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Quality of Service in Wireless Sensor Networks Hwee-Xian Tan

hweexian@comp.nus.edu.sg

Department of Computer Science National University of Singapore

Abstract Wireless Sensor Networks (WSNs) comprise of groups of tiny sensor nodes that are deployed for

collaborative missions such as environmental monitoring, target tracking and surveillance. Due to the miniature size

of the nodes, they are typically deployed in large numbers and communicate via multiple hops through a wireless

shared communication channel. The successful implementation of such networks is dependent on the enabling

technologies (such as digital electronics and wireless communications), as well as the provisioning of Quality of

Service (QoS) in the network. While there have been many efforts in QoS provisioning in traditional networks such

as the Internet and Mobile Ad Hoc Networks (MANETs), these networks have very different characteristics from

that of WSNs. Consequently, the QoS models and protocols that have been designed for the Internet and MANETs

cannot be directly applied to WSNs. In this paper, we look at some of the existing QoS mechanisms in the

networking literature, and the inherent characteristics of WSNs which make it challenging to provision for QoS in

the network. We then identify some key performance metrics for WSN QoS and outline some mechanisms to

achieve QoS in the sensor network. Finally, we propose WISER a framework to enhance QoS in WIreless

SEnsoR networks.

Index Terms Wireless Sensor Networks, Quality of Service, Network Lifetime, Coverage, Spatial Accuracy,

Delay, Topology Management.

I. INTRODUCTION

Wireless Sensor Networks (WSNs) are envisioned to be the next generation of networks

which will form an integral part of mans lives. The sensor nodes are usually small in size, with

multi-modal sensing capabilities which allows them to collect raw data of various physical

parameters such as temperature, salinity, humidity, light intensity, pressure, sound, radiation, etc.

They are equipped with wireless interfaces, enabling them to communicate with each other via

multiple hops using Radio Frequency (RF) techniques. Due to the miniature size of these nodes,

sensor networks can be densely deployed in a distributed manner in any terrain; therefore the

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nodes need to have self-organizing and self-configuring capabilities, as like in ad hoc networks.

With advances in wireless communications, the applications of sensor networks are no longer

limited to that of periodic monitoring of the environment. Wireless sensor networks can be used

for a wide array of applications spanning multiple domains healthcare, biometrics, home

networking, military, automotives, as well as construction and manufacturing industries. The

sensor nodes typically obtain raw data from the environment in which they are deployed, and

then send the collected information back to a centralized sink (or repository) where more

complex processing and real-time analysis can be performed on the data. In some heterogeneous

network which have actuators (also known as SANETs Sensor and Actuator NETworks), the

collated data may be used to trigger the actuators in the network to perform some form of

collaborative missions or tasks. Hence, sensor networks can now be used for the following

purposes: (i) data acquisition the collection of data from the environment; (ii) data

dissemination the delivery of information to other nodes in the network; (iii) data distribution

the delivery of information or instructions from the centralized sink to one or more sensor nodes

in the network; and (iv) M2M (Machine-to-Machine) communication [1] the provision of a

platform for interactions between machines and the environment, without unnecessary human

intervention.

The successful implementation and deployment of such intelligent wireless sensor networks

requires several enabling technologies, such as Micro-Electro-Mechanical Systems (MEMS),

digital electronics and wireless communications [2], as well as the provisioning of Quality of

Service (QoS) support to the applications that run on top of the network. While different

applications may have specific QoS requirements, some of the more commonly used QoS

metrics used to measure network performance are delay, throughput, bandwidth and efficiency

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of the protocol in use.

There has been much effort invested into QoS support in the Internet and MANETs (Mobile

Ad Hoc NETworks) within the last decade, leading to the proliferation of Internet QoS models

such as Integrated Services (IntServ) [3] and Differentiated Services (DiffServ) [4], and MANET

QoS models such as the Flexible QoS Model for MANETs (FQMM) [5] and integrated MANET

QoS (iMAQ) model [6]. However, as these networks have fundamentally different

characteristics from that of wireless sensor networks, the protocols and algorithms that are used

to provide QoS in the Internet and MANETs cannot be directly applied to sensor networks. In

addition, in such networks with higher resource availability and predictability, providing QoS

beyond best effort is already a challenge. It is therefore even more difficult to achieve QoS in

wireless sensor networks, which have unpredictable and limited resources.

