Optimizing QoS in Wireless Sensors Networks using a Caching Platform

emy L

eone

, Paolo Medagliani

and J

emie Leguay

UPMC, 4 Place Jussieu, Paris, France

Thales Communications & Security, 4 Avenue des Louvresses, Gennevilliers Cedex, France

Keywords:

Wireless Sensor Networks, Contiki, CoAP, Service Oriented Architecture, Embedded Web Services, Network

Architecture, Internetworking.

Abstract:

This paper addresses monitoring and surveillance applications using Wireless Sensors Networks (WSNs). In

this context, several remote clients are interested in receiving the information collected by the nodes of a WSN.

As WSN devices are most of the time constrained in energy and processing, we present a caching architecture

that will help reducing unnecessary communications and adapting the network to application needs. Our aim

here is to cache information in order to improve the overall network lifetime, while meeting requirements of

external applications in terms information freshness. We ﬁrst describe and evaluate the performance of our

caching system using a Constrained Application Protocol (CoAP)-HTTP proxy. We then extend this work

by showing how the cache could be enriched and exploited using cross-layer data. Based on information

from routing packets and estimations of nodes power consumption, we derive an optimization strategy which

allows to either maximize the user satisfaction, expressed in terms of freshness of cached data, in the presence

of constraints on network lifetime, or jointly maximize network lifetime and user satisfaction, obtaining a set

of non-dominated Pareto optimal solutions.

1 INTRODUCTION

Sensors are an interface between a physical phe-

nomenon of interest and a numerical information

world (Kovatsch et al., 2012). Recent developments

in technology have brought to the use of Wireless

Sensor Networks (WSNs) in the Internet of Things

(IoT), a new vision of the Internet aiming at push-

ing IP connectivity into smart objects. Nodes inside

a WSN are typically low-power and low-processing

constrained devices. The networks formed using

these devices are commonly referred to as Low-power

and Lossy-Networks (LLNs). Due to the typical ap-

plication ﬁelds of a WSN, such as road tunnel ﬁre

monitoring, intrusion detection, wildlife monitoring

and surveillance scenarios, devices are required to op-

erate for long periods of time, for instance decades,

without human intervention, thus the need for high

energy efﬁciency and self conﬁguring capabilities.

WSNs are proving to be one of the most interesting

innovation in logistic, environment and military tech-

nologies (Akyildiz et al., 2002). Due to their con-

strained nature, it is necessary to introduce optimiza-

tion and self-conﬁguring strategies to increase their

efﬁciency and autonomy.

First, in this paper we present an architecture for

caching data collected from a WSN. When a request

from a remote client arrives to the caching node, if it

has already a stored value which is “fresh” enough,

it replies to the remote client without forwarding the

request inside the WSN. This paper presents an im-

plementation and the evaluation of this caching so-

lution within a framework running the Constrained

Application Protocol (CoAP) protocol. Relying on

CouchDB to store data, our caching system runs on

the gateway node of a WSN and it is coupled with

Californium, a REST proxy for CoAP. To the best

of our knowledge, this is the ﬁrst paper presenting a

working HTTP-CoAP proxy and evaluating the im-

pact of CoAP caching on energy consumption in a

WSN.

Then, we extend this work by showing how the

caching mechanism could be enriched with cross-

layer data and exploited to reach energy optimization.

Relying on the routing tree created by the RPL pro-

tocol and on a simple analytical model describing the

energy consumption of the WSN nodes, we will de-

rive an optimization strategy which allows to either

meet a given network lifetime while satisfying Qual-

ity of Service (QoS) on the freshness of the cached

values or jointly maximize QoS and network lifetime.

The remainder of this paper is structured as fol-

Léone R., Medagliani P. and Leguay J..

Optimizing QoS in Wireless Sensors Networks using a Caching Platform.

DOI: 10.5220/0004314400230032

In Proceedings of the 2nd International Conference on Sensor Networks (SENSORNETS-2013), pages 23-32

ISBN: 978-989-8565-45-7

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

lows: In Section 2, we present a list of related works.

In Section 3, we introduce the main WSN protocols

we will consider in this paper. Then, in Section 4

we describe the caching architecture we have imple-

mented and we show the performance improvements

given to the use of the caching. In Section 5, we

present the energy model and the cross-layer multi-

objective optimization strategy. Finally, Section 6

concludes this paper.

2 RELATED WORK

IETF drafts from CoRE working group are the foun-

dations of the recent innovation in CoAP application

layer protocols(Shelby, 2012). Software and architec-

ture used in this paper and in most of academic publi-

cations are built on top of these speciﬁcations.

Gather information and send it to a single remote

user is not enough. In an IoT perspective, this infor-

mation has to be made easily and immediately avail-

able to several clients without any further operation.

