A Location-based Vertical Handover algorithm for ...infocom.uniroma1.it/.../2008_WiMob_A_Location-basedVerticalHandover.pdf · In this paper we propose a location-based vertical handover

Abstract — In this paper a location-based algorithm is

proposed for managing soft mobile-controlled vertical handover between wireless systems. The case of a dual-mode terminal equipped with UMTS and IEEE 802.11 network interface cards is analyzed in detail, and an novel approach for optimizing goodput and limit the so-called ping-pong effect is defined. The novel algorithm is based on a preliminary handover initiation phase triggered on the basis of mobile node location. Handover is then carried out by following a goodput estimation phase allowed by a transient of by casting during soft handovers. Experimental results provide an assessment on the use of location information to drive handover decisions.

Index Terms— Vertical handover, ping-pong effect, goodput, localization information.

I. INTRODUCTION urrent research efforts in the networking area strongly

address the proliferation and coexistence of multiple wireless technologies like IEEE 802.11, UMTS, and WiMAX, especially in the urban environment. Next-generation mobile devices will be equipped with multiple network interfaces, e.g. they will be multi-mode terminals. When simultaneous coverage from multiple interface is assured to a roaming mobile terminal, intelligent mechanisms could allow it to select the network providing the best QoS over time, and then move from a network to the other thus implementing the so-called Vertical Hand-Over (VHO). Location information can be suitably exploited for this purpose.

The classic way to initiate an handover procedure is to detect significant changes of the RSSI (Received Signal Strength Indication) level, though other approaches can be used. In [1] a Quality-of-Service (QoS) based VHO makes handover decision by quality, either subjective and objective. In addition, handover decisions can be taken when the observed quality metrics reach a critical value (reactive approach). Location information can drive handover initiation allowing a mobile node to select the network which currently assures the best performance on the basis of its proximity to the nearest wireless cell.

VHO classical approaches include two main classes, i.e. the Mobile Terminal-Controlled Handover (MCHO) and the Network-Controlled Handover (NCHO), where the HO procedure is initiated and controlled by the Mobile or by the

Network, respectively [2]. MCHO is in fact the most common case, and is employed, for example, in IEEE 802.11. On the other side, NCHO constitutes the typical operator approach for resource optimization and load management to preserve the best possible QoS level. More recent technologies developed frameworks supporting both approaches: for example, WiMax Forum considers an architecture that integrates both NCHO and MCHO, [3].

Variants to the classical MCHO and NCHO encompass a negotiation between the serving network (SN) and the mobile terminal (MT) in order to decide the VHO [4]. In [5] a new VHO for next-generation heterogeneous networks considers a neighbor node (NN) to decide the initiation of VHO. Namely, the NN takes over the VHO procedures from the MT and carries out handover procedures requiring large latency such as registration and authentication before handover initiation. Then, in [1] the VHO from UMTS to WLAN is driven by a functional entity, called QoS-based Decision Engine (QDE), that communicates with the MT during VHO procedure.

Many handover algorithms incorporate a hysteresis cycle within handover decisions so as to prevent a mobile node moving along the boundary of a wireless cell to trigger handover attempts continuously. This phenomenon is well known in the literature under the name of ping-pong effect and hysteresis is largely adopted in practical implementations.

In this paper we propose a location-based vertical handover approach which aims at the twofold goal of maximizing the goodput and limiting the ping-pong effect. Results obtained by Matlab simulations provide an assessment of the potentialities of using location information for VHO decisions, especially in the initiation process.

The paper is structured as follows. In Section II the proposed vertical handover approach is described. In Section III simulation results are discussed to evaluate the performances of the proposed algorithm. Finally, some conclusions are drawn in Section IV.

II. HANDOVER APPROACH We are proposing a vertical handover algorithm for dual-

mode mobile terminals provided with a UMTS and a WIFI network interfaces. Our vertical handover approach is mobile-driven, soft, based on mobile location information and measured goodput which is performed during handover attempts. In order to limit the so called ping-pong effect a hysteresis cycle is introduced in handover decisions.

A Location-based Vertical Handover algorithm for limitation of the ping-pong effect

T. Inzerilli, A. M. Vegni, A. Neri, and R. Cusani

C

Namely, we use mobile terminal’s location information to initiate handovers, that is, when the distance of the MT from the centre of the cell of the new network towards which a handover is attempted (hereafter referred to as new network) possesses an estimated goodput, i.e. GPNEW, significantly greater than the goodput of the current network, i.e. GPCURR.. Following a handover initiation a channel goodput estimation procedure is performed for both the current network, which terminates with an effective handover execution if the goodput of the new network is effectively greater than the goodput of the current network.

In our handover approach we are interested in defining a simple mechanism (handover initiation) to initiate handovers to estimate GP, followed by a more accurate estimate (handover assessement) which actually enables or prevent handover execution.

A. Handover Initiation The goodput experienced by a MT in a wireless cell

depends on the bandwidth allocated to the mobile for the requested services and the channel quality. When unelastic traffic, e.g. real-time flows over UDP, is conveyed the goodput is given by:

( )1 ,outGP BW P= ⋅ − where BW is the bandwidth allocated to the mobile and Pout is the service outage probability. When elastic traffic is conveyed (typically when TCP is used), throughput tends to decrease with increasing values of Pout.

BW is a function of the nominal capacity, of the MAC algorithm which is used in a specific technology and sometimes of the experienced Pout. The maximum theoretical BWWIFI is equal to 23Mbps (out of a nominal capacity of 54Mbs) in a IEEE 802.11a link [6], though it tends to decrease rapidly with the number of users because of the contention-based MAC. The maximum BWUMTS is equal to 14.4Mbps for a HSDPA network, which however, decreases rapidly with Pout. In handover initiation we are considering the maximum value of BW, i.e. BWmax which is obtained in the case of a single MT in the cell and with a null Pout, as the actual goodput will be measured directly in the handover assessment phase.