In this paper, we first take a look at the various QoS mechanisms that exist today, and present

an overview of the challenges and issues that exist in the deployment of wireless sensor

networks. We then look at the how QoS can be achieved in these networks which are

characterized by multi-hop communications, dynamic environments and limited resources. The

rest of this paper is organized as follows: The next section describes the related work and the

motivation for QoS provisioning in WSNs. Section III details the multi-faceted challenges

involved when considering QoS support in wireless sensor networks. Section IV presents some

of the key performance metrics that can be used in WSN QoS and outlines some mechanisms

which can be used to achieve QoS in the network. In Section V, we propose WISER, which is a

framework to enhance QoS in WIreless SEnsoR networks. We conclude with directions for

future work in Section VI.

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II. RELATED WORK AND MOTIVATION

There is evidence of increasing research efforts in QoS provisioning in the networking

literature; however, majority of the related work are targeted at cellular mobile telephony, wired

Internet and MANETs. A few definitions for QoS in varying networks have also emerged in

recent years Crawley et al in [6] define QoS (in the Internet) as a set of service requirements to

be met by the network, while transporting a flow while Nikaein et al in [8] propose that QoS (in

a MANET) is the provision of a set of parameters in order to adapt the applications to the

quality of the network while routing them through the network. Despite the slightly varying

definitions that have been proposed for different types of networks, the QoS of any particular

network can generally be considered to be its ability to deliver a guaranteed level of service to its

users and/or applications. The service requirements can be specified in the form of performance

metrics, which are typically computed in one of the three following ways: (i) concave (e.g.

minimum bandwidth along each link); (ii) additive (e.g. total delay along a path); and (iii)

multiplicative (e.g. packet delivery ratio along the entire route). Although some performance

metrics such as throughput, delay, jitter (delay variance), bandwidth, packet delivery ratio

(PDR), reliability, etc are more widely used than other metrics, each application has its own

unique set of service parameters to be satisfied, while possibly compromising on other sets of

metrics. Loss-tolerant applications such as multimedia applications are not adversely affected by

occasional data loss, but are highly sensitive to delay and bandwidth. In contrast, other

applications involving sensitive data integrity, such as electronic mail and banking transactions,

require fully reliable data transfer, but may not require stringent delay constraints and can work

with elastic bandwidth.

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A. Existing QoS Mechanisms

The existing QoS mechanisms can be classified into two main categories: (i) resource

reservation; and (ii) traffic classification. In resource reservation, network resources such as

bandwidth are allocated according to the QoS requirements of the application. This necessitates

the presence of a signaling or handshaking protocol such as RSVP [9], to reserve the required

resources in each node along the path of the data packet, before the application is actually

allowed to run. In addition, a call admission control protocol such as that proposed by Iraqi and

Boutaba in [10] which regulates the permissible number of connections that can be allowed

into the system while still fulfilling the QoS requirements of the application is required.

Traffic classification involves the categorization of data packets into different levels of

priority, or classes of service (CoS), based on the application requirements. These data packets

are marked with their respective classes at the edge of the network, and preferential treatment is

then given to the traffic classes with higher priority as they pass through each hop along the path.

Traffic prioritization can be in the form of congestion management schemes such as scheduling

[11], congestion avoidance or rate limiting. In general, data packets with lower priority are

usually dropped at a higher rate than those with higher priority; this allows data flows with

higher priority to have better QoS performance metrics such as reliability, end-to-end delay and

throughput.

B. QoS Provisioning in the Internet

There are currently two Internet QoS models:

1) Integrated Services (IntServ): IntServ provides per-flow end-to-end guarantees through

resource reservation mechanisms [3]. It is a service model that incorporates best effort service,

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real-time service and controlled link sharing into the basic Internet architecture. IntServ is

realized via a framework comprising of four components: (i) packet scheduler; (ii) admission

control routine; (iii) classifier; and (iv) reservation protocol. Due to the large amount of state

information that is required to be stored, IntServ is hardly scalable and therefore not suitable for

use in distributed and autonomous systems like wireless sensor networks.

2) Differentiated Services (DiffServ): DiffServ provides per-class service differentiation via

traffic differentiation and prioritization [4]. Services and applications are classified in a simple

and coarse method, thus avoiding the scalability problem that is inherent in IntServ. A traffic

prioritization mechanism is used to classify network traffic into different classes of service, and

preferential treatment is allocated to classes that are identified as having more stringent

requirements. Per-flow state and other sophisticated classifications, marking, policing and

shaping operations are pushed to the network edge. Each data packet is treated on an aggregate

basis, which is also known as the Per-Hop-Behavior (PHB).