Web service is a solution to achieve this. Several pa-

pers have been published to relate a Service Oriented

Architecture (SOA) and WSNs in order to create Web

Services. The ﬁrst paper related to this perspective

was (Delicato et al., 2003), where the authors intro-

duced the concept of connection between a WSN and

the Internet. In (Leguay et al., 2008), instead the au-

thors presented a SOA architecture adapted to energy

constrained devices.

Connecting a WSN with web applications has

been explored and several works such as (Kuladinithi

et al., 2011; ?; ?) introduce an architecture to bridge

a WSN to the web. The main point of these works

is to present a reliable architecture that implemented

a basic HTTP/CoAP (HC) gateway without proxying

functionalities.

The use of in-network caching in WSNs in order

to avoid redundant transmission is a known technique

for efﬁcient time and energy saving. In (Duquennoy

et al., 2011), this mechanism is adopted to improve

the performance of software updates in incrementally

deployed sensor networks

In this paper, leveraging on a simple yet effective

HC proxy architecture with caching capabilities, we

implement a lifetime optimization strategy, which ex-

ploits cross-layering techniques to meet requirements

on overall network lifetime, given some constraints

on the freshness of cached information.

3 PRELIMINARIES ON

WIRELESS SENSOR

NETWORKS

Traditional operating systems and Internet protocols

are not suited for operating with WSN devices. In or-

der to meet the typical strong requirements of WSNs

in terms of energy consumption, it is preferable to

let the nodes sleep for an as long as possible, wak-

ing them up only when a packet needs to be transmit-

ted. The basic mechanism proposed in Contiki OS, an

operating system for low-power devices, is the Con-

tiki MAC (Dunkels, 2011). The nodes remain sleep-

ing, that is with radio interfaces switched off) for the

largest part of the duty cycle. When a node needs

to transmit a message, it starts cyclically sending the

packet as soon as the receiving node acknowledges

the reception of the packet. During two consecutive

packet transmission attempts, the transmitting node

waits for a short time interval in order to detect an

eventual incoming acknowledgment packet.

Traditional IPv6 routing protocols are not de-

signed with unstable and intermitting communication

links in mind. Therefore, the IETF ROLL Work-

ing Group has deﬁned an IPv6 Routing Protocol for

Low power and lossy networks (RPL) optimized to

deal with multipoint-to-point trafﬁc, as well as point-

to-multipoint trafﬁc and point-to-point trafﬁc (Winter

et al., 2012). RPL allows the creation of a tree to

transfer information to (or from) a root from (or to)

the remote nodes. The node which has knowledge of

the structure of the tree is the border router.

RESTful web services and the HTTP protocol are

widely used to publish the status of resources (Ko-

vatsch et al., 2012). However, web services are not

suited for constrained networks due to the exaggerate

overhead introduced by HTTP and the presence of the

TCP congestion window. For these reasons, the CoRE

IETF working group aims at realizing the REST ar-

chitecture in a suitable form for LLNs through the

deﬁnition of a protocol referred to as Constrained Ap-

plication Protocol (CoAP) (Shelby, 2012).

When a HTTP client wants to receive the status of

a resource running CoAP, the HTTP request must be

translated into a CoAP request (and vice versa for the

response). Normally, the protocol translation is car-

ried out by a HC proxy. Unlike HTTP, CoAP requests

and responses are not sent over a previously estab-

lished connection. In fact, whilst HTTP is based on

the TCP transport protocol, CoAP is based on UDP,

which introduces less overhead and has no need for

session maintenance. In order to reduce response

time and network bandwidth consumption, the CoAP

servers or end-points may cache responses.

SENSORNETS2013-2ndInternationalConferenceonSensorNetworks

4 THE CACHING MODEL

In this section we will describe the caching mecha-

nism implementation and we will a simple analytical

model which well approximates the performance in

terms of requests served by the caching platform.

4.1 Justiﬁcation

One of the main reasons to develop a caching archi-

tecture for WSNs is to let sensors save energy by han-

dling in the caching node the information that has

already been requested by a remote client. In this

way the WSN devices can continue to stay in a sleep

state since remote requests are directly served by the

caching node.

In addition, exploiting suitable opti-

mization strategies and cross-layering techniques, we

can also use the information stored in the cache in or-

der to compute route optimization and graceful degra-

dation on the QoS of the WSN, in order to meet a

given overall network lifetime.

According to the CoAP protocol, the goal of the

cache is to reuse a previously stored, if recent enough,

response to satisfy a client request, reducing energy

consumption in the WSN. If the collected value is

older than a given threshold parameter, then a request

to the resource is performed. Otherwise, the value

stored in the cache is directly sent back to the client.

