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Proceedings ArticleDOI

Handover triggering in IEEE 802.11 networks

14 Jun 2015-pp 1-9

TL;DR: An anticipation-based handover solution that uses a Kalman filter to predict the short term evolution of the received power allows a mobile device to proactively start scanning and executing a handover as soon as better APs are available.

AbstractThe current and future IEEE 802.11 deployment could potentially offer wireless Internet connectivity to mobile users. The limited AP radio coverage forces mobile devices to perform frequent handovers while current operating systems lack efficient mechanisms to manage AP transition. Thus we propose an anticipation-based handover solution that uses a Kalman filter to predict the short term evolution of the received power. This mechanism allows a mobile device to proactively start scanning and executing a handover as soon as better APs are available. We implement our mechanism in Android and we show that our solution provides a better wireless connection.

Topics: Handover (56%), Wireless network (56%), IEEE 802.11 (55%), Mobile telephony (53%), Mobile device (52%)

Summary (4 min read)

Introduction

  • Under this condition, a mobile user may be able to connect to community networks and compensate the low AP coverage area by transiting between APs.
  • First, operators have not deployed the necessary infrastructure to allow mobile users to perform handovers without being disconnected at the application layer, i.e., after a handover on-going application flows are interrupted.
  • The authors propose Kalman-filter-based HAndover Trigger algorithm (KHAT) that succeeds in intelligently triggering handovers and reducing the scanning impact on the mobile device.
  • Section IV introduces KHAT which is evaluated indoor and outdoor in Section V. Section VI concludes the paper.

III. HANDOVER IMPACT

  • This is because, usually, when a MS triggers a handover, the link quality does not allow exchanging frames anymore, and because the MS is often switching operating channel.
  • And determine which parameters influence the scanning latency and success rate.the authors.
  • This testbed consists of nine Cisco Aironet 1040 APs installed in the roof of their building at the locations given in Fig.
  • The authors also use a dedicated server for traffic generation and tracing.
  • For each experiment, the authors walk from AP1 to AP6 and then back again to AP1.

A. Operating Systems Benchmark

  • To illustrate how the handover is currently impacting data flows, the authors have performed a set of experiments to evaluate the degradation of TCP performance for different devices and Operating Systems (OS).
  • Table I shows the number of handovers and the average TCP throughput the authors have observed for the same path and same MS speed using different devices and operating systems.
  • As a baseline, the authors also show the maximum achieved throughput for each device remaining static and connected to a single AP.
  • Fig.1b shows the evolution of the downloaded data for each case.
  • Additionally, the authors have observed that for the Windows device, the average round-trip time (RTT) is the lowest one (103 ms) having also a low standard deviation.

B. Scanning Interactions with Data Traffic

  • The authors focus on active scanning where an MS sends Probe Requests on each channel to discover potential APs, instead of just waiting for periodic beacons (passive scanning).
  • If the handover phases are done one after the other, all packets that arrive during the handover process will be lost.
  • The authors can see that the scan is starting just before the time 1 s, at which no more data packets are received from the server.
  • This technique can also be used to split a scanning phase into several sub-phases where only a subset of channels are scanned.
  • To scan the 13 channels, an MS could sequentially scan three times a subset of 4 (or 5) channels each time, interleaving these sub-phases with the data mode with the current AP to retrieve data packets.

C. Scanning Parameters

  • The authors analyze the scanning performance under different values of timers used to wait for Probe Responses (from 5 ms to 100 ms) and different number of scanned channels during a sub-phase (between 1 and 13).
  • In the standard IEEE 802.11 scanning algorithm, the MS is supposed to scan each channel using two timers namely MinCT and MaxCT (see section II).
  • As a baseline, the authors consider that all the available APs are discovered when scanning the full channel sequence (i.e., 13 channels) using AT=100 ms.
  • In the other cases, the MS discovers only a fraction of the APs, since it either does not wait long enough to receive all AP Probe Responses, or because only a subset of channels are scanned.
  • The authors have also observed that when using a short AT, even if the MS discovers a low number of APs, those APs have a high RSSI.

IV. KHAT: PROACTIVE HANDOVER ALGORITHM

  • The authors propose a handover algorithm called Kalman Filter-based HAndover Triggering (KHAT for short) that provides link going down detection, optmized scanning strategy, and new AP selection.
  • An MS monitors its link quality with its current AP, and when the signal strength is degrading, it starts alternating between scan periods and data communication with the current AP.
  • The scan periodicity and the timer values are determined according to the current link quality and whether a candidate AP has already been found.
  • Once the candidate AP becomes better than the current AP, the handover is triggered.

A. RSSI modelling

  • One way of keeping track of the changing radio condition is to track the RSSI on the MS.
  • In the case of mobile MSs and/or data traffic the median values are smaller (around 110 ms for MS with data traffic, but the standard deviation is always larger).
  • In order to mitigate the effect of these periods, the authors pre-process the RSSI samples, using a time-varying exponential average, before applying the Kalman filter.
  • In particular the authors have used the local linear trend model (see, for example, Durbin and Koopman [9]): Zi = µi +.
  • The Kalman filter can also be used to predict future values.

B. Algorithm design

  • KHAT adapts the scanning strategy, the scanning period and the handover trigger by comparing an estimate of the link quality and the quality of candidate AP as presented in Fig.
  • The main process consists in continuously monitoring the RSSI of the current link and detect a link going down event.
  • To achieve this, the authors use the Kalman filter to obtain the current value of the RSSI (µ) and the slope (ν).
  • The scanning strategy itself is also adapted depending on the current link condition.
  • The reason is that for smaller scan times, the authors only find APs with high RSSI (as shown in section III-C) and as they are in fairly good condition, the MS would only be interested in AP with high RSSI.

A. Methodology and implementation

  • The authors have implemented their solution on the Android ICS 4.0.3 system working on a Samsung Nexus S (GT-I9023) smartphone.
  • It involves modifications in the Android Java Framework, the WPA Supplicant and the Linux driver (BCM4329).
  • The authors moved at the same time two identical smartphones, one with KATH implementation and the other with stock Android, in two different environments.
  • The mobile user walks at a roughly constant speed and each connection lasts for 120 s.
  • In all cases, the authors use iperf to generate the TCP traffic for both MSs and generate several connections for more than one hour.

B. General results

  • Fig. 5, 6, 7 and 8 show the RSSI and the received TCP data for the two considered environments, over one connection duration, while Table IV shows the average over all connections that the authors made.
  • Sometimes, as at Time=400s of the outdoor connection, KHAT manages to find an intermediate AP between those chosen by Stock Android which allows to signigicantly increase both the RSSI and the TCP download.
  • The first handover at Time=233s is made prior to the Legacy handover, but still a stagnation in the received data is observed before and after the handover.
  • While KHAT avoids to trigger handover when it is not for a significantly better AP, in areas where all APs offer low RSSI, KHAT may trigger several handovers.
  • It finally finds AP11 at Time=721s which was providing a good coverage area.

VI. CONCLUSION

  • IEEE 802.11 is one of the most popular wireless standard to offer high data rate Internet connection.
  • The authors have shown in this paper that current devices are able to transit between APs, but the handover performance is quite low.
  • The prediction of the link going down is achieved with a Kalman filter which estimates the slope of the RSSI to determine the link condition.
  • To address the tradeoff between the scanning latency and the AP discovery, the MS is scanning with AT=20 ms if a handover is imminent, AT=10 ms when the link quality is medium and AT=5 ms when the link quality is good.
  • In two different environments (indoor and outdoor), the authors compared a Stock Android with a KHAT smartphone.

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Handover Triggering in IEEE 802.11 Networks
Nicolas Montavont, Alberto Blanc, Renzo Efrain Navas, Tanguy Kerdoncu,
German Castignani
To cite this version:
Nicolas Montavont, Alberto Blanc, Renzo Efrain Navas, Tanguy Kerdoncu, German Castignani.
Handover Triggering in IEEE 802.11 Networks. IEEE 16th International Symposium on A World
of Wireless, Mobile and Multimedia Networks (WoWMoM), Jun 2015, Boston, United States.
�10.1109/WoWMoM.2015.7158126�. �hal-01759108�

Handover Triggering in IEEE 802.11 Networks
Nicolas Montavont, Alberto Blanc, Renzo Navas and Tanguy Kerdoncuff
Institut Mines Telecom / Telecom Bretagne
2, rue de la chataigneraie
35576 Cesson
Email: firsname.lastname@telecom-bretagne.eu
German Castignani
University of Luxembourg / SnT
4, rue Alphonse Weicker
L-2721 Luxembourg
Email: german.castignani@uni.lu
Abstract—The current and future IEEE 802.11 de-
ployment could potentially offer wireless Internet con-
nectivity to mobile users. The limited AP radio coverage
forces mobile devices to perform frequent handovers while
current operating systems lack efficient mechanisms to
manage AP transition. Thus we propose an anticipation-
based handover solution that uses a Kalman filter to
predict the short term evolution of the received power.
This mechanism allows a mobile device to proactively start
scanning and executing a handover as soon as better APs
are available. We implement our mechanism in Android
and we show that our solution provides a better wireless
connection.
I. INTRODUCTION
Due to the proliferation of Wifi hot-spots and com-
munity networks, we have recently observed a great
evolution of IEEE 802.11 networks especially in urban
scenarios. These 802.11-based networks allow mobile
users to get connected to the Internet, providing a high
throughput but a limited mobility due to the short cover-
age area of access points (APs). In our previous work [1]
we have shown that community networks appear to be
highly dense in urban areas, generally providing several
APs (15 in median) per scanning spot. Under this condi-
tion, a mobile user may be able to connect to community
networks and compensate the low AP coverage area
by transiting between APs. We call such AP transition
a handover. However, two main issues currently limit
mobile users from using community networks in such
a mobility-aware scenario. First, operators have not
deployed the necessary infrastructure to allow mobile
users to perform handovers without being disconnected
at the application layer, i.e., after a handover on-going
application flows are interrupted. This limitation may be
addressed by deploying a Mobile IP [2] infrastructure, in
which the application flows may be tunnelled through
a Home Agent that belongs to the operator. Second,
independently from the first issue, there is still a lack of
mechanism to intelligently manage a layer 2 handover
between two APs. In current mobile devices, when a
handover occurs, we observe a degradation of on-going
flows corresponding to a dramatic reduction of the TCP
congestion window (CWND) and of the throughput. In
this paper, we focus on this latter issue by analyzing
the impact of layer 2 handovers on mobile users. We
propose Kalman-filter-based HAndover Trigger algo-
rithm (KHAT) that succeeds in intelligently triggering
handovers and reducing the scanning impact on the
mobile device. We propose a complete implementation
of our handover mechanism in Android ICS (4.0) and
show a comparative study to show that our approach
outperforms the handover mechanism that is currently
implemented on these devices.
The paper is organized as follows. Section II
presents the litterature on handover optimization and
Section III analyzes the handover impact on on-going
communications. Section IV introduces KHAT which is
evaluated indoor and outdoor in Section V. Section VI
concludes the paper.
II. HANDOVER PROCESS AND RELATED WORK
The IEEE 802.11 standard defines a handover as a
three steps process: scanning, authentication and asso-
ciation. The standard proposes two different scanning
algorithms namely passive and active scanning. In pas-
sive scanning, the mobile station (MS) simply tunes its
radio on each channel and listens for periodic beacons
sent by the APs. In active scanning, the MS proactively
sends requests in each channel and waits for responses
during a pre-defined timer.
Once candidate APs have been found, the MS se-
lects one of the APs and attempts authentication and
association. If the association is successful, the MS
can send and receive data through the new AP, if this
new AP is on the same IP subnet as the previous AP.
If the new AP belongs to another IP subnet, the MS
needs additional processing to update its IP address
and redirect data flows to its new point of attachment.
Such Layer 3 handover may be handled by specific
protocols like Mobile IP [2]. Note that in this paper
we do not address IP mobility and any layer 3 mobility
management protocol can be use on top of our proposal
if needed.978-1-4799-8461-9/15/$31.00
c
2015 IEEE

In 2012, the IEEE has published new amendments
for IEEE 802.11 handover optimization, aimed at re-
ducing its duration and its impact on higher layers. The
IEEE 802.11k amendment proposes mechanisms for
radio resource measurement for seamless AP transition,
including measurement reports of signal strength (RSSI)
and load of nearby APs. Additionally, the IEEE 802.11r
amendment contains a Fast Basic Service Set Transition
(FT), which avoids exchanging 802.1X authentication
signaling under special conditions by caching authenti-
cation data.
While these features may enhance the handover per-
formance, they heavily rely on a cooperation between
APs, which might not always be a viable solution. In
addition, users may access various networks operated
by different providers. In that case, operators should
share network information and performance among
them, which is quite an unlikely scenario. In this paper,
we focus on MS-based solutions, where the MS itself
handles the handover without the help from the network.
Several works have been proposed in the literature so
far. In general, those studies cover different aspects of
the handover mechanism. We may group them into three
main categories:
Handover triggering: when to decide that a
disconnection with the current AP will occur.
AP discovery: how to search for APs on differ-
ent channels by minimizing the impact on the
higher layers.
Best AP selection: with which AP to associate,
among the discovered ones.
The simplest mechanism to trigger a handover is to
monitor the RSSI as an estimation of the link quality
and start the handover process if the current RSSI is
lower than a pre-established threshold (commonly set
at 80 dBm). Fig. 1a shows the relationship between
the RSSI measured on an MS and the TCP throughput
that we have gathered during more than 600 connections
to community networks in a urban area in Rennes,
France [1]. We observe that the TCP throughput is ex-
tremely variable for high RSSI, but starts degrading for
RSSI lower than 70 dBm, and it becomes significantly
low around 80, dBm.
Some works focus on the anticipation of the han-
dover triggering in order to minimize the impact on
ongoing communications. Mhatre et al. [3] propose
a set of handover algorithms based on continuously
monitoring the wireless link, i.e., listening to beacons
from the current and neighboring channels. These ap-
proaches give handover latencies varying between 150
and 800 ms. However, since these approaches need to
listen to beacons from neighboring channels, it is neces-
sary to modify the firmware of the wireless card, which
may not always be possible. Yoo et al. [4] propose a
number of handover triggering mechanisms based on
predicting RSSI samples at a given future time using
Least Mean Square (LMS) linear estimation. In this
algorithm, the device continuously monitors the RSSI
and computes the LMS prediction if the RSSI is below
a certain threshold (P
Pred
). Then, if the predicted RSSI
value is lower than a second threshold, P
Min
, the MS
starts a handover.
Wu et al. [5] propose a handover mechanism aiming
at decoupling the AP discovery phase from the AP
selection and reconnection phase. The MS alternates
between scanning phases and a (normal) data mode
where the MS is connected to its current AP. The
time interval between two scanning phases is adapted
depending on the current signal level and varies between
100 and 300 ms. In each scanning phase, the sequence of
channels to scan is selected based on a priority list that
is built based on the results of a periodic full scanning
(i.e., here all channels are scanned).
As far as Android devices are concerned, Silva et
al. [6] present a mobility management solution based on
IEEE 802.21. They propose a mapping of IEEE 802.21
primitives for handover initiation, preparation, execution
and completion to existent Android OS methods and
functions.
III. HANDOVER IMPACT
During an L2 handover, the MS is not able to send
or receive application flows. This is because, usually,
when a MS triggers a handover, the link quality does
not allow exchanging frames anymore, and because
the MS is often switching operating channel. In this
section we evaluate the handover and scanning impact
on application flows, and determine which parameters
influence the scanning latency and success rate.
This testbed consists of nine Cisco Aironet 1040
APs installed in the roof of our building at the locations
given in Fig. 2. All APs are connected to a dedicated
wired LAN. APs broadcast a single SSID, correspond-
ing to an open-system authentication network belonging
to a single IP subnet. We also use a dedicated (fixed)
server for traffic generation and tracing. iPerf is used
to generate TCP downlink traffic to the MS. For each
experiment, we walk from AP
1
to AP
6
and then back
again to AP
1
.
A. Operating Systems Benchmark
To illustrate how the handover is currently impacting
data flows, we have performed a set of experiments
to evaluate the degradation of TCP performance for
different devices and Operating Systems (OS). Table I
shows the number of handovers and the average TCP
2

-20
0
20
40
60
80
100
120
-90 -80 -70 -60 -50 -40 -30
Throughput / KB/s
Signal Strength / dBm
Throughput Std. Dev. Throughput Average
(a) RSSI and TCP Throughput Relation (b) Downloaded data for different OS
0 1 2 3 4
0
2
4
6
Time /s
Downloaded Data /MBytes
(c) Scanning impact on TCP download
Fig. 1: Various TCP performance
Fig. 2: Campus AP Deployment
throughput we have observed for the same path and
same MS speed using different devices and operating
systems. As a baseline, we also show the maximum
achieved throughput for each device remaining static
and connected to a single AP. Using Windows, we
observe the best result, since the MS performs up
to four handovers, reaching an average throughput of
0.875 MB/s. Additionally we observe that for Win-
dows, the time in which no data is downloaded (i.e., the
disconnected time) is relatively short compared to the
other OSs. The netbook running Ubuntu reacts slowly
to changing channel conditions: in this case the MS is
disconnected for more than 20 s and executes only two
handovers, indicating that the MS waits until the quality
of the radio link is significantly degraded. Fig.1b shows
the evolution of the downloaded data for each case.
Additionally, we have observed that for the Windows
device, the average round-trip time (RTT) is the lowest
one (103 ms) having also a low standard deviation. This
differs from the other devices that reach larger RTT
values.
B. Scanning Interactions with Data Traffic
We focus on active scanning where an MS sends
Probe Requests on each channel to discover potential
APs, instead of just waiting for periodic beacons (pas-
sive scanning). We chose active scanning because it
allows spending less time in each channel to determine
the AP availability. If the handover phases are done
one after the other, all packets that arrive during the
handover process will be lost. In order to reduce the
impact of handovers on applications flows, it is possible
to introduce a gap between the scanning phase and
the other handover steps, i.e., the decision to handover,
the authentication and the association, as presented
in [5]. An MS may use the power saving mode defined
in IEEE 802.11 to request its current AP to buffer
incoming packets during the time the MS scans other
channels. This way, instead of loosing packets during
the scanning phase, an MS can receive the packets after
the scanning phase, albeit with an extra delay. This
behavior is illustrated in Fig. 1c, where we plot the
sequence number of the received packets of a TCP flow
when an MS is performing one scan of the 13 channels
with an active timer set at 50 ms. We can see that the
scan is starting just before the time 1 s, at which no more
data packets are received from the server. Once the scan
is finished, around 850 ms after, the MS comes back to
its current AP, and starts receiving TCP packets again.
This technique can also be used to split a scanning
phase into several sub-phases where only a subset of
channels are scanned. For example, to scan the 13
channels, an MS could sequentially scan three times
a subset of 4 (or 5) channels each time, interleaving
these sub-phases with the data mode with the current AP
to retrieve data packets. The impact of the number of
scanned channels, and the timers used in each channel
is given in the next subsection.
C. Scanning Parameters
We analyze the scanning performance under differ-
ent values of timers used to wait for Probe Responses
(from 5 ms to 100 ms) and different number of scanned
channels during a sub-phase (between 1 and 13). In
the standard IEEE 802.11 scanning algorithm, the MS
is supposed to scan each channel using two timers
3

Device OS Version Chipset Static Avg. Mob. Avg. Number of Mean RTT σRTT
Thr. (MB/s) Thr. (MB/s) handovers (ms) (ms)
Asus N10J Win XP SP2 AR5006 5.19 0.878 4 103 43
Asus N10J Ubuntu 10.04 AR5006 4.88 0.601 2 161 360
Nexus S Android 4.0.3 BCM4329 3.80 0.568 5 129 114
MacBook MAC OS 10.7.4 BCM4322 8.44 0.613 3 167 276
TABLE I: Handover performance of different OS
Nb. of AT=5 AT=10 AT=20 AT=50 AT=100
channels (%) (%) (%) (%) (%)
1 3.11 5.76 10.62 22.28 25.24
3 6.45 18.28 32.61 58.18 88.24
5 9.28 21.02 38.83 68.94 89.31
8 10.44 23.61 40.46 70.43 96.58
13 11.74 28.62 45.76 79.88 100.00
RSSI -67.16 -70.07 -76.02 -81.28 -83.26
TABLE II: Percentage of discovered APs for different
values of AT and number of scanned channels
namely MinCT and MaxCT (see section II). However,
the IEEE 802.11 Android driver uses a single timer,
namely Active Timer (AT) for scanning. AT is defined as
the time an MS waits for Probe Responses on a channel.
We ran 60 scanning sub-phases for each AT and
subset of scanned channels and measured the average
number of discovered APs, the RSSI distribution of the
discovered APs and the average duration of the scanning
(i.e., the scanning latency). Results are presented in
Table II. As a baseline, we consider that all the available
APs are discovered when scanning the full channel
sequence (i.e., 13 channels) using AT=100 ms. In the
other cases, the MS discovers only a fraction of the
APs, since it either does not wait long enough to receive
all AP Probe Responses, or because only a subset of
channels are scanned.
We have also observed that when using a short
AT, even if the MS discovers a low number of APs,
those APs have a high RSSI. On the other hand, when
using higher AT values, the MS discovers more APs
but a large part of them have a low RSSI. This can be
observed in Fig. 3a, where we see that for AT=5 ms the
average RSSI of candidate APs is 67 dBm, while for
AT=20 ms, this decreases up to 76 dBm.
IV. KHAT: PROACTIVE HANDOVER ALGORITHM
We propose a handover algorithm called Kalman
Filter-based HAndover Triggering (KHAT for short) that
provides link going down detection, optmized scanning
strategy, and new AP selection. An MS monitors its
link quality with its current AP, and when the signal
strength is degrading, it starts alternating between scan
periods and data communication with the current AP.
The scan periodicity and the timer values are determined
according to the current link quality and whether a can-
didate AP has already been found. Once the candidate
AP becomes better than the current AP, the handover is
triggered.
A. RSSI modelling
One way of keeping track of the changing radio
condition is to track the RSSI on the MS. While far
from being perfect, the RSSI has the advantage of being
always available, whether the MS is exchanging data or
not, as it is updated not only whenever the MS receives
data frames but also when it receives beacon frames,
which are typically sent every 100 ms by most APs.
As the RSSI can fluctuate rapidly, especially when a
user is moving, its instantaneous value is not necessarily
representative. At the same time, its local average and
trend are more useful in deciding whether the radio
channel conditions are improving or not and whether
they are reaching the point where communication is no
longer possible. Using the well known Kalman filter,
it is possible to extract this information from the RSSI
measurements. Many authors have already use Kalman
filter and other time series techniques in order to model
radio channels and the received signal strength, see, for
example, the works by Jiang et al. [7], by Baddour et
al. [8] and references therein.
More formally, let X(t
i
) , X
i
be the received
signal strength at time t
i
. In our case, we sample
the RSSI roughly every 100 ms; but, as we rely on
software timers, there are no guarantees that the t
i
s
will be equally spaced. Figure 3b shows the empirical
distribution of t
i
= t
i
t
i1
for a subset of the traces
we collected. The average is 96 ms and the standard
deviation is 8.2 ms. Given that roughly 90% of the
samples are within less than 100 ms of each other, it
seems reasonable to “re-sample” the time series with a
time-step of 100 ms.
In all the traces we have collected, it is often the
case that several consecutive samples have the same
value, indicating that the received signal strength is
often constant during periods that are longer than the
average distance between samples. The presence of sev-
eral samples with exactly the same value is an obstacle
when one is trying to estimate the local trend of a signal
as, in this case, the estimated slope would be exactly
0. The Kalman filter does not perform well in these
circumstances. As we rely on the values reported by the
802.11 driver, we wondered whether these consecutive
samples with the same values were caused by the driver
4

Citations
More filters

Journal ArticleDOI
TL;DR: A novel software-defined wireless network architecture that integrates coordination mechanisms to enhance the capabilities of a set of central managed Wi-Fi access points (APs) is presented, resulting in a solution that is able to provide smart functionalities using low-cost commercial APs.
Abstract: This paper presents a novel software-defined wireless network architecture that integrates coordination mechanisms to enhance the capabilities of a set of central managed Wi-Fi access points (APs). The global architecture is presented in detail, where the handoff mechanism is integrated with a set of active and passive monitoring tools and other functionalities, resulting in a solution that is able to provide smart functionalities using low-cost commercial APs. The framework includes a central controller that has all the information available, and is therefore able to make smart decisions about the assignment of clients to APs. This avoids the problem of the “sticky client” that remains connected to the original AP it is associated with, rather than moving to a nearby AP, which would be a better choice. Two different test scenarios are used to compare a proactive and a reactive handoff mechanism in realistic conditions, with different walking speeds. The results illustrate the advantage of the proactive handoff, as it is more scalable and allows a better integration with other functionalities such as load balancing. The delay incurred by the handoff between APs in different channels is measured with three wireless devices, using five values for the inter-beacon time, proving that fast and seamless handoffs are possible in the scenario. The paper shows that these advanced functionalities, usually available in proprietary solutions, can also be achieved using off-the-shelf equipment.

13 citations


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21 Nov 2017
TL;DR: This work proposes to move the AP association decision to a periodically-running central controller which aims to maximize the proportionally-fair network throughput, and devise several heuristics requiring various degrees of knowledge, e.g., pairwise user-AP link rates, throughput demand of each user.
Abstract: Efficient resource allocation in enterprise wireless local area networks(WLAN) has become more paramount with the shift of traffic toward WLANs and increasing share of the video traffic. Unfortunately, current practise of client-driven association to APs has several shortcomings, e.g., sticky client problem. As a remedy, we propose to move the AP association decision to a periodically-running central controller which aims to maximize the proportionally-fair network throughput. After formulating the optimal mapping problem, we devise several heuristics requiring various degrees of knowledge, e.g., pairwise user-AP link rates, throughput demand of each user. Our analysis via simulations on realistic scenarios (conference, office, and shopping mall) shows the superior performance of our proposals in terms of aggregate logarithmic throughput. While the utility gain over the conventional client-driven approach is modest, up to 18%, the resulting increase in the weakest user's throughput is significant (71-120%) as well as that of AP load balance and fairness of user throughputs. Moreover, our evaluations reveal a very small optimality gap (between 0.1-5%). The highest gain is observed in the conference setting where the users are unevenly distributed in the network and hence there is a huge load imbalance among the APs. While schemes requiring more knowledge, i.e., on handover-cost and traffic demands, perform the best, a naive approach which runs periodically and assigns each user to the AP providing the highest signal level to that user maintains up to 41% gain in the weakest user's throughput over the client-driven handover approach.

9 citations


Journal ArticleDOI
TL;DR: It is argued that the findings presented in this study can help develop more stable handoff algorithms for dense wireless networks, by analyzing the implementation used by the most common devices and conducting comparative tests in a real network.
Abstract: Association instability is a common phenomenon in dense networks. The decision of whether or not to perform a handoff between access points in an infrastructured IEEE 802.11 network is taken exclusively by the wireless client stations. Even without mobility, static client devices may decide to migrate to another access point with the goal of improving performance. However, the criteria used to perform handoffs are not defined by the IEEE 802.11 standard and, thus, are dependent on specific vendor implementations. In this paper, we use data from a real large scale network and run experiments to demonstrate that such implementations are commonly deficient, resulting in high levels of association instability in dense environments. By analyzing the implementation used by the most common devices and conducting comparative tests in a real network, we were able to conclude that this instability, known as the “ping-pong effect”, results from the direct usage of RSSI samples which are highly variable. Also, we conclude that, despite being effective, common solutions for the ping-pong effect can cause additional delay to the handoff process, which is undesirable. Finally, we analyze the behavior of RSSI in indoor environments showing that its time series presents multimodal distribution. We argue that the findings presented in this study can help develop more stable handoff algorithms for dense wireless networks.

3 citations


Proceedings ArticleDOI
01 Jun 2019
TL;DR: By analyzing the implementation used by the most common devices, it is concluded that this instability, known as the “ping-pong effect”, results from the direct usage of RSSI samples which are highly variable.
Abstract: Association instability is a common phenomenon in dense networks. The decision of whether or not to perform a handoff between access points in an infrastructured IEEE 802.11 network is taken exclusively by the wireless client stations. Even without mobility, static client devices may decide to migrate to another access point with the goal of improving performance. However, the criteria used to perform handoffs are not defined by the IEEE 802.11 standard and, thus, are dependent on specific vendor implementations. In this paper, we use data from a real large scale network and run experiments to demonstrate that such implementations are commonly deficient, resulting in high levels of association instability in dense environments. By analyzing the implementation used by the most common devices, we were able to conclude that this instability, known as the “ping-pong effect”, results from the direct usage of RSSI samples which are highly variable. Finally, we analyze the behavior of RSSI in indoor environments showing that its time series presents multimodal distribution. We argue that the findings presented in this study can help develop more stable handoff algorithms for dense wireless networks.

2 citations


Cites background from "Handover triggering in IEEE 802.11 ..."

  • ...Works that address the ping-pong effect commonly propose the use of three techniques that can be combined to avoid this problem: 1) connection quality thresholds [4], [14]–[16]; 2) hysteresis margins [4], [5], [14], [17]; and 3) filtering [4], [14], [17], [18]....

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Dissertation
11 Jul 2019
Abstract: Nos ultimos anos, os Veiculos Aereos Nao Tripulados (UAVs) estao a ser usados de forma crescente em inumeras aplicacoes, tanto militares como civis. A sua miniaturizacao e o preco reduzido abriram o caminho para o uso de enxames de UAVs, que permitem melhores resultados na realizacao de tarefas em relacao a UAVs independentes. Contudo, para permitir a cooperacao entre UAVs, devem ser asseguradas comunicacoes continuas e fiaveis.Alem disso, os enxames de UAVs foram identificados pela comunidade cientifica como meio para permitir o acesso a Internet a utilizadores terrestres em cenarios como prestacao de socorros e Eventos Temporarios Lotados (TCEs), tirando partido da sua capacidade para transportar Pontos de Acesso (APs) Wi-Fi e celulas Long-Term Evolution (LTE). Solucoes que dependem de uma Estacao de Controlo (CS) capaz de posicionar os UAVs de acordo com as necessidades de trafego dos utilizadores demonstraram aumentar a Qualidade de Servico (QoS) oferecida pela rede. No entanto, estas solucoes introduzem desafios importantes no que diz respeito ao encaminhamento do trafego.Recentemente, foi proposta uma solucao que tira partido do conhecimento da CS sobre o estado futuro da rede para atualizar dinamicamente as tabelas de encaminhamento de modo a que as ligacoes na rede voadora nao sejam interrompidas, em vez de se recuperar da sua interrupcao, como e o caso na maioria dos protocolos de encaminhamento existentes. Apesar de nao considerar o impacto das reconfiguracoes na rede de acesso, como consequencia da mobilidade dos APs, ou o balanceamento da carga na rede, esta abordagem e promissora e merece ser desenvolvida e implementada num sistema real.Esta dissertacao tem como foco a implementacao de um protocolo de encaminhamento para redes voadoras baseado em Software-Defined Networking (SDN). Especificamente, aborda os problemas de mobilidade e de balanceamento da carga na rede de uma perspetiva centralizada, garantindo simultaneamente comunicacoes ininterruptas e de banda-larga entre utilizadores terrestres e a Internet, permitindo assim que os UAVs se possam reposicionar e reconfigurar sem interferir com as ligacoes dos terminais a rede.

Cites background from "Handover triggering in IEEE 802.11 ..."

  • ...Standard Cloud MAC [48] Anyfi [49] KHAT [50] EmPOWER [51]Nakauchi [52] MSDN [53] MPSDWN [54] Odin [55] Wi-5 [47]...

