BBS: An Energy Efficient Localized Routing Scheme for Query Processing in Wireless Sensor Networks.

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International Journal of Distributed Sensor Networks, 2: 23–54, 2006
Copyright © Taylor & Francis Group, LLC
ISSN: 1550-1329 print/1550-1477 online
DOI: 10.1080/15501320500330711
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UDSN1550-13291550-1477Journal of Liposome Research, Vol. 15, No. 03, October 2005: pp. 0–0International Journal of Distributed Sensor Networks
BBS: An Energy Efficient Localized Routing
Scheme for Query Processing in Wireless
Sensor Networks
BBS: An Energy Efficient Localized Routing SchemeLian et al.
JIE LIAN, KSHIRASAGAR NAIK, and GORDON B. AGNEW
Department of Electrical and Computer Engineering, University of Waterloo,
Waterloo, Ontario, Canada
LEI CHEN
Database Research Group, Department of Computer Science, Hong Kong University of
Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
M. TAMER ÖZSU
Database Research Group, School of Computer Science, University of Waterloo,
Waterloo, Ontario, Canada
A wireless sensor network (WSNET) can support various types of queries. The energy resource of
sensors constrains the total number of query responses, called query capacity, received by the sink.
There are four problems in the existing approaches for energy-efficient query processing in
WSNETs:
1. the fact that sensors near the sink drain their energy much faster than distant sensors has
been overlooked,
2. routing trees (RT) are rooted at the sink, and therefore, aggregative queries are less energy-
efficient,
3. data reception cost has been ignored, and
4. flooding is used in query distribution or RT construction.
In this paper, we propose a Broadcasting-Based query Scheme (BBS) to address the above
problems. BBS reduces the energy depletion rate of sensors near the sink, builds different localized
RTs for different query types, and eliminates the flooding cost of query distribution. Compared to the
existing approaches, simulation studies show that BBS produces significant improvement in the
query capacity for non-holistic queries (10%—100% capacity improvement) and holistic queries
(up to an order of magnitude of capacity improvement).
Keywords
Wireless Sensor Networks; Query Processing; Routing Protocols; Localized Routing Tree
1. Introduction
A wireless sensor network (WSNET) can be treated as a distributed sensor database sys-
tem which supports various types of query services [1, 2, 3]. Each query response is based
Address correspondence to Jie Lian, Department of Electrical and Computer Engineering, University of
Waterloo, Waterloo, Ontario, Canada, N2L 3G1. E-mail: jlian@swen.uwaterloo.ca

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Lian et al.
on a group of sensor readings bounded by query constraints. Since sensors are powered by
batteries, the energy constraint of sensors challenges the designs of energy-efficient query
processing at all levels in the sensor network protocol stack [25]. The routing level and
application (or data processing) level have a significant impact on query processing per-
formance. Thus, we consider these two levels together in the design stage.
Energy-efficient query processing has been studied in the literature [3, 4, 5, 6, 7, 10,
11, 14] to support various types of queries, such as Max, Min, Average (Avg), Sum,
Median, Distinct Count, Histogram, and event-based query. If responses of these queries
are generated by sensed data from sensors within a sub-area, called a zone, of the network,
these queries are named zone-based query [4, 11]. An example of zone-based queries is
“return the average temperature sensed by sensors in a given zone every 5 minutes.” From
the viewpoint of sensor readings, aggregative queries can be classified into holistic que-
ries and non-holistic queries [3]. Max, Min, and Avg of readings of sensors are examples
of non-holistic queries. Median, Distinct Count, and Histogram are holistic queries.
Meanwhile, an event-based query [11] is triggered by a set of specific events, such as
“find wheeled vehicles in a specific area.” Depending on the sensed data, an event-based
query can be either holistic or non-holistic. Existing approaches can fully or partially
support the zone-based aggregative queries discussed above [3, 4, 5, 6, 7, 10, 11, 14].
However, four major problems are overlooked or ignored in those approaches.
• In the routing tree (RT) construction and data collection phases, all the sensors behave
the same way, while ignoring the fact that sensors near the sink drain their energy much
faster than far-away sensors. Consequently, the sink is disconnected from the network
sooner than expected. Simply reducing the total energy cost of all the sensors is not a
satisfactory solution—sensors near the sink need to receive special considerations.
• The existing approaches build uniform RTs rooted at the sink for all types of que-
ries. Since different query types require different structures of the underlying RTs,
uniform RTs may not efficiently support different types of queries. For example,
the existing approaches can not efficiently support holistic queries, such as Median,
because sensors in the query zone have to forward their sensed data to the sink
without data aggregation.
• The existing approaches take into account the cost of data transmissions (TX),
while overlooking the cost of data receptions (RX). However, experimental studies
[4, 8, 12] show that the RX cost is comparable to the TX cost.
• The existing approaches use flooding, which is an expensive operation, to dissemi-
nate queries and build RTs.
In this paper, we tackle these problems and propose a novel energy-efficient approach,
called Broadcasting-Based query Scheme (BBS). BBS takes into account the design consider-
ations of both the routing level and the data processing level to efficiently support various types
of zone-based queries. The advantages of BBS, i.e. the contributions of this paper, are as
follows.
• BBS constructs RTs rooted at sensors within the query zone, and builds different
localized RTs for different query types to improve performance. Therefore, zone-
based aggregative queries, including holistic queries and non-holistic queries, can
be efficiently supported.
• In the cases that the RX cost can not be ignored, BBS uses a refinement technique
to reduce the energy consumption due to RXs in sensors near the sink.
• If the sink can be equipped with a power adjustable transmitter, BBS can eliminate
the flooding cost involved in the query distribution and RT construction phases.