Pout is a function of various parameters. In the UMTS network it can be calculated theoretically [7], using the following formula:

( ) ( ), 12

0

Pr ,d

UMTSb TxUMTS UMTS UMTS

outN UMTS

EP A r

Iμ

γσ−

⎧ ⎫⎪ ⎪= ⋅ ≤⎨ ⎬+⎪ ⎪⎩ ⎭

where ,

UMTSb TxE is the bit energy in the received signal, μ and

γ are parameters dependent on the signal and interference statistics, σ2

N is the receiver noise power, Ad (rUMTS) is the signal attenuation factor dependent on the MT’s distance rUMTS from the centre of the cell, and I0 is the inter and intracell

interference power. I0 can be rewritten in terms of the number interfN of effective interfering users as follows:

0 ,interfb

spread

NI E

G=

where Gspread is the WCDMA spreading factor.

The service outage probability for a WiFi network PoutWIFI

can be calculated theoretically in a similar fashion using the following formula:

( ) ( ), 12

Pr ,d

WIFIb TxWIFI WIFI WIFI

outN WIFI

EP A r μ

γσ−

⎧ ⎫⎪ ⎪= ⋅ ≤⎨ ⎬⎪ ⎪⎩ ⎭

We define as the radius of a wireless cell Rcell the distance

from the cell center beyond which the signal to noise ratio or the signal to interference ratio falls below the minimum acceptable value μ. Rcell can be obtained resolving the above equations or empirically, through measurement on the network. As an alternative, typical value for well-known technologies can be used, e.g. Rcell

WIFI ≈120m for IEEE 802.11 a outdoor [8], and 100m ≤ Rcell

UMTS < 1km for a UMTS micro-cell [9].

The path loss ( )dA r is approximately proportional to rγ, and the received power SNR (r) can be written as, [10]:

( ) .celld

RSNR r A

r

γ

μ δ⎡ ⎤⎛ ⎞= +⎢ ⎥⎜ ⎟⎝ ⎠⎢ ⎥⎣ ⎦

Maximum GP in a WIFI and UMTS cell can be calculated

with the following approximated formulas

γ

max max

γ

max max

Pr 1 ,

Pr 1 ,

UMTSUMTS UMTS cell

dUMTS

WIFIWIFI WIFI cell

dWIFI

RGP BW A

r

RGP BW A

r

δ

δ

⎧ ⎧ ⎫⎛ ⎞⎪ ⎪⎪ = ⋅ + <⎨ ⎬⎜ ⎟⎪ ⎝ ⎠⎪ ⎪⎪ ⎩ ⎭⎨

⎧ ⎫⎪ ⎛ ⎞⎪ ⎪= ⋅ + <⎪ ⎨ ⎬⎜ ⎟⎝ ⎠⎪ ⎪ ⎪⎩ ⎭⎩

they will be regarded as zero out of cells.

Handover initiation will be performed when the estimated goodput of the new network is greater than the current one. Namely, in the case of vertical handover from WIFI to UMTS, the following equations apply respectively:

WIFIUMTS GPGP maxmax <

It is worth noticing that when handover executions are taken too frequently, the quality as perceived by the end user can degrade significantly in addition to wasting battery charge. This phenomenon is well-known and called in the literature ping-pong effect. It can be useful to limit handover frequency by imposing a minimum interval of time between two consecutive handovers, which can be set different when connected to WIFI or UMTS. We refer hereafter to the

minimum interval between consecutive handovers with the parameter Twait_UMTS/WIFI

B. Handover assessment When a handover procedure is initiated a soft transition

from the current network to the new network is attempted. In this phase channel goodput through the two networks is estimated for a short transient of time during which the sessions currently received are bycasted to both the current and new network interface. During the transient, the goodput experienced from the two interfaces is measured. The assessment of the goodput experienced through two interfaces can be done with various channel estimation methods, e.g. using the Weighted Moving Average, which is defined as follows.

Let us consider a convenient interval of time, divided into K subintervals of duration Δt, during which the UMTS and WiFi channels are estimated. Let i be the discrete time variable.

If channel estimation begins at time N, i will then range in [N, N+K]. Let gi be the received amount of data in the i-th subinterval over Δt, the WMA-based goodput estimation at time N is given by:

, .N K

N i ii N

GP a g N K+

=

= ⋅ ≥∑

Following the handover assessment, the goodput measured

for UMTS and WIFI are compared and if an improvement of the goodput is expected in case of change of network the handover is executed, that is, if GPN

NEW is greater than GPN

CURR is performed.

C. Handover algorithm flowchart The steps of the proposed LB-VHO algorithm is depicted in

Fig. 1. It starts selecting by default the WIFI network if the measured power from the WIFI NIC, i.e. PWIFI, is bigger than the MT’s WIFI receiver sensitivity, i.e. Pmin

WIFI, generally set to −100dBm. Otherwise, the MT detects UMTS network availability.

If either WIFI or UMTS connectivity is not available, the algorithm performs attempts to select a network at regular interval of times till a network is available and is selected.

Different Twait_UMTS/WIFI values were chosen in the simulation setup, as this parameter affects the algorithm’s performances, in terms of ping-pong effect limitation. In our simulations, we considered the following Twait_UMTS/WIFI values:

_ / 10, 0,1, 2,..., 6,wait UMTS WIFIT i i= ⋅ =

respectively, corresponding to no wait, 10s, 20s, and so on until 60s. So, if the MT moves at 0.5m/s, a 10s waiting time results to 10m walked.

Movement from one network to another is performed, as detailed in Section II, and limited with the Twait_UMTS/WIFI parameter discussed in Section II A.

III. SIMULATION RESULTS Some simulation results are now presented to assess

performance of the Location-based Vertical Handover (LB-VHO) algorithm. We have compared performance of the LB-VHO described in Section II with a corresponding handover with a traditional Power-based vertical handover, (PB-VHO), [11] i.e. using power measurements in order to initiate VHOs instead of mobile location information.

We modeled movements of a MT by Matlab environment, over a grid of 400x400 square zones, each with an edge of 5m. Three UMTS cells and 20 IEEE 802.11b WIFI cells are located. The MT moves with a low speed (i.e. 0.5m/s), corresponding to a man walking speed, inside an heterogeneous map with UMTS and WIFI coverage for 12500 seconds. The MT’s path is generated randomly.

