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Chapter VIIIA Prediction Based Flexible

Channel Assignment in Wireless Networks using Road Topology Information

G. SivaradjePondicherry Engineering College, India

R. NakkeeranPondicherry Engineering College, India

P. DananjayanPondicherry Engineering College, India

Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

AbstrAct

In this paper, a novel prediction technique is proposed, which uses road topology information for predic-tion. The proposed scheme uses real time positioning information and road topology information, which matches with the real environment. The scheme uses flexible channel assignment to maintain a better tradeoff between forced termination and call blocking probabilities. For reservation of resources in advance, the information about future handoffs is obtained from the road topology prediction technique. To show the effectiveness of the prediction scheme and flexible channel assignment scheme, this work aims at simulation of other channel assignment strategies viz., fixed and dynamic channel assignment strategy with and without incorporating the prediction based on road topology information. It gives accurate prediction results which helps to maintain a better QoS and resource management.

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A Prediction Based Flexible Channel Assignment

IntRoductIon

Today’s navigation systems are mostly based on a quite complex positioning system involving multi-sensor systems (differential odometers, gyros, and magnetic field sensors) in the mobile vehicle coupled with a global positioning system (GPS). Mobility prediction for mobile terminal (MT) traveling in a road is possible through GPS but that is not accurate and indeed not available in all areas. However, research shows that 85% of mobility activities in road traffic occur in urban areas where the availability of GPS signals is around 15 to 40%. Rare availability of satellite means due to masking and multi-path effects in urban areas, magnetic disturbances, wheel slips, and unfavorable error propagation lead to loss of position and in principle to total system failures. To make positioning systems available for every mobile vehicle (cars, motorbike, bicycle, pedestrian), development of navigation systems based on a personal digital assistant (PDA), digi-tal maps, mobile phones, and GPS module are initiated (Zhao, 1997; Kyamakya, et al., 2002; Syrjärinne, 2001; Kaplan, 1996; Schwarz, & El-Sheimy, 1999).

Mobility prediction is an exciting research area in which mobile positioning is extremely valuable. The use of real-time positioning information for mobility prediction could potentially give rise to better accuracy and greater adaptability to time-varying conditions than previous methods (Adusei, et. al., 2002; Hellebrandt, Mathar, & Scheibenbogen, 1997; Hellebrandt & Mathar, 1999). The availability for a practical and ac-curate mobility prediction technique could open the door to many applications such as resource reservation, location tracking and management, location-based services, and others that have yet to be identified. If the system has prior knowledge of the exact trajectory of every MT, it could take appropriate steps to reserve resources so that qual-ity of service (QoS) may be guaranteed during the MT’s connection lifetime (Pathirana, Svkin,

& Jha, 2004). However, such an ideal scenario is very unlikely to occur in real life. Instead, much of the work on resource reservation has adopted a predictive approach (Aljadhai & Znati, 2001; Soh & Kim, 2001; Choi & Shin, 1998). A generalized framework for both describing the mobility and updating location information is considered based on a state-based mobility model (Song, Kang, & Park, 2005), and by caching and batch processing (Lee, Zhu, & Hu, 2005). Semantics prefetching strategy is developed, which, utilizes users’ in-formation to manage location dependent data’s (Sang, Song., Park, & Hwang, 2005). Indexing schemes for location dependent queries (Waluyo, Srinivasan, & Taniar, 2005a) and data broadcast-ing are introduced in (Waluyo, Srinivasan, & Taniar, 2005b; Waluyo, Srinivasan, Taniar, & Ra-hayu, 2005). Management of data items in mobile databases and broadcasting system is proposed (Waluyo, Srinivasan, & Taniar, 2004). A mobile query processing approach is proposed when the user’s location moves from one base station to another (Jayaputera & Taniar, 2005).

