An Adaptive Data Dissemination Strategy for Wireless Sensor Networks.

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International Journal of Distributed Sensor Networks, 3: 23–40, 2007
Copyright © Taylor & Francis Group, LLC
ISSN: 1550-1329 print/1550-1477 online
DOI: 10.1080/15501320601067725
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UDSN1550-13291550-1477International Journal of Distributed Sensor Networks, Vol. 3, No. 1, November 2006: pp. 1–36International Journal of Distributed Sensor Networks
An Adaptive Data Dissemination Strategy for
Wireless Sensor Networks*
Adaptive Data Dissemination Strategy for WSNS.S. Selvakennedy and S. Sinnappan
S. SELVAKENNEDY
School of Information Technologies, J12 University of Sydney,
NSW, Australia
S. SINNAPPAN
School of Economics and Information Systems, University of Wollongong,
Wollongong, NSW, Australia
Future large-scale sensor networks may comprise thousands of wirelessly connected sensor nodes
that could provide an unimaginable opportunity to interact with physical phenomena in real time.
However, the nodes are typically highly resource-constrained. Since the communication task is a
significant power consumer, various attempts have been made to introduce energy-awareness at
different levels within the communication stack. Clustering is one such attempt to control energy
dissipation for sensor data dissemination in a multihop fashion. The Time-Controlled Clustering
Algorithm (TCCA) is proposed to realize a network-wide energy reduction. A realistic energy dissi-
pation model is derived probabilistically to quantify the sensor network's energy consumption using
the proposed clustering algorithm. A discrete-event simulator is developed to verify the mathemati-
cal model and to further investigate TCCA in other scenarios. The simulator is also extended to
include the rest of the communication stack to allow a comprehensive evaluation of the proposed
algorithm.
Keywords
Data Dissemination; Clustering; Routing; Energy-Efficiency; Lifetime; Simulation
1. Introduction
Even though physical sensing capability is a rather old technology, there is a renewed
interest in it due to the recent technological advances in micro-electromechanical systems
and the integration of wireless communication capability. The realization of wireless sen-
sor networks (WSNs) has opened a whole set of new application domains, which were
not possible with the traditional wired “dumb” sensors. New application areas such as
forest fire detection, sophisticated structural monitoring, earthquake monitoring, and bat-
tlefield surveillance are beginning to benefit from this technology [1]. As many of these
applications require non-intrusive participation of sensor nodes, they have to be small in
size while remaining functional for a rather long period, possibly a couple of years. As
these nodes are equipped with severely limited battery sources, this poses significant
challenges in the design of network and communication stack to realize an energy efficient
solution.
*An earlier version of this paper appeared in HWISE-2005, co-located with ICPADS 2005.
Address correspondence to S. Selvakennedy, School of Information Technologies, J12 Madsen Bldg. F09,
University of Sydney, NSW 2006, Australia. E-mail: skennedy@it.usyd.edu.au

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S. Selvakennedy and S. Sinnappan
Most possible WSN applications could either be classified as a periodic monitoring or
event-triggered type application. Even the query-based applications such as TinyDB [2]
and Cougar [3], may fall under one of the these types as they dynamically configure the
network. In the periodic monitoring type applications, the task of data dissemination that
happens at predefined intervals towards the network sink has to be managed efficiently to
prolong network lifetime. In many cases, the sink node would not be in the radio range of
most source nodes, therefore making direct transmission infeasible. Organizing these
nodes into clusters is one of the popular mechanisms used to control data dissemination, as
it could improve the scalability of a network [4]. The clustering process divides the net-
work into subsets of nodes (i.e. a cluster) with each cluster being managed by a cluster-
head (CH). Due to the spatial proximity of the nodes within a cluster, upon receiving
sensory data from them, a CH could even aggregate them with minimal information loss
as these readings may exhibit significant correlations. As such, large-scale multihop net-
works become feasible due to this trade-off [5]. For WSNs with a large number of energy-
constrained nodes, it is crucial to design a fast distributed algorithm to organize the nodes
into clusters.
