Immersion simulates progressively immersing the entire topographic surface
in a water container.
2. Raining simulates the rain fall over a topographic surface. The raining can
be considered as a local method because each droplet follows on its own way
not considering neighbouring droplets.
On at areas of the surface, the motion of the water droplet is directed towards
the nearest brim of a downward slope and it stops when it reaches a regional
minimum. An important aspect of designing a parallel asynchronous algorithm
is the exploitation of the data locality for minimisation of the communication
overhead. Aiming at the goal, we propose here a reformulation of the raining
watershed segmentation due to its suitability for parallel implementation. The
presented implementation is capable of computing the Watershed transform ac-
cording to local conditions. The approach proposed in this paper generates the
same result using only one point of synchronisation, thus decreasing the running
time without degradation in the eectiveness of the segmentation. Both asser-
tions are demonstrated throughout this paper and supported by experimental
results.
The paper is organised as follows: Section 2 describes the Watershed algo-
rithm. Section 3 illustrates the suitability of this algorithm for WSNs. Section 5
presents the parallel asynchronous implementation of the Watershed algorithm.
The performance of the proposed implementation is evaluated in Section 6. Sec-
tion 7 concludes the work.

2 Description of the Watershed Algorithm
This section presents Meyer's formalism [9] that is based on local conditions
(rain simulation). The Watershed proposed by Meyer is the base for our pa-
rallel asynchronous implementation. Roedink et al. [15] summarised approaches
for parallel implementations of the Watershed algorithm on powerful (memory
and computation) devices. Before our synchronous and parallel implementation
details are given, some preliminary denitions are introduced as described by
Meyer [9].
Let f (p) be a function of of grey levels, signifying a continous digital image
with the domain Ω ∈ Z2 . Every pixel p ∈ Ω has a grey level f (p) and a set of
neighbouring pixels p ∈ N (p) with a distance function d(p, p ) to each neighbour;
in most published algorithms a 4− to 8−neighbours.
(Regional minimum) Is a point or a set of connected points with the same
grey level, where none of them have a neighbour with a lower grey level [3]. In
keeping with the rain simulation analogy, a regional minimum is the area of the
surface where the rain water would get trapped without owing to lower leveles.
(Lower slope) If p exists, the lower slope (LS)denes the maximum steepness
from a pixel to its lower neighbours.

(LS) = max f (p)−f (p )
∀p ∈N (p) |f (p )≤f (p)
dist(p,p )

(Steepest decending path) ∀p ∈ Ω, SDP (p) is the set of points p ∈ N (p)
dened as follows:
f (p) − f (p )
p ∈ N (p) = LS (p) , f (p ) f (p)
dist (p, p )
i.e. the SDP is a series of connected points where each point presents a grey level
strictly lower than the previous one. There may exist multiple decending paths
from a given point, the choice between them depends on the implementation. A
descending path is said to be a steepest path if each point in the path is connected
to the neighbour with the lowest grey level. According to rain simulation analogy,
the SDP is the path a drop of water would follow when travelling down to a
regional minimum.
(Cost function based on lower slope) The cost, cost (pi−1 , pi ), for walking on the
topographic surface from point pi−1 to pi ∈ N (pi−1 ) is:

LS (pi−1 ) .dist (pi−1 , pi )
 f (pi−1 ) f (pi )
LS (pi ) .dist (pp−1 , pi ) f (pi−1 ) f (pi )
1
2 (LS (pi−1 ) + LS (pi )) .dist(pi−1 , pi ) f (pi−1 ) = f (pi )


(Topographic distance) The topographic distance between two points p and q on
a surface is the minimal π−topographical distance among all paths π between p
and q on the suraface:
π
T Df (p, q) = inf T Df (p, q)

where T Df (p, q) = n
i=2 cost(pi−1 , pi ) is the topograpical distance of a path π =
(p = p1 , p2, ..., pn = q), such that ∀i , pi ∈ N (pi−1 ) and pi ∈ Ω
(Catchment basin based on topographic distance) CBT D (mi ) of a local mini-
mum mi is the set of points p ∈ Ω where the topographical distance is closer
to mi than to any other regional minimum mj , based on the topographical dis-
tance and the grey level of the minima:

CBT D (mi ) = {p |f (mi ) + T Df (p, mi ) + T Df (p, mj ) ∀j = i }
In other words, the CB is formed by a regional minimum and all the points
whose steepest decsending path ends in that minimum [2]. According to the rain
simulation analogy, a CB is an area of a topographic surface, such that when a
droplet of rain fall in any point in that area, it would poure in to its minimum
following the steepest descending path of that point.

