Abstract

Modeling the forwarding feature and analyzing the performance theoretically for opportunistic routing in wireless multihop network are of great challenge. To address this issue, a generalized geometric distribution (GGD) is firstly proposed. Based on the GGD, the forwarding probability between any two forwarding candidates could be calculated and it can be proved that the successful delivery rate after several transmissions of forwarding candidates is irrelevant to the priority rule. Then, a discrete-time queuing model is proposed to analyze mean end-to-end delay (MED) of a regular opportunistic routing with the knowledge of the forwarding probability. By deriving the steady-state joint generating function of the queue length distribution, MED for directly connected networks and some special cases of nondirectly connected networks could be ultimately determined. Besides, an approximation approach is proposed to assess MED for the general cases in the nondirectly connected networks. By comparing with a large number of simulation results, the rationality of the analysis is validated. Both the analysis and simulation results show that MED varies with the number of forwarding candidates, especially when it comes to connected networks; MED increases more rapidly than that in nondirectly connected networks with the increase of the number of forwarding candidates.

1. Introduction

Recently, opportunistic routing (OR) for wireless multihop networks has drawn much attention due to its robustness in practical dynamic environments with frequent transmission failures. Traditional routing protocols, that is, dynamic source routing (DSR) [1], just rely on a (preselected) single fixed path to deliver packets from a source to a destination; therefore, the performance is easily affected by the wireless link. While in OR a packet can be received independently with a certain successful probability from each forwarding candidate, the OR mainly exploits the inherent broadcast nature of wireless transmission to mitigate the impact of poor wireless links. This feature could guarantee the robustness of the transmission. As a result, OR can cope well with the unreliable and varying link quality that is typical of wireless networks [2].

In OR, each forwarding candidate is labeled with a priority which is set according to a certain metric, that is, the distance to the destination. Once a forwarding candidate receives a packet, it would store the packet in the local buffer and then start a timer. If this forwarding candidate receives an acknowledgement from any node with a higher priority before timer elapses, it means that the packet has been forwarded by other nodes. The forwarding candidate will drop this packet from the buffer. Otherwise, the node transmits the packet when the timer elapses [2]. The buffering time, also called the queueing delay, which represents the duration from the time when one packet arrives at the node to the time when this packet is ready to be transmitted, is the major component of mean end-to-end delay (MED).

Besides the robustness against communication failures, time efficiency is also of primary importance in wireless multihop networks due to the applications of real-time nature, that is, disaster relief, military operation [3]. It is known that the MED is the most popular criterion for time efficiency. Moreover, the MED is inversely proportional to the average throughput, and the total average throughput can also be obtained by the derivation of MED [4]. For this reason, we strive to find appropriate methodology and model to study the generative mechanism and characterization of the MED in this paper.

Modeling the forwarding feature and analyzing the MED theoretically for OR in wireless multihop network are a great challenge. There are several reasons: distributed architecture, varying wireless environment, dynamic topology, and so on. To deal with these issues, we firstly introduce a regular OR, which avoids the interchannel interference by setting different orders of access channel. Then we analyze the queueing delay of OR by dividing the network topology into two categories as shown in Figure 1. One of the network topologies is directly connected network in which all the nodes are in the communication range of each other. Another network topology is nondirectly connected network in which not all the nodes could communicate with each other directly. This analysis may be the cornerstone of modeling the MED for OR strategies and could allow us to have a comprehensive understanding about queueing delay features. The main contributions of this paper are summarized as follows.(1)A new mathematical distribution called generalized geometric distribution (GGD) is proposed to model the forwarding feature of OR in wireless multihop networks.(2)A new methodology for analyzing the OR’s MED is proposed. With the knowledge of priority rule and delivery probability, the forwarding probability could be calculated based on GGD. Afterwards, the generating function of the queue length distribution could be derived. According to the property of the multivariate generating function, closed form expressions of MED are derived. These analysis results could be applied to arbitrary directly connected networks and some special nondirectly connected networks.(3)An approximate analysis is also developed for the general cases in the nondirectly connected networks. Meanwhile, a large number of simulation studies have been performed. We have observed that analysis results coincide with the experimental data very well.

