Load Balance Routing Using Packet Success Rate for Mobile Ad hoc Networks Fengfu Zou, Xinming Zhang, Xuemei Gao, Dong Shi, Enbo Wang Department of Computer Science and Technology University of Science and Technology of China Hefei, 230027, P.R. China
[email protected] Abstract—Mobile Ad hoc networks (MANET) are composed of many mobile nodes equipped with wireless antennas, and one of the critical challenges in the design of MANET is the development of efficient routing protocols. Conventional routing protocols such as Dynamic Source Routing (DSR), often prefer to choose “the shortest path” for data forwarding. However, this mechanism often results in two tendencies, making performance declined. One is so-called “hot spot” and the other is “unstable nodes” selected as intermediate nodes. The proposed load balance routing customarily circumfuse one of these two problems and isolate network layers. Therefore, we combine Medium Access Control (MAC) with routing layer and propose a new load balance routing protocol (LBPSR), using packet success rate (PSR) which we define as the probability of a node can send out a packet successfully. Simulation results show that LBPSR prolong the network lifetime and improve the performance of MANET. Keywords-cross-layer design; lifetime; load balance; mobile ad hoc network; routing protocol
I.
INTRODUCTION
In MANETs [1] ˈone of the critical challenges is the development of efficient routing protocols that can provide high-quality communications among mobile hosts and routing protocols [2] including AODV, DSDV and DSR have been proposed. Conventionally, routing protocols such as DSR [3] use “the smallest number of hops” as the metric to determine the optimal path and perform well in “loose” environment However there are two tendencies that cause these routing protocols’ performance declined. One is so-called “hot spot” and the other is “unstable nodes” selected as intermediate nodes. Several attempts [4][5][6][7][8][9][10][11] have been made to solve such problems and improve the performance of ad hoc networks. A load-balanced ad hoc routing (LBAR) algorithm [4] defined a new routing metric known as the degree of nodal activity. A load aware routing ad hoc (LARA) algorithm [5] used the average queue length of a node as a routing metric. Delay-oriented shortest path routing [6] mainly considered the delay in MAC level and Delay Oriented Adaptive Routing (DOAR) [7] is based on a “minimum prediction delay” mechanism. In [8] a new metric “Leisure Degree” is presented for denoting the transmission state of the
node. Mobility- Adaptive Routing (MARio) [11] used route lifetime to represent abstract mobility. However, [4][5][6][11] only focus on either MAC level or Routing level. A recent study [12] showed that the traditional hierarchical network design isn’t an effective method for ad hoc network and a new cross-layer route discovery framework was proposed in it. Also the metrics used in [4][5][6][11] don’t integrate the impact of traffic load and mobility. Our original work in [7][8][9] seems not very close to MAC. We propose a loadaware routing protocol which considers MAC layer channel contention information and the number of packets in the interface queue [10]. In this paper, we use a cross-layer design of MANET architecture which joint the MAC with routing layer and propose a new efficient routing protocol, namely, Load Balance routing using Packet Success Rate (LBPSR), based on the concept of balancing the traffic load which uses the PSR (packet success rate) gained from MAC layer as the metric to select the optimal route. Performance results show that LBPSR outperforms DSR in high load and fast-changing environment. This paper is organized as follows, the section II gives a brief introduction of MAC 802.11. In section III, the details of the proposed LBPSR’s scheme are described. In section IV, the simulation results and analysis are reported. Finally, In section V, we draw a conclusion and give our future work. II.
THE MAC 802.11
In MAC 802.11[13], if a station A needs to send packets to station B, the protocol schedules the following steps if no packet is being transmitted around them. (Precondition: A and B can communicate with each other directly.). 1) A sends RTS to all of his neighbors to tell them that it want to send packet to B and require them to keep “quiet”. 2) On receiving the RTS from A, if B can receive data now (means no collision will happen as it has known), it will broadcast CTS. 3) If A receives CTS from B, then it will start to transmit the data packet to B. 4)if B has received the data packet correctly ,it transmits ACK packet to A. 5) If A has received ACK, protocol enters next schedule period , or else if overtime occurs, RTS will be retransmitted. As detailed in section 3, we can see that the more frequently congestion happens because of “hot spot” or high mobility, the more times overtime will
This paper is partially supported by the National Natural Science Foundation of China under Grant No. 60673171; the National Grand Fundamental Research 973 Program of China under Grant No.2006CB303006 the Open Foundation of Anhui Province Key Laboratory of Software in Computing and Communication 2005-2006.