According to the protocol drafts and recommenda-

tions (Shelby, 2012; ?), how the proxy actually sat-

isﬁes the request is an implementation choice. How-

ever, we can assume several scenarios where the bor-

der router/proxy may be resource constrained. There-

fore, in some cases this solution may not be appro-

priate and one could prefer to avoid the use of such a

proxy, falling back to an end-to-end CoAP communi-

cation.

4.2 The Caching System Architecture

The caching system has been implemented relying on

the IETF protocols. We made this choice both be-

cause some libraries, which can be reused to speed

up the implementation, are already available and be-

cause IETF protocols have been speciﬁcally designed

to meet the constraints typical of IoT architectures.

In order to test and validate the performance of our

caching architecture, we have chosen Contiki OS,

since it provides for a simulation environment like

Cooja and implementations of the RPL and CoAP

protocols. As one of the goal of this paper is energy

We remark that power consumption in sleep state is or-

ders of magnitude lower than those in reception and trans-

mission states.

CoAP servers

Border

router

CoAP HTTP

Cache

CoAP

router

RPL

root

Pub/sub

HC proxy

Figure 1: Scheme of the implemented caching system.

saving inside the WSN, we have decided to use Con-

tikiMac MAC protocol, which provides for duty cycle

capabilities and it has been optimized to not occupy

too much RAM memory with its footprint. In ad-

dition, implementations of CoAP and RPL protocols

are already available in Contiki OS, thus allowing us

to quickly have a working and running testbed.

In Figure 1, we show a logical scheme of the im-

plemented caching system, where N CoAP servers ex-

pose one resource to be queried. The running routing

protocol is RPL. All the requested information and

the notiﬁcations from the nodes are gathered to the

root of the RPL tree, which overlaps with the WSN

gateway. The HC proxy will then send to the ﬁnal

user the requested information.

Our HC proxy implementation is based on the

Californium open source framework (Kovatsch et al.,

2012). The main reason of this choice is that Cal-

ifornium already implements in JAVA a basic set of

CoAP functionalities. We have then introduced two

information storing mechanisms. The ﬁrst one is the

caching database, where all the information from the

WSN are gathered and stocked and for which we have

chosen CouchDB, since it offers a REST HTTP API

to query stored results. The second one is a subscrip-

tions register to efﬁciently manage the subscribers to

the CoAP resources.

The arrival rate of each request made by a remote

user to the i-th resource is distributed as a Poisson pro-

cess of parameter λ

(dimension: [s

−1

]). The requests

are intercepted by the HC proxy which handles also

the eventual response from the given CoAP server. If

the proxy has a stored value which is fresh enough,

that is whose lifetime is smaller than a given value c

(as explained below), it directly replies to the request

from a remote client, without forwarding it into the

WSN. Otherwise, if the required value is not present

or it is older than c

, it transfers the request to the i-

th CoAP server. Additionally, the proxy stores the

sensor responses in the cache, in order to make them

available for other eventual incoming requests.

A similar approach is used for the publish-

subscribe register. In the case of observation requests

issued by a remote client, the proxy handles them by

maintaining a list of observed resources and a list of

OptimizingQoSinWirelessSensorsNetworksusingaCachingPlatform

root b

1 hop

2 hops

3 hops

Figure 2: The considered network topology. N = 12 nodes

(depicted as octagons) are placed in a regular grid with the

root (round node) in the middle. The number of lines of the

contour of each octagon refers to the distance, in terms of

hops, from the root.

interested clients. Each time a notiﬁcation for a re-

source update is sent from the node to the proxy, the

proxy will forward this messages to all the interested

subscribers.

Caching and observe mechanisms can operate to-

gether as well. For instance, an information already

requested by an observe request can be cached and

made available to another request not related to the

previous observation. In fact, the caching and the

observation mechanism are independent from each

other. Nevertheless, the two system should be used

together because information obtained by observation

can help the caching operations.

4.3 Experimental Validation of the

Caching Architecture

4.3.1 Experimental Setup

In order to evaluate the previously described caching

model, we introduce the following simulation setup.

A grid of N = 12 Contiki nodes running CoAP server

is deployed. These Contiki-CoAP nodes are emulated

using COOJA. Due to limitations of Contiki tools to

obtain and analyze the energy consumption, we have

preferred using the number of requests served by the

HC proxy as a metric to measure of saved energy. In

order to have a small memory footprint, each of the

CoAP server will only have a single resource and this

resource will be a ﬁxed text message. An illustrative

example of the considered topology is depicted in Fig-

ure 2. According to the scheme shown in Figure 1, a

remote client ﬁrst randomly chooses one of the CoAP

servers and then issues to it a GET request for know-

ing the status of the selected resource. The interarrival

time between two consecutive requests is distributed

according to an exponential distribution of parame-

ter λ.