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Compared to TAG [3], the simulation studies show that BBS considerably increases the
total number of query responses received by the sink for holistic and non-holistic queries
even without using a power adjustable sink.
The rest of the paper has been organized as follows. In Section 2, we state the prob-
lems in the existing approaches and summarize previous work. The detailed descriptions
of BBS are given in Sections 3 and 4. An analytical performance evaluation of BBS is
given in Section 5. Simulation studies are presented in Section 6. Finally, we make some
concluding remarks in Section 7.
2. Problem Statement and Related Work
2.1. Problem Statement
A. Problem of Non-uniform Energy Depletion Rate of Sensors in WSNETs. In a WSNET
with randomly deployed homogenous sensors and a stationary sink, if all sensors report
approximately the same amount of sensed data, sensors close to the sink drain their energy
much faster than the rest of the sensors. In Fig. 11, the area within the smallest circle cen-
tered at the sink is called the first critical region (Region-1), which has the same TX radius
of a sensor. The area between the smallest circle and the second smallest circle is called
the second critical region (Region-2), and so on. Sensors in Region-1 have the highest for-
warding workload [10]. The mathematical model in reference [10] concluded that for a
large WSNET up to 90% of the total initial energy of all sensors is left unused after the
sensors in Region-1 have depleted their energy. Hence, an important objective in the
design of routing protocols and query processing is to slow down the energy depletion rate
of sensors in Region-1.
The existing approaches focus on minimizing the average energy cost while over-
looking the energy depletion rate of sensors in Region-1. Furthermore, the existing
approaches use the average total energy cost of queries to measure the performance of
query processing. In fact, the average energy cost is not a proper performance metric. An
1Sensors are randomly deployed in a rectangular area. The dark points denote sensors with odd
number of shortest hops to the sink and the gray points denote sensors with even numbers of hops to
the sink. The odd and even hop sensors are interleaved with each other.
FIGURE 1 Distribution of random deployed sensors.