C. QoS Provisioning in MANETs

Mobile Ad Hoc Networks (MANETs) are a class of multi-hop networks with self-organizing

and self-configuring properties. There is neither central administration nor fixed infrastructure in

the network; each node acts as both a host and a router to forward packets to other nodes in the

network. The current Internet QoS models such as IntServ are unsuitable for use in MANETs

because of the vast differences in characteristics between the two types of networks. Due to the

mobility of the nodes in a MANET, as well as the inherent erratic behavior of the wireless

channel, MANETs are characterized by dynamic topology, complex route maintenance, temporal

link connectivity as well as unpredictable and varying resource availability [12][13].

Consequently, QoS provisioning in MANETs is a multi-faceted problem which requires the

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cooperation and integration of the various networking layers [14], viz. physical layer, Medium

Access Control (MAC) layer, network layer, transport layer and application layer. The existing

developments in MANET QoS can be broadly classified into QoS models, QoS resource

reservation signaling, QoS routing and QoS MAC.

D. QoS Provisioning in WSNs

Wireless sensor networks have been envisioned for a wide range of applications, some of

which may involve the collection of sensitive or critical data. For example, if underwater sensor

nodes are thrown into the sea to monitor seismic activities and forewarn the possible occurrence

of an earthquake or tsunami, the sensor network is not very useful if it is unable to inform the

sink in time, on the impending natural disaster. Although delay is not a very crucial factor in

sensor network applications such as periodic monitoring, shorter delays will nevertheless, be

much desired over higher delays. Future WSNs may also be able to capture videos or snapshots

of the physical environment and transmit these images (or videos) back to the sink for real-time

data analysis. As such, there is a demand for QoS support in WSNs; however, existing Internet

QoS and MANET QoS mechanisms are not directly applicable to WSNs due to the difference in

the characteristics of such networks. In addition, there is currently no standardization in the

networking community, on a framework and/or general guidelines on how QoS can be achieved

in WSNs. This motivates the need for more research work and efforts in QoS provisioning in

WSNs.

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III. WIRELESS SENSOR NETWORKS CHARACTERISTICS, ISSUES AND CHALLENGES

In this section, we describe some of the unique characteristics of wireless sensor networks

which make them different from conventional wireless ad hoc networks. We then look into some

of the issues and challenges that are involved in the deployment of sensor networks.

A. Network Characteristics

1) Autonomous: As like ad hoc networks, sensor networks can be deployed in an autonomous

manner without the need for existing infrastructure. They can be set up anytime, anywhere

without the need for any central administration. As such, each node in the network has to act

both as a host, as well as a router to forward data packets to the sink (or other nodes in the

network).

2) Dense and random deployment: Due to the tiny size of the sensor nodes and the nature of

the applications, the network is expected to be quite densely and randomly deployed, covering

several hundreds or even thousands of sensor nodes in the terrain. As compared to ad hoc nodes

which typically have 4-8 neighbours, each sensor node can have up to tens of neighbours [15].

Consequently, there is also higher spatial correlation among the nodes as more than one node

will be sensing the same physical phenomenon or event.

3) Limited resources: As sensor nodes are typically small in size and battery-powered, they

have limited processing power, memory storage and energy supply. In addition, as these nodes

are usually deployed in hostile or unreachable terrains, they cannot be easily retrieved for the

purpose of replacing or recharging the batteries, and the lifetime of the network is usually

limited. The use of a wireless mode of communication between the nodes also puts a constraint

on the amount of bandwidth that can be used for data forwarding.

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4) Susceptibility to failures: The physical environment in which the sensor networks are

deployed is usually harsh, such as in battlefields, large warehouses or even on the ocean floor.

Sensor nodes are more susceptible to failures because they are in close contact with both the

physical phenomenon to be monitored, as well as the physical environment. For example, sensor

nodes that are deployed in the open areas may be tampered by humans or wildlife, and sensors

that are thrown in large numbers into chemically contaminated fields may corrode and become

faulty.

