Mobile Information Systems

Volume 2016, Article ID 4819349, 10 pages

http://dx.doi.org/10.1155/2016/4819349

## Congestion Control Mechanism for Intermittently Connected Wireless Network

Chongqing University of Posts and Telecommunications, Chongqing, China

Received 24 September 2016; Accepted 13 November 2016

Academic Editor: Laurence T. Yang

Copyright © 2016 Ruyan Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Based on the “storing-carrying-forwarding” transmission manner, the packets are forwarded flexibly in Intermittently Connected Wireless Network (ICWN). However, due to its limited resources, ICWN can easily become congested as a large number of packets entering into it. In such situation, the network performance is seriously deteriorated. To solve this problem, we propose a congestion control mechanism that is based on the network state dynamic perception. Specifically, through estimating the congestion risk when a node receives packets, ICWN can reduce the probability of becoming congested. Moreover, due to ICWN’s network dynamics, we determine the congestion risk threshold by jointly taking into account the average packet size, average forwarding risk, and available buffer resources. Further, we also evaluate the service ability of a node in a distributed manner by integrating the recommendation information from other intermediate nodes. Additionally, a node is selected as a relay node according to both the congestion risk and service ability. Simulation results show that the network performance can be greatly optimized by reducing the overhead of packet forwarding.

#### 1. Introduction

Recently, Intermittently Connected Wireless Network (ICWN) has received wide attentions from academia and industry [1]. Due to the sparsity of the node distribution and random movements, a connection between two nodes is dynamic. As a result, the transmitted packets between two nodes can be easily lost, which will lead to the frequent route reestablishment and recovery in ICWN [2]. On the other hand, in practice, the node movement can improve the probability of establishing a connection and thus the network capacity can be improved [3, 4]. Taking advantage of such temporary connections, researchers propose ICWN and design the corresponding architecture [5], in which the nodes except the source and destination nodes can work as the relay. Instead of using the traditional packet forwarding manner (i.e., storing-forwarding), ICWN carries packets in a “storing-carrying-forwarding” way with the help of relay nodes and finally sends packets to reach their destinations.

To realize successful packet transmission and reduce delivery delay, multiple copies of the same packet are injected into ICWN. However, due to limited network resources in ICWN, the buffers of nodes can get saturated quickly. As a result, nodes getting full cannot accommodate more packets, which will result in network congestion. Thus, we have to investigate the network congestion control problem, so as to greatly improve the QoS (Quality of Service) and effectively enhance resource utilization [6].

To address the problem, we propose a dynamic network state perception based on network congestion control mechanism (DNSP-CCM) in this paper. Specifically, we evaluate a node’s congestion risk before it receives packets. Particularly, we evaluate the congestion risk in a distributed manner. We also set up the congestion risk threshold that will be adjusted dynamically according to the dynamic network condition. Moreover, we also evaluate a node’s service ability, (i.e., a node’s message forwarding capability). The service ability can be determined according to the direct encounter probability with other nodes and the indirect encounter probability with the same nodes. We select the node with higher service ability to carry and forward packets. As a result, packets can be transmitted to their destinations in a cost efficient manner so as to effectively alleviate the congestion.

The main contributions of this paper are summarized as follows.

First, we propose a congestion risk evaluation method. The congestion risk level is measured by considering the network status and the buffer size of a node. In particular, the congestion risk threshold is dynamic and changed according to the link condition.

Second, we evaluate a node’s service ability. The service ability is evaluated by jointly considering the direct and indirect encounter probability.

Third, we design an adaptive network congestion control strategy based on the congestion risk and service ability. Specifically, we propose an adaptive buffer separation method, (i.e., the forwarding buffer and replacing buffer). According to the transmission status and a link’s capacity of a local buffered packets, a node needs to determine the packets needed for forwarding or replacing to enhance the network performance.

The remainder of this paper is organized as follows. Section 2 surveys some research works related to the current congestion control methods. The proposed congestion risk evaluation method is described in Section 3. Section 4 examines an estimation method to measure a node’s service capability. Then, an adaptive congestion control mechanism is designed based on the evaluation results of the congestion risk and service capability in Section 5. We show the simulation results in Section 6. Finally, we conclude this paper in Section 7.

#### 2. Related Works

So far, researchers have made great efforts to solve the problem of the network congestion in ICWN. Lo and Lu [7] proposed a mechanism combining with node’s neighborhood buffers and node’s encounter probability. By obtaining neighborhood nodes’ buffer status, a node can dynamically adjust packets quota in order to avoid nodes congestion. This method can alleviate the possibility of changing to congestion status. However, once a node turns into the congestion status, this method only chooses packets to drop by hop counts, which is not reasonable. Besides, only considering the direct encounter probability is unable to well estimate the node’s packet forwarding ability.

To make full use of a node’s social attributes in ICWN, Daly and Haahr [8] proposed a mechanism which chooses relay nodes based on node relations. However, it ignores the fact that so many nodes are having some relations and thus too many redundant copies of a message occur. Thus, it is easy to cause the network congestion [9]. The node’s historical encounter information was utilized to estimate three basic parameters (i.e., the probabilities for head-of-line-blocking, reliability, and deletion in [10]). Nodes make their decisions by these parameters.

On the other hand, researchers considered to use the local congestion status as the network congestion in the area [11]. The forwarding priority is determined according to the degree of data diffusion at the local buffer. Moreover, redundant message copies can be deleted through the active response mechanism. However, the network status cannot be derived from a single node’s congestion status. To speed up the data transmission, [12] uses “interest return” and “opportunity consumption” to evaluate the influence on a node’s local congestion status and then decide whether to receive messages. This method can alleviate the network congestion and improve network performance to some extent. However, it adopts a fixed congestion threshold, and thus it cannot perceive the really current resource usage.