Since the requests for the CoAP resources are

equiprobable, exploiting the superposition propriety

of the Poisson processes, we can say that the arrival

rate of the requests for each node is still a Poisson dis-

tribution of parameter λ

= λ/N, where i denotes the

resource on the i-th node, therefore the arrival rate of

the requests for a generic i-th node is a Poisson pro-

cess of parameter λ

The requests from the remote client are inter-

cepted by the HC proxy, which translates HTTP re-

quests into CoAP requests and, conversely, translates

the CoAP responses into HTTP responses. In addi-

tion, the proxy caches the responses carried out by

the CoAP nodes in order to make them available for

eventual other incoming request. The freshness of the

cached value for the i-th node is denoted as c

. If a

request for the status of the i-th resource arrives, the

proxy ﬁrst veriﬁes in the cache if it already has an

available value to be sent back to the client, that is a

value whose lifetime is smaller than c

, otherwise it

forwards the request to the WSN node to obtain an

updated response. Additionally, the HC proxy stores

the collected value in the cache for future requests and

forward the collected information to the ﬁnal client.

Like in every caching system, refresh parameters are

highly critical. If c

is too large, information won’t

be sufﬁciently renewed and cached values may often

be different from the real values. If c

is too small,

then the proxy will be underperforming, wasting en-

ergy and not reaching the optimal performance.

The simulations have been carried out generat-

ing 50 requests from the remote client to the CoAP

servers in the WSN. In order to bound possible statis-

tical ﬂuctuations in the simulation results, the simula-

tion results are obtained by averaging over 10 consec-

utive runs.

The main parameters of the overall system model

are listed in Table 1. The power consumption pa-

rameters presented in this table have been taken from

the internal data-sheet of a prototype sensor node by

Thales. Like other well-known commercial sensor

nodes using the CC2420 chipcon (e.g., Crossbow Mi-

caZ, Berkeley Telos (Polastre et al., 2005)), power

consumption is higher in the reception mode than in

the full-power transmission mode.

In order to simplify the simulations, we have con-

sidered the same values of λ

, c

, and the same duty

cycle for each CoAP node in the WSN.

We remark that is λ is the arrival rate of the requests to

the HC proxy.

SENSORNETS2013-2ndInternationalConferenceonSensorNetworks

Table 1: System parameters considered in the simulations.

Node transmission rate R 250 kbps

Interval between two consecutive strobed preambles T

0.4 ms

Time to detect an incoming Ack packet T

0.16 ms

GET packet length L

GET

87 bytes

Response packet length L

Ans

96 bytes

Ack transmission duration S

Ack

0.608 ms

Transmission power consumption P

0.0511 W

Reception power consumption P

0.0588 W

Sleep power consumption P

sleep

2.4 · 10

−7

Number of nodes in the network N 12

4.3.2 Analytical Model of the Caching

Architecture

The mechanism of caching can be summarized as fol-

lows: a client issue a request to one of the nodes in the

network. This means that if the proxy doesn’t have an

updated replica of the required value, it transfers the

request to the WSN, caching the response for eventual

other requests to the same resource. The interarrival

time T

(dimension: [s]) between two consecutive re-

quests can be modeled as an exponentially distributed

random variable of parameter λ

. Since in our simu-

lation the destination of the request is randomly cho-

sen, on average, we can say that we must wait for N

consecutive requests before having a new request on

the same node i. Therefore, assuming that λ

is the

same for all the N nodes in the network, the time in-

terval between the different requests to the same node

is distributed with an exponential law of parameter

T = NT

= N/λ

The cache miss ratio (MR) can be deﬁned as the

probability that a request is not served by the caching

architecture. Therefore, MR corresponds to the prob-

ability that the interarrival time between two consec-

utive requests to the same node is larger than c

, that

MR =

∞

−

dt = e

−

. (1)

Therefore, the cache hit ratio (CH) can be expressed

as the complementary of MR, that is

CH = 1 − e

−

. (2)

According to the scheme depicted in Figure 1, the

remote clients send a resource observation request to

one of the N randomly chosen CoAP servers. The

request arrival rate is distributed according to a Pois-

son distribution of parameter λ

, where i denotes the

node to which the request is directed. As explained

in the previous subsection, for every observation it is

possible to deﬁne a freshness parameter c

, that is for

how long the collected data is valid before the data is

discarded by the proxy and a new data request must

be issued to the CoAP server.

In truth, as explained

later, for every observed value we can deﬁne a min-

imum and a maximum admittable freshness values,

denoted as c

min

and c

max

respectively.

If the resource required by the remote client is not

in the cache or the cached value is outdated, the proxy

issues a CoAP request to the given CoAP server.