Figure 1 

Examples for directly and nondirectly connected networks.

The organization of this paper is as follows. The next section summarizes related works. The system model is described in Section 3. Section 4 introduces the GGD and some basic definitions. Based on the system model and basic definitions, we analyze the MED for different kinds of networks in Section 5. In Section 6, numerical results and experimental data are presented and discussed. In the last section, we discuss future research directions and conclude the paper.

2. Related Works

Since the milestone piece of work in this area was proposed in [5], OR has aroused much concern. A wide range of literature has been proposed recently, that is, [6–16]. References [6–10] emphasized particularly on researching opportunistic algorithms and protocols. In the following part, we only discuss some of the main theoretical achievements.

Reference [11] proposed a very general analytical model to describe OR and then derived a closed form expression about the average number of transmissions for successfully delivering a packet to the destination. In [12], the expected transmission count (ETX) of different candidate selection algorithms were profoundly evaluated based on a very useful discrete-time Markov chain. Similarly, a mathematical model was proposed to compute the total number of transmissions of the whole network in [13]. It showed that the main reason behind retransmissions is that forwarder with lower priority is unable to hear the transmission from its neighbor with higher priority. Reference [14] formulated the end-to-end throughput bound as a linear programming problem. Then a heuristic algorithm was proposed to find a feasible scheduling of opportunistic forwarding priorities to achieve the maximum capacity. Considering the link-level interference among the nodes, a closed form expression of maximum achievable throughput was provided for directly connected multihop wireless networks in [15]. Reference [16] mainly focused on calculating the end-to-end energy consumption of each potentially available route for both traditional routing and opportunistic routing. In summary, [11–13] focused on the number of transmissions for successfully delivering a packet to the destination. References [14, 15] provided insights into the system throughput. Reference [16] studied the energy efficiency. These theoretical studies differ from our works.

The works which are most related to ours are [4, 17]. Reference [17] was the first paper which analyzed MED by deriving the steady-state joint generating function of the queue length distribution. However, the analysis was limited to the tandem queueing network. Reference [4] analytically derived saturation throughput and MED for an interference aware opportunistic relay selection protocol. Unfortunately, it is only applicable to two- or three-hop networks. Here, our work well modeled the broadcast characteristic of wireless communication in OR and the analysis methodology for OR could be applied in a very general multihop network.

3. System Model

We consider that a network consists of nodes. Node 0 is assumed as the destination and other nodes could transmit fixed-length packets to node 0. The behaviors of all the nodes in the data forwarding are coordinated by an OR protocol. The system operates in the time slotted and synchronized fashion. The time is divided into slots of size corresponding to the transmission time of a packet. A packet arriving during a slot cannot be forwarded before the beginning of the next slot. Each node is regarded as a first come first serve (FCFS) server.

The main principles of the forward protocol analyzed in this paper are summarized as follows.(P1)Forwarding candidates are coordinated based on a priority rule. In this paper, the priorities are set in accordance with the distance to the destination. The shorter the distance to the destination, the higher the priority that would be set for the forwarding candidate. In Figure 1, the priorities of the forwarding candidates from node to node 1 increase in turn. In the viewpoint of implementation, each node could obtain the global knowledge about the priority easily by the forward candidates discovery process as elaborated in [9].(P2)Since the nodes share a common radio channel, to avoid the collision, channel access is controlled in accordance with the preassigned priorities. More specifically, a node is allowed to transmit in a given slot only if the nodes with higher priority within its communication range have empty queues.(P3)After the upstream node broadcasts a packet, each node within its communication range may hear the packet. To avoid the duplicate transmission, the packet is received and further forwarded by only one node. Current node would drop the packet which would be received by other node with higher priority. That is, a given packet should be received only once by one of the forwarding candidates according to its corresponding priority order from high to low.