Various clustering algorithms in different contexts have been proposed in the litera-
ture. Most algorithms are heuristic in nature and aim at generating the minimum number
of clusters and transmission distance. These algorithms also distinguish themselves by
how the CHs are elected. The LEACH algorithm [6] and its related extensions [7] use
probabilistic self-election, where each sensor node has a probability p of becoming a CH
in each round of monitoring. It guarantees that every node will be a CH only once in 1/p
rounds. This rotation of the energy-intensive CH function aims to distribute the power
usage for prolonged network life. However, LEACH allows only one-hop clusters and
assumes that all nodes are able to reach the sink directly. Another clustering algorithm
proposed in [8] aims to maximize the network lifetime, but it assumes that the nodes are
aware of the entire network topology. This assumption, however, may not be reasonable in
many scenarios. Some of these algorithms were designed to generate stable clusters in
environments with mobile nodes. In a typical WSN, the sensor nodes are stationary and
the instability of clusters due to mobility of these nodes may not be an issue.
Bandyopadhyay et al. proposed a hierarchical multihop clustering algorithm, and then
derived simplified formulas for computing the optimal number of clusters and number of
hops k using results in stochastic geometry to minimize the total energy spent [9]. It was
assumed that the sink is situated at the network center, and the nodes have unit energy
consumption for each communication task. In [10], the authors presented a Hybrid
Energy-Efficient Distributed clustering (HEED) protocol that periodically selects CHs
according to some primary and secondary parameters, for example a node’s residual
energy and a node degree (or its proximity to neighbors), respectively. It exploited the
availability of multiple power levels at a node such as on the Berkeley motes [11]. It was
proven that this clustering process terminates in constant time, and achieves fairly uniform
CH distribution across the network. However, HEED requires a number of parameters to
be specified such as intra- and inter-cluster transmission power levels to ensure connectiv-
ity among the CHs. The configuration of these parameters requires the knowledge of the
entire network. Also, this protocol only allows one-hop clusters. A fixed clustering algo-
rithm that also involves an optimal power allocation was introduced in [12]. To our
knowledge, this is the first paper to consider both clustering and its influence on the rout-
ing protocol in an integrated manner.
In this paper, we introduce the Time-Controlled Clustering Algorithm (TCCA) that
allows multihop clusters, and uses message timestamp and time-to-live (TTL) to control
the cluster formation. During the CH election, a node could also consider its residual

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energy before volunteering. A probabilistic model to quantify its energy usage is then
given, derived using a realistic first-order radio energy dissipation model with the objec-
tive of minimizing the energy spent in data dissemination. A discrete-event simulator is
also developed to verify the mathematical results as well as to investigate the influence of
sink placement on this algorithm. In order to perform a comprehensive evaluation of this
algorithm, the simulator is further extended to incorporate the overhead of the rest of the
communication stack to present an accurate energy usage pattern in an integrated study.
The rest of the paper is organized as follows. Section 2 presents the perspective of this
area of research. Various clustering algorithms proposed in literature are discussed there.
In Section 3, the details of the new clustering algorithm are described. Subsequently, its
energy dissipation is mathematically derived. The simulator used to verify this mathemat-
ical model is described in Section 4. Section 5 presents and analyzes various experiments
and their corresponding results for different scenarios. The main findings of this work are
summarized in the final section with some directions for further work.
2. Related Work
Intense research in the field of sensor network technology in recent years has prompted
further development in micro-sensor technology and low-power analog/digital electronics.