3 Modied Watershed Segmentation
1- Neighbourhood denition using Shepard method In most published
Watershed algorithm variations, a 4− to 8−pixel neighbours are used. Because
the distance from a node to its neighbours can vary, we advocate the use Shepard
method [16] to select nearby nodes. Shepard dened two criteria:
(a) Arbitrary distance criterion: All data points within radius r of the
point p are included in computation.
(b) Arbitrary number criterion: Only the closest n data points are conside-
red in the computation of any interpolated value.
Shepard has chosen a mix of the two criteria which combined their advan-
tages. An initial radius r is dened depending on the overall density of data
points such that seven data points are included on average in a circle of radius r.
r is written as follows:

7A
πr2 =
N
were A is the area of the largest polygon enclosed by the data points. The
suitability of this method for WSN is presented in [6].

2- Inclusion of the number of hops in the calculation of the steepest
path In Section 2, the SDP (Denition 3) was dened as a series of connected
points where each point presents a grey level strictly lower than the previous
one. There may exist multiple descending paths from a given point, the choice
between them depends on the Watershed algorithm implementation. For WSNs
applications, the most energy ecient descending path should be chosen. Accor-
ding to [13], communication is the most power hungry operation. Communication
cost can be computed from transmission distance, hop count, delay, link quality,
and other factors. Hop count is widely used factor to measure energy require-
ment of a routing task and for grouping nodes in energy ecient clusters. The

power consumed in data transmission is directly proportional to the square of
the transmission distance between the sending and receiving nodes. The power
consumption does not only depend on the transmission distance, but also on the
scale of the network [18]. Therefore, the lower distances must be computed as a
function of the number of hops as well as the inter-sensor Euclidean distances.
The new distance function is the sum of the individual inter-node Euclidean
distances multiplied by the hop count.
dist(pi , pj )
Dist(p, p ) =
hc
where i, j hc − 1 and hc is the hope count.

4 Abstractions for Local Interactions
In this section, we give a brief description of how Watershed segmentation al-
gorithm can be used for dening abstractions for local interactions and place
our work in the context of other existing work in the area. To demonstrate the
usefulness of the proposed abstractions we consider events that are caused by
multiple elements targets, e.g. a herd of animals in a habitat monitoring ap-
plication or toxic gas diusion. Elements triggering these events often exhibit
idiosyncratic behaviour, such as splitting into several groups or merging into a
single group. This type of events involves several neighbouring sensor nodes to
collaborate to acquire aggregate features of the target such as shape, location,
coverage, moving direction, speed, etc. Events involving this kind of targets are
submitted by [8] as `region-like' targets. In this paper, Watershed segmentation
is proposed to build a cooperative node structure over every network segment
covered by the events to collect and aggregate related information.
Watershed groups nodes sharing some common group state into segments.
A segment is described as a collection of spatially distributed nodes with an
example being the set of nodes in a geographic area with sensor readings in a
specic range. Therefore, Watershed segmentation combines the advantages of
data-centric (e.g. [4] and topologically (e.g. [22]) dened group abstractions. This
makes the generated segments capable of expressing a number of local behaviours
powerfully, which allows network programmers to write programs that express
higher level behaviour beyond that of the usual query-based methods. In fact,
the programmer is concerned not only with the application logic, but also with
identifying the network portions to be involved in extracting a particular piece
of information and how to reach them. Dealing with this new requirement neces-
sitates new programming abstractions to localise complexity without scarifying
eciency. The logical segments of nodes generated by the Watershed algorithm
replace the tight physical neighbourhood provided by wireless broadcast with
a higher level, application dened abstractions. Segments are created such that
the span of a logical neighbourhood is specied dynamically and declaratively
based on the attributes of nodes, along with requirements about communication
costs (specically the diameter of the segment). A network segment formed with