(P2) and (P3) could be easily implemented when designing a practical OR protocol. Take nondirectly connected network as a general example. Assume that the communication range is two hops for each node in, which means node 3 may only be influenced by nodes . When receiving a packet from node 4 or node 5, node 3 would not transmit the packet until node 2 and node 1 finish their transmission. What is noteworthy is that node 1 or node 2 may also receive the same packet from node 5 or node 4. If node 3 receives this packet from node 1 and node 2 during its waiting time, it would drop this packet because the packet has been forwarded by nodes with higher priority.

In light of the property for multivariate generating function, the steady-state average queue length at node iswhere is the steady-state joint generating function of the queue length distribution and its common definition is where we use the notation and let denote the number of packets at node at time . Here, we assume that the Markov chain is ergodic; namely, . The normalization condition is .

Let denote the arrival rate of packets at node . The MED in the system is obtained by applying Little’s law to the whole system and it is given byFor convenience, we employ the following notations:where and .

4. Basic Definitions

In this section, the GGD is presented. Based on the GGD, the forwarding probability of OR is calculated. Finally, some other basic definitions for the analysis are presented.

4.1. Generalized Geometric Distribution

Definition 1. Assumed that events denoted as are mutual independence. The corresponding probability for each event is , respectively. Let ; namely, the events do not happen but happens. The probability distribution of the events is defined as a GGD.

Let denote the probability of event ; we have where and it could be proved thatThe traditional geometric distribution is a specific case of GGD with and .

Theorem 2. For the events , which are obtained through rearranging the events according to another metric, one has where is the corresponding probability for the reordered events.

Proof. Let us change the order of any adjacent events in a given events set . We get new events denoted as with corresponding probability . We getEquation (8) means (7) still exists when changing the order of any adjacent events. Since any reordered events set could be obtained by changing the order of adjacent events in the original events set for several times, Theorem 2 is proved.

GGD can be widely used to reveal the radio characteristics of wireless transmission. Specifically, could be the event of the th transmission between any two nodes and could be the event that these two nodes have transmitted for times before being successful. Applying to OR, could be the event that the th forwarding candidate forwards the packet successfully. If it failed, the th forwarding candidate would forward the packet and so forth. is the delivery ratio decided by the underline propagation model and is the actual forwarding probability.

As shown in Figure 2, is the delivery ratio from node to node 0. If the priorities are decided by the distance to the node 0, we have and . If the priorities are decided by another metric such as the ETX, the higher the delivery to the destination, the higher the priority that would be set for the forwarding candidate. We have and . This result is consistent with (7), which means that the sum of ratio of successful transmission is irrelevant to the order of the transmissions. Because the priorities which control the order of access channel have little impact on the performance of OR in accordance with Theorem 2, the analysis for the involved OR in this paper could be promoted to several other ORs with different priority rules.

Figure 2 

An example for GGD.

4.2. Other Basic Definitions

Let be the number of packets generated at node in the interval and its steady-state joint generating function of the input process is expressed asOn the basis of the property for multivariate generating function, we have

Let be the number of packets sent out by node at the beginning of the slot . Based on (P2), we getwhere is a indicator function denoted asAnd is the number of nodes with higher priorities within node ’s communication range. When , this network becomes a -node tandem system where the packets are forwarded hop-by-hop. Generally, the number of forwarding candidates are more than two nodes in OR ().

The explanation for (11) is that node could transmit a packet when its own buffer is not empty while the buffers of its neighbors with higher priorities () are empty.

Let denote the delivery rate from node to node . Based on Definition 1, the forwarding probability of a packet that is transmitted from node to node is calculated as follows:The interpretation for (13) is that, for a packet transmitted by the upstream node , node is forbidden to receive it unless nodes have not received it successfully.

Let be a binary-valued variable that takes value 1 if a packet is successfully transmitted from node to node during the interval . denotes the fact that the packet is not transmitted successfully to any other nodes during the interval and would be retransmitted in the next slot. The state space of is and the corresponding probabilities are , respectively. For convenience, let us define . Thus, for , the steady-state joint generating function of isUsing the definitions of and , it is easy to see that for and The third term of (15) represents the number of packets received from the neighbor nodes of node .