The use of GPS has been advocated as a promising choice for avoiding the poor network utilization due to unnecessary reservations (Er-bas, Kyamakya, Steuer, & Jobmann, 2002). Ac-cording to Zhao (2002), it is very reasonable to expect assisted GPS positioning methods to yield an accuracy of less than 20 m over 67% of the time period. Predicting the trajectory of mobile terminals (Chiu & Bassiouni, 2000) and call-level QoS (Soh & Kim, 2003) to perform resource reservations based on geographic information system (GIS) information is suitable for uniform traffic pattern only. Unfortunately, however, there exist some situations in which part of the GPS signal may be obstructed to the extent that the GPS receiver may not “see” enough satellites for positioning. This signal-obstruction problem was successfully overcome by integrating GPS with other positioning systems (Wang, et al., 2000). Augmenting GPS is not limited to sensor integration but it is possible with computer-based

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A Prediction Based Flexible Channel Assignment

tools, such as GIS, for efficient data collection and analysis.

GPs/GIs Integration

The GIS is a computer-based tool capable of acquiring, storing, manipulating, analyzing, and displaying spatially referenced data (Drane, Macnaughtan, & Scott, 1998; Hofmann-Wal-lenhnohof, Lichtenegger, & Collins, 1997). Spatially referenced data is identified according to its geographic location (e.g., features such as streets, light poles, and fire hydrants). Spatial or geographic data can be obtained from a variety of sources such as existing maps, satellite imag-ery, and GPS. Once the information is collected, GIS stores it as a collection of layers in the GIS database. The GIS can then be used to analyze the information and decisions can be made efficiently. If this GPS/GIS is incorporated in the MT, it will be possible to accurately predict the user move-ments and resource reservations made based on this prediction would be quite appropriate.

GPs/cellular Integration

Wireless network operators can either use the network-based location or the handset-based location. Most network-based caller location systems employ either the time difference of arrival (TDOA) approach or the angle of arrival (AOA) approach to determine the caller’s location. Handset-based location technology integrates GPS with cellular communication through the installation of a GPS chipset in the handset of the wireless phone. Unlike network-based technology, handset-based location technology is very simple to implement and does not require the installation of additional equipment at the base stations (e.g., GPS timing receivers etc.). This method is simple, cost-effective, and flexible and hence, it will be the probable choice in future.

In cellular networks, MT that is carried in vehicles would encounter more frequent handoffs;

they are the ones that would benefit most from mobility predictions. In reality, mobile terminals move according to the presence of highways, streets, and roads. The mobile terminals do not move randomly, rather they follow some pat-terns that are somewhat predictable. Therefore, incorporation of road topology information into the prediction algorithm could potentially yield better accuracy (Östergren & Juhlin, 2005). Safar (2005) proposed an approach to efficiently and accurately evaluate nearest neighbor queries in mobile information systems that use GPS- and GIS-based spatial network databases, which is computationally expensive to compute the distances between objects. Most of the works in the literature have considered cell boundary to regular hexagon, which doesn’t match with reality due to terrain characteristics and the existence of obstacles that interfere with radio wave propaga-tion (Aljadhai, et al.; Choi et al.; Liu, Bahl, & Chlmtac, 1998).

Proposed algorithm utilizes the real-time po-sitioning information obtained from augmented GPS for mobility prediction and the associated bandwidth reservation considering a fuzzy type of cell with irregular boundaries. This inte-grated framework assumes base stations (BS) are equipped with road-map information and that the mobile terminals are equipped with GPS devices. Prediction accuracy, blocking probability, handoff probability, and forced termination are obtained for the proposed algorithm and are compared with the existing algorithms.

system descRIPtIon

A cellular network with two-dimensional cell layout is considered in which each cell is adjacent to several other cells. The minimum granularity of bandwidth resources that could be allocated to any call is assumed to be one bandwidth unit (BU). Each BS has a capacity C(j), which is assumed to be constant for flexible channel as-

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A Prediction Based Flexible Channel Assignment

signment (FCA). In DCA, the total capacity of the system CT is shared by all the base stations; in FCA scheme, there are both fixed number of channels C(j) for BS in reserve to the dynamic channels, which is common for all the cells. Given the bandwidth demand of individual connections, the BS performs admission control to ensure that the total demand of all active connections are below or equal to C(j). Although it is sug-gested that some adaptive applications might be able to accept a lower bandwidth at the expense of lower call quality during congestion, that is considered here. Such an assumption is likely to reduce probability of forced termination (PFT), but it may make harder to visualize the advantages of using mobility predictions, which is the main aim of this work.