5) Topological changes: As the sensor nodes have limited power supplies and are susceptible

to node failures, they may die and cause the network topology to change. Although most sensor

networks are assumed to be relatively static, a sensor node may still deviate from its initial

deployed location under the influence of its physical surroundings such as winds, currents, or

even wildlife which also causes the topology to change.

6) Data-centric: In contrast with ad hoc networks which are address-centric, sensor networks

are usually data-centric. Instead of point-to-point communications between individual nodes in

the network, the flow of data in a sensor network is predominantly unilateral, towards a

centralized sink.

7) Application specific: The types of applications that can be supported by sensor networks

span across many different domains and have varying application requirements. It is unlikely

that any particular protocol design or solution is suitable for all the different types of application

scenarios [16]. Therefore, protocols and algorithms that are designed for wireless sensor

networks are likely to be application-specific.

B. Issues and Challenges

The design of a sensor network is influenced by many factors, such as its characteristics and

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other physical constraints. Here, we highlight some of the issues that must be taken into

consideration when designing protocols for use in wireless sensor networks.

1) Self-configuration and self-maintenance: One of the main rationales for deploying sensor

networks is to enable remote sensing with minimal human intervention. In addition, sensor

networks are usually randomly deployed in hostile terrains in large numbers. As such, it is

almost impossible to manually configure each and every one of the sensor nodes; these nodes

must thus be equipped with self-configuration and self-maintenance capabilities.

2) Scalability: As sensor nodes are often deployed in large numbers over very large physical

terrains, protocols for sensor networks must be able to scale; the performance of the network

must not deteriorate significantly even if the number of nodes in the network increases.

3) Energy efficiency: Sensor nodes expend energy during data sensing and communication

with the other nodes in the network. When the energy of a node depreciates, the node will die

and this may cause the network to become partitioned a situation whereby communication gaps

exist in the network such that some nodes may be unable to communicate with each other. The

presence of network partitions will usually render a sensor network useless, because some parts

of the network will no longer be under coverage; hence, some researchers consider that the

sensor network lifetime has expired when the network becomes partitioned. Sensor network

protocols must therefore be energy-efficient so as to extend the network lifetime and usefulness

of the network [17].

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4) Fault tolerance: Due to the limited energy of the nodes, as well as harshness and hostility

of the environment in which the nodes are deployed in, the sensor nodes are prone to failures.

With fault tolerance, the sensor network should be able to continue with its network

functionalities (such as sensing and communication) even in the presence of node failures. This

helps to increase the robustness of the sensor network and improves its QoS delivery to the

applications.

5) Adaptability: The network topology and characteristics of a sensor node may be quite

dynamic due to the influence of the physical environment that the network is deployed in. As

such, the sensor network should demonstrate adaptability to the prevailing network conditions

and physical environment in order to provide good network performance.

6) Transmission media: Sensor nodes typically communicate over a shared wireless

transmission medium because the environment in which they are deployed in does not allow for

infrastructure (such as centralized base stations or wires) to be setup easily. Depending on the

environment that the sensor nodes operate in, different transmission media may be used.

Terrestrial sensor networks typically make use of radio links while underwater sensor networks

utilize acoustic links for communications; each of these transmission media have their own

characteristics, such as the optimum operating frequency, permissible transmission range, etc.

7) Security: The openness of the physical environment and the transmission media subjects

sensor networks to a multitude of security attacks ranging from Denial of Service (DoS) to

malicious attempts to modify sensitive data information. Consequently, it is quintessential to

ensure that sensor networks incorporate security mechanisms into their protocols to protect the

integrity of the data collected.

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8) Data processing: Sensor nodes are expected to have low computing powers and hence,

unable to do complex processing on-board. However, if each sensor node is able to do some

simple data processing such as data aggregation before forwarding the sensed data to the sinks,

this may improve the overall network performance because it lowers the contention of the data

packets and improves the packet delivery ratio. In SANETs, on-board data processing may also

help to improve the response time of the network to changes in the physical environment.

9) Data storage: In large scale wireless sensor networks, the content of the sensed data

collected is more important than the identity of the node that gathers them. Hence, making

effective use of the enormous amounts of collected data requires scalable, self-organizing and

energy-efficient algorithms. Using this approach, the data must be named and communication

abstractions must use these naming conventions rather than the network address of the node. By

making use of efficient and novel data storage algorithms such as Data-Centric Storage (DCS)

[18], relevant data can be stored by name at nodes within the sensor network. In this way,

queries for data of a particular name can be sent to the node storing the named data, without

having to go through the flooding process which is used in some data-centric routing proposals.