In the simulations, we set the following parameters: − the transmitted power in the middle of UMTS and WIFI

cells are about 43 and 30dBm, according to UMTS and WIFI cell requirements, respectively;

− the UMTS/WIFI receiver sensitivies are set at −100dBm, according to UMTS/WIFI cell requirements, respectively;

UMTS and WIFI cell radius are set to 600 and 120m, respectively. For the channel model, we considered a typical AWG (Additive White Gaussian) one, and we referred to the Okomura-Hata model for the signal power attenuation [12].

As PB approach is based on power threshold [ISCE], we considered the PUMTS-TH and PWIFI-TH parameters, both set at −100dBm. This value is equal to the UMTS/WIFI sensitivy, PUMTS/WIFI-min, respectively. Power threshold represents one parameter for vertical handover initiation, in PB approach. As explained in [11], when the measured power on the new interface is greater than a power threshold, old connection is moved from the current interface to the new one.

Move to UMTSMove to WiF i

wait Twait_WIF I wait Twait_UMTS

S oft Handover S oft Handover

NY

N

Y

Y Y

Y N

Y

N

PWIFI > PminWIFI

GPmaxWIFI > GPmax

UMTSGPmaxUMTS> GPmax

WIFI

NGPN

UMTS > GPNWIFI GPN

WIFI > GPNUMTS

S TAR T

Fig. 1. Flowchart for the proposed Location based Vertical Handover.

On the other side, LB approach is based on localization information, evaluated at the beginning of the algorithm, just after a waiting time period.

We have collected statistics on the total amount of bits received by the MT, for 4 cases, i.e. a dual-mode WIFI/UMTS terminal using LB-VHO algorithm, a dual-mode WIFI/UMTS terminal using PB-VHO algorithm, a WIFI single-mode terminal, and a UMTS single-mode terminal used. Each scenario differs from the other in terms of the UMTS/WIFI cell location and the path of the MT on the grid.

Table I, shows the statistics collected for S=20 randomly generated scenarios. Performance are assessed against: 1. the cumulative received bits (CRB) from the beginning to

the end of the simulations with the LB and PB strategies; 2. the number of vertical handovers performed by the user

moving in the grid. Table I shows statistics on the CRB collected in the

simulations. For each approach LB and PB and waiting time 3 parameters are reported related to the CRB, i.e. the mean value (in Gigabit), the standard deviation (in Gigabit) and the dispersion index, defined as the ratio of the standard deviation over the mean value. The three values for LB and PB are reported for a waiting time Twait of 0s and 60s, respectively.

The LB approach brings about reduction of CRB between 6.5% for a null waiting time and 20% for waiting time equal to 60s, which suggests that the waiting time constraint should not be applied to LB approach to reduce number of vertical handovers in order to have a limited reduction of CRB.

Table II shows results of the number of VHO experienced with the LB and PB approach, still in terms of the mean value, standard deviation and dispersion index for various waiting

time values. It can be noticed that the number of vertical handovers with LB is on average significantly smaller, (i.e. ranging in [9.65, 3.70] than that experienced with PB approach, i.e. ranging in [9.15, 329.85]). This demonstrates that the PB approach really requires a constraint on handover frequency limitations, while we have already shown that this measure is counterproductive with LB.

In Fig. 2, the mean values of vertical handovers with LB and PB vs. the waiting time constraint are depicted. This shows even more clearly how the LB approach, providing a more accurate assessment for handover initiation, is able selfcontrol handover initiations and prevents handover execution which can bring about little performance gain. PB approach is unstable even for high values of waiting time, as it can be noticed from the fact that the PB curve is not monotone. On the other side, LB approach is stable to limit vertical handover frequency. Difference between the number of vertical handovers for 0s and 60s is 3. This represents the stabilization of ping-pong effect, independent by waiting time parameters.

In Fig. 3 (a) and 3 (b) are reported the dynamics of the

CRB over the mobile terminal steps during the simulation (i.e. a step is performed every 5 seconds) for a null waiting time and a waiting time of 60s. Curves in Fig. 3 (a) do not follow the same profile, unlike in Fig. 3 (b). This shows the reduction of CRB for LB approach when no waiting time constraint is applied. On the other side, the PB approach follows almost the same curve for CRB, either for 0s and 60s waiting time.

In Fig. 3 (a), up to 500 steps, CRB for LB approach does not follow the PB performance, and at the end of the simulation, the gap between two curves is very strong. Instead, in Fig. 3 (b), LB and PB performances are the same until up to 1000 steps, and the gap at the end of the simulation is very small. This represents a tradeoff between high CRB values and limitation of vertical handoff frequency. So, LB can represent a good compromise.

TABLE II STATISTICS FOR NUMBER OF VHO WITH

LB AND PB APPROACH

Waiting Time[s]

LB Mean

LB Stand. Dev

LB Disp. index

PB Mean

PB Stand, Dev.

PB Disp. Index

0 9.65 2.00 20.73 329.85 794.50 240.87

10 7.25 1.15 15.93 30.20 46.36 153.51

20 5.85 2.31 39.48 19.90 22.54 113.26

30 5.15 1.15 22.42 14.10 16.29 115.53

40 4.35 1.15 26.54 11.80 12.49 105.85

50 4.20 2.00 47.62 9.80 10.58 107.99

60 3.70 1.15 31.21 9.15 7.57 82.75

TABLE I STATISTICS OF THE CRB FOR LB AND PB APPROACH

Waiting Time

[s]

LB Mean [Gb]

LB Stand. Dev [Gb].

LB Disp. Index [Gb].

PB Mean [Gb].

PB Stand. Dev. [Gb].

PB Disp. Index [Gb].

0 5.82 2.38 40.91% 6.23 2.30 36.90 %

60 4.59 2.34 50.88% 5.76 2.14 37.13 %

-10 0 10 20 30 40 50 60 70

0

5

10

15

20

25

VHO frequency comparison

waiting time, [s]

VH

O

PB-VHOLB-VHO

Fig. 2. Number of vertical handovers in PB-VHO and LB-VHO cases.

IV. CONCLUSION In this work we have presented a location-based vertical

handover approach for dual-mode terminals with WIFI and UMTS interfaces, which is able to control the ping-pong effect without specific constraint on minimum interval of time between consecutive handovers, while keeping performance high.