In order to prioritize handoffs over new calls, each cell must reserve some bandwidth that can only be utilized by incoming handoffs. Specifi-cally, each BS shall have a “reservation target” Rtarget that is being updated regularly based on mo-bility predictions. A new call request is accepted if the remaining bandwidth after its acceptance is at least Rtarget, for example,

, arg( ) ( ) ( )x j new t etC j b b R j− + ≥∑ (1)

where,

bnew is the bandwidth required by the new call request.

bx,j is the bandwidth currently being used by an existing connection x in cell j.

The BS can only attempt to meet this target by rejecting new call requests while waiting for some existing calls within the cell to release bandwidth when they are terminated or handed off to other cells. For a handoff request, the admission control rule is more lenient--it is admitted so long as there is sufficient remaining capacity for the handoff, regardless of the value of Rtarget(j)

,( ) x j handoffC j b b− ≥∑ (2)

where,

bhandoff is the bandwidth needed by the handoff.

When a new call request is rejected, it is as-sumed that it is cleared. Subsequent new call re-quests are assumed to be independent of previous requests. When the BS has insufficient bandwidth to accommodate an incoming handoff-request, it is assumed that it is forced to terminate. Handoff queuing is not assumed, although it would likely improve the performance of proposed scheme, such extensions may make it difficult to visualize the advantages of using mobility predictions.

RoAd toPoloGy-bAsed PRedIctIon scheme

In this technique, the serving BS receives regular updates about each active MT’s position for every ∆T (say 1 sec). The output of each prediction has the form of a 3-tuple: target cell, prediction weight, and prediction limit. The target cell is the MT’s predicted handoff cell. The prediction weight is a real number between 0 and 1 that indicates how likely the prediction is correct. The lower predic-tion limit (LPL) gives a statistical bound for the actual remaining time from handoff. Each MT may have more than a single 3-tuple to represent different paths from its current position that may lead to a handoff within the threshold time, Tth.

The prediction tasks are assigned to indi-vidual BSs, which are expected to have sufficient computational and storage resources. In order to incorporate the road information into mobility predictions, each BS needs to keep a database of the roads within its coverage area. The road between two neighbouring junctions is treated as a road segment, and each segment is identified using a junction pair (J1, J2), where a junction can

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A Prediction Based Flexible Channel Assignment

be interpreted as an intersection of roads (e.g., T-junction). The approximate coordinates of each junction are to be stored in the database. Since the road segment may contain bends, it can be broken down further into piecewise-linear line segments. The coordinates defining these line segments within each road segment are also recorded. All the previous coordinates could be easily extracted from existing digital maps previously designed for GPS-based navigational devices. Infrequent updates to these maps are foreseen because new roads are not constructed very often, while existing road layouts are seldom modified. The database also stores some important information about each road segment. Since two-way roads would probably have different characteristics for each direction, the database shall store information corresponding to opposite directions separately. To summarize, the base station has the following information that is stored in the database.

1. Identifying of neighbouring segments at each junction.

2. Probability that a MT traveling along a segment would select each neighbouring

segment. Note that this transition prob-ability could be easily computed from the previously observed paths of other MTs.

3. Statistical data of time taken to transit each segment.

4. Statistical data about possible handoffs along each segment, such as probability of handoff, time in segment before handoff and handoff positions.

With the exception of the first item listed above, the other database entries will be updated periodically since they are dependent on current traffic conditions. In reality, the transition prob-abilities among road segments would probably vary with time and traffic conditions. For sto-chastic processes whose statistics vary slowly with time, it is often appropriate to treat the problem as a succession of stationary problems. The transition between road segments is modeled as a first-order Markov process, and it is assumed stationary between database update instances so as to simplify the computations.

Based on this model, the conditional distribu-tion of a MT chooses, a neighboring segment,

Figure 1. Utilization of road topology information for mobility prediction

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A Prediction Based Flexible Channel Assignment

given all its past segments that are assumed to be dependent only on the current segment and the immediate prior segment.

Using the road topology shown in Figure 1 as an illustration, consider two MTs (MT1 and MT2) that are currently traveling from junction B towards junction E. MT1 came from segment CB previously, while MT2 came from segment AB. Based on the assumed model, the conditional prob-ability of MT1 going to segment EF will be

1[ | ]k kP s EF s BE+ = = (3)

where, sk is the current segment that the MT transits.