a logical notion of proximity determined by applicative information is, therefore,
capable to return specied information with high condence.
Segments generated by Watershed algorithm are automatically labelled with
a marker, which is an integer identier. All nodes within the segment satisfy
the logical constraints encoded in the Watershed algorithm. This logical and
intuitive Watershed template serves as the membership function that dynami-
cally determines and updates which nodes belong to the segment. Programmers
manipulate segments instead of nodes within communication range. The pro-
grammers can still reason in terms of nodes and broadcast messages, but now
they can specify declaratively which portions of the network to consider and
therefore control the span of communication to save energy.
Macroprogramming [22] has been put forward in the literature as an e-
cient approach to information extraction that provides a more general-purpose
approach to distributed computation. Many macroprogramming approaches aim
at programming the network as a whole rather than programming the individual
nodes that compose the network. Global behaviour can be specied, program-
med and then translated to node level code transparently from low level details
like network topology, radio communication or power capacity. A signicance
class of macroprogramming systems are the application-dened, in-network abs-
tractions that are used in data processing. The Regiment [22] and Hood systems
are examples of neighbourhood-based abstractions that handle many nodes col-
lectively and a set of operations on it to enable the programmer to extract
information about the state of the group. EnviroTrack [1] is a programming abs-
traction specically for target-tracking applications, where a group is dened
as the set of sensors that detected the same event. In SPIDEY [10], a node is
represented as a logical node that has multiple exported attributes (static and
dynamic). However, utilising the network topology as an abstraction can require
some rigidity in the programming model. It can also be inecient for systems
with mobile nodes due to the cost and complexity of maintaining the mapping
between the physical topology and the logical topology. The parallel Watershed-
based abstractions are dierent from these approaches in one important aspect;
segmentation runtime loosely synchronises state across nodes, attaining grater
robustness and higher eciency.

5 Parallel Asynchronous Watershed Implementation
Each node, (x, y, z), is depicted as a pixel on the image and the colour of the
pixel located at (x, y) is obtained as the node sensed modality (z). The reso-
lution of the image is the total number of nodes in the network per area unit.
In parallel operation, rain falling simulation Watershed uses less communica-
tion between nodes, compared to immersion which has a highly global nature.
The new segmentation algorithm can be viewed as an asynchronous relaxation
of the `Hill Climbing' algorithm [9]. All processing is local to each node, which
runs a simple nite state machine associated with a single sensed modality.
Non-blocking communications allow each node to run independently. Since syn-

chronisation is limited to N (u) and non-blocking communications are used, each
node operates independently and the algorithm needs no global scheduling.
The role of the Watershed process is to label each non-minimum node by wal-
king downward on a steepest slope path towards the minimum. Initially, all nodes
in the network are considered as non-minima and will be ooded from dierent
sub-domains. At this stage of the segmentation process, nodes are assigned tem-
porary labels because the segmentation results depends on neighbouring nodes
readings. When a node detects a steepest neighbour, it changes it status to a
non-minimum node and gets labelled from the steepest neighbour or from its
predecessor that has the shortest distance among its neighbouring nodes. When
the minimum is reached, its label is assigned to all the nodes upward along the
path. If no non-minimum were detected, the steepest distances must be calcu-
lated based on the entire set of lower borders. Process termination is locally
detected on each node. This reduces the amount of communication and the idle
time in nodes.
The algorithm builds several paths with dierent origins and destinations
from data communications between nodes. This does not introduce additional
cost due to the broadcast nature of wireless communications. Typically, any
changes in one node readings may aect all nodes on the steepest slope line
between that node and the minimum. Therefore, nodes must keep monitoring
and updating their membership. A ag, called reset, is maintained by each node
to record whether changes occurred or not since the last communication. One
advantage of this implementation is that no relabeling and no synchronisation
between nodes are needed frequently.
One disadvantage of this parallel asynchronous implementation is that the
middle nodes on plateaus cannot locally decide whether they are on a minimum
or non-minimum plateau, (N P ), and necessitate global synchronisation points
to identify and label the minimum plateaus. To avoid global synchronisation, all
middle nodes are labelled as minimum or Plateau, (M P ), to allow them makes
local decisions. After that, the propagation of data over a non-minimum plateau
allows the middle nodes of that plateau ooded by a neighbour to switch to
non-minimum.
Algorithm 1 presents the pseudocode of the segmentation process in each
node.