Given that a request for the i-th node arrives, on aver-

age, every T

s, it can be proved that the average period

(dimension: [s]) between two consecutive requests

to the same node can be deﬁned as:



if T

≤ c

else.

(3)

In the following subsection, we will provide the per-

formance of the caching system in terms of cache hit

ratio and network lifetime.

4.3.3 Performance Validation of the Caching

Architecture

In order to evaluate the performance of the imple-

mented caching system, we ﬁrst evaluate the cache

hit ratio CH as a function of the freshness parame-

ter c

. We indicate, for each performance curve, the

conﬁdence interval 2σ, where σ is the standard devia-

tion, over consecutive simulation runs, with respect to

their average value. In Figure 3 (a), the results are de-

picted. Both simulation (solid lines) and theoretical

(dashed lines) results ares shown considering differ-

ent values of the parameters λ

. Of course, the higher

the value of λ

, the higher the number of requests per

unit of time. All the curves shown in Figure 3 (a)

are growing when the cache duration parameter in-

creases. Clearly, the larger the cache duration, the

In our derivation, we assume that each node has only

one available CoAP resource, therefore the freshness of the

i-th observed value corresponds to the freshness of the i-th

node.

OptimizingQoSinWirelessSensorsNetworksusingaCachingPlatform

0 2 4

8 10

[s]

0.0

0.2

0.4

0.6

0.8

= 2, sim.

= 2, theo.

= 1, sim.

= 1, theo.

= 0.5, sim.

= 0.5, theo.

0 2 4

8 10

[s]

120

Lifetime [days]

= 2

= 1

= 0.5

(a) (b)

Figure 3: (a) CH as a function of the cache freshness c

. Simulation (solid lines) and theoretical (dashed lines) results are

presented. (b) Network lifetime as a function of the cache freshness c

. In both cases, different values of λ

have been

considered: λ

= 2 (lines with circles), λ

= 1 (lines with squares), and λ

= 0.5 (lines with triangles).

higher the probability that the request is served by the

proxy, not being forwarded to the WSN nodes. Sim-

ilarly, for a given value of cache duration, the higher

the number of arrival per unit of time, the higher the

probability that the proxy replies to the client with

a cached value. The beneﬁcial effect of the use of

caching is not limited only to the energy saving re-

lated to the lower number of packets transferred to the

WSN with the use of proxy. When the trafﬁc is high,

in fact, it becomes more and more likely that a cached

value may serve several requests from some remote

clients. Therefore, the introduction of a caching sys-

tem allows to reduce the requests transferred to the

WSN and, consequently to signiﬁcantly reduce packet

retransmissions or losses.

The use of a caching system also allows to save

energy and extend the network lifetime, deﬁned as the

time required to the network to have at least one node

running out of energy. In Figure 3 (b) we can see the

impact of caching on the lifetime of our system. As

the intuition may suggest, the larger c

, the larger the

network lifetime, since a larger number of requests

are served by the cache without being transferred to

the WSN. The “stair” behavior of the curve with λ

0.5 is due to the fact that, according to the deﬁnition of

in (3), different values of c

lead to the same number

of requests transferred to the WSN.

5 ADVANCED CACHE

EXPLOITATION

5.1 Parameters of the Optimization

Framework

The choice of the optimal caching lifetime is appli-

cation dependent. According to the energy level of

a node, we can decide to increase the lifetime of the

cached values, accepting less updated values but, on

the other side, extending the network lifetime, due

to the reduced number of requests transferred to the

WSN.

In order to derive an optimization framework, we

must rely on the hypothesis that the WSN nodes are

static and the quality of the communication links are

fair enough to consider the transmissions as error free,

so no energy is wasted for packet retransmissions. We

also assume that no transmission phase locking mech-

anism has been used at the MAC layer. We need to in-

troduce some parameters to describe the optimization

framework within the caching architecture:

• c

min

and c

max

are the lower and upper bounds, re-

spectively, of the amount of time that a requested

information can live in the cache. It’s useless to

request an information that is fresher than c

min

Similarly, an information older than c

max

is not

meaningful anymore.

• c

is the cache lifetime of the i-th resource, with

∈ [c

min

max

]. This information can be changed

according to the current state of our system in or-

der to fulﬁll our optimization goals. When an

updated information is needed, the c

parameter

can be set to 0, so that the caching node becomes

transparent.