#### 3. Congestion Risk Evaluation

According to the basic principle of packets forwarding in ICWN, after encountering between two nodes, they need to exchange the packets which are not in the buffer [13]. Obviously, for forwarding multicopy packets, network congestion risk brought by the received packets is directly related to the buffer space. If nodes get more packets, the capacity of continuing to receive packets from other nodes will decrease, resulting in more and more nodes’ residual buffer resource decreasing because of the limited buffer resource. If this situation goes on, some areas would generate congestion and then make the whole network congestion. What is more, if nodes have more buffer resources, they can carry more packets to forward, making the probability of dropping packets decrease. So packets can be carried by more nodes which lead to improve delivery ratio. In order to enable nodes to estimate the real-time network congestion level, nodes can achieve the congestion controlling. The risk of network congestion caused by the received packets has to be evaluated.

The packet transmission requires cooperation among multiple relay nodes. Meanwhile, in a given period, the more times a relay node encounters with other nodes, the more chance of diffusion of packets can be gained. Therefore, with the encounter interval between nodes, the larger amount of copies will be injected into the network, which possibly results in higher congestion probability. In addition, the larger Expected Node Meeting Time (ENM), which is defined as a mathematical expectation of nodes encounter interval, the more relay nodes used to carry the packets. Therefore, the forwarding risk level of the forwarding node is defined as follows: where is the probability of occurring the network congestion when packets are injected into ICWN. Moreover, the value of can be obtained where and denote the time of first encounter and the last encounter, respectively, between the two nodes, and denotes the total encounter counts.

As can be seen, in order to get the value of forwarding risk level, nodes need to know encounter time showing the number of times from current time to the next encounter time between two nodes. The law of nodes movement indicates that ENM of nodes in ICWN obeys the exponential distribution with parameter and the value of ENM is . It can be described as Generalized Stationary Random Process, whose autocorrelation is only related to time interval [14, 15]. Thus, the encounter time of the next future can be estimated by the node’s historical information.

Therefore, the expectation of encounter interval between two nodes can be obtained where denotes the expectation of encounter interval between node and node , denotes the duration from last encounter to current encounter, denotes the each interval between nodes encounter, and denotes the encounter times between nodes.

Using multicopy packet forwarding scheme, a relay node can continuously receive the copies of a packet. As a result, its buffer is getting crowded. If the residual buffer resource is very low, the node is unable to accept newly arriving packets. In this case, we say that this node comes into the congestion status. To evaluate the congestion level, we consider the packet length and available buffer resources. With the longer packet length, the temporary link will be occupied longer. On the other hand, the higher buffer utilization means that the network load is higher, which results in the higher increase in the forwarding risk and congestion risk. Combining both factors, we can obtain the congestion risk before receiving newly arrived packets.

Therefore, combining the average forwarding risk , the average packet length , and the occupied buffer , we can determine the congestion threshold. where and can be obtained as follows: where denotes the length of packet and denotes the number of packets in the buffer.

#### 4. Service Ability Estimation

The node encounter probability and the number of successfully delivered packets are two important parameters used to describe the service capability. Moreover, as ICWN adopts the “storing-carrying-forwarding” method, packets are stored in multiple relay nodes and no direct path is between two nodes. Therefore, we evaluate the packet delivery status by jointly considering the direct and indirect encounter probabilities.

Obviously, the encounter probability can be obtained directly by the encounter times between two nodes. Thus, direct encounter probability of given node pair is shown as follows.

*Definition 1. *The direct encounter probability between node and is defined as the ratio of encounter times * for* node encountering and the total times node encountering all other nodes; that is, The indirect encounter probability is limited to indirect encounter time interval, average indirect encounter time interval, the total of indirect encounter time interval, and the number of indirect encounters. The indirect encounter time interval means a duration when a node meets another node, node , after node having met another node, node . For an example, an engineer, considered as node , goes to company. Before meeting his partner, considered as node , he may meet with security officer of his company, considered as node . So node can help node to forward packets to node . The indirect encounter time interval means that the duration node meets with node after its meeting with node . Within a given period of time, the more the number of encounters between nodes, the greater the probability of encounter. Therefore, we use indirect encounter time interval as the estimation parameter of indirect encounter probability. It can make the estimation result more accurate and is conducive to improve the network performance. The definition of indirect encounter probability is shown as follows.

*Definition 2. *Indirect encounter time interval denotes the duration from the node encountering with node after node having met with .

Assume that the meeting time between node and nodes and is recoded as and .Consequently, the total indirect time interval for node which encounters with node after meeting with node can be obtained aswhere . The average value of can also be obtained as follows: where is the indirect encounter times.

Obviously, the indirect encounter probability is determined by the indirect meeting interval. The lower average value, the higher encounter frequency. Therefore, we use its average value to evaluate the indirect encounter probability in this paper. The definition of indirect encounter probability is shown as follows.

*Definition 3. *Indirect encounter probability denotes the probability of node encountering with node and consequently encountering with node .where is the given period, denotes the indirect encounter time interval, denotes the total indirect encounter time interval, and is the average indirect encounter time interval.

If the encountered node is the destination of a packet, the indirect encounter probability is where is the average meeting interval of node and node and can be obtained as follows:where denotes the encounter times and and denote the first encounter time and last encounter time between node and node , respectively.

In ICWN, each node maintains an encounter information table, within which each item will be updated timely. We show the encounter information table in Table 1, where are the ID of encountered nodes and are the every historical encounter time.