We have evaluated performance of the location-based approached with a corresponding approach which is power based, [11]. We have used the goodput as overall performance metrics for the two handovers and we have monitored the number of vertical handovers executed by the two algorithms over 20 simulation scenarios. Statistics collected with the two approaches have shown that the LB is more stable than the PB one. Specific limitation on minimum interval of time between consecutive handovers turns out to be counterproductive.

REFERENCES [1] A. M. Vegni, M. Carli, A. Neri, and G. Ragosa, “QoS-based Vertical

Handover in heterogeneous networks”, in Proc. on Wireless Personal Multimedia Communication, WPMC 2007, Jaipur, India, 4-6 Dec. 2007.

[2] H. D. Cho, et al., “A study on the MCHO method in Hard handover and Soft handover between WLAN and CDMA”, Proc. on International Conference on Consumer Electronics, ICCE 2005, pp. 391-392, 8-12 Jan. 2005.

[3] Document 8F/1359-E, “Special Meeting of Working Party 8F to complete consensus on the addition of a new IMT-2000 radio interface”, (Study Group 8).

[4] V. Jesus, et al., “Mobility with QoS Support for Multi-Interface Terminals: Combined User and Network Approach”, in Proc. IEEE Symposium on Computers and Communications, ISCC 2007, pp. 325-332, 1-4 July 2007.

[5] H. H. Choi, and D. H. Cho, “Takeover: a new vertical handover concept for next-generation heterogeneous networks”, in Proc. IEEE 61st Vehicular Technology Conference, VTC 2005, Vol. 4, pp. 2225-2229, 30 May-1 June 2005.

[6] “Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications. High-speed Physical Layer in the 5 GHz Band”, supplement to IEEE Standard for Information technology - Telecommunications and information exchange between systems - Local and metropolitan area networks, Specific requirements, 1999.

[7] J. Laiho, A. Wacker, and T. Novosad, “Radio Network Planning and Optimisation for UMTS”, 2nd Edition, Chapter 3, pp. 99-101, Dec. 2005. ISBN: 978-0-470-01575-9

[8] IEEE Standard for Information technology Telecommunications and information exchange between systems. Local and metropolitan area networks. Specific requirements. “Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications”.



[11] T. Inzerilli, and A. M. Vegni, “A reactive vertical handover approach for WiFi-UMTS dual-mode terminals”, to appear in Proc. of 12th Annual IEEE International Symposium on Consumer Electronics (ISCE 2008), CD-ROM no. of pages: 4, Vilamoura (Portugal), 14-16 April 2008, ISBN 978-1-4244-2422-1.

[12] Y. Okumura, et al., “Field strength and its variability in VHF and UHF land-mobile service”, Rev. Elec. Comm. Lab., Vol. 16, No. 9-10, pp. 825-873, 1968.

0 500 1000 1500 2000 25000

1

2

3

4

5

6

7

8

9

10x 109 LB and PB VHO algorithm comparison

MT'steps

Bits

LB-VHO, Tw ait = 0

PB-VHO, Tw ait = 0

Fig. 3. (a) CRB during a simulated scenario with PB and LB-VHO approaches, for Twait = 0s.

0 500 1000 1500 2000 25000

1

2

3

4

5

6

7

8

9

10x 109 LB and PB VHO algorithm comparison

MT'steps

Bits

LB-VHO, Tw ait = 60

PB-VHO, Tw ait = 60

Fig. 3. (b) CRB during a simulated scenario with PB and LB-VHO

Sponsorship

Fourth IEEE INTERNATIONAL CONFERENCE ON Wireless and Mobile Computing, Networking

and Communications

Technical

co-sponsorship

CALL FOR PAPERS

COMMITEES • GENERAL CHAIR:

Abderrahim Benslimane, University of Avignon, France

• STEERING COMMITTEE Abderrahim Benslimane, University of Avignon, France Pierre Boucher, Ericsson Research Canada

Mieso Denko, University of Guelph, Canada

Ibrahim Habib, City University of New York, USA

Hussein Mouftah, University of Ottawa, Canada

Samuel Pierre, École Polytechnique de Montréal, Canada

Do Van Thanh, Telenor & Norwegian University of Science and Technology, Norway Guy Pujolle, University of Paris 6, France

• FINANCE CO-CHAIRS Christine Nora, IEEE France Section, Christophe Pruvost, University of Avignon • TPC CO-CHAIRS Chadi Assi, CIISE, University of Concordia, Montréal, Canada

Hsiao-Hwa Chen, National Cheng Kung University, Taiwan

• TUTORIAL CO-CHAIRS Azzedine Boukerche, University of Ottawa

Mischa Dohler, France Telecom R&D

• WORKSHOP CO-CHAIRS Peter Mueller, IBM Zurich Research Laboratory

Mieso Denko, University of Guelph, Canada

• PUBLICITY CO-CHAIRS Nirwan Ansari New Jersey Institute of Technology, USA Ronald Beaubrun Université Laval, Canada Abbas Jamalipour

University of Sydney, Australia Congduc Pham, University of Pau, France • CONTACT INFORMATION Abderrezak Rachedi, University of Avignon, [email protected]

The IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2008) will be held in Avignon, France, from 12-14 October. • SCOPE The research area of mobile computing has become more important following the recent widespread drive towards mobile ad hoc networks, wireless sensor networks and vehicular ad hoc networks tracking technologies and their applications. The availability of the high bandwidth 3G infrastructures and the pervasive deployment of low cost WiFi infrastructure and WiMAX to create hotspots around the world serve to accelerate the development of mobile computing towards ubiquitous computing. This conference aims to stimulate interactions among participants and enable them to exchange new ideas and practical experience in these areas. WiMob 2008 addresses three main areas: Wireless Communications, Mobile Networking, Ubiquitous Computing and Applications. This conference will be comprised of the following three symposia:

Wireless Communications Mobile Networking, Mobility and Nomadicity

•Broadband Wireless Communication Systems

•Wireless Personal Communications

•Advances in Satellite Communication

•Broadband Wireless Communications

•Modulation and Coding

•Channel Measurement and Characterization

•OFDM and CDMA Technologies and Systems

•MIMO Channels

•Multiuser Detection

•Signal Separation and Interference Rejection

•Multimedia Communications over Wireless

•DSP Applications to Wireless Systems

•Adaptive Antennas for Wireless Systems

•Multiple Access Techniques

•Location Estimation and Tracking

•Resource Allocation and Interference Management

•Multirate and Multicarrier Communications

•Mobile IP Networks

•Cellular Systems, PCS Modeling and Configuration

•Multimedia over Wireless

•Mobility and Location Management

•Wireless Networks Standards and Protocols

•Design and Analysis of Wireless LAN/WAN

•Designing and Modeling High Availability Mobile Networks

•Mobile Network Modeling and Simulation

•Architectures and Protocols for Mobile Networks

•Security, Privacy and Authentication in Mobile Environments

•Wireless Sensor Networks

•Cross-layer Design and Optimization

•Ad-hoc Wireless Networks

•Mobile Internet

•Home and Ubiquitous Networks

•Smart Sensors and Sensor Networks

•Inter-working of 2G, 3G and 4G Wireless Networks

Ubiquitous Computing, Services and Applications •Distributed Computing and Distributed Databases

•Location-based Services

•Virtual Home Environments (VHE)

•M-Commerce

•M-Learning

•Streaming Applications

•Portable Devices and Wearable Computers

•Web Services

•Home and Office Appliances

•Enabling Technologies for Ubiquitous and Pervasive

Computing

•Security, Privacy and Infrastructure for Ubiquitous

Computing

•Systems and Services (e.g. Mobile satellites, WLANs)

•Security and Privacy for Ubiquitous Computing

•Home and Ubiquitous Networks

•Mobile Ad-Hoc Computing

•Gaming Applications in Ubiquitous Computing Environments

•Passive, Active and Smart Tags for Ubiquitous Computing

•Location-dependent and Context-aware Computing

•Software Architecture for Home/Smart Appliances

•Human Computer Interaction in Ubiquitous Computing

Full papers must be submitted for review. Only original papers, unpublished nor submitted for publication elsewhere, can be submitted. Please visit http://www.lia.univ-avignon.fr/wimob2008 for details and submission information. Only timely submissions through EDAS at http://edas.info will be accepted.

IMPORTANT DATES

Submission deadline extended to 30 May, 2008 Tutorial and special session proposals due: May 16, 2008 Notification of acceptance: August 1, 2008

Final manuscripts due: August 14, 2008 Registration and full payment due: August 20, 2008

Program for IEEE International Conference on Wireless and Mobile Computing, Networking and Communications

Day Time Plenary

Mon 10:30 AM-12:30 PM M.1.1:Wireless Sensor Networks I,

M.2.1: MIMO and OFDMA,

M.3.1: Mobility and resource management

02:00 PM-04:00 PM M.1.2: Mobile Ad Hoc Networks (MANETs),

M.2.2: Scheduling I,

M.3.2: Internet and Technologies

04:30 PM-06:00 PM M.1.3: WLAN and 802.11,

M.2.3: MIMO,

M.3.3: Localization

Tue 10:30 AM-12:30 PM T.1.1: Wireless Sensor Networks II,

T.2.1: WiMAX,

T.3.1: Wireless networks and applications

11:45 AM-12:30 PM T.2.2: Coding

02:00 PM-04:00 PM T.1.2: Scheduling II,

T.2.3: MIMO and OFDM,

T.3.2: Media Streaming and QoS

04:30 PM-06:00 PM T.1.3: Cross Layer design,

T.2.4: OFDM,

T.3.3: Security

------------------------------------------------------------------------------------------------------------

Monday, Oct 13

------------------------------------------------------------------------------------------------------------

10:30 AM - 12:30 PM

M.1.1: Wireless Sensor Networks I

Rate Allocation with Lifetime Maximization and Fairness for Data Aggregation in Sensor Networks

Shouwen Lai (Virginia Tech, USA); Binoy Ravindran (Virginia Tech, USA); Hyeonjoong Cho (ETRI, Korea)

Adaptive Data Gathering Schemes with Mobile Collectors for Wireless Sensor Networks

Azzedine Boukerche (Univ. of Ottawa, Canada); xin fei (university of ottawa, Canada)

Home Tutorials Committees Paper Submission Conference Program General Information Registration

WiMob 2008: Technical Sessions file:///C:/Users/annamaria/Desktop/Per%20Pap%C3%A0_ARTICOLI...

1 di 8 08/09/2008 11.58

Low-Power 2.4 GHz Wake-Up Radio for Wireless Sensor Networks

Philippe Le-Huy (Université Laval, Canada); Sebastien Roy (Laval University, Canada)

GRIDS: Geographically Repulsive Insomnious Distributed Sensors – An efficient node selection mechanism usingPassive Clustering (PC)

Abdel Hafid (University of Montreal, Canada); Taek Jin Kwon (Telcordia Technologies, USA); Driss El Ghanami (Ecole Mohammadia d'Ingénieurs, Morocco)

Directional Sensor Placement with Optimal Sensing Range, Field of View, and Orientation

Yahya Osais (Carleton University, Canada); Marc St-Hilaire (Carleton University, Canada); Fei Richard Yu (Carleton University, Canada)

SOLIST or How To Look For a Needle in a Haystack?