It is necessary to deﬁne some metrics of interest

in order to characterize the optimization strategy in-

troduced in this paper. First of all, we deﬁne the net-

work lifetime as the time interval between the net-

work start-up and the ﬁrst node running out of en-

ergy. In our approach, we consider, as a metric of

user satisfaction, the freshness c

of the values stored

in the cache, that is, the shorter the cache duration,

SENSORNETS2013-2ndInternationalConferenceonSensorNetworks

the higher the user satisfaction. Of course, referring

to the results presented in Figure 3 (a), when the du-

ration of the cached values is small, it is likely that

an incoming request is transferred to the WSN, thus

resulting in energy consumption and reduction of the

network lifetime. On the other side, the maximization

of the network lifetime requires to minimize the trans-

missions inside the WSN, that is to keep the duration

of the cached values as elevated as possible. The user

satisfaction for the i-th resource can be then deﬁned

max

− c

max

− c

min

∗ 100, (4)

that is a user satisfaction of 0% when c

= c

max

and a

user satisfaction of 100% when c

= c

min

5.2 Energy Model of the Cached WSN

In order to describe the energy consumption associ-

ated to the collection of resources, we assume that the

impact of RPL messages is neglectable. Roughly, we

can say that the energy consumption of each node is

given by the sum of the energy consumptions of its

hardware components. For the sake of simplicity, we

only integrate in the energy model contributions from

the communication sub-unit (radio transceiver). The

network lifetime is deﬁned as the time needed for the

average residual energy E

to be lower than a (given)

threshold value E

, which can be used to model the

physical behavior of a node. The residual energy of

node i at time t, denoted as E

(t), can be expressed as

(t) = E

− Ω

tot

t (5)

where E

is the initial energy of the i-th node and

Ω

tot

(dimension: [W]) is the power consumption as-

sociated to its communication operations. For the

sake of simplicity, we assume that all nodes have

the same initial energy. According to the descrip-

tions of the Contiki MAC protocol in Section 3 and

in (Dunkels, 2011), there are four possible states for

a node: (i) transmission, (ii) reception, (iii) sleep,

and (iv) channel listening, with corresponding power

consumptions denoted as Ω

, Ω

, and Ω

(dimension:[W]), respectively. Before starting the

derivation we need to deﬁne the period T

MAC

as the

time interval between two subsequent active phases

of the i-th node, and T

sleep

as the duration of the sleep

phase of the i-th node.

The Ω

tot

in (5) can be ex-

pressed as follows:

Ω

tot

= Ω

+ Ω

(6)

The duration of the active phase T

act

can be evaluated

as T

act

= T

MAC

− T

sleep

where: Ω

is the power consumption associated with

the sleep phase of the i-th node; Ω

is the power

required when performing the channel listening op-

erations (over a period of duration T

MAC

); Ω

is the

power used by a node to receive a packet; Ω

is the

power used to transmit an alert packet.

According to the RPL protocol, there are three

kinds of nodes: (i) the CoAP servers, (ii) the routers,

acting also as CoAP servers, and (iii) the root of the

tree. Since the length of the GET messages and the

observed values are different in length, we will dis-

tinguish between the time interval required to trans-

mit/receive a GET packet, deﬁned as S

GET

= L

GET

/R,

and the time required to transmit/receive the response

from a CoAP server, deﬁned as S

Ans

= L

Ans

/R, where

GET

and L

Ans

are the packet lengths of the GET

packet and the observation packet, respectively, and

R is the transmission rate of the nodes. According to

the Contiki MAC protocol, as described in (Dunkels,

2011) when a node needs to transmit a packet, it must

be for a period of time S

pck−Tx

in transmission and

for a period of time S

pck−Tx

in reception, in order

to receive the acknowledgment (ACK) message from

the receiving node. This period of time spent in trans-

mission of the packet can be expressed as

pck−Tx

3 + b

sleep

−S

pck

(7)

whereas the period of time spent in reception during

the transmission phase can be expressed as

pck−Tx

3 + b

sleep

−S

pck

+ S

ack

(8)

where S

pck

indicates a generic transmitted packet (it

must be replaced with S

GET

or S

Ans

depending on

whether the node is transmitting a GET or a response

packet), T

is the time required to successfully de-

tect an acknowledgment from the receiver, and S

ack

is the time required to transmit an ACK. Equation

(7) has been derived averaging between the best case

of packet transmission, that is the node starts trans-

mitting when the receiving node is waken up, and the

worst case, that is the receiving node has just switched

into the sleep phase when the transmitting node be-

gins sending the packet. Similarly, when a node needs

to receive a packet, it spends a part of the time in re-

ception and a part of the time in transmission since

it has to send the ACK to the transmitting node. The

period of time spent receiving the packet can be ex-

pressed as

pck−Rx

pck

+ T

(9)

OptimizingQoSinWirelessSensorsNetworksusingaCachingPlatform

whereas the period of time spent in transmission dur-

ing the reception phase can be expressed as

pck−Rx

= S

ACK

(10)

where T

is the interval between each packet trans-

mission and S

ACK

is the duration of the transmission

of an ACK message. We point out that equation (9)

has been obtained averaging the energy consumption

between the best, that is the receiving node needs to

receive only the packet once, and the worst case of

packet arrival, that is the receiving node wakes up just

after the beginning of the transmission of the packet

by the transmitting node, so it has to wait for the next

packet transmission to correctly receive it.