The stationary assumption implies that the previous conditional probabilities are independent of the value of “k.” A road segment is described as a “handoff-probable segment” (HPS) if MTs have previously requested handoffs while traveling through it. For each HPS, the handoff probability is obtained as the ratio of MTs that made handoff requests and the segment. Also, for those MTs that made handoff-requests, their target handoff cell is recorded, and information about the time

Start

Monitor an outgoing calland track the position of

the MT

Compare the MT’s endjunction with that of

HP’s end junction

Matchfound

Estimate the time beforehandoff and

the target cell

The MT is consideredfor reservation of

channel in the target cell

Stop

Calculate the probability tochoose the HPS

thP TT <

thT PP <

)( TP

)( PT

YesNo

Yes

Yes

No

No

Figure 2. Road topology-based prediction algorithm flowchart

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A Prediction Based Flexible Channel Assignment

Neighbouring cell (B)

Reference cell (A)

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Figure 3. Procedure performed for every Tpredict to reserve bandwidth

Step Number Actions

1 Reference cell A sends Tth(A) to neighbouring cell B

2Neighbouring cell B performs predictions

3

Neighbouring cell B returns MT_ID, bandwidth require-ment, prediction limit for MTs likely to handoff to ref-erence cell A within Tth(A)

4Reference cell A computes Rtarget(A)

Table 1. Sequence of actions performed to reserve bandwidth

Mobility Model forRoad Segments

ConnectivityDatabase

Cell Cove rage MapDat abase from Base

Stat ion

Digital Road MapDat abase from Base

Stat ion

Accurate Positioningthrough

GPS, Assisted-GPS

Traff ic TrendAnalysisDatabase

User Inter faceAssist anceModule

Positioninginformation through

Back-up systemsPredic tionAlgorithm

Procedure toRenew / Update

the Database

Dynamic BandwidthReservation Scheme

Channel AssignmentStrategy

Fixed ChannelAllocation

Dynamic ChannelAllocation

Flexible ChannelAllocation

Figure 4. Block diagram of the proposed integrated framework

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spent by them in the HPS before handoffs is col-lected, as well as their handoff positions. Using the model previously described, the conditional probabilities of handing off at each of the HPSs from segments that are several hops away is determined via the chain rule. It is also possible to predict the remaining time before handoff for each of these possible paths, using previously col-lected statistical information from each segment along the path.

In between database updates, the BS collects all the relevant data required for the subsequent update. The procedure begins by emptying both set of handoff-probable segments (SHPS) and set of segments in which MTs may be considered for reservations (SRSV) so that they can be regener-ated based on the newly collected data. Each and every road segment within the BS’s coverage area is sequentially examined one at a time. Then the first order transition probabilities are evaluated from the segment examined to its neighboring segments. After that, it evaluates the average speed of the MT in the particular segment so the time spent by MTs in the segment can be obtained. Then the probability that a MT would request a handoff while transiting the segment is computed. If handoffs have occurred along this segment previously, then the segment is identi-fied as a HPS, and is entered into both SHPS and SRSV. Its membership in SRSV signifies that MTs traveling in this segment are potential candidates for resource reservation.

One important point to emphasize for the previous database update algorithm is that all the previous database entries only need to be calcu-lated once during each database update, which occurs very infrequently. Therefore, they should be well within the computational capability of a dedicated, average processor at the BS. Figure 2 shows the flowchart of the road topology-based prediction algorithm, which is self-explanatory. It should be noted apart from predicting MTs probable route to handoff it also estimates the time before handoff.

RoAd toPoloGy InfoRmAtIon-bAsed chAnnel ReseRvAtIon

In the proposed scheme, it is assumed that the system is having a perfect knowledge about future handoffs up to time Tth and an incoming handoff into the current cell will lead to a positive change in the bandwidth used, while an outgoing handoff will lead to a negative change. By summing up all the bandwidth changes over the time interval (0, Tth), it is realized that the maximum peak bandwidth to be reserved (Rtarget(j)) is fixed as one BU.

The predictions used to compute Rtarget(j) are made periodically every Tpredict. If the predic-tions are performed very frequently, they are more accurate but a more powerful processor will be required at each BS. On the other hand, their accuracy may deteriorate if they are far apart, causing the tradeoff between PFT and PCB to become less efficient. A two-cell structure is considered for bandwidth reservations as shown in Figure 3 and the sequences of actions performed to reserve bandwidth are listed in Table 1. In an actual cellular network, several neighboring cells usually surround each cell; Steps 1, 2, and 3 are simultaneously performed for every neighbour-ing cell.

Based on the bandwidth reservation obtained from the proposed road topology-based predic-tion algorithm, channels can be assigned through FCA, DCA, and FLCA. Figure 4 gives a detailed overview of the proposed integrated framework. The information is received from the mobility model for road segments connectivity, cell cover-age, digital road map details from base station, positioning information through GPS systems and other backup systems, traffic trend analysis, and user information is used to update the database of the proposed system. Based on the database details, the prediction algorithm reserves the re-sources prior to handoff occurrence then flexible channel allocation admits the call. This combined

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frame of real time data is from various sources, prediction analysis, and flexible channel alloca-tion will provide a better trade-off between call dropping and call blocking probability, thereby improving the resource utilization and system performance.

sImulAtIon Results And dIscussIon

To facilitate the evaluation of the proposed scheme, a novel simulation model is designed and gener-ated whenever an MT reaches the end of a road segment; it randomly selects the next junction from the array and moves along the selected seg-ment until it reaches the selected junction. In the simulation, MTs new position is updated every second when it moves from one position to other, which is determined by computing the distance moved using the speed of the MT and slope of the road segment in which the MT is traveling. This step is repeated until the simulation is ended for each user.

Figure 5 shows a simulated road map model of Pondicherry Boulevard (India). Although the cell layout shown in the background adapts hexago-nal cell model for determining BS locations, the simulation model does not assume handoffs occur at the hexagonal boundary. Instead, it identifies N points around each BS, which influences handoff and is viewed as handoff influence points (HIP). When the MT comes to one or more of these HIPs, handoff will occur during its transit through this region. Parameters used for simulations are listed in Table 2.

Figure 6 shows the predicted and actual handoffs at each time instant for a cell. Figure 7 shows the consolidated prediction result. It is inferred that predicted handoff is higher than the actual handoff in most of the cases. This clearly indicates the handoff-dropping probability will be the minimum possible if this prediction algorithm is incorporated.

Figure 8 depicts the blocking probability at each time instant and Figure 9 shows the average blocking probability for the proposed flexible channel assignment scheme with prediction. It is

North

Figure 5. Simulated road map of Pondicherry Boulevard (India)

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PARAMETER DETAILS

Area simulatedPondicherry Boulevard (India)

Total area 5 sqkm

Radius of cell Around 600 meters

Number of cells 5

Area of each cell 1 sqkm

Number of channels 100

Velocity 30 Kmph to 110 Kmph

Call holding time 25 s to 125

Call arrival Poisson Distribution

Selection of Road segment at junction Random

Position update Every 1 s

Table 2. Parameters used for simulation

Figure 6. Prediction result at each time instant

Figure 7. Consolidated prediction result

Figure 8. Blocking probability at each time instant

inferred that the blocking probability is always less than 0.1 and hence the proposed scheme is more efficient.

Figure 10 shows forced termination prob-ability of the reference cell at each time instant and Figure 11 shows average forced termination probability for different cells. Here also it is in-

Figure 9. Average blocking probability

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ferred that proposed scheme is better and offers optimum QoS.

conclusIon

In this article, a novel prediction technique is introduced, which uses GIS,GPS, and cellular

integration based on road topology information prediction, which matches with the real environ-ment. Flexible channel assignment implemented using the information obtained from this inte-grated prediction framework offers better tradeoff between forced terminations and call blocking probabilities. It gives accurate prediction results

Figure 10. Forced termination probability at each time instant

Figure 11. Average forced termination prob-ability

Figure 12. Probability of forced termination Vs call blocking

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which helps to maintain a better QoS and resource management.

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A Prediction Based Flexible Channel Assignment

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This work was previously published in Int. Journal of Information Technology and Web Engineering, Vol 1, Issue 4, edited by G. Alkhatib and D. Rine, pp. 37-48, copyright 2006 by IGI Publishing (an imprint of IGI Global).