Yann Busnel (IRISA / University of Rennes 1, France); Marin Bertier (IRISA/INSA, Rennes, France); Anne-Marie Kermarrec (IRISA, France, France)

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M.2.1: MIMO and OFDMA

On Performance of MIMO Link Adaptation in the Presence of Channel Uncertainty

Jelena Mirkovic (RWTH Aachen University, Germany); Bernhard Walke (RWTH Aachen University, Germany)

Proportional Fairness for MIMO Multi-User Schedulers with Traffic Arrival Process

Masoomeh Torabzadeh (University of Québec at Montréal, Canada); Wessam Ajib (Universite de Quebec a Montreal, Canada)

Adaptive Single-Cell OFDMA Resource Allocation for heterogeneous data traffic

Antonis Gotsis (National Technical University of Athens, Greece); Philip Constantinou (National Technical University of Athens, Greece)

AN EFFICIENT CQI FEEDBACK SCHEME FOR MULTI-USER AND SINGLE USER MIMO/OFDMA

Myeon-gyun Cho (Semyung University, Korea)

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M.3.1: Mobility and resource management

Global Mobility Management Scheme with Interworking between PMIPv6 and MIPv6

Kang-Won Lee (Kyungpook National University, Korea); Seo Won Kyeong(Kyoungpook National University, Korea); Dong-Won Kum (Kyungpook National

University, Korea); You-Ze Cho (Kyungpook National University, Korea)

IEEE802.21-assisted Cross-Layer Design and PMIPv6 Mobility Management Framework for Next GenerationWireless Networks

Linoh Magagula (University of Cape Town, South Africa); H Anthony Chan (Huawei Technologies, USA)

Multi-Access Mobility in Heterogeneous Wireless Networks: Today and Tomorrow

Leo Bhebhe (Nokia Siemens Networks, Finland)

Resource management strategies for Mobile Web-based services

Claudia Canali (University of Modena and Reggio Emilia, Italy); Michele Colajanni (University of Modena, Italy); Riccardo Lancellotti (University of Modena and

Reggio Emilia, Italy)

ARPA: An Arbitration Protocol based on Advanced Channel Feedback for Radio Frequency Identification

Flaminio Borgonovo (Politecnico di Milano, Italy); Matteo Cesana (Politecnico di Milano, Italy)

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2:00 PM - 4:00 PM

M.1.2: Mobile Ad Hoc Networks (MANETs)

On Gateway Selection Protocol for DYMO-based MANET

Takeshi Matsuda (Tohoku University, Japan); Hidehisa Nakayama (Tohoku University, Japan); Sherman Shen (University of Waterloo, Canada); Yoshiaki

Nemoto (Tohoku University, Japan); Nei Kato (Tohoku University, Japan)


2 di 8 08/09/2008 11.58

AVERT: Adaptive Service and Route Discovery Protocol for MANETs

Christopher Ververidis (Athens University of Economics and Business, Greece); George Polyzos (Athens University of Economics and Business, Greece)

Accurately Predicting Residual Energy Levels in MANETs

Thomas Kunz (Carleton University, Canada)

An Energy Efficient Algorithm for Clustering Mobile Nodes in MANETs

Haidar Safa (American University of Beirut, Lebanon); Omar Mirza (American University of Beirut, Lebanon); Hassan Artail (American University of Beirut,

Lebanon)

Exploiting Parallel Networks in Intermittently- connected Mobile Environemts

Lara Deek (American University of Beirut, Lebanon); Sarah Thoubian (American University of Beirut, Lebanon); Serouj Jamijian (American University of

Beirut, Lebanon); Khaled Harras (Carnegie Mellon University, USA); Hassan Artail (American University of Beirut, Lebanon)

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M.2.2: Scheduling I

Impact of inter-cell interference on flow level performance of scheduling schemes for the UMTS EUL

Desislava Dimitrova (University of Twente, The Netherlands); Hans van den Berg (University of Twente, The Netherlands); Geert Heijenk (University of

Twente, The Netherlands); Remco Litjens (TNO Information and Communication Technology, The Netherlands)

A Novel Centralized Resource Scheduling Scheme in OFDMA-based Two-hop Relay-enhanced Cellular Systems

Liping Wang (the Graduate University for Advances Studies, Japan); Yusheng Ji (National Institute of Informatics, Japan); Fuqiang Liu (Tongji University,

Shanghai, China, P.R. China)

Throughput and Performance Optimization Using an Adaptive Coded Cooperation Protocol

Faisal Alazem (Ecole Polytechnique De Montreal, Canada); Jean-François Frigon (École Polytechnique de Montréal, Canada); David Haccoun (Ecole

Polytechnique de Montréal, Canada)

Compensated Proportional Fair Scheduling in Multiuser OFDM Wireless Networks

Cedric Gueguen (Universite Pierre et Marie Curie, France); Sebastien BAEY (Universite Pierre et Marie Curie, France)

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M.3.2: Internet and Technologies

VENETA: Serverless Friend-of-Friend Detection in Mobile Social Networking

Marco von Arb (ETH Zurich, Switzerland); Matthias Bader (ETH Zurich, Switzerland); Michael Kuhn (ETH Zurich, Switzerland); Roger Wattenhofer (ETH

Zurich, Switzerland)

Movement and Connectivity Algorithms for Location-based Mobile Social Networks

Bayrem Chelly (Université Pierre et Marie Curie - Paris 6, France); Naceur Malouch (University of Paris 6, France)

Towards a Uniform IMS Client on Heterogeneous Devices

Paal Engelstad (University of Oslo / Telenor R&I, Norway); Do Van Thanh (Telenor R&D / Norwegian University of Science and Technology, Norway); van

Thuan Do (Linus AS, Norway); Ivar Jørstad (Ubisafe AS, Norway); Tore Jønvik (Oslo University Colleg, Norway)

A Mechanism for Requesting Hierarchical Documents in XACML

Assadarat Khurat (Nokia Siemens Networks, Germany); Jörg Abendroth (Nokia Siemens Networks GmbH & Co KG, Germany)

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4:30 PM - 6:00 PM

M.1.3: WLAN and 802.11

Impact of Wireless Channel on VoIP QoS and Admission Regions in IEEE 802.11g WLANs


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Armelle Gnassou (Ecole Polytechnique de Montréal, Canada); Jean-François Frigon (École Polytechnique de Montréal, Canada); Brunilde Sanso (Ecole

Polytechnique de Montreal, Canada)

Performance Analysis of Multi-hop IEEE 802.11 DCF Backhaul Networks

Puttipong Mahasukhon (University of Nebraska-Lincoln, USA); Hamid Sharif (University of Nebraska-Lincoln, USA); Michael Hempel (University of Nebraska-

Lincoln, USA); Ting Zhou (University of Nebraska-Lincoln, USA); Wei Wang (Univ of Nebraska Lincoln, USA); Tad Wysocki (UOW, Australia)