10) Hardware constraints: According to [2], a sensor node comprises of a sensing unit, a

processing unit, a transceiver unit and a power unit. As each of these components must fit into a

small sensor node, it is unlikely that they have stringent constraints such as low computational

power, low data rates and limited power source. The sensor network must be able to work with

these hardware constraints in mind and protocols must not assume the capabilities of complex

mathematical processing on-board.

IV. QOS IN WIRELESS SENSOR NETWORKS

In the previous section, we have highlighted some of the main characteristics of wireless

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sensor networks, and elaborated on how these features may provide challenges when designing

protocols for use in the network. In this section, we will look into the challenges of QoS

provisioning in WSNs and some of the existing developments in QoS mechanisms for WSNs.

A. Difficulties of QoS provisioning in WSNs

The successful deployment of QoS in WSNs is a challenging task because it depends on both

the inherent properties of the network, as well as the physical hardware constraints of the sensor

nodes. According to I. F. Akyildiz et al in [2], the sensor network protocol stack comprises more

than that of a simple multi-layered protocol stack which already includes the physical layer,

data link layer, network layer, transport layer and application layer. It also involves three

additional planes which are perpendicular to the multi-layer stack; viz. task management plane,

mobility management plane and power management plane (see Figure 1).

Figure 1 Sensor networks protocol stack (Source: I. F. Akyildiz et al, Wireless Sensor Networks: A Survey, Computer Networks 38, 2002)

B. QoS Performance Metrics in WSNs

Unlike the Internet and MANETs which can be used for a multitude of applications ranging

from file transfer to multimedia applications, each WSN is usually deployed for a specific

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application such as environmental monitoring or target tracking. In addition, each of these

networks has their own unique characteristics and constraints; consequently, the QoS

performance metrics in WSNs may differ significantly from those that are used in the Internet

and MANETs. Some of the key performance metrics that should be considered in QoS

provisioning in WSNs are as follows:

1) Energy efficiency: The energy limitation in WSNs is one of the most challenging aspects

involved when designing protocols and considering QoS support in the network, because it is

directly related to the lifetime of the network. A sensor node that fails due to lack of energy is

unable to sense the physical environment or communicate with its neighbours. This may lead to

network partitions, which in turn affects the network lifetime.

The network lifetime has been defined in many ways in the literature. [20][21][22][23] refer

to network lifetime as the maximum time before any node in the network drains up its energy.

Although this definition is commonly used, it does not portray an accurate overview of the

network lifetime from the perspective of the application (or user). Due to the high density in

which nodes are usually deployed, the sensed data is highly correlated; therefore, even if a

particular node fails after expending all its energy, a neighbouring node may still be able to

perform the required functionalities of sensing and communication in that spatial location. As

such, network lifetime cannot be considered based on the energy consumption of the nodes

alone, but must also take into account the topological location of the nodes. We therefore adopt

the more generic definition of network lifetime as that proposed by Kumar et al in [24], which is

the time period during which the network continuously satisfies the application requirements,

where application requirements may be specified in terms of coverage or delay and may vary

depending on the specifications of each application.

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2) Coverage: The diversity of sensor network applications has led to a range of interpretations

of sensor network coverage [25]. Despite the discrepancies in the definitions, the main objective

of coverage is somewhat consistent to ensure that each physical region in the space of interest

in within the sensing (and/or communication) range of at least one sensor node [26]. The

coverage of the sensor network is closely correlated with the denseness of the node deployment.

A sparse network results in a sparse coverage, whereby the space of interest is partially covered

by sensors. Dense networks result in dense coverage, whereby the space of interest is (almost)

fully covered by the sensors. In very dense networks which have redundant coverage [27], the

space of interest is covered by multiple sensors, resulting in high spatial correlation in the data

that is observed by nodes which have geographical proximity. As a QoS metric, we define the

coverage of a sensor network as the ratio of the space that is covered by the sensor nodes to the

total space of interest.