Sink
Sensors
Region boundaries
V1
V2
V3
V4
Vk
Vk-1

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Lian et al.
approach using smaller average energy per query does not mean that it is better in terms of
query capacity, and this situation is illustrated in Fig. 2. In Fig. 2, the sink has two neigh-
bors, s1 and s2. Assume that an algorithm B1 is applied in the query zone Q1 (Fig. 2(a)) and
B2 in Q2 (Fig. 2(b)). Assume that B2 consumes twice the amount of average total energy
per query response of B1. For each response, B1 uses two paths to send data to the sink and
B2 uses a single path through either s1 or s2. Not considering the energy available in s1 and
s2, sensors in Q2 deplete their energy faster than sensors in Q1. However, to transmit one
query response to the sink, B1 consumes twice the amount of energy of sensors in Region-1
compared to B2. In Fig. 2(a), s1 and s2 may exhaust their energy faster than the sensors s1
and s2 in Q2 (Fig. 2(b)). Hence, B2 can deliver more query responses than B1, even though
B2 consumes more energy than B1 in total.
B. Problems with Flooding and Routing Trees Rooted at the Sink. Query processing works
in two phases [3, 4]: query distribution and data collection. In the query distribution
phase, the existing approaches use either global flooding or wedge flooding to distribute
queries and construct routing trees (RT). In global flooding, queries are flooded to all sen-
sors in the network. In wedge flooding, sensors within a wedge area (approximately
bounded by two dashed lines in Fig. 3) join the flooding process. High cost is the major
drawback of flooding. Even though wedge flooding consumes less amount of energy,
there is no guarantee that a query will be delivered to all the sensors in the query zone. The
probability of query delivery can be increased by using a large flooding angle θ (Fig. 3) at
a higher cost.
FIGURE 2 Energy consumption of neighbors of the sink.
Sink
s1
Query zone Q1
s2
s3
Sink
s1
Query zone Q2
s2
s3
(a) Multiple paths (b) Single path
FIGURE 3 Illustration of query flooding in a WSNET.
x
y
Sink
Sensor
Query zone
Region-1

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The existing approaches use flooding to build RTs rooted at the sink for all query
types. Consequently, sensors in Region-1 incur a high energy-consumption cost in data
collection. As shown in Fig. 3, if wedge flooding is used to construct the example RT
rooted at the sink, most of the sensors in the intersected area between Region-1 and the
wedge area with flooding angle θ have forwarding tasks. For the non-holistic queries, the
number of forwarding sensors in Region-1 is proportional (by a factor between 0.4 and
0.8) to the number of sensors in the intersected area, as shown in the supplemental docu-
ment. For holistic queries, such as Median, results of sub-aggregations at intermediate
sensors can not offer the correct result at the sink [3], and, thus, all raw data need to be
sent to the sink. The energy consumption of sensors in Region-1 per query response is pro-
portional to the number of sensors in the query zone. However, in either case, if the query
response can be computed by a sensor within the query zone, only a constant amount of
energy of sensors in Region-1 is required to transmit the response to the sink.
C. Problem of Data Reception Cost of Sensors. The existing approaches overlook the
need to reduce the RX cost. If the TX distance is large, the TX cost is much larger than
the RX cost. However, for realistic sensors such as Mica Mote [4], the RX cost is close to
the TX cost. Hence, the RX cost needs to be factored in.
2.2. Related Work
A number of research results have been published both in network and database domains
on energy-efficient query processing for WSNETs. In the network domain, research
results have been published on energy-efficient routing protocols for WSNETs, such as
SPIN [15], LEACH [14], and Directed Diffusion [11]. SPIN is recognized as the earliest
work on data centric routing and is proposed to address the deficiency of flooding. SPIN
saves sensor energy by reducing duplicate copies of messages in the classic flooding and
by using meta-data to reduce the lengths of transmitted original data. Moreover, SPIN
uses negotiation to help ensure only useful information is transferred. LEACH is a scal-
able adaptive clustering protocol in which nodes are organized into clusters. Sensors in a
cluster communicate with the cluster header (CH) and CH directly transmits the processed
data to the sink. LEACH uses a random process to vote CHs to balance the energy con-
sumption of CHs, and, the system lifetime is therefore extended. Directed Diffusion is
another data centric protocol in which sinks periodically floods queries into the network.
During the flooding, sensors set up gradients of data propagation. Based on the received
data, sinks select and reinforce some “good” paths for further data delivery.
In the database domain, Madden et al. [3, 4] proposed TAG to reduce the energy cost
of processing aggregative queries by in-network aggregation techniques. In TAG, an RT
is built by using flooding or wedge flooding. TAG adaptively adjusts the sampling rate of
sensors based on the query constraints and energy of sensors. Through reducing sampling
rates, energy spent on sensing is reduced. Cougar [5] also addressed query processing by
investigating such techniques as partial aggregation and data packet merging to reduce the
packet payload. Many other works have also been proposed based on TAG to address
energy-efficient query processing, such as TinA [6] and location aware routing [7]. TinA
utilizes the temporal coherence among data collected from the same sensor to reduce the
number of transmissions. The location aware routing technique builds RTs with the goal
of reducing message size for group by queries.
The preceding techniques normally assume that homogeneous sensors are uniformly
distributed, which leads to a serious energy drainage problem of sensors in Region-1. To
solve this problem, two techniques have been proposed: