A Study about the Impact of Encryption Support on a Mobile Cloud
Computing Framework
Francisco A. A. Gomes, Paulo A. L. Rego, Fernando A. M. Trinta, Windson Viana,
Francisco A. Silva, Jos
e A. F. de Mac
edo and Jos
e N. de Souza
Group of Computer Networks, Software Engineering and Systems (GREat) Federal University of Cear
a (UFC),
Offloading, Encryption, Performance Evaluation.
Mobile Cloud Computing joins two complementary paradigms by allowing the migration of tasks and data
from resource-constrained devices into remote servers with higher processing capabilities in an approach
known as offloading. An essential aspect of any offloading solution is the privacy support of the informa-
tion transferred among mobile devices and remote servers. A common solution to address privacy issues in
data transmission is the use of encryption. Nevertheless, encryption algorithms impose additional processing
tasks that impact both on the offloading performance and the power consumption of mobile devices. This
paper presents a study on the impact of using cryptographic algorithms in the CAOS offloading platform.
Results from experiments show that the encryption time represents 2.17% to 5.35% of the total offloading
time depending on the amount of offloaded data, encryption key size, and the place to run the offloaded task.
Similar behavior occurred regarding energy consumption.
According to (Chen and Hao, 2018), Mobile Cloud
Computing (MCC) is a novel approach for mobile ap-
plications (apps) aiming at providing a range of ser-
vices, equivalents to the cloud, adapted to the capac-
ity of resource-constrained devices, besides perform-
ing improvements of telecommunications infrastruc-
ture to improve the service provisioning. Accord-
ing to a research report published by MarketsandMar-
, the edge computing market size is expected
to grow from $2.8 billion in 2019 to $9.0 billion by
2024. The key factors driving the edge computing
market include the growing adoption of the Internet
of Things (IoT) across industries and rising demand
for low-latency processing and real-time.
MCC addresses especially apps that are very sen-
sitive to high network delays due to the commu-
nication overhead between mobile devices and data
centers resources located in the core of the current
Internet infrastructure, and far away from the net-
work edge. Some examples include real-time mobile
games, crowdsensing systems, and augmented reality
applications (Varghese and Buyya, 2018). In MCC,
the most common research topic is offloading, which
represents the idea of moving data and processes from
mobile devices with scarce resources to more power-
ful machines (Abbas et al., 2018). Many research has
been done on the offloading topic, and several frame-
works have been proposed to provide offloading fea-
tures in mobile apps (Rego et al., 2016). One of these
solutions is CAOS (Gomes et al., 2017b), a software
infrastructure to support the development of mobile
context-aware applications based on the Android plat-
form. CAOS provides offloading features to enable
the processing of contextual data from mobile devices
into cloud platforms. CAOS also has a version called
CAOS Device-to-Device (D2D), which supports of-
floading between mobile devices (Dos Santos et al.,
2018). Both CAOS and CAOS D2D monitor the ap-
plication life-cycle on a local mobile device, and de-
cide whether it is worthy or not to offload a method
and its parameters to a remote mobile device. De-
spite its potential, MCC has several challenges, such
as the privacy and security of sensitive data used on
offloadable processes (Gupta et al., 2018). Protect-
ing the user privacy enforces consumers’ trust in a
mobile or cloud platform. However, it is challeng-
ing to achieve privacy on MCC systems once the data
Gomes, F., Rego, P., Trinta, F., Viana, W., Silva, F., F. de Macêdo, J. and N. de Souza, J.
A Study about the Impact of Encryption Support on a Mobile Cloud Computing Framework.
DOI: 10.5220/0009420804000407
In Proceedings of the 10th International Conference on Cloud Computing and Services Science (CLOSER 2020), pages 400-407
ISBN: 978-989-758-424-4
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
transferred between mobile devices and remote nodes
(such as methods parameters) may include user’s sen-
sitive data (Mollah et al., 2017). So, MCC scenarios
require techniques such as encryption to offload meth-
ods into untrusted nodes. However, this approach in-
creases the overall round-trip time to exchange mes-
sages between mobile devices and offloading servers,
such as cloudlets(Satyanarayanan et al., 2009). En-
cryption also increases energy consumption, which is
a crucial problem for current mobile applications in
This paper addresses this issue by presenting a
study about the impact of supporting encryption in
both CAOS and CAOS D2D frameworks. Our re-
search goal is to measure the overhead of introducing
cryptographic algorithms in both frameworks. The re-
mainder of this paper is organized as follows. Sec-
tion 2 presents related works to our study. The third
section presents both CAOS and CAOS D2D frame-
works. Section 4 describe evaluation experiments per-
formed to measure CAOS performance and energy
consumption after including encryption mechanisms.
Finally, Section 5 concludes the paper and outlines
possible future work.
We can find in the scientific literature several works
focusing on the computation offloading topic (Sanaei
et al., 2014), (Fernando et al., 2013), (Zhang et al.,
2012), (Dinh et al., 2011). However, few of them take
into account message privacy using encryption in the
offloading between mobile devices and remote envi-
ronments. Most of them does not evaluate the im-
pact of encryption on offloading performance or en-
ergy consumption. For instance, two studies in the
literature have designed frameworks to ensure pri-
vacy and authentication in mobile cloud services. The
Mobile Cloud Authenticator framework (Donald and
Arockiam, 2015) provides authentication of mobile
devices in MCC. The framework protects users cre-
dentials and prevents unauthorized access to cloud
services. (Gomes et al., 2017a) introduces COP, a
service to support the design of mobile apps that use
contextual data from multiple users, such as those
on crowdsourcing scenarios. The COP service aims
at storing and processing the contextual data gath-
ered from multiple mobile devices into cloud/cloudlet
servers. COP enables privacy by filtering the data ex-
changed between mobile devices and remote services
using specific policies. The end-user is responsible
for choosing which contextual information he wants
to share. However, COP does not support encryp-
tion on the exchanged data. Silva et al. (Silva et al.,
2017) studied how to improve mobile apps running
on devices with scarce resources by using the MCC
paradigm. The authors extended the MpOS frame-
work (Costa et al., 2015) to add cryptographic mech-
anisms - symmetric (AES) and asymmetric (RSA) al-
gorithms - in an offloading environment. Their exper-
iments show that encryption affects energy consump-
tion and performance, but the authors did not measure
the amount of time spent on encrypting/decrypting
tasks separately, neither presented the impact of the
network connection in the performance result. Be-
sides that, they did not evaluate any solution that per-
forms offloading among mobile devices. In (Diro
et al., 2018), the authors state that the Internet of
Things (IoT) presents new challenges concerning se-
curity support and claim that these challenges should
not be addressed by IoT devices, once they have
scarce resources to process any additional task, such
as encryption ones. A possible solution is Fog Com-
puting, where encryption tasks may be offloaded to
fog nodes to reduce computational and storage loads
on IoT devices. Diro et al. state that lightweight cryp-
tographic functions, such as elliptic curve cryptogra-
phy, have proven to be suitable for embedded sys-
tems, rather than solutions such as asymmetric cryp-
tography. Unlike our work, they did not measured
the impact of network quality on the offloading pro-
cedures, neither they evaluated energy consumption
on their experiments. Padhi et al. (Padhi et al.,
2016) propose a cloudlet-based solution for oppor-
tunistic mobile networks called SecOMN. Their so-
lution introduces a hybrid encryption algorithm that
combines both symmetric and asymmetric key cryp-
tography systems. SecOMN relies on offloading tasks
encryption to remote nodes with better processing ca-
pabilities. However, they presented no experiments
CAOS is a software infrastructure for the devel-
opment of mobile context-aware applications based
on the Android platform, which provides offloading
mechanisms to delegate the migration and process-
ing of contextual data from mobile devices into cloud
platforms (Gomes et al., 2017b). CAOS allows An-
droid programmers to mark which methods should be
offload to remote servers. The framework uses a hy-
brid decision-making strategy to decide if it is worthy
to migrate a particular method to a remote node, such
a cloud or cloudlet node. CAOS D2D is an alterna-
tive version of CAOS that provides a subset of the
A Study about the Impact of Encryption Support on a Mobile Cloud Computing Framework
original CAOS framework but specially designed to
offload methods into other mobile devices (Dos San-
tos et al., 2018). Both CAOS and CAOS D2D are
based on a client/server architecture, as shown in Fig-
ure 1. Components present two corresponding mod-
ules, on both client and server side. Figure 1 shows
the CAOS/CAOS D2D architecture where its main
components are divided into three tier: CAOS API,
CAOS Server, and CAOS D2D Server.
The CAOS API runs on client mobile device and
is composed by 6 (six) components: Discovery and
Deployment Client, Profile Monitor, Authentication
Client, Security Service, Offloading Client, and Con-
text Client. The Discovery Client uses a mechanism
based on UDP/Multicast to discover CAOS Servers
running in the user’s local network (i.e., CAOS D2D
server or cloudlets). The Deployment Client injects
dependencies on the CAOS Server. Dependency is a
mobile app copy (APK) that needs to be saved on the
server. The Authentication Client sends device data
to the server-side to keep the list of devices attached
to a specific CAOS infrastructure and allow the ex-
change of keys between the client and server-side of
the framework Authentication Service (Section 3.3).
CAOS monitors the mobile application life-cycle and
intercepts its execution flow whenever an annotated
method is called. In CAOS, the application’s methods
can be marked with a Java annotation - @Offloadable,
which denotes that those methods must run, prefer-
ably out of the device. After intercepting the method
call, the CAOS starts the offloading process or not.
CAOS performs a decision-making process to decide
whether it is worthy or not to perform a method of-
floading. The process is performed in two steps: one
at the server-side, and another on the mobile side.
The cloud side keeps receiving profiling data from
each mobile device connected to its infrastructure and
creates a decision tree-based structure with the main
metrics used on the decision-making process, such
as latency, parameter types, amount of data, and so
on. This data structure is sent back to the mobile de-
vice that has only to enforce the decision based on
the current values of the monitored data (Rego et al.,
2019). By using a decision tree-based structure cre-
ated on the CAOS server, the CAOS can decide lo-
cally on the mobile device when it is worth to per-
form an offloading process. If the answer is negative,
the method execution flow is resumed and the method
is performed locally. Otherwise, the CAOS requests
the Offloading Client to start the method offloading
process, which in turn, transfers the method and its
parameters to the Offloading Service in the cloud side.
The Profile Monitor module is responsible for moni-
toring the mobile device environment (e.g., network
bandwidth and latency, power, and memory status).
It sends such information periodically to the Profile
Services. CAOS server-side uses these data to create
the decision tree structure based on the mobile device
information and then sends it back to the mobile side.
All context information of each mobile device con-
nected to the CAOS is sent by Context Client to the
Context Service to keep a database of contextual in-
formation history. The idea is to explore the global
context (i.e., the context of all mobile devices) to pro-
vide more accurate and rich context information. The
Context Client exchanges contextual data between the
mobile and the cloud sides. In CAOS, filters can be
performed in context information repositories. If an
application has an @Offloadable marked method that
accesses context information using the filter concept,
and it can benefit itself from the offloading process to
access the global context repository. CAOS provides
two classes of filters: one to be performed locally (on
the mobile device) and other that runs in the global
context repository when the method is offloaded to
the cloud. The components of the contextual data do
not exist in CAOS D2D.
3.2 The Server Mobile/Cloudlet/Cloud
The CAOS server tier is divided by mobile and
cloudlet/cloud. The first runs the CAOS D2D version
and second runs the CAOS version for cloudlet/cloud.
Both versions present five components: Discovery
and Deployment Service, Profile Service, Authenti-
cation Service, Security Service and Offloading Ser-
vice. The Discovery Service provides the correct end-
points for clients access to the CAOS Services. The
Deployment Service receive dependencies from client
applications, and store them in the server file system,
to enable offloading for those applications. The Au-
thentication Service controls which devices are cur-
rently connected to the CAOS services, besides en-
suring secure authentication by means of the Security
Service (see Section 3.3). The Profile Service is a set
of services that receive device data related to connec-
tivity quality and local execution time of offloadable
methods, to keep a historical evaluation of the elapsed
time for these methods. These records may be used
to decide if a method should be offloadable or not.
The Offloading Service receives offloading requests
directly from Offloading Client and redirects to VM
Pool Service, in cloudlet/cloud tier, or directy on mo-
bile server. When the offloading process finishes, the
CLOSER 2020 - 10th International Conference on Cloud Computing and Services Science
Mobile Device
Discovery and
Deployment lient
Profile Monitor
Offloading Client
CAOS Server
Discovery and
Profile Service
Security Service
Offloading Service
Security Service
CAOS D2D Server
Discovery and
Profile Service
Security Service
Offloading Service
Mobile Device
Context Client Context Service
VM Pool Service
Figure 1: Overview of the CAOS/CAOS D2D Architecture.
Public and Private Keys
are created
Secret Key
is created
Request Objectis encrypted
with shared secret key
Encrypted Secret key is
sent back to the Server
Method is
Result Objectis encrypted
with the shared secret key
Result Object is decrypted
with the shared secret key
Server send its
public key to the Client
Secret key is encrypted
using Server's public key
Secret key is decrypted
using Server's private Key
Result Objectis decrypted
with the shared secret key
4 5
Figure 2: Security Service Flow.
Offloading Service returns the result to the Offloading
Client and persists offloading information. The VM
Pool Service, in cloudlet/cloud tier, is responsible for
providing an environment that redirects offloading re-
quests to a proper Android Virtual Machine where the
offloading execution happens. The Context Service, in
cloudlet/cloud tier, acts as a global context repository,
and stores all context information data sent from all
mobile devices connected to the CAOS Services.
3.3 Security Service
In order to protect offloaded data, the Security Service
component has been added to the CAOS framework.
This service provides modules on both client and
server sides and is responsible for providing encryp-
tion support to the entire migration process. Symmet-
ric cryptographic schemes have lower computational
cost and faster encryption/decryption speed but less
secure as compared to asymmetric key schemes. A
hybrid cryptosystem, including both symmetric and
asymmetric algorithms, provides an efficient security
solution without compromising on any of their fea-
tures(Bhatia and Verma, 2017). Our proposed module
uses a hybrid encryption solution, which consists of
combining symmetric (AES) and asymmetric (RSA)
encryption algorithm recommended for key exchange
by NIST(Barker et al., 2018). The Figure 2 represents
the flow of the security service. Each flow action is
detailed as follows: 1. When the server is started,
both public and private keys (asymmetric encryption)
are generated; 2. When the client is started, the se-
cret key (symmetric encryption) is generated; 3. The
generated public key is sent to the client through the
Authentication Service; 4. On the client-side, we use
the server’s public key to encrypt the secret key cre-
ated by the client; 5. The encrypted secret key is sent
back to the server; 6. The encrypted secret key is de-
crypted using server’s private key; 7. Before offload-
ing a method, the client encrypts the request object
(the method and its parameters) using the shared se-
cret key; 8. The server decrypts the request object
A Study about the Impact of Encryption Support on a Mobile Cloud Computing Framework
using the shared secret key; 9. Then the method is
executed on the server side; 10. After executing the
method, the server encrypts the result object using the
shared secret key and sends it to the client; 11. The
client receives the result object, and decrypts it using
the shared secret key.
This section presents the experiments performed to
evaluate the performance of mobile applications and
energy consumption of a mobile device when using
CAOS and CAOS D2D to offload methods with en-
crypted data. To the best of our knowledge, there are
no available benchmarks for MCC. For this reason,
we used an image processing application (Rego et al.,
2016). That allows users to apply photo effects using
filters into images with different resolutions, where
each one of the filters requires distinct computation
4.1 Experimental Groups and
We divided the experiment into two parts. For the first
part of the tests, we used an Android mobile device
and a cloudlet running the CAOS platform. For the
second part, we used two Android mobile devices -
one acting as a client and one acting as a CAOS D2D
server. For each of these two scenarios, one mobile
device had the image processing application installed.
A method of this app was marked using the annotation
@Offloadable. This method is responsible for apply-
ing a red color filter to the selected image (RedTone).
During the experiments, the method was offloaded to
a cloudlet (first scenario) and a mobile device (second
scenario). We executed the method using a picture
with different resolutions (1MP, 2MP, and 4MP) and
varying the AES encryption key size (128, 192, and
256 bits). For each setup (i.e., scenario, image size,
key size), we executed 30 times the method, totalising
eighteen distinct setups and 540 method executions.
4.2 Material and Methods
For the first part of the tests, we used an Android de-
vice and a cloudlet running the CAOS platform. The
device was a Samsung S4 Active handset (H1) that
runs Android 5.0.2 and has 2 GB of RAM memory
and processor Snapdragon 600 Qualcomm (1.9 GHz
quad-core). As a cloudlet, we used a laptop running
Linux Mint 17.2 64 bit operating system, with 8 GB
RAM and Core i5-4200U (1.6 GHz Quad-Core) pro-
cessor. These devices were connected through a ded-
icated 802.11n wireless network access point. For the
second part of the experiment, the handset used in the
first experiment (H1) played the role of a client, while
the server device (SD) was an LG G3 Beat handset
with Qualcomm Snapdragon 400 MSM8226 Cortex-
A7 1.2 GHz Quad-Core and 1 GB RAM, running An-
droid 5.0.2. These devices were connected through
the same access point used in the previous experi-
ment. Besides measuring the execution time of the
methods, we used the Monsoon Power Monitor
measure the energy consumption of both client and
server devices during the methods’ execution.
4.3 Experiments Results
Figure 3 presents the mean and the 95% confidence
interval of the time required for H1 to offload the im-
age processing method to the cloudlet using CAOS.
The total time ranges from the client’s request until
the response from the server arrives, which includes
the times required to upload the method’s arguments,
execute the method on the cloudlet, download the
method’s return data, besides encryption-related and
other overhead times. These times are presented in
different colors in the graphics. As we can see, the
larger the image to be processed, the longer the total
offloading time. The offloading times range approxi-
mately from 1.5 seconds to 6.0 seconds, depending on
the image resolution. In most cases, there is no statis-
tical difference when comparing the execution time
with different sizes of cryptographic keys. Indeed, in
some cases, we can even notice that the offloading
time is shorter when using the larger cryptographic
key (e.g., when H1 processes a 1MP image) - mainly
because of the other parameters that compose the to-
tal offloading time. Figure 4 shows the mean and the
95% confidence interval of the time required for H1
to offload the image processing method to the hand-
set running the CAOS D2D server. One can see that
the larger the image to be processed, the longer the
total offloading time and the total offloading time us-
ing CAOS D2D is two to four times greater than using
CAOS, mainly because of the method execution time
on SD is greater than on the cloudlet.
As we can see, the total offloading time mainly
depends on the size of the image that will be pro-
cessed, the processing power of the equipment where
the method will be offloaded to and executed, and
the communication (i.e., upload/download speed) be-
tween client and server. In order to understand the real
impact of encryption on offloading performance, we
CLOSER 2020 - 10th International Conference on Cloud Computing and Services Science
Figure 3: The Total Offloading Time between H1 and the Cloudlet Using CAOS.
Figure 4: The Total Offloading Time between H1 and the SD Using CAOS D2D.
Table 1: H1’s Encryption Time (Ms) When Offloading to Cloudlet and SD Using Different Image Sizes (MP).