5. Analysis

If is determined, MED could be calculated from (3) and (1). In this section we first focus on for directly connected networks. Then we analyze that in nondirectly connected networks.

5.1. Directly Connected Networks

Based on (2), (4)–(15), and using a standard technique proved in Appendix A, we obtainThe term represents the event that the buffer of node is not empty while the buffers of nodes are empty and in such a case a packet is transmitted from node to one of the forwarding candidates or retransmitted by node as shown in the term .

In general, delivery probabilities between any nodes and the generating processes of the packets are known; namely, and (calculated based on Definition 1) in (16) are known. Thus, in the following part, we would derive through determining and boundary terms ().

5.1.1. Determination of the Constant

For , by substituting , let approach 1 in (16); then using the normalization condition , we obtain linear equations with the constants .

For , we get

For , we get

For , we getSolving these equations, we obtainwhere .

5.1.2. Determination of the Boundary Terms

Note that , . For convenience, by the definition of , (16) can be changed to Here we first solve the boundary term . Applying Rouche’s theorem, we can prove that, for given with , the equations shown in (22) have a unique solution over in the unit circles . Let denote the unique solution. ConsiderBy substituting (22) and the solution in (21), we obtainwhere . Since has already been obtained in (20), we obtain as shown in (23).

Similarly, we discuss for . For given , with ; solving the equations for over , we get the unique solution denoted as in the unit circles .

By substituting and the solution in (21), we have where .

Since has already been obtained in (23), we can get for in (24). In the same way, by backward recursive substitution of for in (24), we could obtain .

Given all the boundary terms, the joint generating function is uniquely determined. Recalling the derivation, it is observed that is mainly determined by the priority rule and delivery probability.

5.2. Nondirectly Connected Networks

In this kind of networks, the analysis becomes quite complex because node and node may succeed in their transmissions simultaneously. To simplify the analysis, we assume that it is a linear network, the packets are only generated at node , and all the nodes have the same communication range denoted as hops.

In the following part, we first study MED for the case shown in Figure 3. Then, we propose an approximate analysis for MED in the general scene with . The motivation for considering such a special case is threefold. Firstly, for the cases , nodes , which are already analyzed in Section 5.1, are in the communication range of each other. Secondly, it is the simplest nondirectly connected networks in which channel could be reused (node and node may succeed in their transmissions simultaneously if the other nodes are silent). Thirdly, it will serve us as a crucial building stone in developing our approximate analysis of general nondirectly connected networks.

Figure 3 

Approximate analysis in nondirectly connected networks.

5.2.1. Special Networks with

Theorem 3. For , the steady-state joint generating function of the queue length distribution is

Proof. We obtain based on Appendix A as follows:Compared with (16), an additional event is considered in (26). It is that the buffers of node and node are not empty while the buffers of other nodes are empty. In such case node and node transmit simultaneously.
Since node is the sole source, it is easy to see that only node could have more than one packet at a time instant. Other nodes can have at most one packet at a time. Considering (2), for , we can define where are two polynomials consisting of . By setting in , two equations could be established over and . By solving the equations, we have
Through substituting for in (27), we getBy substituting (28) into (26), we obtain (25). The proof of theorem is completed.

According to (1), by taking a partial derivative of (27) with respect to , we have

Considering (29), the average queue length is determined by , which could be obtained by setting in , . To solve these terms, we would establish equations in two steps.

Firstly, to simplify (25), we assume that . is a constant which is calculated as follows.

By substituting into (25), we getLet for . We get

This approximation is reasonable. The explanation is that, for close to 1, ( for ) is close to (). The accuracy of the approximation is demonstrated with simulation in Section 6. Thus (25) is simplified toSecondly, by setting in (32), we obtain which shown as follows:where , is the coefficient matrix of the linear equations, and . Obviously, if is determined, we could solve the linear equations.

Based on Cramer’s rule, let and we obtain