3) Spatial accuracy: Wireless sensor nodes are usually deployed in a large terrain, and each

sensor node is able to sense data from only a certain part of the space of interest. The sink, which

collects the sensed data from the sensor nodes via multi-hop communications, is able to make

logical deductions of the observations only if it has spatial (or locality) information of each data

that is collected. For wireless sensor networks that are deployed outdoors, Global Positioning

System (GPS) can be used to provide the location of each node, which will attach this

information to its sensed data before forwarding it to the sink. Unfortunately, GPS is expensive

and cannot be used indoors; therefore localization techniques such as those proposed in

[29][30][31][32] have to be used as an alternative to obtain the (relative) locations of the nodes

and provide spatial accuracy of the sensed data. The type of localization technique being

employed determines the granularity of the location information can be fine (high spatial

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accuracy) or coarse (low spatial accuracy). However, as high spatial accuracy usually results in

higher overheads than low spatial accuracy, the appropriate level of spatial accuracy should also

be based on the application requirements of the network.

4) Temporal accuracy: As like spatial accuracy, temporal accuracy is required in wireless

sensor networks to ascertain the time period during which an event occurs. In wired and

centralized networks such as the Internet, it is possible to achieve high temporal accuracy

because of the high propagation speed of the communication medium. However, wireless sensor

networks typically communicate in a distributed, multi-hop manner, via a shared communication

channel. In terrestrial networks, the wireless links are prone to high bit error rates (BER),

resulting in high link instability. In underwater networks where sensor nodes utilize acoustic

waves instead of radio waves for communication, the speed of propagation is five orders of

magnitude slower than that in terrestrial networks [33]. Such factors like link instability and slow

propagation speed makes it difficult to achieve time synchronization in the network thus

leading to low temporal accuracy. An accurate time synchronization protocol in the network,

such as that proposed in [34], is hence necessary to achieve high temporal accuracy.

5) Delay: Sensor network applications can be classified into two domains periodic

applications such as environmental monitoring, or event-driven applications such as target

tracking. Event-driven applications tend to have stricter delay constraints; the sink must be able

to receive notification that a particular event has occurred in a particular region of the network

within a short time period after the occurrence so that it can react appropriately. However, as

demonstrated by the authors in [35], stringent delay requirements can severely deteriorate the

network lifetime. Henceforth, tradeoffs are involved when designing protocols for use in sensor

networks.

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C. Mechanisms to Achieve QoS in WSNs

In this section, we describe some existing mechanisms that have been proposed in the

literature, which allow WSNs to achieve QoS.

1) Topology Management: Most of the energy that is expended by a node is through

transmission and sensing. To reduce the amount of energy that is consumed by a sensor node in

the network, the nodes can be put to sleep mode when they are not required to sense or transmit

data to their neighbouring nodes. Topology management can be used to achieve this dual goal of

coordinating the sleep schedules of all the nodes, such that data can still be forwarded efficiently

to the sink [36]. It is able to do this by exploiting the high nodal density and high spatial

correlation of the sensed data. As such, topology management helps to increase energy efficiency

(and thus network lifetime) at the expense of higher latency, because nodes that are required for

the data forwarding process may be in sleep mode during the transmission.

2) Localization: Localization provides an alternative mechanism of finding the physical

locations of the sensor nodes in the network instead of making use of GPS, which is costly and

infeasible indoors. It usually involves two phases [31]: (i) ranging, which is the distance

estimation of the node from the sink or other nodes in the network using techniques such as

signal strength, angle-of-arrival (AoA), etc; and (ii) iterative multilateration, which makes use of

the range measurements from the previous phase to calculate a new location estimate. Hence,

localization increases spatial accuracy, at the cost of higher overheads (and transmissions) which

will reduce energy efficiency.

3) Controlled Mobility: One of the main causes of performance deterioration in wireless

sensor networks is node mobility (due to influence from the environment) and random

deployment of nodes (due to the denseness which nodes are usually deployed). As such, the

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resulting network topology is usually not optimized for the protocols which are designed for the

network. To incorporate QoS in the sensor network, controlled mobility [37] using mobile nodes

or Unmanned Autonomous Vehicles (UAVs) can be used to deploy sensor nodes more

efficiently to enhance connectivity and/or coverage.

4) Data Aggregation and/or Fusion: In data aggregation [39], data which is coming from

different sources en route is combined into a single data packet. This helps to reduce redundancy

caused by spatial correlation of the sensed data and minimize the number of transmissions

required to forward the data back to the sink. However, as data processing is required at some (or

all) of the sensor nodes in order to do aggregation, this could potentially result in higher latency,

which should be taken into consideration when designing data aggregation algorithm for use in

sensor networks. Data fusion is similar to data aggregation in that data of different modalities

(such as pressure, temperature and salinity) are combined before data transmission.