128 192 256 128 192 256
1 73.36±13.60 66.56±9.63 72.36±11.28 150.66±13.89 161.83±16.87 172.86±15.53
2 105.63±10.54 114.00±7.82 137.23±14.25 266.76±13.20 245.43±17.31 285.83±20.85
4 181.73±9.50 183.10±12.69 192.03±19.24 404.93±19.91 385.53±15.31 411.63±16.60
Table 2: H1’s Energy Consumption (mJ) When Offloading to Cloudlet and SD Using Different Image Sizes (MP).
128 192 256 128 192 256
1 156,54 ± 29,02 142,03 ± 20,54 154,40 ± 24,07 321,49 ± 29,63 345,32 ± 35,99 368,86 ± 33,13
2 225,40 ± 22,49 243,26 ± 16,68 292,83 ± 30,40 569,23 ± 28,16 523,72 ± 36,93 609,93 ± 44,49
4 387,79 ± 20,27 390,71 ± 27,07 409,77 ± 41,05 864,08 ± 42,48 822,68 ± 32,67 878,37 ± 35,42
further analyze the results. Thus, Table 1 presents the
mean and the 95% confidence interval time required
to perform the encryption process (T EP) throughout
the method’s offloading from client to server (cloudlet
or device). To calculate this value, the following
equation was used:
T EP = T Ereq + TDreq + T Eres + T Dres
in which, T Ereq is the time to encrypt the client-
side request, T Dreq is the time to decrypt the server-
side request, T Eres is the encryption time of the
server-side response, and T Dres is the time to decrypt
the server-side response.
Table 1 shows that the larger the cryptographic
key and the image resolution, the longer the total en-
cryption time when offloading to both cloudlet and
SD. Also, the encryption time is up to 90% longer
when using CAOS D2D because the encryption pro-
cess also depends on the server’s processing capa-
bility, which is a mobile device in this case. The
same behavior can be observed in Table 2, which
presents the mean and the 95% confidence interval
for the energy consumed by the mobile device for all
encryption-related operations. As we can see, the use
of encryption consumes from 142 mJ to 878 mJ de-
pending on the image size, devices, and framework
used. Therefore, the longer the encryption key and
the bigger the data to be transferred, the higher the
energy consumption. We used ANOVA and Tukey
statistical tests to evaluate whether the difference be-
tween encryption times is significant or not when we
change the encryption key size (i.e., 128, 192, 256
bits). As we mentioned, for each image size and sce-
nario, we had three groups of experiments varying the
encryption key size. Each ANOVA test compares the
A Study about the Impact of Encryption Support on a Mobile Cloud Computing Framework
(a) Cloudlet - CAOS Server (b) SD - CAOS D2D
Figure 5: Tukey’s Significance Test for Total Encryption Time on H1.
Table 3: Proportion of Encryption Time and Total Offloading Time (%).
128 192 256 128 192 256
1 4.19±0.56 4.69±0.59 5.35±0.64 3.65±0.29 3.38±0.37 3.65±0.29
2 3.90±0.32 3.82±0.25 3.62±0.30 3.10±0.15 3.06±0.14 3.18±0.19
4 3.70±0.29 3.49±0.29 4.34±0.32 2.17±0.20 2.76±0.13 2.45±0.14
means of these three sample groups. Then, we used
ANOVA to test the null hypothesis. If H0 is rejected,
we applied the Tukey-Kramer procedure to determine
which pairs of means have statistically significant dif-
ferences. Tukey’s plot compares two by two samples
on the left side. If the respective confidence interval
does not contain zero, the corresponding means are
significantly different. Figure 5 illustrates six plots
of the Tukey’s test for H1 to offload the method to
the Cloudlet (a) and the SD (b) using 128, 192 and
256-bits key sizes. The results when offloading to
the Cloudlet (plots (a)) show that, for 1 and 4MP
images, there is no significant difference between all
means when using distinct key sizes. For 1MP im-
ages, ANOVAs p value = 0.66, while for 4MP im-
ages, p value = 0.534, which means we cannot re-
ject the hypothesis that all means are equal. This re-
sult can be explained by the good processing capacity
of H1, which performs encryption in a few time and
with little variation in computation time when com-
pared to the total offloading time. For the 2MP im-
ages, ANOVAs p value = 0, which indicates all
means are not equal. But, as we can see in Tukey’s
result, at least the means using 128 and 192-bits keys
are statistically equal. When offloading to the SD
(plots (b)), we can see the same behavior when con-
sidering 1 and 4MP images. In both cases, ANOVAs
p value = 0.123 and p value = 0.084, respec-
tively, which means we cannot reject the hypothe-
sis that all means are equal. Tukey’s plots indicate
that the difference between the means is not statisti-
cally significant because the range does include zero.