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occur ,in turn ,the greater the number of retransmission of RTS will be. So we can use the rate named PSR which is defined as (ACK times) / (RTS times) to represent the integration status of nodes. The larger PSR is, the better status of node will be in, thus for the node have high probability to send out packet easier. In this paper we use this metric value gained from MAC layer to select the optimal route in routing layer. III.
LOAD BALANCE ROUTING USING PACKET SUCCESS RATE (LBPSR)
A. Description of LBPSR The Routing protocols such as DSR use the “shortest path” will lead to two tendencies which may result in performance declined. One is called “hot spot” problem. The routing protocols use a few “centrally located” nodes in a large number of routes as shown Fig. 1. This causes congestion and contention described as follows at the MAC level Nodes around the “hot spot” broadcast RTS packets requiring send data to it, but no CTS replies them, and the nodes which want to send data have to retransmit RTS packets continuously. Even one of the nodes gets the opportunity to transmit a packet luckily; it has to compete intensely with other nodes in next schedule cycle. Secondly, shown as Fig. 2, some nodes moving quickly are used in routing protocol. Often though the neighbor has move out of the transmission range of the node, the node has to retransmit RTS N times uselessly (N defined as the threshold value) if there isn’t neighbor-detect mechanism. Besides this, [14] shows that the mobility of nodes has impact on Rayleigh fading and there can be no viable communication between the source and the destination when the channel is in a fade. As described, neglecting the real traffic load and mobility of nodes, there might exist several such busy and unstable nodes as are overloaded while some capable nodes cannot make their full contributions to improve the performance of the network simply because they are not located on those “shortest paths”. We make an attempt to avoid such a condition, balance the load on each node according to its capacity. In this paper, we present a description of the capacity of nodes in ad hoc network called as packet success rate (PSR). PSR i denotes the capacity of node i . The bigger PSR i is, the bigger traffic handling capability the node i enjoys, or in other words, node i has lower load and more stable neighbors around it. The heuristic definition of PSRi is expressed as
PSR i = ACK i / RTS i
Now we have got the value of PSRi representing the average capacity of nodes in previous T seconds (used as PSRi sample in formulae (2)). Then we use the well-known exponential weighted moving average method (see (2)) applied to PSRi old and PSRi sample to calculate PSRi which is the estimate value of nodes’ capacity. PSRi old and PSRi sample represent the history and the newly calculated values respectively. In our work, we set T = 6 and α = 0.3 .
PSRi = α × PSRi old + (1 − α ) × PSRi sample (2) The capacity of a node can be expressed by PSRi . Considering per data packet transmitted, the smaller times RTS(s) is (are) (re)transmitted, The larger PSRi is ,in other words, the better status of node will be in, thus for the node have high probability to send out packet easier because of the less contention for link or less link failure. At routing level, suppose there is a route ri = n s ,n1 , n2 ,....nd , where n s is the source and n d is the destination. Then, we can define the PSR of this path as follows:
PSRri =
∏ PSR n
n j ∈ri , n j ≠ ns ,nd
j
(3)
The PSR of a path with multiple hops is simply defined as the product of all the intermediate nodes’ PSR and it is a balance between “maximal PSR” and “shortest path”. Our route selection mechanism operates in the following way: for a given source-destination pair n s and n d , there is a set of possible routing paths r available. Under the condition we choose route ri representing that r ≠ ĭ,
max( PSRri ) among all the possible routes between ri ∈r
n s and n d .
Here figure 1&2 illustrates how our algorithm to avoid selecting node c as intermediate node forwarding packets from S to D. Because PSRS-A-B-D (= 0.72) is bigger then PSRS-C-D (=0.5), the routing protocol select RS-A-B-D rather than RS-C-D as source route for packets from S to D.
(1)
Here ACK i is the number of ACK received by node i in T seconds. RTS i is the number of RST transmitted by node i in T seconds(obviously ACK i