5) Network Topology: Conventional wireless sensor networks have a single centralized sink

that is usually placed in a corner of the network, and all the source nodes have to send data to the

sink in a predominantly unilateral direction. As a result, sensor nodes that are near the sink have

to perform more data forwarding and packet transmissions, which leads to two undesired

behaviors: (i) increased contention and collisions near the sink; and (ii) nodes that are near the

sink will drain up their energy faster, resulting in shorter network lifetime. Subsequently, Seah

and Tan in [40] consider the use of more than one sink in a virtual multi-sink multi-path network

architecture, which provides spatially diverse routes in the network such that source nodes will

avoid sending all the data to one direction and cause network deterioration. This helps to

improve the load distribution of the network and increases the network lifetime, at the expense of

the physical deployment of more sinks.

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6) Cross-Layer Designs: Although traditional networking paradigms promote the usage of a

multi-layered protocol stack in which the different layers have minimal impact on each other,

this does not lead to optimal performance. Cross layered designs such as that proposed by Chen

et al in [41] can help to improve network performance by sharing information across the

different layers, at the cost of eliminating the interdependency between adjacent layers.

V. WISER A FRAMEWORK TO ENHANCE QOS IN WIRELESS SENSOR NETWORKS

Although there have been significant research efforts in the various mechanisms to achieve

QoS in WSNs, such as topology management, localization, etc, there is currently no

standardization on a QoS model or framework in wireless sensor network. In addition, there is

also little work which attempts to relate all these issues together to present an overall guideline

on how QoS can be achieved in wireless sensor network. Sanli et al [42] propose a two phase

protocol, EQos for the purpose of providing QoS in the network while achieving energy

efficiency. However, they only consider QoS metrics such as coverage, connectivity and delay,

which does not reflect the full application requirements of a WSN.

We hence propose WISER, which is a framework to enhance QoS in WIreless SEnsoR

networks (see Figure 2). It comprises of the a few components such as topology management,

localization, data aggregation, etc, all of which aim to improve at least one of the QoS

performance metrics that has been identified in the previous section. Each of the different

components in WISER may span across a few different networking layers of the protocol stack,

and they also interoperate with each other to improve the network performance. As each of these

components will result in tradeoffs in network performances, it is important to consider the

impact of the entire framework as a whole.

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Topology Management

Data aggregation and/or fusion

Controlled Mobility

Figure 2 WISER a framework to achieve QoS in WIreless SEnsoR networks

A. Network Model and Assumptions

We assume a heterogeneous network which has three types of nodes with different

capabilities: (i) wireless sensor nodes which are deployed randomly in possibly large numbers;

(ii) one or more sinks which are placed in strategic locations in the network; and (iii) Unmanned

Autonomous Vehicles (UAVs) which are deployed in lesser numbers due to their cost. The

UAVs are expected to have higher processing capabilities than the sensor nodes, and are also

able to move about independently. It is not necessary for the network to have UAVs, but if these

nodes do exist, then the total number of sensor nodes must be greater than the number of UAVs.

Quality of Service in

Wireless Sensor Network

Localization

Cross Layer Design

Security

Network Topology

Time Synchronization

21

All the nodes in the network (sensor nodes and UAVs) communicate via a wireless shared

channel. For simplicity, it is assumed that the sensing range of each node is the same as the

transmission range, although these values may be different in real life. In addition, the

transmission range of each node is relatively small as compared to the size of the physical

terrain; hence it is expected that nodes will have to communicate via multiple hops.

A node is either in the sleep or wake mode at any one time. If the node is awake, its radio is

either in the idle state, receive state or transmit state. A node may also sense data only if it is

awake; i.e. if the node is in the sleep mode, it can neither communicate with its neighbours nor

sense any physical phenomenon in the environment.

The sensor network is also expected to support both periodic and event-driven applications.

For periodic applications, each sensor node senses its environment at periodic time intervals and

forwards the data back to the sink(s). Event-driven applications are classified into two main

categories: (i) sink-initiated; or (ii) sensor node-initiated. In the latter case, a sensor node upon

sensing an unknown (or pre-specified) physical phenomenon, will send the sensed data back to

the sink immediately. In sink-initiated applications, the sink will send data request messages to

one or more groups of sensor nodes to request for specific data. We assume that event-driven

applications have more stringent delay constraints than the periodic applications, and that both

types of applications need temporal and spatial information, as well as full spatial coverage. No

other assumptions are made with respect to the application requirements.