For the 2MP images, ANOVAs p value = 0.005,
which indicates all means are not equal. Indeed, as
we can see in Tukey’s result, the means using 192
and 256-bits keys are not statistically equal as the
range does not include zero. Finally, Table 3 sum-
marizes the mean proportion between encryption and
total execution times so we can analyze the impact
of adding an encryption module into computation of-
floading frameworks. The table shows that the en-
cryption time represents 2.17% to 5.35% of the to-
tal execution time depending on the image resolu-
tion, key size, and where to offload. The proportion
ranges from 3.49% to 5.35% when H1 offloads to the
cloudlet and 2.17% to 3.65% when H1 offloads to an-
other handset. As we can see, the longer the total exe-
cution time, the lower the impact of encryption. This
is the reason the total encryption time has less impact
when offloading to another mobile device, despite the
encryption time be higher in such a case, as we show
in Table 1.
Security and privacy are essential aspects that any of-
floading solution should support, but few current ones
provide. In this paper, we address this research theme
by including encryption support to an existing of-
CLOSER 2020 - 10th International Conference on Cloud Computing and Services Science
floading platform called CAOS/CAOS D2D. Our ex-
periments show that encryption support may increase
the total offloading time up to 5.35% (±0.64%). But
they also show that the larger the offloading time, the
less the impact of encryption procedures. The encryp-
tion key size seems irrelevant compared with the in-
fluence of downloading and uploading times, so big-
ger keys should be used instead of weaker ones. Re-
garding energy consumption, our experiments show
that the encryption process consumes up to 878 mJ,
and the longer the encryption key and the bigger the
data to be transferred, the higher the energy consump-
tion. As future work, we intend to expand the ex-
perimentation and test a set of other mobile devices
leveraging different wireless technologies (e.g., 4G,
5G), besides using public cloud instances as remote
execution environments and test other cryptographic
The authors would like to thank The Cear
a State
Foundation for the Support of Scientific and Tech-
nological Development (FUNCAP) for the financial
support (grant number 6945087/2019).
Abbas, N., Zhang, Y., Taherkordi, A., and Skeie, T. (2018).
Mobile edge computing: A survey. IEEE Internet of
Things Journal, 5(1):450–465.
Barker, E., Chen, L., Roginsky, A., Vassilev, A., Davis, R.,
and Simon, S. (2018). Recommendation for pair-wise
key-establishment using integer factorization cryptog-
raphy. Technical report, National Institute of Stan-
dards and Technology.
Bhatia, T. and Verma, A. (2017). Data security in mobile
cloud computing paradigm: a survey, taxonomy and
open research issues. The Journal of Supercomputing,
Chen, M. and Hao, Y. (2018). Task offloading for mobile
edge computing in software defined ultra-dense net-
work. IEEE Journal on Selected Areas in Communi-
cations, 36(3):587–597.
Costa, P. B., Rego, P. A. L., Rocha, L. S., Trinta, F. A. M.,
and de Souza, J. N. (2015). Mpos: A multiplatform
offloading system. In Proceedings of the 30th An-
nual ACM Symposium on Applied Computing, SAC
’15, pages 577–584, New York, NY, USA. ACM.
