Global Optimization of Multi Radio Mesh Access

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Global Optimization of Multi Radio Mesh Access Point Location in Underground Area Manani M. Moutairou Underground Communications Research Laboratory/ UQAT, Val d’Or, Quebec, Canada [email protected] Hasnaâ Aniss Underground Communications Research Laboratory/ UQAT, Val d’Or, Quebec, Canada [email protected] Gilles Y. Delisle Technopole Defence and Security/ DRDC Valcartier, Quebec, Quebec, Canada [email protected] Nadir Hakem Underground Communications Research Laboratory/ UQAT, Val d’Or, Quebec, Canada [email protected].

Abstract— This paper shows that with a proper placement of Mesh multi radio devices in a confined area, it is possible to reduce the congestion in the WMN (Wireless Mesh Network) by 50 to 60 percent according to the loaded traffic. The best portal deployment is obtained by minimizing the maximum traffic aggregation based on several constraints such as collision domain; routing and physical layer constraints. The different steps that must be fulfilled to reach this adequate deployment are clearly detailed. The effect of uneven resources utilization due to a non uniform spatial density of the workers in the confined area is also addressed. A well designed traffic distribution and a deployment algorithm for underground confined area are proposed and its effects on the WMN’s performance are evaluated by a Monte Carlo simulation. Index Terms—multi radio, confined area, wireless mesh network, collision domain, aggregate throughput, gateway placement.

I. INTRODUCTION

Wireless network operators are dealing with complex problems when planning network operations, particularly in an underground environment. To permit an acceptable level of automation in the planning process, simulation and optimization tools are being developed on a large scale basis. For this deployment, Mesh access points (AP) which combines APs and routing are considered with each AP having two radio interfaces, (fig.1). There is a radio for the wireless access link (AL) and a radio for the wireless transit link. The access link operates within the 2.4 GHz ISM Band (802.11 b) used to exchange data from end-user mobile stations, whereas transit link (TL)

Manuscript received September 10, 2008; revised January, 2009; accepted February 10, 2009.

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works at a center frequency of 5.8 GHz (802.11 a) used to make backhaul of wireless mesh network. Also, portal (gateway (GW) with AP functionality) location will be tackled. Three heuristic algorithms for optimizing the placement of mesh portals based on a linear program have been proposed by Chandra et al. [1]. Another integer linear program and a polynomial time near-optimal algorithm for the same purpose has also been proposed by Aoun and al. [2]. Both approaches require prior knowledge of the traffic flows and aim to strategically place a minimum number of mesh portals to satisfy the capacity demand. Also, a genetic algorithm for mesh network optimization in radio access networks has been proposed by Ghosh et al. [3]. Wireless Mesh architecture is a first step toward providing high bandwidth network coverage. It is a network that is well matched to changing topology area because it has attractive proprieties which allow it to be self-configurable and self-spread. This paper deals with the complete problem of wireless multi radio Mesh Networks devices deployment. Unlike what is presented in [3], our approach takes into account two wireless radios links. After the wireless Mesh devices are deployed, this study shows the profit of a good portal location in a WMN. In [1-2], the placement of mesh portal take into account some parameters (Relay load and cluster size) based on the density of devices in the area on study (free space area). Yet, in a confined area, those parameters are less important because the topology adopted by the WMN is locally a string and its global topology is drawn by the confined area changing topology. Its performance improvement is limited in a confined area topology because it does not take into account uneven resource utilization due to a non uniform worker spatial density in

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the confined area. Uneven resource utilization can be solved by using conventional layered protocol architecture [4] or a dedicated cross-layer design [5-6]. Cross layer approach has been investigated in recent works without tackling the problem of multiple radio deployment of WMN in a confined area and the improvement that can induce an optimal gateway location under specified traffic model in the same area. In most of the case the traffic distribution of a WMN is skewed and the gateway location is chosen arbitrary even if the cross layer design is also focused on some metric such as hop count, gateway link and path capacity. Load balancing [7], canal assignment, and beamforming technique improvement in a WMN are well studied and well known. In this study, the portal placement is chosen in such a way that the maximum aggregated traffic flow through the gateway is taken into account. This maximum aggregated flow is found by evaluating the collision domains of each link following the routing criteria adopted. The shortest number of hops between nodes is used as routing criteria in the confined area in this work. The problem of non uniform resource utilization is also solved. The different steps of the WMN in confined area are clearly detailed. The best portal locations are the locations which minimize the maximum sum of traffic aggregation based on evoked constraints. II. THEORETICAL BACKGROUND