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References
More filters

Journal Article
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.

229,202 citations


01 Jan 2002
TL;DR: This document specifies protocol enhancements that allow transparent routing of IP datagrams to mobile nodes in the Internet.
Abstract: This document specifies protocol enhancements that allow transparent routing of IP datagrams to mobile nodes in the Internet. Each mobile node is always identified by its home address, regardless of its current point of attachment to the Internet. While situated away from its home, a mobile node is also associated with a care-of address, which provides information about its current point of attachment to the Internet. The protocol provides for registering the care-of address with a home agent. The home agent sends datagrams destined for the mobile node through a tunnel to the care- of address. After arriving at the end of the tunnel, each datagram is then delivered to the mobile node.

2,200 citations


"Handover triggering in IEEE 802.11 ..." refers background in this paper

  • ...This limitation may be addressed by deploying a Mobile IP [2] infrastructure, in which the application flows may be tunnelled through a Home Agent that belongs to the operator....

    [...]

  • ...Such Layer 3 handover may be handled by specific protocols like Mobile IP [2]....

    [...]


Journal ArticleDOI
TL;DR: The general applicability of the autoregressive stochastic models method is demonstrated by examples involving the accurate synthesis of nonisotropic fading channel models, and performance comparisons are made with popular fading generation techniques.
Abstract: Autoregressive stochastic models for the computer simulation of correlated Rayleigh fading processes are investigated. The unavoidable numerical difficulties inherent in this method are elucidated and a simple heuristic approach is adopted to enable the synthesis of accurately correlated, bandlimited Rayleigh variates. Startup procedures are presented, which allow autoregressive simulators to produce stationary channel gain samples from the first output sample. Performance comparisons are then made with popular fading generation techniques to demonstrate the merits of the approach. The general applicability of the method is demonstrated by examples involving the accurate synthesis of nonisotropic fading channel models.

522 citations


"Handover triggering in IEEE 802.11 ..." refers background in this paper

  • ...In the other cases, the MS discovers only a fraction of the APs, since it either does not wait long enough to receive all AP Probe Responses, or because only a subset of channels are scanned....

    [...]


Proceedings ArticleDOI
01 May 2007
TL;DR: It is shown that Proactive Scan does provide fast handoff and satisfactory performance to VoIP applications and is a software-only client-only solution that any mobile device can use in any existing 802.11 networks.
Abstract: It has been a challenging problem to support VoIP-type delay sensitive applications in an 802.11 wireless LAN, because the standard handoff procedure implemented in many current 802.11 products occurs a delay deem unacceptable to VoIP users. To reduce this delay, we have developed a fast handoff scheme called Proactive Scan. It employs two new techniques. The first is to decouple the time-consuming channel scan from the actual handoff, and to eliminate channel scan delay by doing scan early and interleaving it with ongoing traffic in a non-intrusive way. The second technique is a smart trigger that takes into account both uplink and downlink quality and explicitly addresses the link asymmetry which has yet not been touched in previous work. Through implementation and experimentation study, we have shown that Proactive Scan does provide fast handoff and satisfactory performance to VoIP applications. Further, it is a software-only client-only solution that any mobile device can use in any existing 802.11 networks.

182 citations


"Handover triggering in IEEE 802.11 ..." refers background or result in this paper

  • ...Section IV introduces KHAT which is evaluated indoor and outdoor in Section V. Section VI concludes the paper....

    [...]

  • ...Using Windows, we observe the best result, since the MS performs up to four handovers, reaching an average throughput of 0.875MB/s. Additionally we observe that for Windows, the time in which no data is downloaded (i.e., the disconnected time) is relatively short compared to the other OSs....

    [...]


Journal ArticleDOI
TL;DR: An R package focused on Bayesian analysis of dynamic linear models with flexibility to deal with a variety of constant or time-varying, univariate or multivariate models, and the numerically stable singular value decomposition-based algorithms used for filtering and smoothing is described.
Abstract: We describe an R package focused on Bayesian analysis of dynamic linear models. The main features of the package are its flexibility to deal with a variety of constant or time-varying, univariate or multivariate models, and the numerically stable singular value decomposition-based algorithms used for filtering and smoothing. In addition to the examples of "out-of-the-box" use, we illustrate how the package can be used in advanced applications to implement a Gibbs sampler for a user-specified model.

180 citations


Frequently Asked Questions (1)
Q1. What are the contributions in "Handover triggering in ieee 802.11 networks" ?

Thus the authors propose an anticipationbased handover solution that uses a Kalman filter to predict the short term evolution of the received power. The authors implement their mechanism in Android and they show that their solution provides a better wireless connection.