6 Evaluation
Suppose that a WSN has been deployed to monitor temperature of environmen-
tally sensitive areas. An event of interest is predened if temperature readings
with enough numbers go above a certain threshold in a specic geographic area.
In our simulation, an event is triggered at random times in random locations
followed by issuing a query to locate the hottest spots. Query resolution is im-
plemented using three methods: Watershed-based, nodes are logically grouped
into segments that are used to assist query processing; in-network processing
via aggregation of messages up a spanning tree of the network; and centralised,

Algorithm 1 The segmentation process of nodes in each state.
Input node current state of node (u) S(u); f (v) states of neighbours
Output segment membership
case (S (u) = initial)
The node u broadcasts its data d (u) and label l (u) to its neighbours N (u),
waits for data from each neigbhour v ∈ N (u), compute the following:
N (u) all neighbouring readings equal to (u)
=

N (u) all neighbouring readings greater than (u)
N (u) = vi a singleton set such that LSmax (vi ) and vi ∈ N (u)
Ln

if Ln
N (u) =
l (u) ← minv∈N (u)= (l (v)); S (u) ← M P
else
S (u) ← N P
case (S (u) = MP )
The node u waits to receive new data, d (v), from any neighbour
if d (v) d(u)
ln
N (u) ← v; S (u) ← N P
else
l (u) ← min (l (u) , l (v)); S (u) ← M P
case (S (u) = NP )
The node (u)waits for data from N (u)Ln ; S (u) ← N P
In all states, the node (u) sends its data f (u) to all nodes in N (u)≥ whenever
a new reading is recorded

(a) (b) (c)

Figure 1. (a) Original thermal map (b) Watershed segmentation results (c) Extend
segmentation

(a) (b)

Figure 2. Communication overhead and the number of nodes involved in resolving the
query

were all data is sent directly to the sink for analysis. All simulations were carried
out using Dingo [11], which is a scalable python-based package to allow rapid
prototyping of WSNs algorithms. In all experiments, we make use of a thermal
map, Figure 1(a), adopted from goinfrared.com. Figure 1(b) shows the result
of segmentation in which nodes in each segment collaborate to solve a query.
It is easy to extend the segmentation to the whole monitored terrain by using
generalised Voronoi, i.e., each location where there is no sensor node is assigned
to its nearest segment, Figure 1(c).
The cost of the query resolution was measured in terms of the number of
messages exchanged to answer the query. Multiple runs with dierent topologies
and dierent number of nodes were carried out for the three query resolution
methods. These results are presented in Figure 2(a). The energy cost, response
time, and accuracy are aected by which and how many nodes are involved
in answering a query. Excluding nodes irrelevant to a certain query not only
improve answer accuracy but also saves energy and reduce the time required for
data analysis. Figure 2(b) shows the number of nodes involved in resolving the
same query at dierent network densities.
The results obtained in the above experiments indicate that segments can be
used to support in-network query resolution. The results shows that the commu-
nication overhead associated with segment-based query processing is almost 2
folds less than in-network processing and 10 folds less than the centralised query
resolution. These results are clearly explained by the analysis presented in Fi-
gure 2.

7 Conclusion
The preliminary work in this paper indicates that segment-based in-network
query processing produces considerable energy savings over aggregation and
centralised approaches. Watershed logically organises nodes into energy ecient
segments that reduce unecessary data transmissions and improve response ac-
curacy. Compared to standard Watershed segmentation algorithms, the major

improvement of our algorithm is that labelling and climbing along the steepest
paths are concurrently and locally executed based on the node state, during the
entire segmentation process. There are a number of limitations in the work so far
that need to be addressed in the future, for example the cost of segmentation.

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