A. Physical Layer Constraints The propagation in underground area has been a focus point of our research activities and narrow band, wideband and ultra wideband channel characteristics have been obtained and studied in details [8-9]. Our recent studies of the mine tunnel [8] have shown that the received signal power is log normally distributed, as treated elsewhere [9] also. The average quality of Wi-Fi AP’s has also been investigated and it was noticed that WiFi access point coverage in tunnel are efficient.

Figure 1.

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WMN devices

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It is known that 802.11 devices’ range varies according to the radiated power, the environment and the operating rates. The real problems on which this work is focused on is the fact that the two wireless links operate on different radio interfaces, making that links being dependent on each other a fundamental requirement. So, to assume an efficient roaming between Mesh devices, it is clear that the transit link (bold circle) range is conditioned by the access link range (dotted circle) (fig.2) so that the condition about the transit link (backbone) range is fulfilled (fig. 2d). The bold and solid circle indicates if the wireless links established between both nodes are efficient. If both nodes are inside the solid and bold circle, it means that there are in the range of each other and the wireless links established between both nodes are efficient. In contrary, it is assumed that no link can be established between the nodes. Obviously the overlap percentage is of a great importance when deploying 802.11 devices as it is specified in the WLAN standard 802.11 adopted by the Institute of Electrical and Electronics Engineers (IEEE) since 1997. This standard determines the media access control (MAC) and physical (PHY) layers for a LAN with wireless connectivity. If the requirement for 802.11 devices to be on the range of each other to insure connectivity on the backbone is well known, nothing is specified to ensure efficient mobile roaming between AP in a confined area. This question is addressed in this paper. a

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Figure 2. Wireless connections efficiency. Overlap area between cells formed by TL links and AL links ensure efficient backhaul and roaming respectively.

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B. Topology Constraint Structure of underground mining environment is often composed of a set of interconnected curved and straight galleries, with about hundred meters length. Accordingly, the major purpose of underground mining mesh network deployment is to cover these galleries, which requires the use of an interconnected linear or chain topologies, also called string topologies. Based on this kind of topology, wireless mesh network is very sensitive to any AP mesh failure. The network topology is strictly affected by the underground area topology. C. Mac Layer Consideration The goal of gateway selection algorithm is to manage the mobile stations generated traffic among gateways, to avoid bottlenecks generated by strong collision domain and to utilize the network resource more efficiently [10]. The collision domain (fig. 3) is computed for each link in the network. This domain is stronger in case of single channel. To evaluate the gateway placement selection performances, it is assumed that all routers in the backhaul operate on a single channel. Traffic aggregation on the collision domain corresponding to each link in the wireless network backhaul is bounded by the throughput capacity of the MAC layer [11]. The hidden node problem still exists in multihops networks but is not taken into account in this study. The cells formed by the transit wireless link and the wireless access link are assumed circular. For a robust WMN, each node must detect its reachability to at least one gateway. So it’s important to select the best gateway according to a certain metric. This decision is called the gateway selection. The goal is to select a default gateway to provide a better quality of Internet access. The quality can be mainly affected by two factors: the link quality and the traffic load on the route. The former is concerned with the bandwidth and the bit error rate (BER) of each wireless link. The latter is concerned with network congestion. From [11-12], a chain of n = 8 nodes generate and forward traffic to the gateway. Assuming that each node generates traffic (G) to be forwarded to the gateway (GW) and that each node can only receive packets from its immediate neighbors, the first step is to make sure that the traffic that has to be forwarded by each link is computed. It is clear that nodes closer to the gateway have to forward more traffic, easily expressed as n times G. In the second step, the collision domain of every link in the network is constructed.    8 G 7 6 5 4 3 2 1  G G G G G G G G     G 2G 3G 4G 5G 6G 7G 8G Figure 3. String topology of 8 WMN APs devices. Each AP is loaded by a constant charge (G) [11].