According to the different nodes allowed by the

CoAP protocol, the terms Ω

and Ω

will be dif-

ferent for a CoAP server, a router, and the root of

the tree. In the ﬁrst case, the terms will be replaced

by Ω

CoAP−T

and Ω

CoAP−R

, in the second case the

terms will be replaced by Ω

Router−T

and Ω

Router−R

whereas in the third the terms will be replaced by

Ω

root−T

and Ω

root−R

. Considering a generic CoAP

server without routing functionalities, which only re-

ceives a GET message and transmits the observed

value, the power consumption during the transmission

phase can be expressed as

Ω

CoAP−T

Ans−Tx

+ P

Ans−Tx

(11)

whereas the power consumption during the reception

phase can be expressed as

Ω

CoAP−R

GET−Rx

+ P

GET−Rx

(12)

replacing the packet lengths of the GET message and

of its related response into equations (7), (8), (9),

and (10). The terms P

and P

denotes the power

consumptions of a node in transmission and recep-

tion phase. Considering the root node of the network,

instead, the power consumptions to transmit a GET

message to a generic node i and receive back its re-

sponse can be expressed as

Ω

root−T

GET−Tx

+ P

GET−Tx

and

Ω

root−R

Ans−Rx

+ P

Ans−Rx

respectively. Since the root transmits to each of the N

associated children, the power consumption to trans-

mit to all the nodes and to receive a packet from all

the nodes can be expressed as

Ω

root−T

∑

i=1

Ω

root−T

(13)

and

Ω

root−R

∑

i=1

Ω

root−R

, (14)

respectively. Considering the intermediate routing

nodes, since they are in charge of transferring both the

request from the root to the CoAP servers and the re-

sponses from the CoAP servers to the root, we can say

that they act both as root for the children nodes and as

CoAP servers for the root. Therefore, the power con-

sumptions to forward the incoming packets can be ex-

pressed as

Ω

Router−T

∑

j∈m



Ω

CoAP−T

+ Ω

root−T



+ Ω

CoAP−T

(15)

and

Ω

Router−R

∑

j∈m



Ω

CoAP−R

+ Ω

root−R



+Ω

CoAP−R

(16)

where m

, as shown in Figure 1, denotes the number of

children associated to the i-th node. The terms at the

right-hand side of (15) and (16) have been introduced

since these nodes, besides acting as routers, may also

behave as CoAP servers.

Finally, the power consumption in the channel lis-

tening state can be expressed as

Ω

act

MAC

, (17)

whereas the power consumption in the sleep phase

can be denoted as

Ω

sleep

Sleep

MAC

− Γ

, (18)

where P

Sleep

is the power consumed in the sleep state,

and Γ

are two corrective terms, described

in more detail below. During normal operations the

node either performs channel listening and sleep op-

erations or transmits/receives a packet. Γ

and Γ

are used to reﬁne the power consumption due to sleep

operations. In fact, the sleep and also the transmission

and reception intervals overlap for short intervals, so

that without these two terms the power consumption

budget would be higher than the correct one. In par-

ticular, Γ

can be expressed as

= Ω





3 + b

sleep

−S

pck

+ T

) + S

ack





MAC

(19)

whereas Γ

can be expressed as

= Ω



pck

+ T

+ S

ack



MAC

(20)

SENSORNETS2013-2ndInternationalConferenceonSensorNetworks

The term Γ

takes into account the fact that, during

transmission operations, such as (i) strobed transmis-

sion of the packet over an interval of duration T

sleep

(ii) packet transmission, and (iii) acknowledgment re-

ception, a node would normally be in the sleep state.

Thus, the correction factor Γ

is necessary, since

otherwise the energy consumed by the node with this

model would be higher than the real value because

reception and transmission operations would overlap

with normal sleep operations for a period. Similar

considerations can be carried out for the term Γ

. In

fact, when a node is waiting for the acknowledgment

window to transmit an ACK message, to receive the

preamble, and to receive a packet, it would normally

be in the sleep state.

Replacing expressions (19) and (20) into (18) and

expressions (11), (12) (if a CoAP node, otherwise

(15) and (16) for a router or (13) and (14) for the

root), (17), and (18) into (6), it is possible to derive

an expression for the energy consumption depending

on topology and communication parameters.