Numerous issues have been addressed to tackle the challenge of sensor energy conserva-
tion. Some of the main issues are low-power signal processing architectures, low-power
sensing interfaces, energy efficient wireless media access control (MAC), routing proto-
cols, low-power security protocols, and essential key management architectures and local-
ization systems. In order to address the energy-efficient data dissemination need, various
clustering algorithms have been proposed in different context. Initially, these algorithms
focused on the connectivity problem [13–15] but later energy-efficiency was more of
interest in wireless ad hoc and sensor networks [6, 8, 9, 16–18]. However, almost all focus
on reducing the number of clusters formed, which may not necessarily entail minimum
energy dissipation.
Generally, clustering algorithms segment a network into non-overlapping clusters
comprising a CH each. Non-CHs transmit sensed data to CHs, where the sensed data
could be aggregated and transmitted to the sink. Clustering algorithms maybe distin-
guished by the way the CHs are elected. The Linked Cluster Algorithm (LCA) [15] selects
the CH based on the highest id among all nodes within one-hop. This was enhanced by
LCA2 [19] that selects the node with the lowest id among all nodes that is neither a CH
nor is one-hop of the previously selected CHs. In [20], the authors developed a similar dis-
tributed algorithm to LCA2, which identifies the CH by choosing the node with the high-
est degree. Other algorithms such as the Distributed Cluster Algorithm (DCA) [21] and
Weighted Clustering Algorithm (WCA) [22] rely on weights to select CHs.
Load balancing heuristics were proposed in [17] and [23]. In [23], the proposed clus-
tering algorithm was designed to ensure that each cluster has an equal number of nodes
while keeping minimal distance between the nodes and their CH. However, most of the
above algorithms have a restricted scope of application and are suitable for only a small
number of nodes. They generate only one-hop clusters and require synchronized clocks,
which makes them less favorable for practical usage. Moreover, the “load-balanced” algo-
rithms focus mainly on balancing the intra-cluster traffic load without due consideration to
the external traffic. The max-min d-cluster algorithm was proposed to achieve better load
balancing among CHs as well as to reduce the number of CHs as compared to LCA or
LCA2 [18]. It generates d-hop clusters with a run-time of O(d) rounds. In [8], clustering
algorithms were proposed to maximize the network lifetime by varying the cluster size

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S. Selvakennedy and S. Sinnappan
and the duration of a node being nominated as a CH based on the assumption that the loca-
tions are known a priori. These algorithms ignore inter-cluster traffic and also need the
recognition of the whole network topology, which may not be feasible in most cases.
LEACH [6] self-elects CHs using a nominated probability p. The algorithm ensures
that every node will be nominated as a CH only once in 1/p rounds for a certain fixed
duration. Based on LEACH, the authors of [9] proposed a clustering algorithm and
derived a simplified energy model for predicting the optimal p using the results in stochas-
tic geometry. This model was also extended to optimize the cluster radius as well as to
support a hierarchical sensor network. Hybrid Indirect Transmission (HIT) [24], a combi-
nation of LEACH and Power Efficient Gathering in Sensor Information Systems
(PEGASIS) [25], was proposed to allow simultaneous transmissions both among clusters
and within a cluster, without requiring any position knowledge. HIT also looks at the
transmission delay issue with its chaining ability.
In [26], the authors proposed a scheme that incorporates the effect of inter-cluster
traffic and the sink location to maximize the network topological lifetime. However,
energy-load balancing was not considered. In [12], a fixed clustering algorithm that per-
forms energy-balancing to improve network lifetime was proposed. It also takes into con-
sideration the interaction between clustering and routing. Two schemes were introduced.
The first scheme computes the optimal cluster size and the CH locations, whereas the sec-
ond allows a CH to probabilistically choose to either relay the traffic to the next-hop or to
deliver it directly to the sink. It assumes a heterogeneous network, where the CH nodes
have bigger resources than the others.
In this paper, we expanded the work proposed in [6] and [9]. In [9], a node self-elects
with probability p and advertises itself as a CH to the nodes within its radio range. This
information is also passed on to k-hop neighbors. Such nodes are known as volunteer CH.