Dinh, H. T., Lee, C., Niyato, D., and Wang, P. (2011). A
survey of mobile cloud computing: architecture, ap-
plications, and approaches. Wireless Communications
and Mobile Computing.
Diro, A. A., Chilamkurti, N., and Nam, Y. (2018). Analy-
sis of lightweight encryption scheme for fog-to-things
communication. IEEE Access, 6:26820–26830.
Donald, A. C. and Arockiam, L. (2015). A secure authen-
tication scheme for mobicloud. In 2015 International
Conference on Computer Communication and Infor-
matics (ICCCI), pages 1–6.
Dos Santos, G. B., Trinta, F. A., Rego, P. A., Silva, F. A.,
and De Souza, J. N. (2018). Performance and energy
consumption evaluation of computation offloading us-
ing caos d2d. In 2018 IEEE Global Communications
Conference (GLOBECOM), pages 1–7. IEEE.
Fernando, N., Loke, S. W., and Rahayu, W. (2013). Mo-
bile cloud computing: A survey. Future Generation
Computer Systems, 29(1):84 – 106.
Gomes, F., Viana, W., Rocha, L., and Trinta, F. (2017a). On
the evaluation of a contextual sensitive data offload-
ing service: the cop case. Journal of Information and
Data Management, 8(3):197.
Gomes, F. A., Rego, P. A., Rocha, L., de Souza, J. N., and
Trinta, F. (2017b). CAOS: A context acquisition and
offloading system. In 2017 IEEE 41st Annual Com-
puter Software and Applications Conference (COMP-
SAC), pages 957–966. IEEE.
Gupta, B. B., Yamaguchi, S., and Agrawal, D. P. (2018).
Advances in security and privacy of multimedia big
data in mobile and cloud computing. Multimedia
Tools and Applications, 77(7):9203–9208.
Mollah, M. B., Azad, M. A. K., and Vasilakos, A. (2017).
Security and privacy challenges in mobile cloud com-
puting: Survey and way ahead. Journal of Network
and Computer Applications, 84:38–54.
Padhi, S., Tiwary, M., Priyadarshini, R., Panigrahi, C. R.,
and Misra, R. (2016). Secomn: Improved security ap-
proach for opportunistic mobile networks using cyber
foraging. In 2016 3rd International Conference on
Recent Advances in Information Technology (RAIT),
pages 415–421.
Rego, P. A., Trinta, F. A., Hasan, M. Z., and de Souza,
J. N. (2019). Enhancing offloading systems with smart
decisions, adaptive monitoring, and mobility support.
Wireless Communications and Mobile Computing.
Rego, P. A. L., Costa, P. B., Coutinho, E. F., Rocha, L. S.,
Trinta, F. A., and de Souza, J. N. (2016). Performing
computation offloading on multiple platforms. Com-
puter Communications.
Sanaei, Z., Abolfazli, S., Gani, A., and Buyya, R. (2014).
Heterogeneity in mobile cloud computing: Taxonomy
and open challenges. Communications Surveys Tuto-
rials, IEEE, 16(1):369–392.
Satyanarayanan, M., Bahl, P., Caceres, R., and Davies, N.
(2009). The case for vm-based cloudlets in mobile
computing. Pervasive Computing, IEEE, 8(4):14–23.
Silva, B., Sabino, A., Junior, W., Oliveira, E., J
unior, F., and
Dias, K. (2017). Performance evaluation of cryptog-
raphy on middleware-based computational offloading.
In 2017 VII Brazilian Symposium on Computing Sys-
tems Engineering (SBESC), pages 205–210.
Varghese, B. and Buyya, R. (2018). Next generation cloud
computing: New trends and research directions. Fu-
ture Generation Computer Systems, 79:849–861.
Zhang, Y., Huang, G., Liu, X., Zhang, W., Mei, H., and
Yang, S. (2012). Refactoring android java code for
on-demand computation offloading. SIGPLAN Not.,
A Study about the Impact of Encryption Support on a Mobile Cloud Computing Framework