Throughput Analysis of Overlay CSMA/CA for Secondary Networks

Athanassios Adamis (National Technical University of Athens, Greece); Konstantinos Maliatsos (National Technical University of Athens, Greece); Philip

Constantinou (National Technical University of Athens, Greece)

Opportunistic spectrum access with IEEE 802.11 in IEEE P1900.4 framework

Afef Ben Hadj Alaya - Feki (Orange Labs, France); Berna Sayrac (Orange Labs, France); Paul Houze (France Telecom R&D, France); Eric Moulines (Telecom

ParisTech, France)

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M.2.3: MIMO

Multistage Widely-linear DF equalizers for MIMO channels

Davide Mattera (Università degli Studi di Napoli Federico II, Italy); Luigi Paura (Università di Napoli Federico II, Italy); Fabio Sterle (University of Naples

Federico II, Italy)

Performance Analysis of modern Space-Time codes on a MIMO-WiMAX Platform

Karel-Peet Mare (University of Pretoria, South Africa); Bodhaswar Maharaj (University of Pretoria, South Africa, South Africa)

Precoder Designs for MIMO Broadcast Channels with Imperfect CSI

P. Ubaidulla (Indian Institute of Science, India); A. Chockalingam (Indian Institute of Science, India)

A New 2×2 Coordinate Interleaved STBC for High-Rate Wireless Systems

Ertugrul Basar (Istanbul Technical University, Turkey); Umit Aygolu (Istanbul Technical University, Turkey)

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M.3.3: Localization

A Multi-Sensing Range Method for Position Estimations of Passive RFID Tags

Toshihiro Hori (Kansai University, Japan); Tomotaka Wada (Kansai University, Japan); Yuki Ota (Kansai University, Japan); Norie Uchitomi (Kansai University,

Japan); Kouichi Mutsuura (Shinshu University, Japan); Hiromi Okada (Kansai University, Japan)

Wireless Tracking Analysis in Location Fingerprinting

Eddie Chan (The Hong Kong Polytechnic University, Hong Kong); George Baciu (The Hong Kong Polytechnic University, Hong Kong)

Sensitivity Analysis for GPS in land-vehicle navigation

Jordan Shikoski (Ministry of Education and Science, Macedonia); Ustijana Shikoska (EVN- Distribucija Ohrid, Macedonia)

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Tuesday, Oct 14

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10:30 AM - 12:30 PM

T.1.1: Wireless Sensor Networks II

An Autonomous Wireless Sensor Network with Fault Resilience

Jenq-Shiou Leu (National Taiwan University of Science and Technology, Taiwan); Hui-Ching Hsieh (Tsing Hua University, Taiwan)

Generic 3-D Routing Protocols for Wireless Sensor Networks


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Tarek El Salti (University of Guelph, Canada); Nidal Nasser (University of Guelph, Canada)

On the performance and use of a space-efficient Merkle tree traversal algorithm in real-time applications forwireless and sensor networks

Diana Berbecaru (Politecnico di Torino, Italy); Luca Albertalli (Politecnico di Torino, Italy)

Reinforcement Learning on Real WSN Hardware? It works!

Anna Förster (University of Lugano, Switzerland); Amy Murphy (Fondazione Bruno Kessler - IRST, Italy); Kirsten Terfloth (Freie Universität Berlin, Germany);

Jochen Schiller (FU Berlin, Germany)

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T.2.1: WiMAX

User Profile-Driven Resource Reservation for WiMAX networks

Jaime Cesar de Carvalho Jr. (UFRJ, Brazil); Flavia Delicato (Federal University of Rio Grande do Norte, Brazil); Paulo Pires (Federal University of Rio Grande

do Norte, Brazil); Luci Pirmez (Federal University of Rio de Janeiro, Brazil); Luiz Carmo (UFRJ, Brazil); Aline de Souza (UFRJ, Brazil); Marcos Pirmez (UFRJ,

Brazil)

On SDMA Transmission Considering an Efficient MAC Based IEEE 802.16 Relay Station

Christos Antonopoulos (Intracom Telecom Patras, Greece)

Performance Evaluation of Five New Adaptive Contention Slot Allocators for IEEE 802.16 Based Systems

Victor Rangel (National University of Mexico, Mexico); Miguel Lopez-Guerrero (Universidad Autonoma Metropolitana, Mexico); Javier Gomez (National

University of Mexico, Mexico); Raul Santos (University of Colima, Mexico)

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T.3.1: Wireless networks and applications

An Evolutionary Model for the Study of Wireless Networks

Randall Landry (The Mitre Corporation, USA); Jared Burdin (The MITRE Corporation, USA)

Using LED Lighting for Ubiquitous Indoor Wireless Networking

Peter Dib (Boston University, USA); Thomas Little (Boston University, USA); Kandarp Shah (Boston University, USA); Nick Barraford (Boston University,

USA); Beth Gallagher (Boston University, USA)

Subscriber Mobility in Pub/Sub Systems: Pro-Active vs. Reactive Handoffs

Abdulbaset Gaddah (Carleton University, Canada); Thomas Kunz (Carleton University, Canada)

A Location-based Vertical Handover algorithm for limitation of the ping-pong effect

Tiziano Inzerilli (University of Rome Sapienza, Italy); Anna Maria Vegni (University of Roma Tre, Italy); Alessandro Neri (University of ROMA TRE, Italy);

Roberto Cusani (University of Rome "Sapienza", Italy)

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11:45 AM - 12:30 PM

T.2.2: Coding

The spherical bound stack decoder

Rym Ouertani (TELECOM ParisTech, France); Ghaya Rekaya (ENST Paris, France); Abdellatif Salah (TELECOM ParisTech, France)

Decremental Redundancy Compression with Fountain Codes

Bodhaswar Maharaj (University of Pretoria, South Africa, South Africa); Francois Luus (University of Pretoria, South Africa)

A New Channel Reliability Value for Iterative MAP Turbo Decoder

Wangrok Oh (Chungnam National University, Korea)


5 di 8 08/09/2008 11.58

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2:00 PM - 4:00 PM

T.1.2: Scheduling II

A Distributed Scheduling Algorithm with QoS Provisions in Multi-Hop Wireless Mesh Networks

Chi Harold Liu (Imperial College, United Kingdom); Athanasios Gkelias (Imperial College London, United Kingdom); Yun Hou (Imperial College London, United

Kingdom); Kin Leung (Imperial College, United Kingdom)

Maximizing Network Stability in a Mobile WiMax/802.16 Mesh Centralized Scheduling.