Those nodes that do not receive any advertisements within a time duration of t become the
forced CH. The duration is in the order of the time needed for the data to be sent by the
leaf nodes from k-hop away to their CH. The non-CHs join the closest CH forming a
Voronoi tessellation. The CH aggregates data from its cluster members and communicates
the information to the sink. Based on these assumptions, the authors derived the optimal p
as well as k values that minimize the total energy spent. However, a simplified unit energy
model with the same dissipation behavior for the radio’s transmitter and receiver was
assumed. This may not present an accurate energy usage pattern, and it was reported in
[27] that the transmitter consumes almost 2.5 times more energy than the receiver. In this
work, the Time-Controlled Clustering Algorithm (TCCA) is introduced, where the cluster
formation is controlled through the inclusion of timestamp and TTL fields in its messages.
Furthermore, its energy dissipation is analytically derived using a realistic radio model.
The resultant expression is further used to derive the network lifetime metric.
3. The TCCA Algorithm
The operation of TCCA is divided into rounds to enable load distribution among the
nodes, similar to the LEACH algorithm. Each of these rounds comprises a cluster setup
phase and a steady-state phase. During the setup phase, CHs are elected and the clusters
are formed. During the steady-state phase, the cycle of periodic data dissemination that
involves data collection, aggregation and transfer to the sink occurs.
In order to determine the eligibility to be a CH, each node i generates a random num-
ber between 0 and 1. If the number is less than a variable threshold T(i), the node becomes
a CH for the current round r. Besides, a node’s residual energy Eresidual could also be taken
into consideration. Thus, the threshold is computed as follows:

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Where p is the desired CH probability, Emax is a reference maximum energy, Tmin is a min-
imum threshold (to avoid a very unlikely possibility when Eresidual is small), and G is the
set of nodes that have not been CHs in the last 1/p rounds. When a CH has been self-
elected, it advertizes itself as the CH to the neighboring nodes within its radio range. This
advertisement message (ADV) carries its node id, an initial TTL, the node’s residual
energy and a timestamp. Upon receiving, first-hop nodes forward the ADV message fur-
ther as governed by its TTL value. The selection of the TTL value may be based on the
current energy level of the CH and could be used to limit the diameter of the cluster to be
formed. However, in this work, we assume that all nodes use the same fixed k value to
simplify our mathematical derivation.
Any node that receives such an ADV message and is not a CH itself joins the
cluster of the nearest CH from the TTL value. If there is a tie, this node could select
the CH with higher residual energy. Once a node decides to be part of a cluster, it
informs the corresponding CH by generating a join-request message (JOIN-REQ)
consisting of the node’s id, the CH’s id, the original ADV timestamp and the remain-
ing TTL value. The timestamp is included to assist the CH in approximating the rela-
tive distance of its members. Together with TTL, the CH could form a multihop view
of its cluster, which could be used to create a collision-free transmission schedule in a
time-division multiplexed scheme as in [6]. A transmission schedule is created by the
CH based on its number of members and their relative distance to enable the reception
of all sensory data in a collision free manner. At the end of the schedule, the CH com-
municates the aggregated information to the sink. For the nodes that do not receive
any ADV message, they are forced to route their data, using the routing protocol, to
the sink. Their dissemination process does not include any aggregation task but the
transfer to the sink may involve multihop transmissions. To simplify the mathematical
model representation, we will neglect the marginal effect of the cluster setup phase in
the computation of energy dissipation, as the setup phase is substantially shorter than
data transfer. However, the complete overhead of this algorithm is incorporated in the
simulator.
4. The TCCA Energy Dissipation Model
The energy used for the data dissemination towards the sink will depend on the cluster
size controlled through k (i.e. TTL) and distance between the transmitting and receiving
nodes. Since the goal of our work is to organize nodes into clusters with minimal overall
energy consumptions, we need to determine the optimal value of k. For the development
of our model, the following assumptions are made:
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