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In Fig. 3, the constraints of link 2-3 are represented as dotted arrows. Each link in the chain is constrained to transmit only when the other links in its vicinity are inactive. This consideration makes collision domain of a TL link strongest.  For example, the collision domain of link 2-3 is composed of links [2-3, GW-1, 1-2, 3-4, 4-5]. Each collision domain has to be able to forward the sum of the traffic of its links. The collision domain of 2-3 has to forward. 4G+5G+6G+7G+8G=30G

(1)

The collision domain corresponding to each link cannot forward more than the nominal MAC layer capacity B. The bottleneck collision is defined as the collision domain that has to transfer the most traffic in the network. In fig.3, the throughput available to each node Gmax is bounded by Gmax < B/30. B is the throughput that can be achieved at the MAC layer in one hop network with infrastructure and the number 30 represents the sum of the traffic factor in the bottleneck collision domain. This latter is the main index used in this paper to evaluate the congestion in the networks and the Mac bandwidth sharing. D. Routing Layer Consideration Different routing metrics are widely used for the best path selection. As simple metric, link quality evaluated from received signal strength indicator (RSSI) can be used to select a route with best wireless link. However, in a multihop wireless network, the estimation of link quality is more complicated [10]. Many other metrics can be used to optimize routing such as effective number of transmissions, expected transmission account, loss rate, per-hop Round Trip Time and hop count. Most of the gateway selection algorithms are directly based on routing metrics. It means that an optimal placement of the gateway (portal) in the WMN is important to improve the global network performance. The goal of the routing algorithm is thus to determine routes between each traffic aggregation device and the gateway while balancing or sharing the load on the mesh network. Load balancing or a good sharing of the available Mac bandwidth helps to avoid bottleneck links and increases the network resource utilization efficiency. As a simple solution, it is assumed that a node can select a gateway with the minimal hop count. In this study, the routing between two nodes vi and vj is ensured by following the path with the shortest number of hops. It has been observed that shortest paths routing metric gives less improvement than shortest number of hops routing metric. If d (vi,vj) represents the shortest number of hops between vi and vj , it can be written that d ( vi ,w )  d ( w,v j ) d ( vi ,v j )

(2)

where w is the intermediate point(s) which allows a minimum number of hops between vi and vj. One of those two nodes considered must be the gateway position as the routing protocol found the shortest path for each simple node toward the gateway.

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The multi radio wireless Mesh devices are deployed using a genetic algorithm [13-14]. The genetic algorithm (GA) is a popular probabilistic and heuristic optimization method that has been studied as a possible algorithm to solve based station placement by several research groups and individuals [14]. All steps of the WMN devices’ deployment will be detailed in the following section. Each criterion will be clearly evaluated. First, the mathematical dominating set is described. The grid of deployment is presented with the WMN devices positions. Then, criteria are introduced and then expressed in terms of the optimization problem. Second, the optimal gateway position is presented by evaluation the bottleneck collision domain in the network using a Monte Carlo simulation. A. Problem Description And Requirements The dominating set problem [15] can be used to relate test point coverage to preselected candidate AP location. Letting S = {s1,s2,….sn} denote the set of grid points and T = {t1,…tk} denote a set of candidate positions (where possibly t1,…tk represent the different configurations of the same candidate location). Then a bipartite graph may be formed between the sets S and T (denoted Gb). An edge is defined between si and sj if and only if the properties of the candidates’ positions provide adequate reception quality at si when sj is transmitting and at sj when si is transmitting.. For an arbitrary (not necessarily bipartite) graph G= (V,E), a subset V’ Ž V is said to be dominating if for each vertex v  V there exists u  V’ such that (u,v)  E. The minimum dominating set problem is the one that finds the smallest set V’ which has the dominating propriety. It follows that finding the smallest dominating set in Gb corresponds to finding the minimum number of candidates location for adequate area coverage. Note that the minimum dominating set problem is well studied in its own right. However, the dominating set approach needs to be considered in addition to other important basic model requirements. B. Mesh AP Devices Deployment 1.

The deployment area

The planning area (fig. 4) is described by a universal set of points S represented by a grid, (fig. 5). The grid points are set on the selected region following the area of study topology. The different spatial points used in the proposed algorithm are extracted from the mine AutoCAD plan (fig. 4). The experimentation site is located at 70 m below the ground level in the CANMET (CANADA center for Mineral and Energy Technology), mine-laboratory located close to Val d’Or, Québec, Canada. Figure 5 shows two galleries that intercept themselves. The right gallery one denoted section 2 in figure 4.

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Let’s APap and GWgw, denote the optimal set of Mesh AP position and Gateway position respectively. 400

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Level -70 Map sections

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Deployment grid and a selected area on study topology.

In our investigation, criteria are based on the coverage, control of roaming and access points’ number for cost reduction so that immediate devices are in the range of each other on the backbone. Every control nodes defined in APap set have to meet the planning requirements Rn (3) Rn=^ Sap , Sgw , AL, TL, %R`

(3)

S gw < Sap < n 

The set of Gateway position is chosen among the Mesh AP positions. So, this set is mathematically denoted by (4)

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APap = (s1,s2 .....sk ) / fR (s1,s2 .....sk ) d 0,s  Sk n

`

(4)

k = Sap , APap =b,b t 1

where fRn is a function which uses the set of selection criterions, Rn , This function is used to determine the APs’ location. where n represents the number of points in the area of study, where Sgw, Sap are the total and optimal number of gateways and AP Mesh needed to deploy the WMN devices in the study area respectively,

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where b is the number of optimal sets of different solutions found. For example, a deterministic algorithm gives only one set of solution (b = 1). Those criteria will be clearly defined and evaluated in the following paragraphs. 2. The weight and profit index As the grid includes all points located on the area of study, it is possible that some locations are more dedicated for AP position. This can be explained by the fact that some positions (difficult geographical locations as corners) present no interest for the network designer. Such approach avoids some difficulties and helps the algorithms by the use of weight variety where planning is of big interest to find and ensure realistic solution based on locations, greatly enhanced by an adequate site survey. The nodes on the grid are numbered following the same direction. So the points in the solution set are chosen in an order following their number on the deployed grid. For this study, it is assumed that all points in the grid have the same weight. The sections in the total area (fig. 4) are processed one at the time. The sets of nodes, described above, provide the basis for the problem definition. Given a set of control nodes, the Mesh AP and the gateways ideal position must be chosen using efficient algorithms based on specific criteria enumerated later in this work (Rn). These criteria are well defined based on parameters enumerated in (3). Those parameters must be of course chosen based on the operator’s requirement. The overlap ratio %R varies as a function to the coverage area geometry. It is, by the way, the reason why the objective function is based on parameters dependant on the overlap coverage. To evaluate %R, the coverage area of an AP located at a point sj is evaluated by (5).

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Cover_s j = sn  S , dist (s j , sn ) d AL, s j  APap,b and b t 1 , (5) || Cover_s j ||= Ks j

where dist(x,y) is the Euclidian distance between node x and node y. Between two adjacent Mesh APi,j the overlap ratio %Ri,j is evaluated using (6). %Ri, j =

||(Cover_si ˆ Cover_s j )|| ||( Cover_si ‰ Cover_s j )|| - ||(Cover_si ‰ Cover_s j )||

(6)

Assuming that Į is the ratio of the transit link range by the access link range (Į = TL/AL) the overlap ratio is evaluated between two circular cells in free space (7). (%R)-1 =

2 ((1 - Į 2 )× 1- ( Į 2 )2 )

-1

(7)

with Į < 2, AL > 0,TL > 0

Equation (7) shows that in free space it is important to choose an estimated access link noted AL such as TL/AL ” Į, where Į can vary between 0 and 2 included to ensure %R overlap for roaming issue. Indeed, more overlap percentage is needed to reach a same ratio Į, because the underground geometry is non regular. © 2009 ACADEMY PUBLISHER

The optimization algorithm must deploy a minimum number of access points to satisfy the operator’s requirements (which are defined in Rn (3)). The received power at the control nodes varies significantly depending on the location of selected access points and controls nodes. Two closely spaced control nodes served by the same access point can have different power levels depending on terrain variations [10]. 3. GA and Fitness function The GA, a global optimizer algorithm widely known due to its versatility [10] is chosen to perform this study. Solutions in this work are obtained after 150 generations of 100 individuals. Since an individual is a set of chromosomes, chromosomes in this study is consist of binary strings with lc bits chosen to represent a Mesh devices position. To reduce time computation, the size of a chromosome, lc is evaluated so that [11]

§n· 2lc-1 < ¨ ¸ d 2lc ©k ¹

(8)

Let us recall that n is the total number of points located on the grid of study, k is the expected set size of the solution (k-uplet AP Mesh locations). This set is denoted individual and the set of lc bits is called chromosome. So the size of an individual is k*lc. Assume IP, a binary string of length lc, and IP10, the decimal conversion of IP. The AP position is then decoded by using the following function

P . n - 1 +1) M P = INT(I10 2lc - 1

(9)

At each generation, the individuals in the current population of size N are updated by using an evolution scheme. The following techniques are chosen to reach the optimal solution. The main steps of the algorithm are: 1- Extract five best individuals in the current population on the basis of the fitness value. Those individual will represent the first five individuals in the new population. 2- From the individuals who formed the current population except the best individual already chosen: a) Select two individuals based on a tournament scheme. Those individuals are called parents. b) Those parents are crossed to give two new individuals. Those individuals are called children. A two point crossover is applied for generating the new children till a new population is rebuilt and before the current children are set in the new population, they are randomly mutated. The probability of the crossing over is set to 0.75. The probability of mutation is set to 0.05. This new population must be filled till its size is equal to the initial population size (N). 3- The algorithm stops after 150 generations of population if a good solution was not found. More detail about selection scheme, crossover and mutation operator are available in [10].

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Assume that (s1,s2,s3,….sk)  APap and that TLi is the transit link between immediate nodes in the set of AP positions (solutions) found. The fitness function (GA objective function) is defined in (10). ­ 1 if TLi d TL I(TLi ) = ® ¯0 elsewhere A = min (W)× exp(-10× -R + Round (min (K)× 100) )) K = (%R1,2 , %R2,3 ,....., %Rsap -1,sap ) B=

(10)

R 100× max (Round (K))

Fitness =

1 l ¦ k -1 I(TLi )× (A× (1 - )+ B) k - 1 i=1 T

where Round (x) rounds the elements of x to the nearest integer and x is the absolute value of x. Let W= [ w1,w2,…..,wn] be the weight vector. Each element of this vector represents the i-th AP in the set of k–uplet deployment location. K is the overlap ratio between pairs immediate and adjacent nodes in the set of k-uplet deployment location selected. R is a fixed percentage overlap value expected by the network designer. %Ri,j is evaluated as in (6). T is the number of points set on the grid of deployment. l is the number of points which are not covered on the deployed grid. In (10), the expression A contains an exponential function. This function is used as a discrimination factor. The severity level of this discrimination is hold by the exponent of multiplicity (-10) inside the exponential function. A helps to find the set of nodes which maintain a minimum expected overlap ratio R between immediate neighbour nodes. The minimum radio between all pair of immediate nodes in the set of nodes is examined by expression A whereas the maximum expected overlap ratio also R is examined by expression B. So the fitness function helps to choose the set of nodes which intermediated nodes maintain first, the expected overlap radio R on cells established by wireless access link and second efficient wireless transit links between themselves. Finally the total area, covered by the set of nodes to be chosen, is evaluated. The optimal value of the finesse is 1 when all this conditions are reached. Also the set of points which fitness is 1 is considered as optimal set of nodes points’ position for AP deployment in the considered area. It must be recalled that the parameter used in this work and particularly the Mesh AP, ranges (TL and AL) is chosen based on results observed during experimentation campaign [8-9]. Let’s assume that H is the maximum section of all galleries in the confined area and a = min (AL, TL) the Mesh AP ranges, it is important to notice that basically confined area is characterized by H