5.3 An Optimization Framework

The above introduced analytical model can be used

during network conﬁguration in order to ﬁnd the

proper values of c

which allow to meet a given re-

quirement on minimum network lifetime. Of course,

as introduced in Subsection 5.1, considering a min-

imum and a maximum value of cache duration, the

best network lifetime can be reached using c

= c

max

On the other side, if we want to maximize the average

user satisfaction γ, deﬁned as γ =

∑

i=1

/N, we must

set c

= c

min

, with the opposite undesirable effect that

the lifetime is minimized.

The choice of the suitable values of c

introduces

then a trade-off between network lifetime and user

satisfaction. In addition, since some nodes act as

routers, decreasing the number of transmissions to

the i-th node may affect not only the lifetime of

node i, but also the lifetime of the routers which are

on the path between node i and the root. In order

to solve these problems, we use the Non-dominated

Sorting Genetic Algorithm II (NSGA II) (Deb et al.,

2002). Given the minimum target lifetime, the inter-

val [c

min

max

], and the network topology generated

by the RPL protocol, the solver provides for the best

set of c

parameters which satisﬁes the constraints on

lifetime while maximizing the user satisfaction γ.

Considering a scenario with c

min

= 1 s and c

max

9 s, λ

= 1 s

−1

, and the topology with N = 12 nodes

shown in Figure 2, we have set some constraints on

network lifetime, namely at least 25, 50, and 75 days,

and we have evaluated the set of c

opt

which maxi-

Table 2: Performance results of the optimization strategy

compared with the cases with c

= c

min

and c

= c

max

. For

estimating c

opt

, target lifetimes of 25, 50, and 75 days have

been considered.

lifetime [days] γ

min

11.4219 100%

opt

25 95%

opt

50 68%

opt

75 49%

max

101.256 0%

0 20 40

80 100

γ [%]

100

Lifetime [days]

Pareto front

Figure 4: Pareto front of the considered caching architec-

ture.

mizes the user satisfaction while fulﬁlling the network

lifetime requirement. As a comparison, we also show

the results obtained without the optimization tool, that

is those obtained with c

= c

min

and c

= c

max

. The

results are shown in Table 2. The set of c

obtained

through the optimization tool allows to effectively

meet the requirement on network lifetime while max-

imizing the user satisfaction.

If no constraints on lifetime are imposed, ﬁnding

a suitable working point (i.e., a suitable set of values

of c

) for the system can be seen as a multi-objective

optimization problem. In this case, the NSGA II al-

gorithm allows to ﬁnd the non-dominated Pareto front

of solutions, that is the set of values which are Pareto

optimal. According to the deﬁnition of Pareto op-

timality, a solution is Pareto optimal when it is not

possible to improve one objective without reducing

at least one of the other objectives. In Figure 4, the

Pareto front for the presented caching architecture is

shown. According to the presented results, it is possi-

ble to ﬁnd out a set of suitable working points which

allows to achieve the best possible network conﬁgu-

ration. The solver associates to each of these points a

set of parameters c

which can be used at the network

start-up to properly tune the cache duration.

We point out that the above presented optimiza-

tion strategy has been evaluated according to a spe-

OptimizingQoSinWirelessSensorsNetworksusingaCachingPlatform

ciﬁc deﬁnition of QoS. However, it can be easily gen-

eralized in order to encompass other objective func-

tions or other constraints in the optimization process.

6 CONCLUSIONS

This paper has addressed the problem of energy efﬁ-

cient QoS optimization in WSNs using cross-layering

techniques and exploiting a speciﬁcally introduced

caching platform. We have ﬁrst presented the imple-

mentation of a caching solution based on a proxy node

which is in charge of answering, if a cached value is

available, to a request coming from a remote client

without transferring it to the WSN. Simulation results

show that the introduction of a caching architecture

has an impact in terms of energy saving on the system

performance, since it allows to reduce the transmis-

sions inside the WSN. Then, we have introduced an

optimization framework which, exploiting the infor-

mation collected by the RPL protocol and given a set

of constraints on the minimum and maximum values

of cache duration, allows to optimally conﬁgure the

values of the caching lifetimes. The proposed opti-

mization strategy allows to either ﬁnd suitable solu-

tions in the presence of constraints on network life-

time or to ﬁnd out the optimal non-dominated set of

solutions in the case of multi-objective optimization.

Further works include the real-time change of the

routing paths, in order to save the nodes which are

running out of energy, and large-scale experiments,

using the Senslab platform (Senslab Website, 2008),

in order to get real energy consumption data from

physical nodes.

ACKNOWLEDGEMENTS

This work is funded by the European Community’s

Seventh Framework Programme, area “Internetcon-

nected Objects”, under Grant no. 288879, CALIPSO

project - Connect All IP-based Smart Objects. The

work reﬂects only the authors’ views; the European

Community is not liable for any use that may be made

of the information contained herein.

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SENSORNETS2013-2ndInternationalConferenceonSensorNetworks