Jad El-Najjar (Concordia University, Canada); Brigitte Jaumard (Concordia University, Canada); Chadi Assi (Concordia University, Canada)

Improving Coverage-Capacity Tradeoff and Power Consumption of TDMA-CDMA by Coverage-dependent TimeslotAllocation

Hamed Nasrabadi (Sharif University of Technology, Iran); Farid Ashtiani (Sharif University of Technology, Iran)

Scheduling-based Reservation MAC Protocol for Bandwidth and Delay Optimization in Wireless Mesh Networks

Boudour GHALEM (UPS, France); Cedric Teyssie (IRIT - Paul Sabatier University, France); Zoubir Mammeri (Paul Sabatier University, France)

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T.2.3: MIMO and OFDM

Channel Estimation Algorithm for Super-Orthogonal Space Time Trellis Coded OFDM System

Dare Sokoya (CSIR, South Africa)

A Chip Semiblind Multiuser Receiver Based on Adaptive Mixing Parameterisation for MIMO STBC DownlinkMC-CDMA with CPICH Systems

Samphan Phrompichai (MahanakornN University, Thailand)

A Novel SNR per Subcarrier Estimation Scheme for OFDM Systems in Frequency Selective Channels

Athanasios Doukas (University of Patras, Greece)

Channel Estimation for OFDM Systems in Rapidly Time-Variant Channels Using High Degree Channel Approximation

Tariq Jamil Khanzada (University of Magdeburg, Germany); Abbas Omar (University of Magdeburg, Germany)

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T.3.2: Media Streaming and QoS

PGMS: Pseudo-optimal Greedy Media Streaming Algorithm for Heterogeneous Wireless Networks

Ahmed Zahran (University College Cork, Ireland); Cormac Sreenan (University College Cork, Ireland)

Feasibility Study of MPEG-4 Transmission on IEEE 802.15.4 Networks

Antonio-Javier Garcia-Sanchez (Technical University of Cartagena, Spain); Felipe Fgarcia-Sanchez (Polytechnic University of Cartagena, Spain); Joan

Garcia-Haro (Polytechnic University of Cartagena, Spain)

QoS Aware HSDPA Congestion Control

Csaba Vulkan (Nokia Siemens Networks, Hungary); Laszlo Korossy (Nokia Siemens Networks, Hungary)

Seamless Handover Scheme in Proxy Mobile IPv6

Ju-Eun Kang (LGDACOM CORPORATION/Research Institute of Technology, Korea); Dong-Won Kum (Kyungpook National University, Korea); Yang Li

(kyungpook National University, Korea); You-Ze Cho (Kyungpook National University, Korea)

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4:30 PM - 6:00 PM


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T.1.3: Cross Layer design

Frame Size Adaptation in Mobile Wireless Networks with Cross-layer Cooperative Mode

Wei Kuang Lai (National Sun Yat-Sen University, Taiwan); Kai-Ting Yang (NSYSU, Taiwan)

Cross-layer self routing: a self-managed routing approach for MANETs

Mohammad Razzaque (UCD, Dublin, Ireland); Simon Dobson (UCD Dublin, Ireland); Paddy nixon (University College Dublin (UCD), Ireland)

Multi-Hop Cross-Layer Design in Wireless Sensor Networks: A Case Study

Philipp Hurni (University of Bern, Switzerland); Torsten Braun (University of Bern, Switzerland); Bharat Bhargava (Purdue Univ, USA); Yu Zhang (Purdue

University, USA)

Combating Packet Reordering in Vertical Handoff using Cross-layer Notifications to TCP

Laila Daniel (University of Helsinki, Finland); Ilpo Järvinen (University of Helsinki, Finland); Markku Kojo (University of Helsinki, Finland)

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T.2.4: OFDM

Estimation and Mitigation of Intercarrier Interference for OFDM Systems in Multipath Fading Channels

Lorenzo Favalli (University of Pavia, Italy); Pietro Savazzi (Università degli Studi di Pavia, Italy); Anna Vizziello (University of Pavia, Italy)

Improved Decoding of Clipped OFDM Signals through Iteration

Josko Radic (University of Split, Croatia); Nikola Rozic (Univeristy of Split, Croatia)

OFDM ICI Self-Cancellation Scheme Based on Five Weights

Oscar Real (Universidad Politecnica De Valencia, Spain); Vicenc Almenar (Universidad Politecnica De Valencia, Spain)

Hybrid ARQ Scheme with Intercarrier Interference Mitigation for OFDM Systems

Rong-Terng Juang (Industrial Technology Research Institute, Taiwan); Kun-Yi Lin (National Taipei University of Technology, Taiwan); Pangan Ting (Tsing Hua

University, Taiwan); Hsin-Piao Lin (National Taipei University of Technology, Taiwan); Ding-Bing Lin (National Taipei University of Technology, Taiwan)

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T.3.3: Security

A Mechanism Design-Based Secure Architecture for Mobile Ad Hoc Networks

Abderrezak Rachedi (University of Avignon, France); Hadi Otrok (Concordia University, Canada); Noman Mohammed (Concordia University, Canada);

Abderrahim Benslimane (University of Avignon, France)

Identity and Pairing-based Secure Key Management Scheme for Heterogeneous Sensor Networks

Sk. Md. Mizanur Rahman (University of Guelph, Canada); Nidal Nasser (University of Guelph, Canada); Kassem Saleh (Kuwait University, Kuwait)

Prioritizing and Enhancing Vehicular Networks Authentication Process Using DSRC Channels Diversity

Christian Tchepnda (France Telecom R&D, France)

Utilizing Semantic Knowledge for Access Control in Pervasive and Ubiquitous Systems

Anand Dersingh (Dalhousie University, Canada); Ramiro Liscano (University of Ontario Institute of Technology, Canada); Allan Jost (Dalhousie University,

Canada)

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