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B. Network Initialization

During the initial deployment of the network, there is a network initialization period during

which nodes perform various functions such as authentication (for security purposes), time

synchronization (to achieve temporal accuracy), localization (to achieve spatial accuracy) and

route establishment (to the sinks). This initialization phase is triggered by the sink(s)

immediately after the nodes have been distributed in the network. To obtain authentication, time

synchrony and location information, the nodes in the network need to exchange periodic update

messages. To reduce the number of messages that are being exchanged, it is assumed that each

of these messages contain the required information for authentication, time synchronization,

localization and route establishment, i.e. each node does not need to send four separate messages

to perform these four functionalities. After the initialization phase, each node will still need to

send periodic update messages to one another this is one of the self-configuring and self-

maintenance features of the WISER. With the use of these periodic messages, nodes can easily

tell if the neighbouring nodes are in sleep or wake mode, or if the neighbouring node has already

failed. Initially, all the nodes are in the wake mode.

C. Network Components

Depending on the application requirements of the sensor network, different algorithms for

each of the network components may be deployed. For example, if the application does not

require high spatial accuracy, then the network can deploy a coarse-grained granularity

localization algorithm which usually incurs fewer overheads (and other costs) as compared to a

fine-grained granularity localization algorithm. It is also not necessary for the network to have

each and every component that is in the proposed framework if network lifetime is not an issue

23

in the network, it need not make use of a topology management scheme to increase energy

efficiency. However, as mentioned in the previous subsection, the minimum application

requirement is spatial and temporal accuracy; therefore, localization and time synchronization

must are the minimum components of the framework which must be implemented in the

network.

The functionalities of each of the different components in the framework are as follows:

Topology management coordinates sleep/wake schedules of the nodes in the network to

achieve energy efficiency;

Localization provides location information of the nodes without the use of GPS to

achieve spatial accuracy;

Cross-layer design facilitates information sharing among the different layers in the

networking protocol stack by message passing, so as to achieve better performance;

Time synchronization provides a common time base for all the nodes to achieve temporal

accuracy;

Network topology optimizes the topology of the network, possibly with the aid of more

sinks or better node placements;

Security provides authentication of the nodes in the network;

Controlled mobility optimizes the topology of the network for better system

performance, possibly with the aid of UAVs; and

Data aggregation and/or fusion combines data packets or data with different modalities

together, to reduce the number of required transmissions and to improve the energy

efficiency of the nodes in the network.

24

VI. CONCLUSION AND FUTURE WORK

While many diverse applications have been envisioned for use in wireless sensor networks,

there are still many issues that need to be worked on before Quality of Service can be supported

in these networks. In this paper, we have looked at the existing mechanisms to provide QoS in

different networks and also examined some of the constraints in WSNs which makes it difficult

to provision for QoS. We have also proposed WISER a framework which aims to enhance QoS

in WIreless SEnsoR Networks. WISER is made up of a few different network components;

however, it is not quintessential for the wireless sensor network to implement all the modules in

the framework. Only certain components in the framework can be implemented, depending on

the specific application requirements. In addition, as sensor networks are typically application-

specific, we have attempted to make WISER as generic as possible by not putting constraints on

the different protocols that have to be used for each component.

As part of future work, we will verify the correctness and completeness of WISER in

provisioning for QoS in wireless sensor networks. Furthermore, we will also evaluate the

efficiency of WISER as a framework to provide QoS support in the network, by considering the

QoS performance metrics that we have identified earlier on, viz. coverage, temporal accuracy,

spatial accuracy, etc.

25

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INTRODUCTIONRelated Work and MotivationExisting QoS MechanismsQoS Provisioning in the InternetQoS Provisioning in MANETsQoS Provisioning in WSNs

Wireless Sensor Networks Characteristics, Issues and ChallNetwork CharacteristicsIssues and Challenges

QoS in Wireless Sensor NetworksDifficulties of QoS provisioning in WSNsQoS Performance Metrics in WSNsMechanisms to Achieve QoS in WSNs

WISER A Framework to Enhance QoS in WIreless SEnsoR NetworNetwork Model and AssumptionsNetwork InitializationNetwork Components

Conclusion and Future Work